From e835bf6ec80fa35fe9444cb4707aa8637630b79c Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Fri, 27 Sep 2024 10:40:32 -0400 Subject: [PATCH 01/33] Add a draft notebook --- gnomad_toolbox/modules/__init__.py | 1 + gnomad_toolbox/modules/variant_filtering.py | 40 ++ .../use_cases/toolbox_for_gnomad_users.ipynb | 511 ++++++++++++++++++ 3 files changed, 552 insertions(+) create mode 100644 gnomad_toolbox/modules/__init__.py create mode 100644 gnomad_toolbox/modules/variant_filtering.py create mode 100644 gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb diff --git a/gnomad_toolbox/modules/__init__.py b/gnomad_toolbox/modules/__init__.py new file mode 100644 index 0000000..6e03199 --- /dev/null +++ b/gnomad_toolbox/modules/__init__.py @@ -0,0 +1 @@ +# noqa: D104 diff --git a/gnomad_toolbox/modules/variant_filtering.py b/gnomad_toolbox/modules/variant_filtering.py new file mode 100644 index 0000000..ab7b230 --- /dev/null +++ b/gnomad_toolbox/modules/variant_filtering.py @@ -0,0 +1,40 @@ +"""Small functions to filter variants in gnomAD datasets, such as by allele frequency or by variant type.""" + +import hail as hl + + +def get_variant_count( + ht: hl.Table, + afs: list[float] = [0.01, 0.001], + singletons: bool = False, + doubletons: bool = False, +) -> dict: + """ + Count variants with frequency <1%, <0.1%, and singletons (AC == 1). + + .. note:: This function works for gnomAD exomes and genomes datasets, not yet for + gnomAD joint dataset, since the HT schema is slightly different. + + :param ht: Input Table. + :param afs: List of allele frequencies cutoffs. + :param singletons: Include singletons. + :param doubletons: Include doubletons. + :return: Dictionary with counts. + """ + counts = {} + + # Filter to PASS variants. + ht = ht.filter(hl.len(ht.filters) == 0) + if singletons: + n_singletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 1)) + counts["number of singletons"] = n_singletons + if doubletons: + n_doubletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 2)) + counts["number of doubletons"] = n_doubletons + + for af in afs: + n_variants = ht.aggregate(hl.agg.count_where(ht.freq[0].AF < af)) + counts[f"number of variants with AF < {af}"] = n_variants + + # Count variants with frequency <1%, <0.1%, and singletons (AC == 1). + return counts diff --git a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb new file mode 100644 index 0000000..d19f5ea --- /dev/null +++ b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb @@ -0,0 +1,511 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "e77d32b1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + "
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i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"bd49890a-4e5b-4f64-961d-42fb378aed46\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import hail as hl\n", + "from gnomad.resources.grch37.gnomad import public_release as v2_public_release\n", + "from gnomad.resources.grch38.gnomad import public_release as v4_public_release" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6fae5aa5", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the number of variant counts by allele frequencies" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4c6e7dd6", + "metadata": {}, + "outputs": [], + "source": [ + "def get_variant_count(ht: hl.Table, afs: list[float]=[0.01, 0.001], singletons: bool = False, doubletons: bool = False) -> dict:\n", + " \"\"\"\n", + " Count variants with frequency <1%, <0.1%, and singletons (AC == 1).\n", + "\n", + " .. note:: This function works for gnomAD exomes and genomes datasets, not yet for gnomAD joint dataset, since the HT scheme is slightly different.\n", + "\n", + " :param ht: Input Table.\n", + " :param afs: List of allele frequencies cutoffs.\n", + " :param singletons: Include singletons.\n", + " :param doubletons: Include doubletons.\n", + " :return: Dictionary with counts.\n", + " \"\"\"\n", + " counts = {}\n", + "\n", + " # Filter to PASS variants.\n", + " ht = ht.filter(hl.len(ht.filters) == 0)\n", + " if singletons:\n", + " n_singletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 1))\n", + " counts[\"number of singletons\"] = n_singletons\n", + " if doubletons:\n", + " n_doubletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 2))\n", + " counts[\"number of doubletons\"] = n_doubletons\n", + "\n", + " for af in afs:\n", + " n_variants = ht.aggregate(hl.agg.count_where(ht.freq[0].AF < af))\n", + " counts[f\"number of variants with AF < {af}\"] = n_variants\n", + "\n", + " # Count variants with frequency <1%, <0.1%, and singletons (AC == 1).\n", + " return counts" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "112c5065", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing Hail with default parameters...\n", + "/opt/conda/miniconda3/lib/python3.10/site-packages/hailtop/aiocloud/aiogoogle/user_config.py:43: UserWarning:\n", + "\n", + "Reading spark-defaults.conf to determine GCS requester pays configuration. This is deprecated. Please use `hailctl config set gcs_requester_pays/project` and `hailctl config set gcs_requester_pays/buckets`.\n", + "\n", + "Setting default log level to \"WARN\".\n", + "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SPARKMONITOR_LISTENER: Started SparkListener for Jupyter Notebook\n", + "SPARKMONITOR_LISTENER: Port obtained from environment: 48351\n", + "SPARKMONITOR_LISTENER: Application Started: application_1727440474542_0001 ...Start Time: 1727441640185\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Running on Apache Spark version 3.3.2\n", + "SparkUI available at http://qh1-m.c.broad-mpg-gnomad.internal:38205\n", + "Welcome to\n", + " __ __ <>__\n", + " / /_/ /__ __/ /\n", + " / __ / _ `/ / /\n", + " /_/ /_/\\_,_/_/_/ version 0.2.130-bea04d9c79b5\n", + "LOGGING: writing to /home/hail/hail-20240927-1253-0.2.130-bea04d9c79b5.log\n" + ] + } + ], + "source": [ + "v2_ht = v2_public_release(\"exomes\").ht()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c276fb7e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 1:===================================================>(9979 + 16) / 9997]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'number of variants with AF < 0.01': 14795986, 'number of variants with AF < 0.001': 14551940}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "[Stage 1:====================================================>(9995 + 3) / 9997]\r" + ] + } + ], + "source": [ + "print(get_variant_count(v2_ht))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c0243c4b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 5:====================================================>(9992 + 5) / 9997]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'number of singletons': 7763393, 'number of doubletons': 2194502, 'number of variants with AF < 0.01': 14795986, 'number of variants with AF < 0.001': 14551940}\n" + ] + } + ], + "source": [ + "print(get_variant_count(v2_ht, singletons=True, doubletons=True))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6f4a60c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From c9f0e22fa321df45eff6c48710a69caec3e69900 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Mon, 30 Sep 2024 10:55:23 -0400 Subject: [PATCH 02/33] Add more use cases in notebook --- .../use_cases/toolbox_for_gnomad_users.ipynb | 706 ++++++++++++++++-- 1 file changed, 650 insertions(+), 56 deletions(-) diff --git a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb index d19f5ea..93f80a1 100644 --- a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb +++ b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "id": "e77d32b1", "metadata": {}, "outputs": [ @@ -19,7 +19,7 @@ " \n", "
\n", " \n", - " Loading BokehJS ...\n", + " Loading BokehJS ...\n", "
\n" ] }, @@ -191,7 +191,7 @@ " \"\"}};\n", "\n", " function display_loaded() {\n", - " const el = document.getElementById(\"bd49890a-4e5b-4f64-961d-42fb378aed46\");\n", + " const el = document.getElementById(\"f884217f-12c5-4fe1-a1d8-6a1d52499205\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", @@ -297,7 +297,7 @@ " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", - " const cell = $(document.getElementById(\"bd49890a-4e5b-4f64-961d-42fb378aed46\")).parents('.cell').data().cell;\n", + " const cell = $(document.getElementById(\"f884217f-12c5-4fe1-a1d8-6a1d52499205\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " }\n", @@ -313,40 +313,74 @@ " }\n", "}(window));" ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"bd49890a-4e5b-4f64-961d-42fb378aed46\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"bd49890a-4e5b-4f64-961d-42fb378aed46\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"f884217f-12c5-4fe1-a1d8-6a1d52499205\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"f884217f-12c5-4fe1-a1d8-6a1d52499205\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/miniconda3/lib/python3.11/site-packages/hailtop/aiocloud/aiogoogle/user_config.py:43: UserWarning:\n", + "\n", + "Reading spark-defaults.conf to determine GCS requester pays configuration. This is deprecated. Please use `hailctl config set gcs_requester_pays/project` and `hailctl config set gcs_requester_pays/buckets`.\n", + "\n", + "Setting default log level to \"WARN\".\n", + "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SPARKMONITOR_LISTENER: Started SparkListener for Jupyter Notebook\n", + "SPARKMONITOR_LISTENER: Port obtained from environment: 52867\n", + "SPARKMONITOR_LISTENER: Application Started: application_1727699094620_0001 ...Start Time: 1727699722656\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Running on Apache Spark version 3.5.0\n", + "SparkUI available at http://qh1-m.c.broad-mpg-gnomad.internal:43005\n", + "Welcome to\n", + " __ __ <>__\n", + " / /_/ /__ __/ /\n", + " / __ / _ `/ / /\n", + " /_/ /_/\\_,_/_/_/ version 0.2.132-678e1f52b999\n", + "LOGGING: writing to /test_toolbox.log\n" + ] } ], "source": [ "import hail as hl\n", - "from gnomad.resources.grch37.gnomad import public_release as v2_public_release\n", - "from gnomad.resources.grch38.gnomad import public_release as v4_public_release" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6fae5aa5", - "metadata": {}, - "outputs": [], - "source": [ - "# Get the number of variant counts by allele frequencies" + "\n", + "hl.init(\n", + " log=\"/test_toolbox.log\",\n", + " tmp_dir=\"gs://gnomad-tmp-30day\",\n", + " )" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "4c6e7dd6", + "execution_count": 11, + "id": "dd053d78", "metadata": {}, "outputs": [], "source": [ - "def get_variant_count(ht: hl.Table, afs: list[float]=[0.01, 0.001], singletons: bool = False, doubletons: bool = False) -> dict:\n", + "def get_variant_count(\n", + " ht: hl.Table,\n", + " afs: list[float] = [0.01, 0.001],\n", + " singletons: bool = False,\n", + " doubletons: bool = False,\n", + ") -> dict:\n", " \"\"\"\n", " Count variants with frequency <1%, <0.1%, and singletons (AC == 1).\n", "\n", - " .. note:: This function works for gnomAD exomes and genomes datasets, not yet for gnomAD joint dataset, since the HT scheme is slightly different.\n", + " .. note:: This function works for gnomAD exomes and genomes datasets, not yet for\n", + " gnomAD joint dataset, since the HT schema is slightly different.\n", "\n", " :param ht: Input Table.\n", " :param afs: List of allele frequencies cutoffs.\n", @@ -373,115 +407,659 @@ " return counts" ] }, + { + "cell_type": "markdown", + "id": "1ba4bfaf", + "metadata": {}, + "source": [ + "# Get variant count" + ] + }, + { + "cell_type": "markdown", + "id": "df28f17d", + "metadata": {}, + "source": [ + "## Get variant count by AF for a release" + ] + }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, + "id": "d9f96940", + "metadata": {}, + "outputs": [], + "source": [ + "from gnomad.resources.grch38.gnomad import public_release as v4_public_release" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "id": "112c5065", "metadata": {}, + "outputs": [], + "source": [ + "ht = v4_public_release(\"exomes\").ht()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c276fb7e", + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Initializing Hail with default parameters...\n", - "/opt/conda/miniconda3/lib/python3.10/site-packages/hailtop/aiocloud/aiogoogle/user_config.py:43: UserWarning:\n", - "\n", - "Reading spark-defaults.conf to determine GCS requester pays configuration. This is deprecated. Please use `hailctl config set gcs_requester_pays/project` and `hailctl config set gcs_requester_pays/buckets`.\n", - "\n", - "Setting default log level to \"WARN\".\n", - "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n" + "[Stage 1:====================================================>(8782 + 7) / 8789]\r" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "SPARKMONITOR_LISTENER: Started SparkListener for Jupyter Notebook\n", - "SPARKMONITOR_LISTENER: Port obtained from environment: 48351\n", - "SPARKMONITOR_LISTENER: Application Started: application_1727440474542_0001 ...Start Time: 1727441640185\n" + "{'number of variants with AF < 0.01': 68398090, 'number of variants with AF < 0.001': 67709028}\n" + ] + } + ], + "source": [ + "print(get_variant_count(ht))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c0243c4b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 5:====================================================>(8781 + 8) / 8789]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'number of singletons': 34047562, 'number of doubletons': 10161819, 'number of variants with AF < 0.01': 68398090, 'number of variants with AF < 0.001': 67709028}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Running on Apache Spark version 3.3.2\n", - "SparkUI available at http://qh1-m.c.broad-mpg-gnomad.internal:38205\n", - "Welcome to\n", - " __ __ <>__\n", - " / /_/ /__ __/ /\n", - " / __ / _ `/ / /\n", - " /_/ /_/\\_,_/_/_/ version 0.2.130-bea04d9c79b5\n", - "LOGGING: writing to /home/hail/hail-20240927-1253-0.2.130-bea04d9c79b5.log\n" + "\r", + "[Stage 5:====================================================>(8786 + 3) / 8789]\r" ] } ], "source": [ - "v2_ht = v2_public_release(\"exomes\").ht()" + "print(get_variant_count(ht, singletons=True, doubletons=True))" + ] + }, + { + "cell_type": "markdown", + "id": "cc1178c8", + "metadata": {}, + "source": [ + "## Get variant count by AF for a gene" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "c276fb7e", + "execution_count": 15, + "id": "814e2af4", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Stage 1:===================================================>(9979 + 16) / 9997]\r" + "[Stage 7:===========================================================(3 + 1) / 3]\r" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "{'number of variants with AF < 0.01': 14795986, 'number of variants with AF < 0.001': 14551940}\n" + "{'number of singletons': 5282, 'number of doubletons': 1616, 'number of variants with AF < 0.01': 10733, 'number of variants with AF < 0.001': 10656}\n" ] - }, + } + ], + "source": [ + "# Filter to interval, e.g. for ASH1L.\n", + "gene_interval = \"chr1:155335268-155563162\"\n", + "\n", + "# Filter the exome release Hail Table to the ASH1L gene interval.\n", + "ht = hl.filter_intervals(ht, [hl.parse_locus_interval(gene_interval, reference_genome=\"GRCh38\")])\n", + "\n", + "print(get_variant_count(ht, singletons=True, doubletons=True))" + ] + }, + { + "cell_type": "markdown", + "id": "30663beb", + "metadata": {}, + "source": [ + "# Filter to variants by VEP annotations" + ] + }, + { + "cell_type": "markdown", + "id": "9c78ffb2", + "metadata": {}, + "source": [ + "## Filter to LOF variants" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a58ab4ac", + "metadata": {}, + "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\r", - "[Stage 1:====================================================>(9995 + 3) / 9997]\r" + "INFO (gnomad.utils.vep 928): Filtering to canonical transcripts\n", + "INFO (gnomad.utils.vep 931): Filtering to MANE Select transcripts...\n", + "INFO (gnomad.utils.vep 934): Filtering to Ensembl transcripts...\n", + "INFO (gnomad.utils.vep 940): Filtering to genes of interest...\n", + "INFO (gnomad.utils.vep 948): Filtering to variants with additional criteria...\n" ] } ], "source": [ - "print(get_variant_count(v2_ht))" + "from gnomad.utils.vep import filter_vep_transcript_csqs\n", + "# Filter to variants in ASH1L that are LOFTEE high-confidence (with no flags) in the MANE select transcript.\n", + "ht = filter_vep_transcript_csqs(\n", + " ht, \n", + " synonymous=False, \n", + " mane_select=True,\n", + " genes=[\"ASH1L\"],\n", + " match_by_gene_symbol=True,\n", + " additional_filtering_criteria=[lambda x: (x.lof == \"HC\") & hl.is_missing(x.lof_flags)],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6aaba993", + "metadata": {}, + "source": [ + "## Filter to pLOF variants that we used to compute constraint metrics\n", + "pLOF variants meets the following requirements:\n", + "* High-confidence LOFTEE variants (without any flags),\n", + "* Only variants in the MANE Select transcript,\n", + "* PASS variants that are SNVs with MAF ≤ 0.1%, and\n", + "* Exome median depth ≥ 30\n", + "\n", + "**Note: this number should match the number of observed pLOF SNVs on the gene page of gnomAD Browser.**" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "c0243c4b", + "execution_count": 17, + "id": "291d8b7c", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Stage 5:====================================================>(9992 + 5) / 9997]\r" + "[Stage 7:===================(3 + 1) / 3][Stage 10:==================(3 + 1) / 3]\r" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "{'number of singletons': 7763393, 'number of doubletons': 2194502, 'number of variants with AF < 0.01': 14795986, 'number of variants with AF < 0.001': 14551940}\n" + "Number of variants: 18\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 7:=====(3 + 1) / 3][Stage 10:====(3 + 1) / 3][Stage 11:====(3 + 1) / 3]\r" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
freq
coverage
locus
alleles
AC
AF
AN
homozygote_count
csq
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>array<str>int32float64int32int64array<str>float64int32int64float64float64float64float64float64float64float64float64float64
chr1:155337668["G","A"]4593.14e-0414612700["stop_gained"]2.99e+0130218875281.00e+001.00e+001.00e+009.97e-019.86e-019.32e-016.07e-018.96e-046.02e-05
chr1:155337704["G","A"]64.10e-0614617140["stop_gained"]3.02e+0130220423721.00e+001.00e+001.00e+009.99e-019.94e-019.48e-016.21e-018.39e-044.93e-05
chr1:155337735["G","C"]16.84e-0714616940["stop_gained"]3.00e+0130219569141.00e+001.00e+001.00e+009.99e-019.92e-019.37e-016.12e-018.29e-044.51e-05
chr1:155338087["A","T"]16.85e-0714596480["splice_donor_variant"]3.16e+0131231113191.00e+001.00e+009.99e-019.95e-019.89e-019.59e-017.28e-011.35e-027.59e-04
chr1:155338161["G","A"]16.84e-0714618840["stop_gained"]3.19e+0131233084341.00e+001.00e+001.00e+001.00e+001.00e+009.76e-017.42e-011.35e-027.59e-04
chr1:155349380["T","A"]16.84e-0714617680["stop_gained"]3.21e+0132234449991.00e+001.00e+001.00e+001.00e+009.99e-019.76e-017.69e-015.01e-031.85e-04
chr1:155354631["C","T"]16.88e-0714534160["splice_acceptor_variant"]3.04e+0131222097141.00e+001.00e+009.95e-019.83e-019.70e-019.28e-016.53e-014.38e-044.93e-05
chr1:155357583["A","T"]16.84e-0714616800["splice_donor_variant"]3.13e+0131228588871.00e+001.00e+001.00e+009.99e-019.97e-019.79e-017.47e-018.14e-046.29e-05
chr1:155370984["C","T"]32.06e-0614557260["splice_acceptor_variant"]3.12e+0131228021811.00e+001.00e+001.00e+009.99e-019.97e-019.54e-016.97e-011.14e-033.69e-05
chr1:155415924["C","A"]21.48e-0613520320["splice_acceptor_variant"]2.73e+0130199323751.00e+009.84e-019.41e-018.72e-018.17e-017.64e-015.70e-011.35e-031.76e-04
chr1:155478020["G","C"]16.84e-0714618880["stop_gained"]3.26e+0133238300811.00e+001.00e+001.00e+001.00e+001.00e+009.72e-017.53e-013.11e-031.40e-03
chr1:155478203["C","T"]16.84e-0714618740["stop_gained"]3.27e+0133239248711.00e+001.00e+001.00e+001.00e+001.00e+009.72e-017.55e-014.07e-032.20e-03
chr1:155478439["C","T"]16.84e-0714618900["stop_gained"]3.28e+0133239948001.00e+001.00e+001.00e+001.00e+001.00e+009.73e-017.59e-014.64e-032.58e-03
chr1:155478528["G","A"]16.84e-0714618760["stop_gained"]3.28e+0133239977761.00e+001.00e+001.00e+001.00e+001.00e+009.73e-017.59e-014.67e-032.60e-03
chr1:155479767["G","A"]16.84e-0714618580["stop_gained"]3.30e+0133241434591.00e+001.00e+001.00e+001.00e+001.00e+009.74e-017.66e-016.55e-033.89e-03
chr1:155479862["G","T"]16.84e-0714611600["stop_gained"]3.24e+0132236512031.00e+001.00e+001.00e+009.98e-019.91e-019.42e-017.13e-016.42e-033.91e-03
chr1:155521291["C","A"]16.84e-0714618620["stop_gained"]3.30e+0132241144561.00e+001.00e+001.00e+001.00e+001.00e+009.81e-017.72e-011.94e-023.68e-03
chr1:155521474["C","A"]16.84e-0714617240["stop_gained"]3.29e+0132240775511.00e+001.00e+001.00e+009.99e-019.98e-019.79e-017.69e-011.94e-023.67e-03
" + ], + "text/plain": [ + "+----------------+------------+---------+----------+---------+\n", + "| locus | alleles | freq.AC | freq.AF | freq.AN |\n", + "+----------------+------------+---------+----------+---------+\n", + "| locus | array | int32 | float64 | int32 |\n", + "+----------------+------------+---------+----------+---------+\n", + "| chr1:155337668 | [\"G\",\"A\"] | 459 | 3.14e-04 | 1461270 |\n", + "| chr1:155337704 | [\"G\",\"A\"] | 6 | 4.10e-06 | 1461714 |\n", + "| chr1:155337735 | [\"G\",\"C\"] | 1 | 6.84e-07 | 1461694 |\n", + "| chr1:155338087 | [\"A\",\"T\"] | 1 | 6.85e-07 | 1459648 |\n", + "| chr1:155338161 | [\"G\",\"A\"] | 1 | 6.84e-07 | 1461884 |\n", + "| chr1:155349380 | [\"T\",\"A\"] | 1 | 6.84e-07 | 1461768 |\n", + "| chr1:155354631 | [\"C\",\"T\"] | 1 | 6.88e-07 | 1453416 |\n", + "| chr1:155357583 | [\"A\",\"T\"] | 1 | 6.84e-07 | 1461680 |\n", + "| chr1:155370984 | [\"C\",\"T\"] | 3 | 2.06e-06 | 1455726 |\n", + "| chr1:155415924 | [\"C\",\"A\"] | 2 | 1.48e-06 | 1352032 |\n", + "| chr1:155478020 | [\"G\",\"C\"] | 1 | 6.84e-07 | 1461888 |\n", + "| chr1:155478203 | [\"C\",\"T\"] | 1 | 6.84e-07 | 1461874 |\n", + "| chr1:155478439 | [\"C\",\"T\"] | 1 | 6.84e-07 | 1461890 |\n", + "| chr1:155478528 | [\"G\",\"A\"] | 1 | 6.84e-07 | 1461876 |\n", + "| chr1:155479767 | [\"G\",\"A\"] | 1 | 6.84e-07 | 1461858 |\n", + "| chr1:155479862 | [\"G\",\"T\"] | 1 | 6.84e-07 | 1461160 |\n", + "| chr1:155521291 | [\"C\",\"A\"] | 1 | 6.84e-07 | 1461862 |\n", + "| chr1:155521474 | [\"C\",\"A\"] | 1 | 6.84e-07 | 1461724 |\n", + "+----------------+------------+---------+----------+---------+\n", + "\n", + "+-----------------------+-----------------------------+---------------+\n", + "| freq.homozygote_count | csq | coverage.mean |\n", + "+-----------------------+-----------------------------+---------------+\n", + "| int64 | array | float64 |\n", + "+-----------------------+-----------------------------+---------------+\n", + "| 0 | [\"stop_gained\"] | 2.99e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.02e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.00e+01 |\n", + "| 0 | [\"splice_donor_variant\"] | 3.16e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.19e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.21e+01 |\n", + "| 0 | [\"splice_acceptor_variant\"] | 3.04e+01 |\n", + "| 0 | [\"splice_donor_variant\"] | 3.13e+01 |\n", + "| 0 | [\"splice_acceptor_variant\"] | 3.12e+01 |\n", + "| 0 | [\"splice_acceptor_variant\"] | 2.73e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.26e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.27e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.28e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.28e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.30e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.24e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.30e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.29e+01 |\n", + "+-----------------------+-----------------------------+---------------+\n", + "\n", + "+------------------------+-------------------+-----------------+\n", + "| coverage.median_approx | coverage.total_DP | coverage.over_1 |\n", + "+------------------------+-------------------+-----------------+\n", + "| int32 | int64 | float64 |\n", + "+------------------------+-------------------+-----------------+\n", + "| 30 | 21887528 | 1.00e+00 |\n", + "| 30 | 22042372 | 1.00e+00 |\n", + "| 30 | 21956914 | 1.00e+00 |\n", + "| 31 | 23111319 | 1.00e+00 |\n", + "| 31 | 23308434 | 1.00e+00 |\n", + "| 32 | 23444999 | 1.00e+00 |\n", + "| 31 | 22209714 | 1.00e+00 |\n", + "| 31 | 22858887 | 1.00e+00 |\n", + "| 31 | 22802181 | 1.00e+00 |\n", + "| 30 | 19932375 | 1.00e+00 |\n", + "| 33 | 23830081 | 1.00e+00 |\n", + "| 33 | 23924871 | 1.00e+00 |\n", + "| 33 | 23994800 | 1.00e+00 |\n", + "| 33 | 23997776 | 1.00e+00 |\n", + "| 33 | 24143459 | 1.00e+00 |\n", + "| 32 | 23651203 | 1.00e+00 |\n", + "| 32 | 24114456 | 1.00e+00 |\n", + "| 32 | 24077551 | 1.00e+00 |\n", + "+------------------------+-------------------+-----------------+\n", + "\n", + "+-----------------+------------------+------------------+------------------+\n", + "| coverage.over_5 | coverage.over_10 | coverage.over_15 | coverage.over_20 |\n", + "+-----------------+------------------+------------------+------------------+\n", + "| float64 | float64 | float64 | float64 |\n", + "+-----------------+------------------+------------------+------------------+\n", + "| 1.00e+00 | 1.00e+00 | 9.97e-01 | 9.86e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.94e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.92e-01 |\n", + "| 1.00e+00 | 9.99e-01 | 9.95e-01 | 9.89e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 |\n", + "| 1.00e+00 | 9.95e-01 | 9.83e-01 | 9.70e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.97e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.97e-01 |\n", + "| 9.84e-01 | 9.41e-01 | 8.72e-01 | 8.17e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 9.98e-01 | 9.91e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.98e-01 |\n", + "+-----------------+------------------+------------------+------------------+\n", + "\n", + "+------------------+------------------+------------------+-------------------+\n", + "| coverage.over_25 | coverage.over_30 | coverage.over_50 | coverage.over_100 |\n", + "+------------------+------------------+------------------+-------------------+\n", + "| float64 | float64 | float64 | float64 |\n", + "+------------------+------------------+------------------+-------------------+\n", + "| 9.32e-01 | 6.07e-01 | 8.96e-04 | 6.02e-05 |\n", + "| 9.48e-01 | 6.21e-01 | 8.39e-04 | 4.93e-05 |\n", + "| 9.37e-01 | 6.12e-01 | 8.29e-04 | 4.51e-05 |\n", + "| 9.59e-01 | 7.28e-01 | 1.35e-02 | 7.59e-04 |\n", + "| 9.76e-01 | 7.42e-01 | 1.35e-02 | 7.59e-04 |\n", + "| 9.76e-01 | 7.69e-01 | 5.01e-03 | 1.85e-04 |\n", + "| 9.28e-01 | 6.53e-01 | 4.38e-04 | 4.93e-05 |\n", + "| 9.79e-01 | 7.47e-01 | 8.14e-04 | 6.29e-05 |\n", + "| 9.54e-01 | 6.97e-01 | 1.14e-03 | 3.69e-05 |\n", + "| 7.64e-01 | 5.70e-01 | 1.35e-03 | 1.76e-04 |\n", + "| 9.72e-01 | 7.53e-01 | 3.11e-03 | 1.40e-03 |\n", + "| 9.72e-01 | 7.55e-01 | 4.07e-03 | 2.20e-03 |\n", + "| 9.73e-01 | 7.59e-01 | 4.64e-03 | 2.58e-03 |\n", + "| 9.73e-01 | 7.59e-01 | 4.67e-03 | 2.60e-03 |\n", + "| 9.74e-01 | 7.66e-01 | 6.55e-03 | 3.89e-03 |\n", + "| 9.42e-01 | 7.13e-01 | 6.42e-03 | 3.91e-03 |\n", + "| 9.81e-01 | 7.72e-01 | 1.94e-02 | 3.68e-03 |\n", + "| 9.79e-01 | 7.69e-01 | 1.94e-02 | 3.67e-03 |\n", + "+------------------+------------------+------------------+-------------------+" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 7:=====(3 + 1) / 3][Stage 10:====(3 + 1) / 3][Stage 11:====(3 + 1) / 3]\r" ] } ], "source": [ - "print(get_variant_count(v2_ht, singletons=True, doubletons=True))" + "from gnomad.resources.grch38.gnomad import coverage\n", + "\n", + "coverage_ht = coverage(\"exomes\").ht()\n", + "\n", + "#Filter to PASS SNVs with AF <= 0.1% and median exome depth ≥ 30.\n", + "ht = ht.filter(\n", + " (hl.len(ht.filters) == 0) \n", + " & (ht.allele_info.allele_type == \"snv\")\n", + " & (ht.freq[0].AF <= 0.001)\n", + " & (coverage_ht[ht.locus].median_approx >= 30)\n", + ")\n", + "\n", + "print(f\"Number of variants: {ht.count()}\")\n", + "ht.select(\n", + " freq=ht.freq[0],\n", + " csq=ht.vep.transcript_consequences[0].consequence_terms,\n", + " coverage=coverage_ht[ht.locus],\n", + ").show(-1)" + ] + }, + { + "cell_type": "markdown", + "id": "bf479bea", + "metadata": {}, + "source": [ + "# Get 'freq' for specific genetic ancestry groups" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "ee3cb2dd", + "metadata": {}, + "outputs": [], + "source": [ + "ht = v4_public_release(\"exomes\").ht()\n", + "\n", + "# Filter to interval, e.g. for ASH1L.\n", + "gene_interval = \"chr1:155335268-155563162\"\n", + "\n", + "# Filter the exome release Hail Table to the ASH1L gene interval.\n", + "ht = hl.filter_intervals(ht, [hl.parse_locus_interval(gene_interval, reference_genome=\"GRCh38\")])\n", + "\n", + "# Filter to variants with adj.AC > 0 \n", + "ht = ht.filter(ht.freq[0].AC>0)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "01c5051c", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ami', 'asj', 'fin', 'oth', 'remaining'}" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# For this example, we filter to the ancestry that we included in the FAF calculation\n", + "from gnomad.resources.grch38.gnomad import POPS_TO_REMOVE_FOR_POPMAX\n", + "\n", + "POPS_TO_REMOVE_FOR_POPMAX[\"v4\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "905996c8", + "metadata": {}, + "outputs": [], + "source": [ + "from gnomad.utils.filtering import filter_arrays_by_meta\n", + "\n", + "# Remove unwanted stratifications\n", + "items_to_filter1 = ['sex','downsampling','subset']\n", + "freq_meta1, array_exprs1 = filter_arrays_by_meta(\n", + " ht.freq_meta,\n", + " {\n", + " **{a: ht[a] for a in ['freq']},\n", + " \"freq_meta_sample_count\": ht.index_globals().freq_meta_sample_count,\n", + " },\n", + " items_to_filter=items_to_filter1,\n", + " keep=False,\n", + " combine_operator=\"or\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "c7cfb183", + "metadata": {}, + "outputs": [], + "source": [ + "# keep the necessary stratifications for your analysis \n", + "items_to_filter2 = {'gen_anc':['ami', 'asj', 'fin', 'oth', 'remaining'], 'group':['raw']}\n", + "\n", + "freq_meta2, array_exprs2 = filter_arrays_by_meta(\n", + " freq_meta1,\n", + " array_exprs1,\n", + " items_to_filter=items_to_filter2,\n", + " keep=False,\n", + " combine_operator=\"or\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "f39a0853", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[{'group': 'adj'},\n", + " {'gen_anc': 'afr', 'group': 'adj'},\n", + " {'gen_anc': 'amr', 'group': 'adj'},\n", + " {'gen_anc': 'eas', 'group': 'adj'},\n", + " {'gen_anc': 'mid', 'group': 'adj'},\n", + " {'gen_anc': 'nfe', 'group': 'adj'},\n", + " {'gen_anc': 'sas', 'group': 'adj'}]]" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "freq_meta2.collect()" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "f57f9535", + "metadata": {}, + "outputs": [], + "source": [ + "ht = ht.annotate(**{a: array_exprs2[a] for a in ['freq']})\n", + "ht = ht.annotate_globals(\n", + " freq_meta=freq_meta2,\n", + " freq_meta_sample_count=array_exprs2[\"freq_meta_sample_count\"],\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "4792f7d1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
all
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locus<GRCh38>array<str>int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64
chr1:155335497["A","C"]11.79e-0356000NA000NA0000.00e+0013800NA0000.00e+00200NA00
chr1:155335570["T","C"]35.26e-0357000NA000NA0017.25e-0313800NA0000.00e+00200NA00
chr1:155335571["TA","T"]35.26e-0357000NA000NA0017.25e-0313800NA0000.00e+00200NA00
chr1:155335746["G","C"]11.77e-0356400NA000NA0000.00e+0013200NA0000.00e+00200NA00
chr1:155335855["G","A"]11.74e-0357600NA000NA0017.25e-0313800NA0000.00e+00600NA00

showing top 5 rows

\n" + ], + "text/plain": [ + "+----------------+------------+--------+----------+--------+\n", + "| locus | alleles | all.AC | all.AF | all.AN |\n", + "+----------------+------------+--------+----------+--------+\n", + "| locus | array | int32 | float64 | int32 |\n", + "+----------------+------------+--------+----------+--------+\n", + "| chr1:155335497 | [\"A\",\"C\"] | 1 | 1.79e-03 | 560 |\n", + "| chr1:155335570 | [\"T\",\"C\"] | 3 | 5.26e-03 | 570 |\n", + "| chr1:155335571 | [\"TA\",\"T\"] | 3 | 5.26e-03 | 570 |\n", + "| chr1:155335746 | [\"G\",\"C\"] | 1 | 1.77e-03 | 564 |\n", + "| chr1:155335855 | [\"G\",\"A\"] | 1 | 1.74e-03 | 576 |\n", + "+----------------+------------+--------+----------+--------+\n", + "\n", + "+----------------------+--------+---------+--------+----------------------+\n", + "| all.homozygote_count | afr.AC | afr.AF | afr.AN | afr.homozygote_count |\n", + "+----------------------+--------+---------+--------+----------------------+\n", + "| int64 | int32 | float64 | int32 | int64 |\n", + "+----------------------+--------+---------+--------+----------------------+\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "+----------------------+--------+---------+--------+----------------------+\n", + "\n", + "+--------+---------+--------+----------------------+--------+----------+\n", + "| amr.AC | amr.AF | amr.AN | amr.homozygote_count | eas.AC | eas.AF |\n", + "+--------+---------+--------+----------------------+--------+----------+\n", + "| int32 | float64 | int32 | int64 | int32 | float64 |\n", + "+--------+---------+--------+----------------------+--------+----------+\n", + "| 0 | NA | 0 | 0 | 0 | 0.00e+00 |\n", + "| 0 | NA | 0 | 0 | 1 | 7.25e-03 |\n", + "| 0 | NA | 0 | 0 | 1 | 7.25e-03 |\n", + "| 0 | NA | 0 | 0 | 0 | 0.00e+00 |\n", + "| 0 | NA | 0 | 0 | 1 | 7.25e-03 |\n", + "+--------+---------+--------+----------------------+--------+----------+\n", + "\n", + "+--------+----------------------+--------+---------+--------+\n", + "| eas.AN | eas.homozygote_count | mid.AC | mid.AF | mid.AN |\n", + "+--------+----------------------+--------+---------+--------+\n", + "| int32 | int64 | int32 | float64 | int32 |\n", + "+--------+----------------------+--------+---------+--------+\n", + "| 138 | 0 | 0 | NA | 0 |\n", + "| 138 | 0 | 0 | NA | 0 |\n", + "| 138 | 0 | 0 | NA | 0 |\n", + "| 132 | 0 | 0 | NA | 0 |\n", + "| 138 | 0 | 0 | NA | 0 |\n", + "+--------+----------------------+--------+---------+--------+\n", + "\n", + "+----------------------+--------+----------+--------+----------------------+\n", + "| mid.homozygote_count | nfe.AC | nfe.AF | nfe.AN | nfe.homozygote_count |\n", + "+----------------------+--------+----------+--------+----------------------+\n", + "| int64 | int32 | float64 | int32 | int64 |\n", + "+----------------------+--------+----------+--------+----------------------+\n", + "| 0 | 0 | 0.00e+00 | 2 | 0 |\n", + "| 0 | 0 | 0.00e+00 | 2 | 0 |\n", + "| 0 | 0 | 0.00e+00 | 2 | 0 |\n", + "| 0 | 0 | 0.00e+00 | 2 | 0 |\n", + "| 0 | 0 | 0.00e+00 | 6 | 0 |\n", + "+----------------------+--------+----------+--------+----------------------+\n", + "\n", + "+--------+---------+--------+----------------------+\n", + "| sas.AC | sas.AF | sas.AN | sas.homozygote_count |\n", + "+--------+---------+--------+----------------------+\n", + "| int32 | float64 | int32 | int64 |\n", + "+--------+---------+--------+----------------------+\n", + "| 0 | NA | 0 | 0 |\n", + "| 0 | NA | 0 | 0 |\n", + "| 0 | NA | 0 | 0 |\n", + "| 0 | NA | 0 | 0 |\n", + "| 0 | NA | 0 | 0 |\n", + "+--------+---------+--------+----------------------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 7:=====(3 + 1) / 3][Stage 10:====(3 + 1) / 3][Stage 11:====(3 + 1) / 3]\r" + ] + } + ], + "source": [ + "populations = ['all', 'afr', 'amr', 'eas', 'mid', 'nfe', 'sas']\n", + "ht.select(**{pop: ht.freq[i] for i, pop in enumerate(populations)}).show(5)" ] }, { "cell_type": "code", "execution_count": null, - "id": "d6f4a60c", + "id": "027a2120", "metadata": {}, "outputs": [], "source": [] @@ -503,7 +1081,23 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.7" + }, + "toc": { + "base_numbering": 1, + "nav_menu": { + "height": "213px", + "width": "374px" + }, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true } }, "nbformat": 4, From a44e72d3bbc41fc41fe6aabeb89d90e11d3e0419 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Mon, 30 Sep 2024 13:21:13 -0400 Subject: [PATCH 03/33] Add example to get AF for one ancestry for one variant --- .../use_cases/toolbox_for_gnomad_users.ipynb | 150 +++++++++++++++--- 1 file changed, 132 insertions(+), 18 deletions(-) diff --git a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb index 93f80a1..ca5208e 100644 --- a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb +++ b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb @@ -366,7 +366,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "dd053d78", + "id": "e69953f7", "metadata": {}, "outputs": [], "source": [ @@ -503,7 +503,7 @@ }, { "cell_type": "markdown", - "id": "cc1178c8", + "id": "725f9a57", "metadata": {}, "source": [ "## Get variant count by AF for a gene" @@ -512,7 +512,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "814e2af4", + "id": "9f8e1ba4", "metadata": {}, "outputs": [ { @@ -542,7 +542,7 @@ }, { "cell_type": "markdown", - "id": "30663beb", + "id": "7bff63bb", "metadata": {}, "source": [ "# Filter to variants by VEP annotations" @@ -550,7 +550,7 @@ }, { "cell_type": "markdown", - "id": "9c78ffb2", + "id": "b1031947", "metadata": {}, "source": [ "## Filter to LOF variants" @@ -559,7 +559,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "a58ab4ac", + "id": "5b08a706", "metadata": {}, "outputs": [ { @@ -589,7 +589,7 @@ }, { "cell_type": "markdown", - "id": "6aaba993", + "id": "f9b2d921", "metadata": {}, "source": [ "## Filter to pLOF variants that we used to compute constraint metrics\n", @@ -605,7 +605,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "291d8b7c", + "id": "6ce87a77", "metadata": {}, "outputs": [ { @@ -814,16 +814,24 @@ }, { "cell_type": "markdown", - "id": "bf479bea", + "id": "b104a39b", "metadata": {}, "source": [ "# Get 'freq' for specific genetic ancestry groups" ] }, + { + "cell_type": "markdown", + "id": "135565fe", + "metadata": {}, + "source": [ + "## Get 'freq' for multiple groups for an (gene) interval" + ] + }, { "cell_type": "code", "execution_count": 85, - "id": "ee3cb2dd", + "id": "4f78166f", "metadata": {}, "outputs": [], "source": [ @@ -842,7 +850,7 @@ { "cell_type": "code", "execution_count": 86, - "id": "01c5051c", + "id": "8f625a41", "metadata": { "scrolled": true }, @@ -868,7 +876,7 @@ { "cell_type": "code", "execution_count": 87, - "id": "905996c8", + "id": "15729719", "metadata": {}, "outputs": [], "source": [ @@ -891,11 +899,11 @@ { "cell_type": "code", "execution_count": 88, - "id": "c7cfb183", + "id": "d2886179", "metadata": {}, "outputs": [], "source": [ - "# keep the necessary stratifications for your analysis \n", + "# Remove the genetic ancetries/group that you don't need \n", "items_to_filter2 = {'gen_anc':['ami', 'asj', 'fin', 'oth', 'remaining'], 'group':['raw']}\n", "\n", "freq_meta2, array_exprs2 = filter_arrays_by_meta(\n", @@ -910,7 +918,7 @@ { "cell_type": "code", "execution_count": 89, - "id": "f39a0853", + "id": "5471689c", "metadata": {}, "outputs": [ { @@ -937,7 +945,7 @@ { "cell_type": "code", "execution_count": 90, - "id": "f57f9535", + "id": "16170ed7", "metadata": {}, "outputs": [], "source": [ @@ -951,7 +959,7 @@ { "cell_type": "code", "execution_count": 91, - "id": "4792f7d1", + "id": "e3bcf7a0", "metadata": {}, "outputs": [ { @@ -1056,10 +1064,116 @@ "ht.select(**{pop: ht.freq[i] for i, pop in enumerate(populations)}).show(5)" ] }, + { + "cell_type": "markdown", + "id": "fe2e98b8", + "metadata": {}, + "source": [ + "## Get 'freq' for a specific group and a specific variant" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "9a4f26a2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[{'gen_anc': 'afr', 'group': 'adj'}]]" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from gnomad.utils.filtering import filter_arrays_by_meta\n", + "\n", + "ht = v4_public_release(\"exomes\").ht()\n", + "\n", + "# Filter by the location of the variant\n", + "ht = ht.filter((ht.locus.contig == \"chr22\") & (ht.locus.position==15528692))\n", + "\n", + "# Assign th\n", + "items_to_filter1 = {'gen_anc':['afr'], 'group':['adj']}\n", + "freq_meta1, array_exprs1 = filter_arrays_by_meta(\n", + " ht.freq_meta,\n", + " {\n", + " **{a: ht[a] for a in ['freq']},\n", + " \"freq_meta_sample_count\": ht.index_globals().freq_meta_sample_count,\n", + " },\n", + " items_to_filter=items_to_filter1,\n", + " keep=True,\n", + " combine_operator=\"and\",\n", + " )\n", + "\n", + "# if you want to further remove 'downsampling', 'sex', and 'subset'\n", + "items_to_filter2 = ['sex','downsampling','subset']\n", + "freq_meta2, array_exprs2 = filter_arrays_by_meta(\n", + " freq_meta1,\n", + " {\n", + " **{a: ht[a] for a in ['freq']},\n", + " \"freq_meta_sample_count\": ht.index_globals().freq_meta_sample_count,\n", + " },\n", + " items_to_filter=items_to_filter2,\n", + " keep=False,\n", + " combine_operator=\"or\",\n", + " )\n", + "freq_meta2.collect()" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "7e044201", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
locus
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locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>
chr22:15528692["C","G"][(793,5.43e-04,1459438,7)]
" + ], + "text/plain": [ + "+----------------+------------+\n", + "| locus | alleles |\n", + "+----------------+------------+\n", + "| locus | array |\n", + "+----------------+------------+\n", + "| chr22:15528692 | [\"C\",\"G\"] |\n", + "+----------------+------------+\n", + "\n", + "+---------------------------------------------------------------------------+\n", + "| freq |\n", + "+---------------------------------------------------------------------------+\n", + "| array |\n", + "+---------------------------------------------------------------------------+\n", + "| [(793,5.43e-04,1459438,7)] |\n", + "+---------------------------------------------------------------------------+" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht = ht.annotate(**{a: array_exprs2[a] for a in ['freq']})\n", + "ht = ht.annotate_globals(\n", + " freq_meta=freq_meta2,\n", + " freq_meta_sample_count=array_exprs2[\"freq_meta_sample_count\"],\n", + " )\n", + "\n", + "ht.select('freq').show(-1)" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "027a2120", + "id": "6fc82c5c", "metadata": {}, "outputs": [], "source": [] From 4bc936c3c94c046b44b29ca593b5db63ccf9a570 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 1 Oct 2024 10:27:26 -0400 Subject: [PATCH 04/33] Add table of contents and screenshot --- .../use_cases/toolbox_for_gnomad_users.ipynb | 47 ++++++++++++++++--- 1 file changed, 40 insertions(+), 7 deletions(-) diff --git a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb index ca5208e..791362c 100644 --- a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb +++ b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb @@ -1,5 +1,16 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "8e609a46", + "metadata": { + "toc": true + }, + "source": [ + "

Table of Contents

\n", + "
" + ] + }, { "cell_type": "code", "execution_count": 6, @@ -420,7 +431,9 @@ "id": "df28f17d", "metadata": {}, "source": [ - "## Get variant count by AF for a release" + "## Get variant count by AF for a release\n", + "\n", + "**Note: this will take long if your notebook is using multiple nodes.**" ] }, { @@ -588,6 +601,11 @@ ] }, { + "attachments": { + "Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png": { + "image/png": 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" + } + }, "cell_type": "markdown", "id": "f9b2d921", "metadata": {}, @@ -599,7 +617,9 @@ "* PASS variants that are SNVs with MAF ≤ 0.1%, and\n", "* Exome median depth ≥ 30\n", "\n", - "**Note: this number should match the number of observed pLOF SNVs on the gene page of gnomAD Browser.**" + "**Note: this number should match the number of observed pLOF SNVs on the gene page of gnomAD Browser.**\n", + "\n", + "![Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png](attachment:Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png)" ] }, { @@ -804,6 +824,7 @@ " & (coverage_ht[ht.locus].median_approx >= 30)\n", ")\n", "\n", + "\n", "print(f\"Number of variants: {ht.count()}\")\n", "ht.select(\n", " freq=ht.freq[0],\n", @@ -1200,18 +1221,30 @@ "toc": { "base_numbering": 1, "nav_menu": { - "height": "213px", - "width": "374px" + "height": "613.99px", + "width": "526.312px" }, - "number_sections": true, + "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, + "toc_cell": true, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "219.438px" + }, "toc_section_display": true, "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, From 2f1156e60d8d27f012dd08fe275bca330a893d7d Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 1 Oct 2024 11:32:05 -0400 Subject: [PATCH 05/33] Format gnomad_methods in requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 2900720..338124c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,3 @@ # We're using the main branch of gnomad_method on github rather than the pip version -git+https://github.com/broadinstitute/gnomad_methods@main +gnomad_methods@git+https://github.com/broadinstitute/gnomad_methods@main hail From ff64d867263bf21ceb56811826b9a4941512f652 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 1 Oct 2024 11:40:12 -0400 Subject: [PATCH 06/33] Remove setup.py --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 338124c..2900720 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,3 @@ # We're using the main branch of gnomad_method on github rather than the pip version -gnomad_methods@git+https://github.com/broadinstitute/gnomad_methods@main +git+https://github.com/broadinstitute/gnomad_methods@main hail From bc85e6fcc89a269a576208f35fdbb5a1cf33d1ef Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 1 Oct 2024 11:47:29 -0400 Subject: [PATCH 07/33] Remove setup.py --- setup.py | 41 ----------------------------------------- 1 file changed, 41 deletions(-) delete mode 100644 setup.py diff --git a/setup.py b/setup.py deleted file mode 100644 index f0e9818..0000000 --- a/setup.py +++ /dev/null @@ -1,41 +0,0 @@ -"""Setup script.""" - -import setuptools - - -with open("README.md", "r") as readme_file: - long_description = readme_file.read() - -install_requires = [] -with open("requirements.txt", "r") as requirements_file: - for req in (line.strip() for line in requirements_file): - if req != "hail": - install_requires.append(req) - - -setuptools.setup( - name="gnomad_toolbox", - version="0.0.1", - author="The Genome Aggregation Database", - author_email="gnomad@broadinstitute.org", - description="Toolbox to help users process gnomAD release files", - long_description=long_description, - long_description_content_type="text/markdown", - url="https://github.com/broadinstitute/gnomad-toolbox", - packages=setuptools.find_namespace_packages(include=["gnomad_toolbox.*"]), - project_urls={ - "Documentation": "https://broadinstitute.github.io/gnomad-toolbox/", - "Source Code": "https://github.com/broadinstitute/gnomad-toolbox", - "Issues": "https://github.com/broadinstitute/gnomad-toolbox/issues", - "Change Log": "https://github.com/broadinstitute/gnomad-toolbox/releases", - }, - classifiers=[ - "Topic :: Scientific/Engineering :: Bio-Informatics", - "Intended Audience :: Science/Research", - "License :: OSI Approved :: BSD 3-Clause License", - "Programming Language :: Python :: 3", - "Development Status :: 4 - Beta", - ], - python_requires=">=3.9", - install_requires=install_requires, -) From e083fbf4de436e83e54acb5ee60f04ba76773eb3 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 1 Oct 2024 12:10:29 -0400 Subject: [PATCH 08/33] Reorganize the notebook --- .../use_cases/toolbox_for_gnomad_users.ipynb | 155 +++++++----------- 1 file changed, 56 insertions(+), 99 deletions(-) diff --git a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb index 791362c..6b42cfd 100644 --- a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb +++ b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb @@ -8,12 +8,12 @@ }, "source": [ "

Table of Contents

\n", - "
" + "
" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "id": "e77d32b1", "metadata": {}, "outputs": [ @@ -30,7 +30,7 @@ " \n", "
\n", " \n", - " Loading BokehJS ...\n", + " Loading BokehJS ...\n", "
\n" ] }, @@ -202,7 +202,7 @@ " \"\"}};\n", "\n", " function display_loaded() {\n", - " const el = document.getElementById(\"f884217f-12c5-4fe1-a1d8-6a1d52499205\");\n", + " const el = document.getElementById(\"e95c853b-736c-43c1-b869-4fb671ab4b50\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", @@ -308,7 +308,7 @@ " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", - " const cell = $(document.getElementById(\"f884217f-12c5-4fe1-a1d8-6a1d52499205\")).parents('.cell').data().cell;\n", + " const cell = $(document.getElementById(\"e95c853b-736c-43c1-b869-4fb671ab4b50\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " }\n", @@ -324,7 +324,7 @@ " }\n", "}(window));" ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"f884217f-12c5-4fe1-a1d8-6a1d52499205\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"f884217f-12c5-4fe1-a1d8-6a1d52499205\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"e95c853b-736c-43c1-b869-4fb671ab4b50\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"e95c853b-736c-43c1-b869-4fb671ab4b50\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -346,8 +346,8 @@ "output_type": "stream", "text": [ "SPARKMONITOR_LISTENER: Started SparkListener for Jupyter Notebook\n", - "SPARKMONITOR_LISTENER: Port obtained from environment: 52867\n", - "SPARKMONITOR_LISTENER: Application Started: application_1727699094620_0001 ...Start Time: 1727699722656\n" + "SPARKMONITOR_LISTENER: Port obtained from environment: 49385\n", + "SPARKMONITOR_LISTENER: Application Started: application_1727797895584_0002 ...Start Time: 1727798616140\n" ] }, { @@ -355,7 +355,7 @@ "output_type": "stream", "text": [ "Running on Apache Spark version 3.5.0\n", - "SparkUI available at http://qh1-m.c.broad-mpg-gnomad.internal:43005\n", + "SparkUI available at http://qh1-m.c.broad-mpg-gnomad.internal:45603\n", "Welcome to\n", " __ __ <>__\n", " / /_/ /__ __/ /\n", @@ -374,48 +374,27 @@ " )" ] }, + { + "cell_type": "markdown", + "id": "8e713032", + "metadata": {}, + "source": [ + "# Import modules" + ] + }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "id": "e69953f7", "metadata": {}, "outputs": [], "source": [ - "def get_variant_count(\n", - " ht: hl.Table,\n", - " afs: list[float] = [0.01, 0.001],\n", - " singletons: bool = False,\n", - " doubletons: bool = False,\n", - ") -> dict:\n", - " \"\"\"\n", - " Count variants with frequency <1%, <0.1%, and singletons (AC == 1).\n", - "\n", - " .. note:: This function works for gnomAD exomes and genomes datasets, not yet for\n", - " gnomAD joint dataset, since the HT schema is slightly different.\n", - "\n", - " :param ht: Input Table.\n", - " :param afs: List of allele frequencies cutoffs.\n", - " :param singletons: Include singletons.\n", - " :param doubletons: Include doubletons.\n", - " :return: Dictionary with counts.\n", - " \"\"\"\n", - " counts = {}\n", - "\n", - " # Filter to PASS variants.\n", - " ht = ht.filter(hl.len(ht.filters) == 0)\n", - " if singletons:\n", - " n_singletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 1))\n", - " counts[\"number of singletons\"] = n_singletons\n", - " if doubletons:\n", - " n_doubletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 2))\n", - " counts[\"number of doubletons\"] = n_doubletons\n", - "\n", - " for af in afs:\n", - " n_variants = ht.aggregate(hl.agg.count_where(ht.freq[0].AF < af))\n", - " counts[f\"number of variants with AF < {af}\"] = n_variants\n", - "\n", - " # Count variants with frequency <1%, <0.1%, and singletons (AC == 1).\n", - " return counts" + "from gnomad_toolbox.modules.variant_filtering import get_variant_count\n", + "from gnomad.resources.grch38.gnomad import public_release as v4_public_release\n", + "from gnomad.utils.vep import filter_vep_transcript_csqs\n", + "from gnomad.resources.grch38.gnomad import coverage\n", + "from gnomad.resources.grch38.gnomad import POPS_TO_REMOVE_FOR_POPMAX\n", + "from gnomad.utils.filtering import filter_arrays_by_meta" ] }, { @@ -433,22 +412,12 @@ "source": [ "## Get variant count by AF for a release\n", "\n", - "**Note: this will take long if your notebook is using multiple nodes.**" + "**Note: this will take long if your notebook is NOT using multiple nodes.**" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "d9f96940", - "metadata": {}, - "outputs": [], - "source": [ - "from gnomad.resources.grch38.gnomad import public_release as v4_public_release" - ] - }, - { - "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "id": "112c5065", "metadata": {}, "outputs": [], @@ -519,12 +488,14 @@ "id": "725f9a57", "metadata": {}, "source": [ - "## Get variant count by AF for a gene" + "## Get variant count by AF for a gene interval\n", + "\n", + "**Note: This isn't necessarily equal to the number of variants annotated to this gene by VEP.**" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "id": "9f8e1ba4", "metadata": {}, "outputs": [ @@ -532,7 +503,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[Stage 7:===========================================================(3 + 1) / 3]\r" + "[Stage 3:=======================================> (2 + 1) / 3]\r" ] }, { @@ -571,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "id": "5b08a706", "metadata": {}, "outputs": [ @@ -588,7 +559,6 @@ } ], "source": [ - "from gnomad.utils.vep import filter_vep_transcript_csqs\n", "# Filter to variants in ASH1L that are LOFTEE high-confidence (with no flags) in the MANE select transcript.\n", "ht = filter_vep_transcript_csqs(\n", " ht, \n", @@ -624,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "id": "6ce87a77", "metadata": {}, "outputs": [ @@ -632,7 +602,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[Stage 7:===================(3 + 1) / 3][Stage 10:==================(3 + 1) / 3]\r" + "[Stage 4:=======================================> (2 + 1) / 3]\r" ] }, { @@ -646,7 +616,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[Stage 7:=====(3 + 1) / 3][Stage 10:====(3 + 1) / 3][Stage 11:====(3 + 1) / 3]\r" + "[Stage 5:=======================================> (2 + 1) / 3]\r" ] }, { @@ -802,18 +772,9 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 7:=====(3 + 1) / 3][Stage 10:====(3 + 1) / 3][Stage 11:====(3 + 1) / 3]\r" - ] } ], "source": [ - "from gnomad.resources.grch38.gnomad import coverage\n", - "\n", "coverage_ht = coverage(\"exomes\").ht()\n", "\n", "#Filter to PASS SNVs with AF <= 0.1% and median exome depth ≥ 30.\n", @@ -851,7 +812,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 7, "id": "4f78166f", "metadata": {}, "outputs": [], @@ -870,7 +831,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 8, "id": "8f625a41", "metadata": { "scrolled": true @@ -882,27 +843,23 @@ "{'ami', 'asj', 'fin', 'oth', 'remaining'}" ] }, - "execution_count": 86, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# For this example, we filter to the ancestry that we included in the FAF calculation\n", - "from gnomad.resources.grch38.gnomad import POPS_TO_REMOVE_FOR_POPMAX\n", - "\n", "POPS_TO_REMOVE_FOR_POPMAX[\"v4\"]" ] }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 9, "id": "15729719", "metadata": {}, "outputs": [], "source": [ - "from gnomad.utils.filtering import filter_arrays_by_meta\n", - "\n", "# Remove unwanted stratifications\n", "items_to_filter1 = ['sex','downsampling','subset']\n", "freq_meta1, array_exprs1 = filter_arrays_by_meta(\n", @@ -919,7 +876,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 10, "id": "d2886179", "metadata": {}, "outputs": [], @@ -938,7 +895,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 11, "id": "5471689c", "metadata": {}, "outputs": [ @@ -954,18 +911,19 @@ " {'gen_anc': 'sas', 'group': 'adj'}]]" ] }, - "execution_count": 89, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# The order in the meta is order how AC, AF, AN and homozygote_count is stored in freq. \n", "freq_meta2.collect()" ] }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 12, "id": "16170ed7", "metadata": {}, "outputs": [], @@ -979,10 +937,18 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 13, "id": "e3bcf7a0", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-01 16:07:07.655 Hail: WARN: Name collision: field 'all' already in object dict. \n", + " This field must be referenced with __getitem__ syntax: obj['all']\n" + ] + }, { "data": { "text/html": [ @@ -1071,13 +1037,6 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 7:=====(3 + 1) / 3][Stage 10:====(3 + 1) / 3][Stage 11:====(3 + 1) / 3]\r" - ] } ], "source": [ @@ -1095,7 +1054,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 14, "id": "9a4f26a2", "metadata": {}, "outputs": [ @@ -1105,14 +1064,12 @@ "[[{'gen_anc': 'afr', 'group': 'adj'}]]" ] }, - "execution_count": 96, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from gnomad.utils.filtering import filter_arrays_by_meta\n", - "\n", "ht = v4_public_release(\"exomes\").ht()\n", "\n", "# Filter by the location of the variant\n", @@ -1148,7 +1105,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 15, "id": "7e044201", "metadata": {}, "outputs": [ From b5101babf02725304f619a0194deb1fd8b1628f7 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 11:49:33 -0400 Subject: [PATCH 09/33] Function to import data by version --- ...variant_filtering.py => filter_variant.py} | 0 gnomad_toolbox/modules/import_data.py | 32 +++++++++++++++++++ 2 files changed, 32 insertions(+) rename gnomad_toolbox/modules/{variant_filtering.py => filter_variant.py} (100%) create mode 100644 gnomad_toolbox/modules/import_data.py diff --git a/gnomad_toolbox/modules/variant_filtering.py b/gnomad_toolbox/modules/filter_variant.py similarity index 100% rename from gnomad_toolbox/modules/variant_filtering.py rename to gnomad_toolbox/modules/filter_variant.py diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py new file mode 100644 index 0000000..7e1594d --- /dev/null +++ b/gnomad_toolbox/modules/import_data.py @@ -0,0 +1,32 @@ +"""Functions to import gnomAD data.""" + +import hail as hl +from gnomad.resources.grch37.gnomad import public_release as grch37_public_release +from gnomad.resources.grch38.gnomad import public_release as grch38_public_release + + +def get_ht_by_datatype_and_version( + data_type: str = "exomes", version: str = "4.1" +) -> hl.Table: + """ + Get gnomAD HT by data type and version. + + .. note: Available versions for each data type are: + + | Data Type | GRCh38 Versions | GRCh37 Versions | + |-----------------|----------------------------------|----------------------| + | exomes | 4.0, 4.1 | 2.1, 2.1.1 | + | genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 | + | joint | 4.1 | N/A | + + + :param data_type: Data type (exomes or genomes or joint). + :param version: gnomAD version. + :return: Hail Table. + """ + if version in ["2.1", "2.1.1"]: + return grch37_public_release(data_type).ht() + elif version in ["3.0", "3.1", "3.1.1", "3.1.2", "4.0", "4.1"]: + return grch38_public_release(data_type).ht() + else: + raise ValueError(f"Version {version} not found for data type {data_type}.") From 2bd494e65c3ed12cc3081a7b6a67c8f28b813fa5 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 12:10:20 -0400 Subject: [PATCH 10/33] Modify import_data function to match gnomad_methods repo version by datatype --- gnomad_toolbox/modules/import_data.py | 37 ++++++++++++++++++++++----- 1 file changed, 31 insertions(+), 6 deletions(-) diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py index 7e1594d..ac17b35 100644 --- a/gnomad_toolbox/modules/import_data.py +++ b/gnomad_toolbox/modules/import_data.py @@ -1,7 +1,12 @@ """Functions to import gnomAD data.""" import hail as hl +from gnomad.resources.grch37.gnomad import EXOME_RELEASES as GRCh37_EXOME_RELEASES +from gnomad.resources.grch37.gnomad import GENOME_RELEASES as GRCh37_GENOME_RELEASES from gnomad.resources.grch37.gnomad import public_release as grch37_public_release +from gnomad.resources.grch38.gnomad import EXOME_RELEASES as GRCh38_EXOME_RELEASES +from gnomad.resources.grch38.gnomad import GENOME_RELEASES as GRCh38_GENOME_RELEASES +from gnomad.resources.grch38.gnomad import JOINT_RELEASES as GRCh38_JOINT_RELEASES from gnomad.resources.grch38.gnomad import public_release as grch38_public_release @@ -19,14 +24,34 @@ def get_ht_by_datatype_and_version( | genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 | | joint | 4.1 | N/A | - - :param data_type: Data type (exomes or genomes or joint). + :param data_type: Data type (exomes, genomes, or joint). :param version: gnomAD version. :return: Hail Table. + :raises ValueError: If the data type or version is invalid. """ - if version in ["2.1", "2.1.1"]: - return grch37_public_release(data_type).ht() - elif version in ["3.0", "3.1", "3.1.1", "3.1.2", "4.0", "4.1"]: + # Mapping data types to version sets for GRCh38 and GRCh37 + versions_by_type = { + "exomes": (GRCh38_EXOME_RELEASES, GRCh37_EXOME_RELEASES), + "genomes": (GRCh38_GENOME_RELEASES, GRCh37_GENOME_RELEASES), + "joint": (GRCh38_JOINT_RELEASES, []), + } + + # Validate data type + if data_type not in versions_by_type: + raise ValueError( + f"Data type {data_type} is invalid. Choose from 'exomes', 'genomes', or 'joint'." + ) + + # Get GRCh38 and GRCh37 versions for the given data type + grch38_versions, grch37_versions = versions_by_type[data_type] + + # Check version availability for GRCh38 and GRCh37 + if version in grch38_versions: return grch38_public_release(data_type).ht() + elif version in grch37_versions: + return grch37_public_release(data_type).ht() else: - raise ValueError(f"Version {version} not found for data type {data_type}.") + raise ValueError( + f"Version {version} is not available for {data_type}. " + f"Available versions: GRCh38 - {grch38_versions}, GRCh37 - {grch37_versions}." + ) From 3cfb1bdac0743061c2e0bb1336300a26702a1bc2 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 14:07:32 -0400 Subject: [PATCH 11/33] Formatting --- gnomad_toolbox/modules/import_data.py | 1 - 1 file changed, 1 deletion(-) diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py index ac17b35..fb56e57 100644 --- a/gnomad_toolbox/modules/import_data.py +++ b/gnomad_toolbox/modules/import_data.py @@ -27,7 +27,6 @@ def get_ht_by_datatype_and_version( :param data_type: Data type (exomes, genomes, or joint). :param version: gnomAD version. :return: Hail Table. - :raises ValueError: If the data type or version is invalid. """ # Mapping data types to version sets for GRCh38 and GRCh37 versions_by_type = { From 8d904cf8299fc843ac091d4f0ab668bc3f95f157 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 14:12:22 -0400 Subject: [PATCH 12/33] Formatting --- gnomad_toolbox/modules/import_data.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py index fb56e57..f0fb127 100644 --- a/gnomad_toolbox/modules/import_data.py +++ b/gnomad_toolbox/modules/import_data.py @@ -16,7 +16,7 @@ def get_ht_by_datatype_and_version( """ Get gnomAD HT by data type and version. - .. note: Available versions for each data type are: + .. note:: Available versions for each data type are: | Data Type | GRCh38 Versions | GRCh37 Versions | |-----------------|----------------------------------|----------------------| From 49b2cb0633fb09235b8337bd80683ddf4ec31df8 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 14:16:32 -0400 Subject: [PATCH 13/33] Formatting --- gnomad_toolbox/modules/import_data.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py index f0fb127..8dcf16d 100644 --- a/gnomad_toolbox/modules/import_data.py +++ b/gnomad_toolbox/modules/import_data.py @@ -16,8 +16,9 @@ def get_ht_by_datatype_and_version( """ Get gnomAD HT by data type and version. - .. note:: Available versions for each data type are: + .. note:: + Available versions for each data type are: | Data Type | GRCh38 Versions | GRCh37 Versions | |-----------------|----------------------------------|----------------------| | exomes | 4.0, 4.1 | 2.1, 2.1.1 | From a7c8e53314ef1c431290ee29e09cc8392354c902 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 14:21:06 -0400 Subject: [PATCH 14/33] Formatting --- gnomad_toolbox/modules/import_data.py | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py index 8dcf16d..0698693 100644 --- a/gnomad_toolbox/modules/import_data.py +++ b/gnomad_toolbox/modules/import_data.py @@ -16,14 +16,13 @@ def get_ht_by_datatype_and_version( """ Get gnomAD HT by data type and version. - .. note:: - - Available versions for each data type are: - | Data Type | GRCh38 Versions | GRCh37 Versions | - |-----------------|----------------------------------|----------------------| - | exomes | 4.0, 4.1 | 2.1, 2.1.1 | - | genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 | - | joint | 4.1 | N/A | + .. note:: Available versions for each data type are (as of 2024-10-29): + + | Data Type | GRCh38 Versions | GRCh37 Versions | + |-----------------|----------------------------------|----------------------| + | exomes | 4.0, 4.1 | 2.1, 2.1.1 | + | genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 | + | joint | 4.1 | N/A | :param data_type: Data type (exomes, genomes, or joint). :param version: gnomAD version. From 6d3471fd24313854f413dbf99aaca9b29e217347 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 14:29:01 -0400 Subject: [PATCH 15/33] Formatting --- gnomad_toolbox/modules/import_data.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py index 0698693..70f183b 100644 --- a/gnomad_toolbox/modules/import_data.py +++ b/gnomad_toolbox/modules/import_data.py @@ -16,8 +16,9 @@ def get_ht_by_datatype_and_version( """ Get gnomAD HT by data type and version. - .. note:: Available versions for each data type are (as of 2024-10-29): + .. note:: + Available versions for each data type are (as of 2024-10-29): | Data Type | GRCh38 Versions | GRCh37 Versions | |-----------------|----------------------------------|----------------------| | exomes | 4.0, 4.1 | 2.1, 2.1.1 | From 1b847b438bb48226b32b80bd4321b9e4404ea369 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 14:44:30 -0400 Subject: [PATCH 16/33] Formatting --- gnomad_toolbox/modules/filter_variant.py | 7 +++++++ gnomad_toolbox/modules/import_data.py | 1 + 2 files changed, 8 insertions(+) diff --git a/gnomad_toolbox/modules/filter_variant.py b/gnomad_toolbox/modules/filter_variant.py index ab7b230..9c7073c 100644 --- a/gnomad_toolbox/modules/filter_variant.py +++ b/gnomad_toolbox/modules/filter_variant.py @@ -1,6 +1,13 @@ """Small functions to filter variants in gnomAD datasets, such as by allele frequency or by variant type.""" import hail as hl +from gnomad.resources.grch38.gnomad import POPS_TO_REMOVE_FOR_POPMAX, coverage +from gnomad.utils.filtering import filter_arrays_by_meta +from gnomad.utils.vep import ( + CSQ_CODING, + filter_vep_transcript_csqs, + get_most_severe_consequence_for_summary, +) def get_variant_count( diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py index 70f183b..acd93ee 100644 --- a/gnomad_toolbox/modules/import_data.py +++ b/gnomad_toolbox/modules/import_data.py @@ -19,6 +19,7 @@ def get_ht_by_datatype_and_version( .. note:: Available versions for each data type are (as of 2024-10-29): + | Data Type | GRCh38 Versions | GRCh37 Versions | |-----------------|----------------------------------|----------------------| | exomes | 4.0, 4.1 | 2.1, 2.1.1 | From 22c40f746616c93eeb3072db8131c0bbaee0b62f Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 14:49:52 -0400 Subject: [PATCH 17/33] Formatting docstring block --- gnomad_toolbox/modules/filter_variant.py | 83 ++++++++++++++++++++++++ gnomad_toolbox/modules/import_data.py | 12 ++-- 2 files changed, 90 insertions(+), 5 deletions(-) diff --git a/gnomad_toolbox/modules/filter_variant.py b/gnomad_toolbox/modules/filter_variant.py index 9c7073c..7b1d4a6 100644 --- a/gnomad_toolbox/modules/filter_variant.py +++ b/gnomad_toolbox/modules/filter_variant.py @@ -45,3 +45,86 @@ def get_variant_count( # Count variants with frequency <1%, <0.1%, and singletons (AC == 1). return counts + + +def filter_by_csqs(ht: hl.Table, csqs: list[str]) -> hl.Table: + """ + Filter variants by consequence. + + :param ht: Input Table. + :param csqs: List of consequences. + :return: Table with variants with the given consequences. + """ + ht = ht.filter( + hl.any( + hl.map( + lambda x: (x.consequence_terms.contains(csqs)), + ht.vep.transcript_consequences, + ) + ) + ) + + return ht + + +def filter_by_gene_symbol(ht: hl.Table, gene: str) -> hl.Table: + """ + Filter variants in a gene. + + :param ht: Input Table. + :param gene: Gene symbol or. + :return: Table with variants in the gene. + """ + ht = filter_vep_transcript_csqs( + ht, + synonymous=False, + mane_select=True, + genes=[gene], + match_by_gene_symbol=True, + ) + + return ht + + +def filter_to_coding_variants(ht: hl.Table) -> hl.Table: + """ + Filter to coding variants. + + :param ht: Input Table. + :return: Table with coding variants. + """ + ht = filter_vep_transcript_csqs( + ht, + synonymous=False, + canonical=True, + ) + ht = get_most_severe_consequence_for_summary(ht) + + filter_expr = {} + filter_expr["coding"] = hl.any(lambda csq: ht.most_severe_csq == csq, CSQ_CODING) + + ht = ht.filter(filter_expr["coding"]) + + return ht + + +def filter_to_lof_variants(ht: hl.Table) -> hl.Table: + """ + Filter to loss-of-function (LoF) variants. + + :param ht: Input Table. + :return: Table with LoF variants. + """ + ht = filter_vep_transcript_csqs( + ht, + lof=True, + canonical=True, + ) + ht = get_most_severe_consequence_for_summary(ht) + + filter_expr = {} + filter_expr["lof"] = hl.any(lambda csq: ht.most_severe_csq == csq, CSQ_CODING) + + ht = ht.filter(filter_expr["lof"]) + + return ht diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py index acd93ee..8616917 100644 --- a/gnomad_toolbox/modules/import_data.py +++ b/gnomad_toolbox/modules/import_data.py @@ -20,11 +20,13 @@ def get_ht_by_datatype_and_version( Available versions for each data type are (as of 2024-10-29): - | Data Type | GRCh38 Versions | GRCh37 Versions | - |-----------------|----------------------------------|----------------------| - | exomes | 4.0, 4.1 | 2.1, 2.1.1 | - | genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 | - | joint | 4.1 | N/A | + :: + + | Data Type | GRCh38 Versions | GRCh37 Versions | + |-----------------|----------------------------------|----------------------| + | exomes | 4.0, 4.1 | 2.1, 2.1.1 | + | genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 | + | joint | 4.1 | N/A | :param data_type: Data type (exomes, genomes, or joint). :param version: gnomAD version. From 66770eae25b630ca9cdee03ea3973f60bdee4072 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 16:37:55 -0400 Subject: [PATCH 18/33] Function to extract callstats for one variant in one genetic ancestry group --- gnomad_toolbox/modules/extract_freq.py | 50 ++++++++++++++++++++++++++ 1 file changed, 50 insertions(+) create mode 100644 gnomad_toolbox/modules/extract_freq.py diff --git a/gnomad_toolbox/modules/extract_freq.py b/gnomad_toolbox/modules/extract_freq.py new file mode 100644 index 0000000..cb590b6 --- /dev/null +++ b/gnomad_toolbox/modules/extract_freq.py @@ -0,0 +1,50 @@ +"""Extract callstats from 'freq' of gnomAD HTs.""" + +from typing import List, Optional + +import hail as hl +from gnomad.resources.grch38.gnomad import POPS_TO_REMOVE_FOR_POPMAX +from gnomad.utils.filtering import filter_arrays_by_meta + + +def extract_callstats_for_1anc_1variant( + ht: hl.Table, gen_anc: str, contig: str, position: int, alleles: Optional[List[str]] +) -> hl.Table: + """ + Extract callstats for a single genetic ancestry group and a single variant. + + :param ht: Input Table. + :param group: Ancestry Group (e.g., 'afr', 'amr', 'asj', 'eas', 'fin', 'nfe', + 'oth', 'sas'). + :param contig: Chromosome. + :param position: Position. + :param alleles: List of alleles. + :return: Table with callstats for the given group. + """ + # Filter to the variant of interest + ht = ht.filter( + (ht.locus.contig == contig) + & (ht.locus.position == position) + & (ht.alleles == alleles) + ) + + # Format the gen_anc to lowercase if it's fed in as uppercase + gen_anc = gen_anc.lower() + items_to_filter = {"gen_anc": [gen_anc], "group": ["adj"]} + freq_meta, array_exprs = filter_arrays_by_meta( + ht.freq_meta, + { + **{a: ht[a] for a in ["freq"]}, + "freq_meta_sample_count": ht.index_globals().freq_meta_sample_count, + }, + items_to_filter=items_to_filter, + keep=True, + combine_operator="and", + exact_match=True, + ) + ht = ht.select(**{gen_anc: array_exprs["freq"]}) + ht = ht.annotate_globals( + freq_meta=freq_meta, + freq_meta_sample_count=array_exprs["freq_meta_sample_count"], + ) + return ht From 71a33bdcce46d92eab923f31e50a106d46970207 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 18:18:19 -0400 Subject: [PATCH 19/33] Function to extract callstats for genetic ancestry groups --- gnomad_toolbox/modules/extract_freq.py | 72 +++++++++++++++++++++----- 1 file changed, 59 insertions(+), 13 deletions(-) diff --git a/gnomad_toolbox/modules/extract_freq.py b/gnomad_toolbox/modules/extract_freq.py index cb590b6..5411dcb 100644 --- a/gnomad_toolbox/modules/extract_freq.py +++ b/gnomad_toolbox/modules/extract_freq.py @@ -8,18 +8,17 @@ def extract_callstats_for_1anc_1variant( - ht: hl.Table, gen_anc: str, contig: str, position: int, alleles: Optional[List[str]] + ht: hl.Table, gen_anc: str, contig: str, position: int, alleles: List[str] ) -> hl.Table: """ - Extract callstats for a single genetic ancestry group and a single variant. + Extract callstats for a specific ancestry group and single variant. - :param ht: Input Table. - :param group: Ancestry Group (e.g., 'afr', 'amr', 'asj', 'eas', 'fin', 'nfe', - 'oth', 'sas'). - :param contig: Chromosome. - :param position: Position. - :param alleles: List of alleles. - :return: Table with callstats for the given group. + :param ht: Input Hail Table with variant data. + :param gen_anc: Genetic ancestry group (e.g., 'afr', 'nfe'). + :param contig: Chromosome of the variant. + :param position: Variant position. + :param alleles: List of alleles for the variant (e.g., ['A', 'T']). + :return: Filtered Table with callstats for the specified group. """ # Filter to the variant of interest ht = ht.filter( @@ -28,9 +27,14 @@ def extract_callstats_for_1anc_1variant( & (ht.alleles == alleles) ) - # Format the gen_anc to lowercase if it's fed in as uppercase - gen_anc = gen_anc.lower() - items_to_filter = {"gen_anc": [gen_anc], "group": ["adj"]} + # Check if the variant exists + if ht.count() == 0: + hl.utils.warning( + f"No variant found at {contig}:{position} with alleles {alleles}" + ) + + # Format gen_anc to lowercase and filter arrays by metadata + items_to_filter = {"gen_anc": [gen_anc.lower()], "group": ["adj"]} freq_meta, array_exprs = filter_arrays_by_meta( ht.freq_meta, { @@ -42,7 +46,49 @@ def extract_callstats_for_1anc_1variant( combine_operator="and", exact_match=True, ) - ht = ht.select(**{gen_anc: array_exprs["freq"]}) + # Select frequency for ancestry group + ht = ht.select( + **{ + gen_anc: array_exprs["freq"][i] + for i, gen_anc in enumerate([gen_anc.lower()]) + } + ) + ht = ht.annotate_globals( + freq_meta=freq_meta, + freq_meta_sample_count=array_exprs["freq_meta_sample_count"], + ) + return ht + + +def extract_callstats_for_multiple_ancs( + ht: hl.Table, + gen_ancs: List[str], +) -> hl.Table: + """ + Extract callstats for multiple genetic ancestry groups. + + :param ht: Input Table. + :param gen_ancs: List of Ancestry Groups (e.g., 'afr', 'amr', 'asj', 'eas', 'fin', 'nfe', + 'oth', 'sas'). + :return: Table with callstats for the given groups. + """ + # Format the gen_ancs to lowercase if they're fed in as uppercase + gen_ancs = [gen_anc.lower() for gen_anc in gen_ancs] + items_to_filter = {"gen_anc": gen_ancs, "group": ["adj"]} + freq_meta, array_exprs = filter_arrays_by_meta( + ht.freq_meta, + { + **{a: ht[a] for a in ["freq"]}, + "freq_meta_sample_count": ht.index_globals().freq_meta_sample_count, + }, + items_to_filter=items_to_filter, + keep=True, + combine_operator="and", + exact_match=True, + ) + ht = ht.select( + **{gen_anc: array_exprs["freq"][i] for i, gen_anc in enumerate(gen_ancs)} + ) ht = ht.annotate_globals( freq_meta=freq_meta, freq_meta_sample_count=array_exprs["freq_meta_sample_count"], From 80b1f91799a2cee5213152269574192cf30256f8 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 18:24:22 -0400 Subject: [PATCH 20/33] indent correctly --- gnomad_toolbox/modules/extract_freq.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gnomad_toolbox/modules/extract_freq.py b/gnomad_toolbox/modules/extract_freq.py index 5411dcb..ee051e7 100644 --- a/gnomad_toolbox/modules/extract_freq.py +++ b/gnomad_toolbox/modules/extract_freq.py @@ -69,7 +69,7 @@ def extract_callstats_for_multiple_ancs( :param ht: Input Table. :param gen_ancs: List of Ancestry Groups (e.g., 'afr', 'amr', 'asj', 'eas', 'fin', 'nfe', - 'oth', 'sas'). + 'oth', 'sas'). :return: Table with callstats for the given groups. """ # Format the gen_ancs to lowercase if they're fed in as uppercase From 8dcce6c58fcbc1d1abf8829b4890022e19c6d240 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Fri, 1 Nov 2024 19:21:25 -0400 Subject: [PATCH 21/33] Add functions to filter variants by gene symbol, interval and csqs --- gnomad_toolbox/modules/filter_variant.py | 150 +++++++++++++++-------- 1 file changed, 97 insertions(+), 53 deletions(-) diff --git a/gnomad_toolbox/modules/filter_variant.py b/gnomad_toolbox/modules/filter_variant.py index 7b1d4a6..6e3bd87 100644 --- a/gnomad_toolbox/modules/filter_variant.py +++ b/gnomad_toolbox/modules/filter_variant.py @@ -1,13 +1,9 @@ """Small functions to filter variants in gnomAD datasets, such as by allele frequency or by variant type.""" +from functools import reduce + import hail as hl -from gnomad.resources.grch38.gnomad import POPS_TO_REMOVE_FOR_POPMAX, coverage -from gnomad.utils.filtering import filter_arrays_by_meta -from gnomad.utils.vep import ( - CSQ_CODING, - filter_vep_transcript_csqs, - get_most_severe_consequence_for_summary, -) +from gnomad.utils.vep import LOF_CSQ_SET def get_variant_count( @@ -47,23 +43,29 @@ def get_variant_count( return counts -def filter_by_csqs(ht: hl.Table, csqs: list[str]) -> hl.Table: +def filter_by_interval(ht: hl.Table, interval: str) -> hl.Table: """ - Filter variants by consequence. + Filter variants by interval. :param ht: Input Table. - :param csqs: List of consequences. - :return: Table with variants with the given consequences. + :param interval: Interval string. Format has to be "chr:start-end", e.g., "1:1000-2000". + :return: Table with variants in the interval. """ - ht = ht.filter( - hl.any( - hl.map( - lambda x: (x.consequence_terms.contains(csqs)), - ht.vep.transcript_consequences, + if ht.locus.dtype.reference_genome.name == "GRCh38": + interval = "chr" + interval + ht = hl.filter_intervals( + ht, + [ + hl.parse_locus_interval( + interval, + reference_genome=( + "GRCh38" + if ht.locus.dtype.reference_genome.name == "GRCh38" + else "GRCh37" + ), ) - ) + ], ) - return ht @@ -71,60 +73,102 @@ def filter_by_gene_symbol(ht: hl.Table, gene: str) -> hl.Table: """ Filter variants in a gene. + .. note:: + This function is to match the number of variants that you will get in the + gnomAD browser, which only focus on variants in "CDS" regions plus 75bp + up- and downstream. This is not the same as filtering by gene symbol with + our `filter_vep_transcript_csqs` function, which will include all variants. + :param ht: Input Table. - :param gene: Gene symbol or. + :param gene: Gene symbol. :return: Table with variants in the gene. """ - ht = filter_vep_transcript_csqs( - ht, - synonymous=False, - mane_select=True, - genes=[gene], - match_by_gene_symbol=True, + if ht.locus.dtype.reference_genome.name == "GRCh37": + gene_ht = hl.read_table( + "gs://gcp-public-data--gnomad/resources/grch37/browser/gnomad" + ".genes.GRCh37.GENCODEv19.ht" + ) + else: + gene_ht = hl.read_table( + "gs://gcp-public-data--gnomad/resources/grch38/browser/gnomad" + ".genes.GRCh38.GENCODEv39.ht" + ) + + gene_ht = gene_ht.annotate( + cds_intervals=hl.array( + gene_ht.exons.filter(lambda exon: exon.feature_type == "CDS") + ).map( + lambda exon: hl.locus_interval( + hl.if_else( + gene_ht.interval.start.dtype.reference_genome.name == "GRCh38", + "chr" + gene_ht.chrom, + gene_ht.chrom, + ), + exon.start - 75, + exon.stop + 75, + reference_genome=gene_ht.interval.start.dtype.reference_genome, + includes_end=True, + ) + ) ) + intervals = gene_ht.filter(gene_ht.gencode_symbol == gene).cds_intervals.collect()[ + 0 + ] + + ht = hl.filter_intervals(ht, intervals) + return ht -def filter_to_coding_variants(ht: hl.Table) -> hl.Table: +def filter_by_csqs( + ht: hl.Table, csqs: list[str], pass_filters: bool = True +) -> hl.Table: """ - Filter to coding variants. + Filter variants by consequences. :param ht: Input Table. - :return: Table with coding variants. + :param csqs: List of consequences to filter by. It can be specified as the + categories on the browser: pLoF, Missense / Inframe indel, Synonymous, Other. + :param pass_filters: Boolean if the variants pass the filters. + :return: Table with variants with the specified consequences. """ - ht = filter_vep_transcript_csqs( - ht, - synonymous=False, - canonical=True, - ) - ht = get_most_severe_consequence_for_summary(ht) + missense_inframe = ["missense_variant", "inframe_insertion", "inframe_deletion"] - filter_expr = {} - filter_expr["coding"] = hl.any(lambda csq: ht.most_severe_csq == csq, CSQ_CODING) + filter_expr = [] + if "lof" in csqs: + filter_expr.append( + hl.literal(LOF_CSQ_SET).contains(ht.vep.most_severe_consequence) + ) - ht = ht.filter(filter_expr["coding"]) + if "synonymous" in csqs: + filter_expr.append(ht.vep.most_severe_consequence == "synonymous_variant") - return ht + if "missense" in csqs: + filter_expr.append( + hl.literal(missense_inframe).contains(ht.vep.most_severe_consequence) + ) + if "other" in csqs: + excluded_csqs = hl.literal( + LOF_CSQ_SET + missense_inframe + ["synonymous_variant"] + ) + filter_expr.append(~excluded_csqs.contains(ht.vep.most_severe_consequence)) -def filter_to_lof_variants(ht: hl.Table) -> hl.Table: - """ - Filter to loss-of-function (LoF) variants. + if len(filter_expr) == 0: + raise ValueError( + "No valid consequence specified. Choose from 'lof', 'synonymous', 'missense', 'other'." + ) - :param ht: Input Table. - :return: Table with LoF variants. - """ - ht = filter_vep_transcript_csqs( - ht, - lof=True, - canonical=True, - ) - ht = get_most_severe_consequence_for_summary(ht) + # Combine filter expressions with logical OR + if len(filter_expr) == 1: + combined_filter = filter_expr[0] + else: + combined_filter = reduce(lambda acc, expr: acc | expr, filter_expr) - filter_expr = {} - filter_expr["lof"] = hl.any(lambda csq: ht.most_severe_csq == csq, CSQ_CODING) + ht = ht.filter(combined_filter) - ht = ht.filter(filter_expr["lof"]) + if pass_filters: + ht = ht.filter(hl.len(ht.filters) == 0) return ht From 34fab765cd35fb9f4ae23ff9600d36c630a7ca87 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Fri, 1 Nov 2024 22:10:52 -0400 Subject: [PATCH 22/33] Correct small errors --- gnomad_toolbox/modules/filter_variant.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/gnomad_toolbox/modules/filter_variant.py b/gnomad_toolbox/modules/filter_variant.py index 6e3bd87..f73fe73 100644 --- a/gnomad_toolbox/modules/filter_variant.py +++ b/gnomad_toolbox/modules/filter_variant.py @@ -3,7 +3,7 @@ from functools import reduce import hail as hl -from gnomad.utils.vep import LOF_CSQ_SET +from gnomad.utils.vep import CSQ_CODING, LOF_CSQ_SET def get_variant_count( @@ -83,6 +83,9 @@ def filter_by_gene_symbol(ht: hl.Table, gene: str) -> hl.Table: :param gene: Gene symbol. :return: Table with variants in the gene. """ + # Make gene symbol uppercase + gene = gene.upper() + if ht.locus.dtype.reference_genome.name == "GRCh37": gene_ht = hl.read_table( "gs://gcp-public-data--gnomad/resources/grch37/browser/gnomad" @@ -151,10 +154,15 @@ def filter_by_csqs( if "other" in csqs: excluded_csqs = hl.literal( - LOF_CSQ_SET + missense_inframe + ["synonymous_variant"] + list(LOF_CSQ_SET) + missense_inframe + ["synonymous_variant"] ) filter_expr.append(~excluded_csqs.contains(ht.vep.most_severe_consequence)) + if "coding" in csqs: + filter_expr.append( + hl.literal(CSQ_CODING).contains(ht.vep.most_severe_consequence) + ) + if len(filter_expr) == 0: raise ValueError( "No valid consequence specified. Choose from 'lof', 'synonymous', 'missense', 'other'." From 0ccdb5bb4b2eac13b5ff3b986da890bc3191c68e Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Fri, 1 Nov 2024 22:12:25 -0400 Subject: [PATCH 23/33] Update notebook --- .../use_cases/toolbox_for_gnomad_users.ipynb | 1874 ++++++++++++++--- 1 file changed, 1593 insertions(+), 281 deletions(-) diff --git a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb index 6b42cfd..0e081fe 100644 --- a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb +++ b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb @@ -8,14 +8,34 @@ }, "source": [ "

Table of Contents

\n", - "
" + "
" ] }, + { + "cell_type": "markdown", + "id": "853c94b9", + "metadata": {}, + "source": [ + "# README\n", + "\n", + "This toolbox is meant to use Hail tables of gnomAD releases on cloud computing, if you want to query variants for gene(s), you should use gnomAD API (https://gnomad.broadinstitute.org/api).\n", + "\n", + "If you want to import your own data to use other gnomAD notebooks, such as for ancestry inference (https://github.com/broadinstitute/gnomad_qc/blob/main/gnomad_qc/example_notebooks/ancestry_classification_using_gnomad_rf.ipynb), you may use Hail's `import_vcf` functions." + ] + }, + { + "cell_type": "markdown", + "id": "ff73954c", + "metadata": {}, + "source": [] + }, { "cell_type": "code", "execution_count": 1, "id": "e77d32b1", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -30,7 +50,7 @@ " \n", "
\n", " \n", - " Loading BokehJS ...\n", + " Loading BokehJS ...\n", "
\n" ] }, @@ -202,7 +222,7 @@ " \"\"}};\n", "\n", " function display_loaded() {\n", - " const el = document.getElementById(\"e95c853b-736c-43c1-b869-4fb671ab4b50\");\n", + " const el = document.getElementById(\"c9c511bf-e817-434d-8072-f4db620005e8\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", @@ -308,7 +328,7 @@ " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", - " const cell = $(document.getElementById(\"e95c853b-736c-43c1-b869-4fb671ab4b50\")).parents('.cell').data().cell;\n", + " const cell = $(document.getElementById(\"c9c511bf-e817-434d-8072-f4db620005e8\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " }\n", @@ -324,7 +344,7 @@ " }\n", "}(window));" ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"e95c853b-736c-43c1-b869-4fb671ab4b50\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"e95c853b-736c-43c1-b869-4fb671ab4b50\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"c9c511bf-e817-434d-8072-f4db620005e8\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"c9c511bf-e817-434d-8072-f4db620005e8\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -346,8 +366,8 @@ "output_type": "stream", "text": [ "SPARKMONITOR_LISTENER: Started SparkListener for Jupyter Notebook\n", - "SPARKMONITOR_LISTENER: Port obtained from environment: 49385\n", - "SPARKMONITOR_LISTENER: Application Started: application_1727797895584_0002 ...Start Time: 1727798616140\n" + "SPARKMONITOR_LISTENER: Port obtained from environment: 51311\n", + "SPARKMONITOR_LISTENER: Application Started: application_1730470703538_0002 ...Start Time: 1730485367380\n" ] }, { @@ -355,12 +375,12 @@ "output_type": "stream", "text": [ "Running on Apache Spark version 3.5.0\n", - "SparkUI available at http://qh1-m.c.broad-mpg-gnomad.internal:45603\n", + "SparkUI available at http://qh1-m.c.broad-mpg-gnomad.internal:39033\n", "Welcome to\n", " __ __ <>__\n", " / /_/ /__ __/ /\n", " / __ / _ `/ / /\n", - " /_/ /_/\\_,_/_/_/ version 0.2.132-678e1f52b999\n", + " /_/ /_/\\_,_/_/_/ version 0.2.133-4c60fddb171a\n", "LOGGING: writing to /test_toolbox.log\n" ] } @@ -384,17 +404,46 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "e69953f7", "metadata": {}, "outputs": [], "source": [ - "from gnomad_toolbox.modules.variant_filtering import get_variant_count\n", - "from gnomad.resources.grch38.gnomad import public_release as v4_public_release\n", - "from gnomad.utils.vep import filter_vep_transcript_csqs\n", - "from gnomad.resources.grch38.gnomad import coverage\n", - "from gnomad.resources.grch38.gnomad import POPS_TO_REMOVE_FOR_POPMAX\n", - "from gnomad.utils.filtering import filter_arrays_by_meta" + "from gnomad_toolbox.modules.filter_variant import get_variant_count, filter_by_interval, filter_by_gene_symbol, filter_by_csqs\n", + "from gnomad_toolbox.modules.import_data import get_ht_by_datatype_and_version\n", + "from gnomad_toolbox.modules.filter_variant import \n", + "from gnomad_toolbox.modules.extract_freq import extract_callstats_for_1anc_1variant, extract_callstats_for_multiple_ancs\n", + "from gnomad.resources.grch38.gnomad import coverage" + ] + }, + { + "cell_type": "markdown", + "id": "5335a135", + "metadata": {}, + "source": [ + "# Import data\n", + "\n", + "You can choose which version of gnomAD release you want to look at, here we listed the available version per data type per reference build. \n", + "\n", + "Available versions for each data type are (as of 2024-10-29):\n", + "\n", + "| Data Type | GRCh38 Versions | GRCh37 Versions |\n", + "|-----------------|----------------------------------|----------------------|\n", + "| exomes | 4.0, 4.1 | 2.1, 2.1.1 |\n", + "| genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 |\n", + "| joint | 4.1 | N/A |\n", + "\n", + "We use gnomAD v4.1 exomes to demonstrate for examples below. " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "100cf576", + "metadata": {}, + "outputs": [], + "source": [ + "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')" ] }, { @@ -415,16 +464,6 @@ "**Note: this will take long if your notebook is NOT using multiple nodes.**" ] }, - { - "cell_type": "code", - "execution_count": 3, - "id": "112c5065", - "metadata": {}, - "outputs": [], - "source": [ - "ht = v4_public_release(\"exomes\").ht()" - ] - }, { "cell_type": "code", "execution_count": 12, @@ -483,45 +522,205 @@ "print(get_variant_count(ht, singletons=True, doubletons=True))" ] }, + { + "cell_type": "markdown", + "id": "ec659eeb", + "metadata": {}, + "source": [ + "## Get variant count by AF for coding variants" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "d65b0ea8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 21:===================================================>(8784 + 5) / 8789]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'number of variants with AF < 0.01': 23762097, 'number of variants with AF < 0.001': 23643787, 'number of variants with AF < 0.0005': 23569893}\n" + ] + } + ], + "source": [ + "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n", + "\n", + "ht = filter_by_csqs(ht, ['coding'])\n", + "\n", + "print(get_variant_count(ht, afs=[0.01, 0.001, 0.0005]))" + ] + }, + { + "cell_type": "markdown", + "id": "f07ca88f", + "metadata": {}, + "source": [ + "## Get variant count by VEP consequence" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "b515bfc0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "18231426" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# total number of missense variant in exomes data\n", + "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n", + "\n", + "ht.filter(\n", + " hl.any(\n", + " hl.map(\n", + " lambda x: (x.consequence_terms.contains(\"missense_variant\")),\n", + " ht.vep.transcript_consequences,\n", + " )\n", + " )\n", + ").count()" + ] + }, { "cell_type": "markdown", "id": "725f9a57", "metadata": {}, "source": [ - "## Get variant count by AF for a gene interval\n", + "## Get variant count by AF for a gene\n", + "\n", + "**Note: This isn't necessarily equal to the number of variants annotated to this gene by VEP.**\n", + "\n", + "Here we show two ways that you can load a variant table on the gnomAD browser, one is the [gene page](https://gnomad.broadinstitute.org/gene/ENSG00000149295?dataset=gnomad_r4) (filtered to MANE Select transcript of that gene, and only variants located in or within 75 base pairs of a coding exon (CDS)), the other is the [region view](https://gnomad.broadinstitute.org/region/11-113409605-113475691?dataset=gnomad_r4). We use *DRD2* gene as an example. " + ] + }, + { + "attachments": { + "Screenshot%202024-11-01%20at%209.31.00%E2%80%AFPM.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "164a07b7", + "metadata": {}, + "source": [ + "### On region view (the interval of a gene)\n", "\n", - "**Note: This isn't necessarily equal to the number of variants annotated to this gene by VEP.**" + "This is for 'DRD2' gene. \n", + "![Screenshot%202024-11-01%20at%209.31.00%E2%80%AFPM.png](attachment:Screenshot%202024-11-01%20at%209.31.00%E2%80%AFPM.png)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 58, "id": "9f8e1ba4", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The total number of variants in this gene interval is: 8126\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "[Stage 3:=======================================> (2 + 1) / 3]\r" + "[Stage 37:=============================> (1 + 1) / 2]\r" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "{'number of singletons': 5282, 'number of doubletons': 1616, 'number of variants with AF < 0.01': 10733, 'number of variants with AF < 0.001': 10656}\n" + "{'number of singletons': 1390, 'number of doubletons': 384, 'number of variants with AF < 0.01': 2711, 'number of variants with AF < 0.001': 2662}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "[Stage 38:=============================> (1 + 1) / 2]\r" ] } ], "source": [ - "# Filter to interval, e.g. for ASH1L.\n", - "gene_interval = \"chr1:155335268-155563162\"\n", + "# Filter to interval, e.g. for DRD2.\n", + "gene_interval = \"11:113409605-113475691\"\n", + "\n", + "gene_ht = filter_by_interval(ht, gene_interval)\n", "\n", "# Filter the exome release Hail Table to the ASH1L gene interval.\n", - "ht = hl.filter_intervals(ht, [hl.parse_locus_interval(gene_interval, reference_genome=\"GRCh38\")])\n", + "print(\"The total number of variants in this gene interval is: \", gene_ht.count())\n", "\n", - "print(get_variant_count(ht, singletons=True, doubletons=True))" + "print(get_variant_count(gene_ht, singletons=True, doubletons=True))" + ] + }, + { + "attachments": { + "Screenshot%202024-11-01%20at%209.28.50%E2%80%AFPM.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "ac393319", + "metadata": {}, + "source": [ + "### On gene page\n", + "\n", + "To get the number of variants shown on the gene page, if you click 'all' and check 'exomes', 'SNVs', 'Indels', and 'Filtered variants' as this: \n", + "\n", + "![Screenshot%202024-11-01%20at%209.28.50%E2%80%AFPM.png](attachment:Screenshot%202024-11-01%20at%209.28.50%E2%80%AFPM.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "e6bf7236", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 26:> (0 + 1) / 1]\r" + ] + }, + { + "data": { + "text/plain": [ + "1764" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# gene_symbol can be upper or lower case\n", + "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n", + "\n", + "drd2 = filter_by_gene_symbol(ht, 'drd2')\n", + "drd2.count()" ] }, { @@ -529,7 +728,1203 @@ "id": "7bff63bb", "metadata": {}, "source": [ - "# Filter to variants by VEP annotations" + "# Filter to variants by VEP annotations\n", + "\n", + "You can get the variant table either by gene_symbol or gene interval, we recommen you to get by gene_symbol because it's already filtered to MANE Select transcript of a gene. " + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "700582e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
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n_smaller
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n_smaller
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bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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showing top 5 rows

\n" + ], + "text/plain": [ + "+-----------------+------------+\n", + "| locus | alleles |\n", + "+-----------------+------------+\n", + "| locus | array |\n", + "+-----------------+------------+\n", + "| chr11:113410731 | [\"C\",\"A\"] |\n", + "| chr11:113410731 | [\"C\",\"T\"] |\n", + "| chr11:113410735 | [\"G\",\"A\"] |\n", + "| chr11:113410736 | [\"G\",\"A\"] |\n", + "| chr11:113410736 | [\"G\",\"T\"] |\n", + "+-----------------+------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| freq |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [(2,1.37e-06,1461866,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(2,4.... |\n", + "| [(1,6.84e-07,1461866,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n", + "| [(75,5.13e-05,1461876,0),(75,5.13e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n", + "| [(15,1.03e-05,1461882,0),(15,1.03e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n", + "| [(1,6.84e-07,1461882,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------+------------------+------------------+\n", + "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n", + "+------------------+------------------+------------------+\n", + "| int32 | float64 | int32 |\n", + "+------------------+------------------+------------------+\n", + "| 2 | 4.47e-05 | 44724 |\n", + "| 1 | 8.99e-07 | 1112010 |\n", + "| 8 | 7.19e-06 | 1112004 |\n", + "| 15 | 1.35e-05 | 1112010 |\n", + "| 1 | 8.99e-07 | 1112010 |\n", + "+------------------+------------------+------------------+\n", + "\n", + "+--------------------------------+-----------------------+-------------------+\n", + "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n", + "+--------------------------------+-----------------------+-------------------+\n", + "| int64 | str | int32 |\n", + "+--------------------------------+-----------------------+-------------------+\n", + "| 0 | \"amr\" | 2 |\n", + "| 0 | \"nfe\" | NA |\n", + "| 0 | \"nfe\" | 2 |\n", + "| 0 | \"nfe\" | 1 |\n", + "| 0 | \"nfe\" | NA |\n", + "+--------------------------------+-----------------------+-------------------+\n", + "\n", + "+-------------------+-------------------+---------------------------------+\n", + "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n", + "+-------------------+-------------------+---------------------------------+\n", + "| float64 | int32 | int64 |\n", + "+-------------------+-------------------+---------------------------------+\n", + "| 4.57e-05 | 43740 | 0 |\n", + "| NA | NA | NA |\n", + "| 5.71e-06 | 350102 | 0 |\n", + "| 2.86e-06 | 350106 | 0 |\n", + "| NA | NA | NA |\n", + "+-------------------+-------------------+---------------------------------+\n", + "\n", + "+------------------------+\n", + "| grpmax.non_ukb.gen_anc |\n", + "+------------------------+\n", + "| str |\n", + "+------------------------+\n", + "| \"amr\" |\n", + "| NA |\n", + "| \"nfe\" |\n", + "| \"nfe\" |\n", + "| NA |\n", + "+------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| faf |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(7.41e-06,2.77e-06),(0.00e+00,0.... |\n", + "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n", + "| [(4.19e-05,3.82e-05),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n", + "| [(6.16e-06,4.89e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n", + "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+-------------------------+---------------------------------+\n", + "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n", + "+-------------------------+---------------------------------+\n", + "| float64 | str |\n", + "+-------------------------+---------------------------------+\n", + "| 7.41e-06 | \"amr\" |\n", + "| NA | NA |\n", + "| 3.09e-06 | \"nfe\" |\n", + "| 8.10e-06 | \"nfe\" |\n", + "| NA | NA |\n", + "+-------------------------+---------------------------------+\n", + "\n", + "+-------------------------+---------------------------------+\n", + "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n", + "+-------------------------+---------------------------------+\n", + "| float64 | str |\n", + "+-------------------------+---------------------------------+\n", + "| 2.77e-06 | \"amr\" |\n", + "| NA | NA |\n", + "| 2.24e-06 | \"nfe\" |\n", + "| 6.42e-06 | \"nfe\" |\n", + "| NA | NA |\n", + "+-------------------------+---------------------------------+\n", + "\n", + "+--------------------------+----------------------------------+\n", + "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n", + "+--------------------------+----------------------------------+\n", + "| float64 | str |\n", + "+--------------------------+----------------------------------+\n", + "| 7.58e-06 | \"amr\" |\n", + "| NA | NA |\n", + "| 9.50e-07 | \"nfe\" |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+--------------------------+----------------------------------+\n", + "\n", + "+--------------------------+----------------------------------+---------+\n", + "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n", + 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{} | 5.49e-01 | 6.00e+01 |\n", + "+-----------+------------------+----------+----------+----------+\n", + "\n", + "+----------------+-----------------+----------+---------------------+\n", + "| info.MQRankSum | info.QUALapprox | info.QD | info.ReadPosRankSum |\n", + "+----------------+-----------------+----------+---------------------+\n", + "| float64 | int64 | float64 | float64 |\n", + "+----------------+-----------------+----------+---------------------+\n", + "| 0.00e+00 | 3862 | 1.02e+01 | -1.98e-01 |\n", + "| 0.00e+00 | 3862 | 1.02e+01 | -1.98e-01 |\n", + "| 0.00e+00 | 127501 | 1.23e+01 | 3.70e-02 |\n", + "| 0.00e+00 | 12307 | 1.16e+01 | 7.40e-02 |\n", + "| 0.00e+00 | 12307 | 1.16e+01 | 7.40e-02 |\n", + "+----------------+-----------------+----------+---------------------+\n", + "\n", + "+-----------------------+----------+------------+------------+------------+\n", + "| info.SB | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n", + 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[386,195,304,161] | 6.47e-01 | 1013 |\n", + "| -9.23e-01 | [386,195,12,5] | 7.90e-01 | 50 |\n", + "+------------------------+-----------------------+-------------+---------------+\n", + "\n", + "+----------------+----------------------------+------------------------+\n", + "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n", + "+----------------+----------------------------+------------------------+\n", + "| bool | bool | bool |\n", + "+----------------+----------------------------+------------------------+\n", + "| False | NA | NA |\n", + "| True | False | False |\n", + "| False | NA | NA |\n", + "| False | NA | NA |\n", + "| True | False | False |\n", + "+----------------+----------------------------+------------------------+\n", + "\n", + "+-----------+------------+------------------+---------------+----------------+\n", + "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n", + 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|\n", + "+-----------------------+-----------------------+---------------------+\n", + "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"A\"] |\n", + "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"T\"] |\n", + "| [113410734,113410734] | [113410735,113410735] | [\"G\",\"A\"] |\n", + "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"A\"] |\n", + "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"T\"] |\n", + "+-----------------------+-----------------------+---------------------+\n", + "\n", + "+-------------------+-----------+--------+--------------------------------+\n", + "| vep.allele_string | vep.end | vep.id | vep.input |\n", + "+-------------------+-----------+--------+--------------------------------+\n", + "| str | int32 | str | str |\n", + "+-------------------+-----------+--------+--------------------------------+\n", + "| \"C/A\" | 113410731 | \".\" | \"chr11\t113410731\t.\tC\tA\t.\t.\tGT\" |\n", + "| \"C/T\" | 113410731 | \".\" | \"chr11\t113410731\t.\tC\tT\t.\t.\tGT\" |\n", + "| \"G/A\" | 113410735 | \".\" | \"chr11\t113410735\t.\tG\tA\t.\t.\tGT\" |\n", + "| \"G/A\" | 113410736 | \".\" | \"chr11\t113410736\t.\tG\tA\t.\t.\tGT\" |\n", + "| \"G/T\" | 113410736 | \".\" | \"chr11\t113410736\t.\tG\tT\t.\t.\tGT\" |\n", + "+-------------------+-----------+--------+--------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| vep.intergenic_consequences |\n", + "+------------------------------------------------------------------------------+\n", + "| array, impact: st... |\n", + "+------------------------------------------------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+-----------------------------+\n", + "| vep.most_severe_consequence |\n", + "+-----------------------------+\n", + 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[0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n", + "| [0,0,0,0,12,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n", + "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n", + "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n", + "+---------------------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_all.n_smaller |\n", + "+---------------------------------------------+\n", + "| int64 |\n", + "+---------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+---------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_all.n_larger |\n", + "+--------------------------------------------+\n", + "| int64 |\n", + "+--------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + 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[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_alt.bin_freq |\n", + "+--------------------------------------------+\n", + "| array |\n", + "+--------------------------------------------+\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1] |\n", + "+--------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_alt.n_smaller |\n", + "+---------------------------------------------+\n", + "| int64 |\n", + "+---------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+---------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_alt.n_larger |\n", + "+--------------------------------------------+\n", + "| int64 |\n", + "+--------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+--------------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| histograms.qual_hists.dp_hist_alt.bin_edges |\n", + 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[0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0] |\n", + "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2] |\n", + "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0] |\n", + "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0] |\n", + "+--------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| histograms.qual_hists.dp_hist_alt.n_smaller |\n", + "+---------------------------------------------+\n", + "| int64 |\n", + "+---------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+---------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| histograms.qual_hists.dp_hist_alt.n_larger |\n", + "+--------------------------------------------+\n", + "| int64 |\n", + "+--------------------------------------------+\n", + "| 1 |\n", + "| 0 |\n", + "| 32 |\n", + "| 1 |\n", + "| 0 |\n", + 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|\n", + "| 2.69e+00 | 2.67e-01 |\n", + "| 2.28e-01 | NA |\n", + "| 1.86e-01 | NA |\n", + "+-------------------------------------+--------------------------------+\n", + "\n", + "+--------------------------------------+\n", + "| in_silico_predictors.spliceai_ds_max |\n", + "+--------------------------------------+\n", + "| float32 |\n", + "+--------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------+\n", + "\n", + "+------------------------------------------+-----------------------------+\n", + "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n", + "+------------------------------------------+-----------------------------+\n", + "| float64 | float64 |\n", + "+------------------------------------------+-----------------------------+\n", + "| 0.00e+00 | 8.78e+00 |\n", + "| 2.00e-02 | 8.78e+00 |\n", + "| 1.00e-02 | 8.67e+00 |\n", + "| 1.00e-02 | -2.55e-01 |\n", + "| 3.00e-02 | -2.55e-01 |\n", + "+------------------------------------------+-----------------------------+\n", + "\n", + "+-------------------------------+-----------------------------------+\n", + "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n", + "+-------------------------------+-----------------------------------+\n", + "| float64 | float64 |\n", + "+-------------------------------+-----------------------------------+\n", + "| 0.00e+00 | 9.89e-01 |\n", + "| 0.00e+00 | 9.89e-01 |\n", + "| 1.30e-01 | 1.18e-01 |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+-------------------------------+-----------------------------------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# lof, missense, synonymous variants passing filters\n", + "variants_of_interest = filter_by_csqs(drd2,['lof','missense','synonymous'])\n", + "variants_of_interest.show(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "9887fdb0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "[Stage 27:> (0 + 1) / 1]\r" + ] + }, + { + "data": { + "text/plain": [ + "17" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# number of lof variants passing filters\n", + "filter_by_csqs(drd2,['lof']).count()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "86596aaf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "[Stage 28:> (0 + 1) / 1]\r" + ] + }, + { + "data": { + "text/plain": [ + "409" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# number of missense variants passing filters\n", + "filter_by_csqs(drd2,['missense']).count()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "b7e4368b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "[Stage 29:> (0 + 1) / 1]\r" + ] + }, + { + "data": { + "text/plain": [ + "238" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# number of synonymous variants passing filters\n", + "filter_by_csqs(drd2,['synonymous']).count()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "4141ccb3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "[Stage 30:> (0 + 1) / 1]\r" + ] + }, + { + "data": { + "text/plain": [ + "783" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# number of 'Other' variants passing filters\n", + "filter_by_csqs(drd2,['other']).count()" ] }, { @@ -537,7 +1932,7 @@ "id": "b1031947", "metadata": {}, "source": [ - "## Filter to LOF variants" + "## Filter to 'HC' LOF variants for certain genes" ] }, { @@ -580,12 +1975,12 @@ "id": "f9b2d921", "metadata": {}, "source": [ - "## Filter to pLOF variants that we used to compute constraint metrics\n", + "### Filter to pLOF variants that we used to compute constraint metrics\n", "pLOF variants meets the following requirements:\n", "* High-confidence LOFTEE variants (without any flags),\n", "* Only variants in the MANE Select transcript,\n", "* PASS variants that are SNVs with MAF ≤ 0.1%, and\n", - "* Exome median depth ≥ 30\n", + "* Exome median depth ≥ 30 (**This is changing in v4 constraint?**)\n", "\n", "**Note: this number should match the number of observed pLOF SNVs on the gene page of gnomAD Browser.**\n", "\n", @@ -812,12 +2207,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 60, "id": "4f78166f", "metadata": {}, "outputs": [], "source": [ - "ht = v4_public_release(\"exomes\").ht()\n", + "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n", "\n", "# Filter to interval, e.g. for ASH1L.\n", "gene_interval = \"chr1:155335268-155563162\"\n", @@ -831,150 +2226,46 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "8f625a41", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'ami', 'asj', 'fin', 'oth', 'remaining'}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# For this example, we filter to the ancestry that we included in the FAF calculation\n", - "POPS_TO_REMOVE_FOR_POPMAX[\"v4\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "15729719", - "metadata": {}, - "outputs": [], - "source": [ - "# Remove unwanted stratifications\n", - "items_to_filter1 = ['sex','downsampling','subset']\n", - "freq_meta1, array_exprs1 = filter_arrays_by_meta(\n", - " ht.freq_meta,\n", - " {\n", - " **{a: ht[a] for a in ['freq']},\n", - " \"freq_meta_sample_count\": ht.index_globals().freq_meta_sample_count,\n", - " },\n", - " items_to_filter=items_to_filter1,\n", - " keep=False,\n", - " combine_operator=\"or\",\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d2886179", - "metadata": {}, - "outputs": [], - "source": [ - "# Remove the genetic ancetries/group that you don't need \n", - "items_to_filter2 = {'gen_anc':['ami', 'asj', 'fin', 'oth', 'remaining'], 'group':['raw']}\n", - "\n", - "freq_meta2, array_exprs2 = filter_arrays_by_meta(\n", - " freq_meta1,\n", - " array_exprs1,\n", - " items_to_filter=items_to_filter2,\n", - " keep=False,\n", - " combine_operator=\"or\",\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "5471689c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[{'group': 'adj'},\n", - " {'gen_anc': 'afr', 'group': 'adj'},\n", - " {'gen_anc': 'amr', 'group': 'adj'},\n", - " {'gen_anc': 'eas', 'group': 'adj'},\n", - " {'gen_anc': 'mid', 'group': 'adj'},\n", - " {'gen_anc': 'nfe', 'group': 'adj'},\n", - " {'gen_anc': 'sas', 'group': 'adj'}]]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# The order in the meta is order how AC, AF, AN and homozygote_count is stored in freq. \n", - "freq_meta2.collect()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "16170ed7", - "metadata": {}, - "outputs": [], - "source": [ - "ht = ht.annotate(**{a: array_exprs2[a] for a in ['freq']})\n", - "ht = ht.annotate_globals(\n", - " freq_meta=freq_meta2,\n", - " freq_meta_sample_count=array_exprs2[\"freq_meta_sample_count\"],\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "e3bcf7a0", + "execution_count": 61, + "id": "ce7a1e8c", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2024-10-01 16:07:07.655 Hail: WARN: Name collision: field 'all' already in object dict. \n", - " This field must be referenced with __getitem__ syntax: obj['all']\n" + "\r", + "[Stage 39:> (0 + 3) / 3]\r", + "\r", + "[Stage 39:======================================> (2 + 1) / 3]\r" ] }, { "data": { "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "
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chr1:155335497["A","C"]11.79e-0356000NA000NA0000.00e+0013800NA0000.00e+00200NA00
chr1:155335570["T","C"]35.26e-0357000NA000NA0017.25e-0313800NA0000.00e+00200NA00
chr1:155335571["TA","T"]35.26e-0357000NA000NA0017.25e-0313800NA0000.00e+00200NA00
chr1:155335746["G","C"]11.77e-0356400NA000NA0000.00e+0013200NA0000.00e+00200NA00
chr1:155335855["G","A"]11.74e-0357600NA000NA0017.25e-0313800NA0000.00e+00600NA00
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chr1:155335497["A","C"]0NA000NA0000.00e+0013800NA0000.00e+00200NA00
chr1:155335570["T","C"]0NA000NA0017.25e-0313800NA0000.00e+00200NA00
chr1:155335571["TA","T"]0NA000NA0017.25e-0313800NA0000.00e+00200NA00
chr1:155335746["G","C"]0NA000NA0000.00e+0013200NA0000.00e+00200NA00
chr1:155335855["G","A"]0NA000NA0017.25e-0313800NA0000.00e+00600NA00

showing top 5 rows

\n" ], "text/plain": [ - "+----------------+------------+--------+----------+--------+\n", - "| locus | alleles | all.AC | all.AF | all.AN |\n", - "+----------------+------------+--------+----------+--------+\n", - "| locus | array | int32 | float64 | int32 |\n", - "+----------------+------------+--------+----------+--------+\n", - "| chr1:155335497 | [\"A\",\"C\"] | 1 | 1.79e-03 | 560 |\n", - "| chr1:155335570 | [\"T\",\"C\"] | 3 | 5.26e-03 | 570 |\n", - "| chr1:155335571 | [\"TA\",\"T\"] | 3 | 5.26e-03 | 570 |\n", - "| chr1:155335746 | [\"G\",\"C\"] | 1 | 1.77e-03 | 564 |\n", - "| chr1:155335855 | [\"G\",\"A\"] | 1 | 1.74e-03 | 576 |\n", - "+----------------+------------+--------+----------+--------+\n", + "+----------------+------------+--------+---------+--------+\n", + "| locus | alleles | afr.AC | afr.AF | afr.AN |\n", + "+----------------+------------+--------+---------+--------+\n", + "| locus | array | int32 | float64 | int32 |\n", + "+----------------+------------+--------+---------+--------+\n", + "| chr1:155335497 | [\"A\",\"C\"] | 0 | NA | 0 |\n", + "| chr1:155335570 | [\"T\",\"C\"] | 0 | NA | 0 |\n", + "| chr1:155335571 | [\"TA\",\"T\"] | 0 | NA | 0 |\n", + "| chr1:155335746 | [\"G\",\"C\"] | 0 | NA | 0 |\n", + "| chr1:155335855 | [\"G\",\"A\"] | 0 | NA | 0 |\n", + "+----------------+------------+--------+---------+--------+\n", "\n", "+----------------------+--------+---------+--------+----------------------+\n", - "| all.homozygote_count | afr.AC | afr.AF | afr.AN | afr.homozygote_count |\n", + "| afr.homozygote_count | amr.AC | amr.AF | amr.AN | amr.homozygote_count |\n", "+----------------------+--------+---------+--------+----------------------+\n", "| int64 | int32 | float64 | int32 | int64 |\n", "+----------------------+--------+---------+--------+----------------------+\n", @@ -985,53 +2276,41 @@ "| 0 | 0 | NA | 0 | 0 |\n", "+----------------------+--------+---------+--------+----------------------+\n", "\n", - "+--------+---------+--------+----------------------+--------+----------+\n", - "| amr.AC | amr.AF | amr.AN | amr.homozygote_count | eas.AC | eas.AF |\n", - "+--------+---------+--------+----------------------+--------+----------+\n", - "| int32 | float64 | int32 | int64 | int32 | float64 |\n", - "+--------+---------+--------+----------------------+--------+----------+\n", - "| 0 | NA | 0 | 0 | 0 | 0.00e+00 |\n", - "| 0 | NA | 0 | 0 | 1 | 7.25e-03 |\n", - "| 0 | NA | 0 | 0 | 1 | 7.25e-03 |\n", - "| 0 | NA | 0 | 0 | 0 | 0.00e+00 |\n", - "| 0 | NA | 0 | 0 | 1 | 7.25e-03 |\n", - "+--------+---------+--------+----------------------+--------+----------+\n", - "\n", - "+--------+----------------------+--------+---------+--------+\n", - "| eas.AN | eas.homozygote_count | mid.AC | mid.AF | mid.AN |\n", - "+--------+----------------------+--------+---------+--------+\n", - "| int32 | int64 | int32 | float64 | int32 |\n", - "+--------+----------------------+--------+---------+--------+\n", - "| 138 | 0 | 0 | NA | 0 |\n", - "| 138 | 0 | 0 | NA | 0 |\n", - "| 138 | 0 | 0 | NA | 0 |\n", - "| 132 | 0 | 0 | NA | 0 |\n", - "| 138 | 0 | 0 | NA | 0 |\n", - "+--------+----------------------+--------+---------+--------+\n", - "\n", - "+----------------------+--------+----------+--------+----------------------+\n", - "| mid.homozygote_count | nfe.AC | nfe.AF | nfe.AN | nfe.homozygote_count |\n", - "+----------------------+--------+----------+--------+----------------------+\n", - "| int64 | int32 | float64 | int32 | int64 |\n", - "+----------------------+--------+----------+--------+----------------------+\n", - "| 0 | 0 | 0.00e+00 | 2 | 0 |\n", - "| 0 | 0 | 0.00e+00 | 2 | 0 |\n", - "| 0 | 0 | 0.00e+00 | 2 | 0 |\n", - "| 0 | 0 | 0.00e+00 | 2 | 0 |\n", - "| 0 | 0 | 0.00e+00 | 6 | 0 |\n", - "+----------------------+--------+----------+--------+----------------------+\n", - "\n", - "+--------+---------+--------+----------------------+\n", - "| sas.AC | sas.AF | sas.AN | sas.homozygote_count |\n", - "+--------+---------+--------+----------------------+\n", - "| int32 | float64 | int32 | int64 |\n", - "+--------+---------+--------+----------------------+\n", - "| 0 | NA | 0 | 0 |\n", - "| 0 | NA | 0 | 0 |\n", - "| 0 | NA | 0 | 0 |\n", - "| 0 | NA | 0 | 0 |\n", - "| 0 | NA | 0 | 0 |\n", - "+--------+---------+--------+----------------------+\n", + "+--------+----------+--------+----------------------+--------+---------+\n", + "| eas.AC | eas.AF | eas.AN | eas.homozygote_count | mid.AC | mid.AF |\n", + "+--------+----------+--------+----------------------+--------+---------+\n", + "| int32 | float64 | int32 | int64 | int32 | float64 |\n", + "+--------+----------+--------+----------------------+--------+---------+\n", + "| 0 | 0.00e+00 | 138 | 0 | 0 | NA |\n", + "| 1 | 7.25e-03 | 138 | 0 | 0 | NA |\n", + "| 1 | 7.25e-03 | 138 | 0 | 0 | NA |\n", + "| 0 | 0.00e+00 | 132 | 0 | 0 | NA |\n", + "| 1 | 7.25e-03 | 138 | 0 | 0 | NA |\n", + "+--------+----------+--------+----------------------+--------+---------+\n", + "\n", + "+--------+----------------------+--------+----------+--------+\n", + "| mid.AN | mid.homozygote_count | nfe.AC | nfe.AF | nfe.AN |\n", + "+--------+----------------------+--------+----------+--------+\n", + "| int32 | int64 | int32 | float64 | int32 |\n", + "+--------+----------------------+--------+----------+--------+\n", + "| 0 | 0 | 0 | 0.00e+00 | 2 |\n", + "| 0 | 0 | 0 | 0.00e+00 | 2 |\n", + "| 0 | 0 | 0 | 0.00e+00 | 2 |\n", + "| 0 | 0 | 0 | 0.00e+00 | 2 |\n", + "| 0 | 0 | 0 | 0.00e+00 | 6 |\n", + "+--------+----------------------+--------+----------+--------+\n", + "\n", + "+----------------------+--------+---------+--------+----------------------+\n", + "| nfe.homozygote_count | sas.AC | sas.AF | sas.AN | sas.homozygote_count |\n", + "+----------------------+--------+---------+--------+----------------------+\n", + "| int64 | int32 | float64 | int32 | int64 |\n", + "+----------------------+--------+---------+--------+----------------------+\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "+----------------------+--------+---------+--------+----------------------+\n", "showing top 5 rows" ] }, @@ -1040,8 +2319,9 @@ } ], "source": [ - "populations = ['all', 'afr', 'amr', 'eas', 'mid', 'nfe', 'sas']\n", - "ht.select(**{pop: ht.freq[i] for i, pop in enumerate(populations)}).show(5)" + "ht = extract_callstats_for_multiple_ancs(ht, gen_ancs=['afr', 'amr', 'eas', 'mid', 'nfe', 'sas'])\n", + "\n", + "ht.show(5)" ] }, { @@ -1054,84 +2334,79 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "9a4f26a2", + "execution_count": 62, + "id": "4846958a", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "\n", + "\n", + "
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chr22:15528692["C","G"]6351.90e-02333806
" + ], "text/plain": [ - "[[{'gen_anc': 'afr', 'group': 'adj'}]]" + "+----------------+------------+--------+----------+--------+\n", + "| locus | alleles | afr.AC | afr.AF | afr.AN |\n", + "+----------------+------------+--------+----------+--------+\n", + "| locus | array | int32 | float64 | int32 |\n", + "+----------------+------------+--------+----------+--------+\n", + "| chr22:15528692 | [\"C\",\"G\"] | 635 | 1.90e-02 | 33380 |\n", + "+----------------+------------+--------+----------+--------+\n", + "\n", + "+----------------------+\n", + "| afr.homozygote_count |\n", + "+----------------------+\n", + "| int64 |\n", + "+----------------------+\n", + "| 6 |\n", + "+----------------------+" ] }, - "execution_count": 14, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "ht = v4_public_release(\"exomes\").ht()\n", - "\n", - "# Filter by the location of the variant\n", - "ht = ht.filter((ht.locus.contig == \"chr22\") & (ht.locus.position==15528692))\n", - "\n", - "# Assign th\n", - "items_to_filter1 = {'gen_anc':['afr'], 'group':['adj']}\n", - "freq_meta1, array_exprs1 = filter_arrays_by_meta(\n", - " ht.freq_meta,\n", - " {\n", - " **{a: ht[a] for a in ['freq']},\n", - " \"freq_meta_sample_count\": ht.index_globals().freq_meta_sample_count,\n", - " },\n", - " items_to_filter=items_to_filter1,\n", - " keep=True,\n", - " combine_operator=\"and\",\n", - " )\n", + "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n", "\n", - "# if you want to further remove 'downsampling', 'sex', and 'subset'\n", - "items_to_filter2 = ['sex','downsampling','subset']\n", - "freq_meta2, array_exprs2 = filter_arrays_by_meta(\n", - " freq_meta1,\n", - " {\n", - " **{a: ht[a] for a in ['freq']},\n", - " \"freq_meta_sample_count\": ht.index_globals().freq_meta_sample_count,\n", - " },\n", - " items_to_filter=items_to_filter2,\n", - " keep=False,\n", - " combine_operator=\"or\",\n", - " )\n", - "freq_meta2.collect()" + "# When a variant exists...\n", + "extract_callstats_for_1anc_1variant(ht, gen_anc='AFR', contig='chr22', position=15528692, alleles=['C','G']).show(-1)" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "7e044201", + "execution_count": 64, + "id": "9f4c689b", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-02 02:02:23.969 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n" + ] + }, { "data": { "text/html": [ - "\n", - "\n", - "
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chr22:15528692["C","G"][(793,5.43e-04,1459438,7)]
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locus<GRCh38>array<str>int32float64int32int64
" ], "text/plain": [ - "+----------------+------------+\n", - "| locus | alleles |\n", - "+----------------+------------+\n", - "| locus | array |\n", - "+----------------+------------+\n", - "| chr22:15528692 | [\"C\",\"G\"] |\n", - "+----------------+------------+\n", - "\n", - "+---------------------------------------------------------------------------+\n", - "| freq |\n", - "+---------------------------------------------------------------------------+\n", - "| array |\n", - "+---------------------------------------------------------------------------+\n", - "| [(793,5.43e-04,1459438,7)] |\n", - "+---------------------------------------------------------------------------+" + "+---------------+------------+--------+---------+--------+\n", + "| locus | alleles | afr.AC | afr.AF | afr.AN |\n", + "+---------------+------------+--------+---------+--------+\n", + "| locus | array | int32 | float64 | int32 |\n", + "+---------------+------------+--------+---------+--------+\n", + "+---------------+------------+--------+---------+--------+\n", + "\n", + "+----------------------+\n", + "| afr.homozygote_count |\n", + "+----------------------+\n", + "| int64 |\n", + "+----------------------+\n", + "+----------------------+" ] }, "metadata": {}, @@ -1139,19 +2414,56 @@ } ], "source": [ - "ht = ht.annotate(**{a: array_exprs2[a] for a in ['freq']})\n", - "ht = ht.annotate_globals(\n", - " freq_meta=freq_meta2,\n", - " freq_meta_sample_count=array_exprs2[\"freq_meta_sample_count\"],\n", - " )\n", + "# When a variant doesn't exist...\n", + "extract_callstats_for_1anc_1variant(ht, gen_anc='AFR', contig='chr22', position=15528692, alleles=['C','A']).show(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "6fc82c5c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Copying gs://gnomad-qin/qin_notebooks/toolbox_for_gnomad_users.ipynb...\n", + "/ [1 files][747.1 KiB/747.1 KiB] \n", + "Operation completed over 1 objects/747.1 KiB. \n", + "[NbConvertApp] WARNING | Config option `extra_template_paths` not recognized by `EmbedHTMLExporter`. Did you mean `template_path`?\n", + "[NbConvertApp] Converting notebook toolbox_for_gnomad_users.ipynb to html_embed\n", + "[NbConvertApp] Writing 943138 bytes to toolbox_for_gnomad_users.html\n", + "Copying file://toolbox_for_gnomad_users.html [Content-Type=text/html]...\n", + "/ [1 files][921.1 KiB/921.1 KiB] \n", + "Operation completed over 1 objects/921.1 KiB. \n" + ] + } + ], + "source": [ + "notebook_name='toolbox_for_gnomad_users'\n", + "\n", + "#Uncomment top lines for the first time exporting on a cluster\n", + "#!/opt/conda/default/bin/conda create -n save-html-env --clone /opt/conda/default\n", + "#!/opt/conda/miniconda3/envs/save-html-env/bin/pip install \"nbconvert<6\" jinja2==3.0.3 jupyter_contrib_nbextensions\n", "\n", - "ht.select('freq').show(-1)" + "# Download the notebook from Google Cloud Storage\n", + "!gsutil -u broad-mpg-gnomad cp gs://gnomad-qin/qin_notebooks/{notebook_name}.ipynb .\n", + "\n", + "# Convert the notebook to HTML with embedded resources\n", + "! /opt/conda/miniconda3/envs/save-html-env/bin/jupyter nbconvert \\\n", + " --CodeFoldingPreprocessor.remove_folded_code=True --to html_embed \\\n", + " --template \"/opt/conda/miniconda3/envs/save-html-env/lib/python3.11/site-packages/jupyter_contrib_nbextensions/templates/toc2.tpl\" \\\n", + " {notebook_name}.ipynb\n", + "\n", + "# Upload the converted HTML back to Google Cloud Storage\n", + "!gsutil -u broad-mpg-gnomad cp {notebook_name}.html gs://gnomad-qin/qin_notebooks/{notebook_name}.html" ] }, { "cell_type": "code", "execution_count": null, - "id": "6fc82c5c", + "id": "a9e4c9bb", "metadata": {}, "outputs": [], "source": [] From c1834707ed35fbcd7ed326bc95ff2e412591b0bc Mon Sep 17 00:00:00 2001 From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com> Date: Mon, 9 Dec 2024 10:49:50 -0700 Subject: [PATCH 24/33] - Restructure files - Add a description of the repo structure to the README - Add some potential requirements - Update the documentation and make sure it works - Create a notebook specific to loading gnomAD release data and just showing what each dataset looks like. --- README.md | 30 + docs/generate_api_reference.py | 45 +- gnomad_toolbox/analysis/general.py | 42 + gnomad_toolbox/filtering/__init__.py | 1 + gnomad_toolbox/filtering/constraint.py | 1 + gnomad_toolbox/filtering/coverage.py | 1 + .../frequency.py} | 5 +- gnomad_toolbox/filtering/interval.py | 84 + gnomad_toolbox/filtering/pext.py | 1 + gnomad_toolbox/filtering/vep.py | 64 + gnomad_toolbox/load_data.py | 111 + gnomad_toolbox/modules/filter_variant.py | 182 - gnomad_toolbox/modules/import_data.py | 60 - .../notebooks/intro_to_release_data.ipynb | 6622 +++++++++++++++++ .../toolbox_for_gnomad_users.ipynb | 1963 ++++- requirements.txt | 7 + 16 files changed, 8590 insertions(+), 629 deletions(-) create mode 100644 gnomad_toolbox/analysis/general.py create mode 100644 gnomad_toolbox/filtering/__init__.py create mode 100644 gnomad_toolbox/filtering/constraint.py create mode 100644 gnomad_toolbox/filtering/coverage.py rename gnomad_toolbox/{modules/extract_freq.py => filtering/frequency.py} (95%) create mode 100644 gnomad_toolbox/filtering/interval.py create mode 100644 gnomad_toolbox/filtering/pext.py create mode 100644 gnomad_toolbox/filtering/vep.py create mode 100644 gnomad_toolbox/load_data.py delete mode 100644 gnomad_toolbox/modules/filter_variant.py delete mode 100644 gnomad_toolbox/modules/import_data.py create mode 100644 gnomad_toolbox/notebooks/intro_to_release_data.ipynb rename gnomad_toolbox/{use_cases => notebooks}/toolbox_for_gnomad_users.ipynb (72%) diff --git a/README.md b/README.md index 10ead05..52bdd94 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,32 @@ # gnomad-toolbox This repository provides a set of Python functions to simplify working with gnomAD Hail Tables. It includes tools for data access, filtering, and analysis. + +## Repository structure +``` +ggnomad_toolbox/ +│ +├── load_data.py # Functions to load gnomAD release Hail Tables. +│ +├── filtering/ +│ ├── __init__.py +│ ├── constraint.py # Functions to filter constraint metrics (e.g., observed/expected ratios). +│ ├── coverage.py # Functions to filter variants or regions based on coverage thresholds. +│ ├── frequency.py # Functions to filter variants by allele frequency thresholds. +│ ├── interval.py # Functions to filter variants within specified genomic intervals and genes. +│ ├── pext.py # Functions to filter variants using predicted expression (pext) scores. +│ ├── vep.py # Functions to filter variants based on VEP (Variant Effect Predictor) annotations. +│ +├── analysis/ +│ ├── __init__.py +│ ├── general.py # General analysis functions, such as summarizing variant statistics. +│ +├── notebooks/ +│ ├── intro_to_release_data.ipynb # Jupyter notebook introducing the loading of gnomAD release data. +``` + +## Getting started +### Install +pip install -r requirements.txt + +### Opening the notebooks +jupyter lab diff --git a/docs/generate_api_reference.py b/docs/generate_api_reference.py index a38ba3e..34930cf 100755 --- a/docs/generate_api_reference.py +++ b/docs/generate_api_reference.py @@ -21,16 +21,6 @@ EXCLUDE_PACKAGES = ["tests"] -EXCLUDE_TOP_LEVEL_PACKAGES = [] -""" -List of packages/modules to exclude from API reference documentation. - -This should be a list of strings where each string is the full name (from the top level) -of a package or module to exclude. For example, if 'gnomad_toolbox' includes a -'example_notebooks' that you want to exclude, you would add -'gnomad_toolbox.example_notebooks' to this list. -""" - PACKAGE_DOC_TEMPLATE = """{title} {package_doc} @@ -119,7 +109,11 @@ def write_module_doc(module_name): write_file(doc_path, doc) -def write_package_doc(package_name): +def write_package_doc( + package_name, + package_doc = None, + doc_path = None, +): """Write API reference documentation file for a package.""" package = importlib.import_module(package_name) @@ -139,32 +133,21 @@ def write_package_doc(package_name): doc = PACKAGE_DOC_TEMPLATE.format( title=format_title(package_name), - package_doc=package.__doc__ or "", + package_doc= package_doc or package.__doc__ or "", module_links="\n ".join(module_links), ) - doc_path = package_doc_path(package) + doc_path = doc_path or package_doc_path(package) write_file(doc_path, doc) if __name__ == "__main__": - packages = setuptools.find_namespace_packages( - where=REPOSITORY_ROOT_PATH, include=["gnomad_toolbox.*"] - ) - top_level_packages = [ - pkg for pkg in packages if pkg.count(".") == 1 and pkg not in EXCLUDE_TOP_LEVEL_PACKAGES - ] - - for pkg in top_level_packages: - write_package_doc(pkg) - - root_doc = PACKAGE_DOC_TEMPLATE.format( - title=format_title("gnomad_toolbox"), - package_doc="", - module_links="\n ".join( - f"{pkg.split('.')[1]} <{pkg.split('.')[1]}/index>" - for pkg in top_level_packages + write_package_doc( + "gnomad_toolbox", + package_doc=( + "This repository provides a set of Python functions to simplify working " + "with gnomAD Hail Tables. It includes tools for data access, filtering, " + "and analysis." ), + doc_path=os.path.join(DOCS_DIRECTORY, "api_reference", "index.rst"), ) - - write_file(os.path.join(DOCS_DIRECTORY, "api_reference", "index.rst"), root_doc) diff --git a/gnomad_toolbox/analysis/general.py b/gnomad_toolbox/analysis/general.py new file mode 100644 index 0000000..7334f43 --- /dev/null +++ b/gnomad_toolbox/analysis/general.py @@ -0,0 +1,42 @@ +"""Set of general functions for gnomAD analysis.""" + +import hail as hl + + +def get_variant_count( + ht: hl.Table, + afs: list[float] = [0.01, 0.001], + singletons: bool = False, + doubletons: bool = False, +) -> dict: + """ + Count variants with frequency <1%, <0.1%, and singletons (AC == 1). + + .. note:: + + This function works for gnomAD exomes and genomes datasets, not yet for gnomAD + joint dataset, since the HT schema is slightly different. + + :param ht: Input Table. + :param afs: List of allele frequencies cutoffs. + :param singletons: Include singletons. + :param doubletons: Include doubletons. + :return: Dictionary with counts. + """ + counts = {} + + # Filter to PASS variants. + ht = ht.filter(hl.len(ht.filters) == 0) + if singletons: + n_singletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 1)) + counts["number of singletons"] = n_singletons + if doubletons: + n_doubletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 2)) + counts["number of doubletons"] = n_doubletons + + for af in afs: + n_variants = ht.aggregate(hl.agg.count_where(ht.freq[0].AF < af)) + counts[f"number of variants with AF < {af}"] = n_variants + + # Count variants with frequency <1%, <0.1%, and singletons (AC == 1). + return counts diff --git a/gnomad_toolbox/filtering/__init__.py b/gnomad_toolbox/filtering/__init__.py new file mode 100644 index 0000000..6e03199 --- /dev/null +++ b/gnomad_toolbox/filtering/__init__.py @@ -0,0 +1 @@ +# noqa: D104 diff --git a/gnomad_toolbox/filtering/constraint.py b/gnomad_toolbox/filtering/constraint.py new file mode 100644 index 0000000..edbb4ee --- /dev/null +++ b/gnomad_toolbox/filtering/constraint.py @@ -0,0 +1 @@ +# noqa: D104, D100 diff --git a/gnomad_toolbox/filtering/coverage.py b/gnomad_toolbox/filtering/coverage.py new file mode 100644 index 0000000..edbb4ee --- /dev/null +++ b/gnomad_toolbox/filtering/coverage.py @@ -0,0 +1 @@ +# noqa: D104, D100 diff --git a/gnomad_toolbox/modules/extract_freq.py b/gnomad_toolbox/filtering/frequency.py similarity index 95% rename from gnomad_toolbox/modules/extract_freq.py rename to gnomad_toolbox/filtering/frequency.py index ee051e7..1c028a3 100644 --- a/gnomad_toolbox/modules/extract_freq.py +++ b/gnomad_toolbox/filtering/frequency.py @@ -1,9 +1,8 @@ -"""Extract callstats from 'freq' of gnomAD HTs.""" +"""Functions for filtering the gnomAD sites HT frequency data.""" -from typing import List, Optional +from typing import List import hail as hl -from gnomad.resources.grch38.gnomad import POPS_TO_REMOVE_FOR_POPMAX from gnomad.utils.filtering import filter_arrays_by_meta diff --git a/gnomad_toolbox/filtering/interval.py b/gnomad_toolbox/filtering/interval.py new file mode 100644 index 0000000..0a3b810 --- /dev/null +++ b/gnomad_toolbox/filtering/interval.py @@ -0,0 +1,84 @@ +"""Functions to filter the gnmoAD sites HT by interval.""" + +import hail as hl + + +def filter_by_interval(ht: hl.Table, interval: str) -> hl.Table: + """ + Filter variants by interval. + + :param ht: Input Table. + :param interval: Interval string. Format has to be "chr:start-end", e.g., "1:1000-2000". + :return: Table with variants in the interval. + """ + if ht.locus.dtype.reference_genome.name == "GRCh38": + interval = "chr" + interval + ht = hl.filter_intervals( + ht, + [ + hl.parse_locus_interval( + interval, + reference_genome=( + "GRCh38" + if ht.locus.dtype.reference_genome.name == "GRCh38" + else "GRCh37" + ), + ) + ], + ) + return ht + + +def filter_by_gene_symbol(ht: hl.Table, gene: str) -> hl.Table: + """ + Filter variants in a gene. + + .. note:: + This function is to match the number of variants that you will get in the + gnomAD browser, which only focus on variants in "CDS" regions plus 75bp + up- and downstream. This is not the same as filtering by gene symbol with + our `filter_vep_transcript_csqs` function, which will include all variants. + + :param ht: Input Table. + :param gene: Gene symbol. + :return: Table with variants in the gene. + """ + # Make gene symbol uppercase + gene = gene.upper() + + if ht.locus.dtype.reference_genome.name == "GRCh37": + gene_ht = hl.read_table( + "gs://gcp-public-data--gnomad/resources/grch37/browser/gnomad" + ".genes.GRCh37.GENCODEv19.ht" + ) + else: + gene_ht = hl.read_table( + "gs://gcp-public-data--gnomad/resources/grch38/browser/gnomad" + ".genes.GRCh38.GENCODEv39.ht" + ) + + gene_ht = gene_ht.annotate( + cds_intervals=hl.array( + gene_ht.exons.filter(lambda exon: exon.feature_type == "CDS") + ).map( + lambda exon: hl.locus_interval( + hl.if_else( + gene_ht.interval.start.dtype.reference_genome.name == "GRCh38", + "chr" + gene_ht.chrom, + gene_ht.chrom, + ), + exon.start - 75, + exon.stop + 75, + reference_genome=gene_ht.interval.start.dtype.reference_genome, + includes_end=True, + ) + ) + ) + + intervals = gene_ht.filter(gene_ht.gencode_symbol == gene).cds_intervals.collect()[ + 0 + ] + + ht = hl.filter_intervals(ht, intervals) + + return ht diff --git a/gnomad_toolbox/filtering/pext.py b/gnomad_toolbox/filtering/pext.py new file mode 100644 index 0000000..edbb4ee --- /dev/null +++ b/gnomad_toolbox/filtering/pext.py @@ -0,0 +1 @@ +# noqa: D104, D100 diff --git a/gnomad_toolbox/filtering/vep.py b/gnomad_toolbox/filtering/vep.py new file mode 100644 index 0000000..f851598 --- /dev/null +++ b/gnomad_toolbox/filtering/vep.py @@ -0,0 +1,64 @@ +"""Functions to filter gnomAD sites HT by VEP annotations.""" + +from functools import reduce + +import hail as hl +from gnomad.utils.vep import CSQ_CODING, LOF_CSQ_SET + + +def filter_by_csqs( + ht: hl.Table, csqs: list[str], pass_filters: bool = True +) -> hl.Table: + """ + Filter variants by consequences. + + :param ht: Input Table. + :param csqs: List of consequences to filter by. It can be specified as the + categories on the browser: pLoF, Missense / Inframe indel, Synonymous, Other. + :param pass_filters: Boolean if the variants pass the filters. + :return: Table with variants with the specified consequences. + """ + missense_inframe = ["missense_variant", "inframe_insertion", "inframe_deletion"] + + filter_expr = [] + if "lof" in csqs: + filter_expr.append( + hl.literal(LOF_CSQ_SET).contains(ht.vep.most_severe_consequence) + ) + + if "synonymous" in csqs: + filter_expr.append(ht.vep.most_severe_consequence == "synonymous_variant") + + if "missense" in csqs: + filter_expr.append( + hl.literal(missense_inframe).contains(ht.vep.most_severe_consequence) + ) + + if "other" in csqs: + excluded_csqs = hl.literal( + list(LOF_CSQ_SET) + missense_inframe + ["synonymous_variant"] + ) + filter_expr.append(~excluded_csqs.contains(ht.vep.most_severe_consequence)) + + if "coding" in csqs: + filter_expr.append( + hl.literal(CSQ_CODING).contains(ht.vep.most_severe_consequence) + ) + + if len(filter_expr) == 0: + raise ValueError( + "No valid consequence specified. Choose from 'lof', 'synonymous', 'missense', 'other'." + ) + + # Combine filter expressions with logical OR + if len(filter_expr) == 1: + combined_filter = filter_expr[0] + else: + combined_filter = reduce(lambda acc, expr: acc | expr, filter_expr) + + ht = ht.filter(combined_filter) + + if pass_filters: + ht = ht.filter(hl.len(ht.filters) == 0) + + return ht diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py new file mode 100644 index 0000000..b40dc10 --- /dev/null +++ b/gnomad_toolbox/load_data.py @@ -0,0 +1,111 @@ +"""Functions to import gnomAD data.""" + +import gnomad.resources.grch37.gnomad as grch37_gnomad +import gnomad.resources.grch38.gnomad as grch38_gnomad +import hail as hl + +GNOMAD_BY_BUILD = { + "GRCh37": grch37_gnomad, + "GRCh38": grch38_gnomad, +} +DATASETS = { + "variant": "public_release", + "all_sites_an": "all_sites_an", + "coverage": "coverage", +} +DATA_TYPES = ["exomes", "genomes", "joint"] +RELEASES_GLOBAL = { + "variant": { + "exomes": "EXOME_RELEASES", + "genomes": "GENOME_RELEASES", + "joint": "JOINT_RELEASES", + }, + "all_sites_an": { + "exomes": "EXOME_AN_RELEASES", + "genomes": "GENOME_AN_RELEASES", + }, + "coverage": { + "exomes": "EXOME_COVERAGE_RELEASES", + "genomes": "GENOME_COVERAGE_RELEASES", + }, +} +RELEASES = { + dataset: { + data_type: { + build: getattr(res, release_global, None) + for build, res in GNOMAD_BY_BUILD.items() + } + for data_type, release_global in data_types.items() + } + for dataset, data_types in RELEASES_GLOBAL.items() +} + + +def get_gnomad_release( + data_type: str = "exomes", + version: str = grch38_gnomad.CURRENT_EXOME_RELEASE, + dataset: str = "variant", +) -> hl.Table: + """ + Get gnomAD HT by dataset, data type, and version. + + .. table:: Available versions for each dataset and data type are (as of 2024-10-29) + :widths: auto + + +--------------+-----------------+----------------------------------+----------------------+ + | Dataset | Data Type | GRCh38 Versions | GRCh37 Versions | + +==============+=================+==================================+======================+ + | variant | exomes | 4.0, 4.1 | 2.1, 2.1.1 | + | +-----------------+----------------------------------+----------------------+ + | | genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 | + | +-----------------+----------------------------------+----------------------+ + | | joint | 4.1 | N/A | + +--------------+-----------------+----------------------------------+----------------------+ + | coverage | exomes | 4.0 | 2.1 | + | +-----------------+----------------------------------+----------------------+ + | | genomes | 3.0.1 | 2.1 | + +--------------+-----------------+----------------------------------+----------------------+ + | all_sites_an | exomes | 4.1 | N/A | + | +-----------------+----------------------------------+----------------------+ + | | genomes | 4.1 | N/A | + +--------------+-----------------+----------------------------------+----------------------+ + + :param data_type: Data type (exomes, genomes, or joint). Default is "exomes". + :param version: gnomAD version. Default is the current exome release. + :param dataset: Dataset type. One of "variant", "all_sites_an", "coverage". Default + is "variant". + :return: Hail Table for requested dataset, data type, and version. + """ + # Get all releases for the given dataset. + releases = RELEASES.get(dataset) + + # Validate dataset. + if releases is None: + raise ValueError(f"{dataset} is invalid. Choose from {RELEASES.keys()}") + + # Get all releases for the given dataset and data_type. + data_type_releases = releases.get(data_type) + + # Validate data type. + if data_type_releases is None: + raise ValueError( + f"Data type {data_type} is invalid. Choose from 'exomes', 'genomes', or " + "'joint'." + ) + + # Check version availability for GRCh38 and GRCh37. + if data_type_releases["GRCh38"] and version in data_type_releases["GRCh38"]: + return ( + getattr(grch38_gnomad, DATASETS[dataset])(data_type).versions[version].ht() + ) + elif data_type_releases["GRCh37"] and version in data_type_releases["GRCh37"]: + return ( + getattr(grch37_gnomad, DATASETS[dataset])(data_type).versions[version].ht() + ) + else: + raise ValueError( + f"Version {version} is not available for { + data_type} in the {dataset} dataset. " + f"Available versions: GRCh38 - {data_type_releases['GRCh38']}, " + f"GRCh37 - {data_type_releases['GRCh37']}." + ) diff --git a/gnomad_toolbox/modules/filter_variant.py b/gnomad_toolbox/modules/filter_variant.py deleted file mode 100644 index f73fe73..0000000 --- a/gnomad_toolbox/modules/filter_variant.py +++ /dev/null @@ -1,182 +0,0 @@ -"""Small functions to filter variants in gnomAD datasets, such as by allele frequency or by variant type.""" - -from functools import reduce - -import hail as hl -from gnomad.utils.vep import CSQ_CODING, LOF_CSQ_SET - - -def get_variant_count( - ht: hl.Table, - afs: list[float] = [0.01, 0.001], - singletons: bool = False, - doubletons: bool = False, -) -> dict: - """ - Count variants with frequency <1%, <0.1%, and singletons (AC == 1). - - .. note:: This function works for gnomAD exomes and genomes datasets, not yet for - gnomAD joint dataset, since the HT schema is slightly different. - - :param ht: Input Table. - :param afs: List of allele frequencies cutoffs. - :param singletons: Include singletons. - :param doubletons: Include doubletons. - :return: Dictionary with counts. - """ - counts = {} - - # Filter to PASS variants. - ht = ht.filter(hl.len(ht.filters) == 0) - if singletons: - n_singletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 1)) - counts["number of singletons"] = n_singletons - if doubletons: - n_doubletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 2)) - counts["number of doubletons"] = n_doubletons - - for af in afs: - n_variants = ht.aggregate(hl.agg.count_where(ht.freq[0].AF < af)) - counts[f"number of variants with AF < {af}"] = n_variants - - # Count variants with frequency <1%, <0.1%, and singletons (AC == 1). - return counts - - -def filter_by_interval(ht: hl.Table, interval: str) -> hl.Table: - """ - Filter variants by interval. - - :param ht: Input Table. - :param interval: Interval string. Format has to be "chr:start-end", e.g., "1:1000-2000". - :return: Table with variants in the interval. - """ - if ht.locus.dtype.reference_genome.name == "GRCh38": - interval = "chr" + interval - ht = hl.filter_intervals( - ht, - [ - hl.parse_locus_interval( - interval, - reference_genome=( - "GRCh38" - if ht.locus.dtype.reference_genome.name == "GRCh38" - else "GRCh37" - ), - ) - ], - ) - return ht - - -def filter_by_gene_symbol(ht: hl.Table, gene: str) -> hl.Table: - """ - Filter variants in a gene. - - .. note:: - This function is to match the number of variants that you will get in the - gnomAD browser, which only focus on variants in "CDS" regions plus 75bp - up- and downstream. This is not the same as filtering by gene symbol with - our `filter_vep_transcript_csqs` function, which will include all variants. - - :param ht: Input Table. - :param gene: Gene symbol. - :return: Table with variants in the gene. - """ - # Make gene symbol uppercase - gene = gene.upper() - - if ht.locus.dtype.reference_genome.name == "GRCh37": - gene_ht = hl.read_table( - "gs://gcp-public-data--gnomad/resources/grch37/browser/gnomad" - ".genes.GRCh37.GENCODEv19.ht" - ) - else: - gene_ht = hl.read_table( - "gs://gcp-public-data--gnomad/resources/grch38/browser/gnomad" - ".genes.GRCh38.GENCODEv39.ht" - ) - - gene_ht = gene_ht.annotate( - cds_intervals=hl.array( - gene_ht.exons.filter(lambda exon: exon.feature_type == "CDS") - ).map( - lambda exon: hl.locus_interval( - hl.if_else( - gene_ht.interval.start.dtype.reference_genome.name == "GRCh38", - "chr" + gene_ht.chrom, - gene_ht.chrom, - ), - exon.start - 75, - exon.stop + 75, - reference_genome=gene_ht.interval.start.dtype.reference_genome, - includes_end=True, - ) - ) - ) - - intervals = gene_ht.filter(gene_ht.gencode_symbol == gene).cds_intervals.collect()[ - 0 - ] - - ht = hl.filter_intervals(ht, intervals) - - return ht - - -def filter_by_csqs( - ht: hl.Table, csqs: list[str], pass_filters: bool = True -) -> hl.Table: - """ - Filter variants by consequences. - - :param ht: Input Table. - :param csqs: List of consequences to filter by. It can be specified as the - categories on the browser: pLoF, Missense / Inframe indel, Synonymous, Other. - :param pass_filters: Boolean if the variants pass the filters. - :return: Table with variants with the specified consequences. - """ - missense_inframe = ["missense_variant", "inframe_insertion", "inframe_deletion"] - - filter_expr = [] - if "lof" in csqs: - filter_expr.append( - hl.literal(LOF_CSQ_SET).contains(ht.vep.most_severe_consequence) - ) - - if "synonymous" in csqs: - filter_expr.append(ht.vep.most_severe_consequence == "synonymous_variant") - - if "missense" in csqs: - filter_expr.append( - hl.literal(missense_inframe).contains(ht.vep.most_severe_consequence) - ) - - if "other" in csqs: - excluded_csqs = hl.literal( - list(LOF_CSQ_SET) + missense_inframe + ["synonymous_variant"] - ) - filter_expr.append(~excluded_csqs.contains(ht.vep.most_severe_consequence)) - - if "coding" in csqs: - filter_expr.append( - hl.literal(CSQ_CODING).contains(ht.vep.most_severe_consequence) - ) - - if len(filter_expr) == 0: - raise ValueError( - "No valid consequence specified. Choose from 'lof', 'synonymous', 'missense', 'other'." - ) - - # Combine filter expressions with logical OR - if len(filter_expr) == 1: - combined_filter = filter_expr[0] - else: - combined_filter = reduce(lambda acc, expr: acc | expr, filter_expr) - - ht = ht.filter(combined_filter) - - if pass_filters: - ht = ht.filter(hl.len(ht.filters) == 0) - - return ht diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py deleted file mode 100644 index 8616917..0000000 --- a/gnomad_toolbox/modules/import_data.py +++ /dev/null @@ -1,60 +0,0 @@ -"""Functions to import gnomAD data.""" - -import hail as hl -from gnomad.resources.grch37.gnomad import EXOME_RELEASES as GRCh37_EXOME_RELEASES -from gnomad.resources.grch37.gnomad import GENOME_RELEASES as GRCh37_GENOME_RELEASES -from gnomad.resources.grch37.gnomad import public_release as grch37_public_release -from gnomad.resources.grch38.gnomad import EXOME_RELEASES as GRCh38_EXOME_RELEASES -from gnomad.resources.grch38.gnomad import GENOME_RELEASES as GRCh38_GENOME_RELEASES -from gnomad.resources.grch38.gnomad import JOINT_RELEASES as GRCh38_JOINT_RELEASES -from gnomad.resources.grch38.gnomad import public_release as grch38_public_release - - -def get_ht_by_datatype_and_version( - data_type: str = "exomes", version: str = "4.1" -) -> hl.Table: - """ - Get gnomAD HT by data type and version. - - .. note:: - - Available versions for each data type are (as of 2024-10-29): - - :: - - | Data Type | GRCh38 Versions | GRCh37 Versions | - |-----------------|----------------------------------|----------------------| - | exomes | 4.0, 4.1 | 2.1, 2.1.1 | - | genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 | - | joint | 4.1 | N/A | - - :param data_type: Data type (exomes, genomes, or joint). - :param version: gnomAD version. - :return: Hail Table. - """ - # Mapping data types to version sets for GRCh38 and GRCh37 - versions_by_type = { - "exomes": (GRCh38_EXOME_RELEASES, GRCh37_EXOME_RELEASES), - "genomes": (GRCh38_GENOME_RELEASES, GRCh37_GENOME_RELEASES), - "joint": (GRCh38_JOINT_RELEASES, []), - } - - # Validate data type - if data_type not in versions_by_type: - raise ValueError( - f"Data type {data_type} is invalid. Choose from 'exomes', 'genomes', or 'joint'." - ) - - # Get GRCh38 and GRCh37 versions for the given data type - grch38_versions, grch37_versions = versions_by_type[data_type] - - # Check version availability for GRCh38 and GRCh37 - if version in grch38_versions: - return grch38_public_release(data_type).ht() - elif version in grch37_versions: - return grch37_public_release(data_type).ht() - else: - raise ValueError( - f"Version {version} is not available for {data_type}. " - f"Available versions: GRCh38 - {grch38_versions}, GRCh37 - {grch37_versions}." - ) diff --git a/gnomad_toolbox/notebooks/intro_to_release_data.ipynb b/gnomad_toolbox/notebooks/intro_to_release_data.ipynb new file mode 100644 index 0000000..c84c0d7 --- /dev/null +++ b/gnomad_toolbox/notebooks/intro_to_release_data.ipynb @@ -0,0 +1,6622 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "853c94b9", + "metadata": {}, + "source": [ + "# Introduction to gnomAD Hail release files\n" + ] + }, + { + "cell_type": "markdown", + "id": "5cf83cfe-0fce-40ae-add7-c9f2c20c1e85", + "metadata": {}, + "source": [ + "In this notebook we will explore all of the available [gnomAD v4 release files](https://gnomad.broadinstitute.org/data#v4) that are in Hail formats." + ] + }, + { + "attachments": { + "afcd4ecc-4e90-464b-91c7-9cd80b0e92ba.png": { + "image/png": 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" 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" + } + }, + "cell_type": "markdown", + "id": "f97431a4-78a0-473f-99a0-67eeefb8bbc6", + "metadata": {}, + "source": [ + "![image.png](attachment:2f957256-5d08-40e1-b77c-5faa4f771fb2.png)" + ] + }, + { + "cell_type": "markdown", + "id": "8e609a46", + "metadata": { + "tags": [], + "toc": true + }, + "source": [ + "

Table of Contents

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "8e713032", + "metadata": {}, + "source": [ + "## Import modules" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e69953f7", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:40.044647Z", + "start_time": "2024-12-06T20:08:37.758061Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + "
\n", + " \n", + " Loading BokehJS ...\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " function drop(id) {\n", + " const view = Bokeh.index.get_by_id(id)\n", + " if (view != null) {\n", + " view.model.document.clear()\n", + " Bokeh.index.delete(view)\n", + " }\n", + " }\n", + "\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + "\n", + " // Clean up Bokeh references\n", + " if (id != null) {\n", + " drop(id)\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim()\n", + " drop(id)\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(\"e0412447-897b-4b15-8731-38893ba83dd5\");\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "if (force === true) {\n", + " display_loaded();\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(\"e0412447-897b-4b15-8731-38893ba83dd5\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"e0412447-897b-4b15-8731-38893ba83dd5\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"e0412447-897b-4b15-8731-38893ba83dd5\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import hail as hl\n", + "from gnomad_toolbox.load_data import get_gnomad_release" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8649f215-0afc-4f66-920a-53b707f41c4a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Welcome to\n", + " __ __ <>__\n", + " / /_/ /__ __/ /\n", + " / __ / _ `/ / /\n", + " /_/ /_/\\_,_/_/_/ version 0.2.132-678e1f52b999\n", + "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241208-2218-0.2.132-678e1f52b999.log\n" + ] + } + ], + "source": [ + "hl.init(backend=\"local\")" + ] + }, + { + "cell_type": "markdown", + "id": "5335a135", + "metadata": { + "tags": [] + }, + "source": [ + "## Variant data\n", + "\n", + "Available versions for each data type and reference build are (as of 2024-10-29):\n", + "\n", + "| Data Type | GRCh38 Versions | GRCh37 Versions |\n", + "|-----------------|----------------------------------|----------------------|\n", + "| exomes | 4.0, 4.1 | 2.1, 2.1.1 |\n", + "| genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 |\n", + "| joint | 4.1 | N/A |\n", + "\n", + "For a description of all fields the the HT, see the [Help/FAQ](https://gnomad.broadinstitute.org/help/v4-hts) page." + ] + }, + { + "cell_type": "markdown", + "id": "d1a4ae8933ba6421", + "metadata": { + "tags": [] + }, + "source": [ + "### v4.1 exomes Hail Table" + ] + }, + { + "cell_type": "markdown", + "id": "318a034c-ac84-4147-9f25-e5e8783e9b91", + "metadata": {}, + "source": [ + "#### Load the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "100cf576", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:59.197890Z", + "start_time": "2024-12-06T20:08:50.859108Z" + } + }, + "outputs": [], + "source": [ + "ht = get_gnomad_release(data_type='exomes', version='4.1')" + ] + }, + { + "cell_type": "markdown", + "id": "77d7a05e31c1f37a", + "metadata": {}, + "source": [ + "#### Print the schema of the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "95c14f2c8cc3e699", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:00.032524Z", + "start_time": "2024-12-06T20:09:00.029271Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "Global fields:\n", + " 'freq_meta': array> \n", + " 'freq_index_dict': dict \n", + " 'freq_meta_sample_count': array \n", + " 'faf_meta': array> \n", + " 'faf_index_dict': dict \n", + " 'age_distribution': struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int32, \n", + " n_larger: int32\n", + " } \n", + " 'downsamplings': dict> \n", + " 'filtering_model': struct {\n", + " filter_name: str, \n", + " score_name: str, \n", + " snv_cutoff: struct {\n", + " bin: int32, \n", + " min_score: float64\n", + " }, \n", + " indel_cutoff: struct {\n", + " bin: int32, \n", + " min_score: float64\n", + " }, \n", + " snv_training_variables: array, \n", + " indel_training_variables: array\n", + " } \n", + " 'inbreeding_coeff_cutoff': float64 \n", + " 'interval_qc_parameters': struct {\n", + " per_platform: bool, \n", + " all_platforms: bool, \n", + " high_qual_cutoffs: dict>, \n", + " min_platform_size: int32\n", + " } \n", + " 'tool_versions': struct {\n", + " cadd_version: str, \n", + " revel_version: str, \n", + " spliceai_version: str, \n", + " pangolin_version: array, \n", + " phylop_version: str, \n", + " dbsnp_version: str, \n", + " sift_version: str, \n", + " polyphen_version: str\n", + " } \n", + " 'vrs_versions': struct {\n", + " vrs_schema_version: str, \n", + " vrs_python_version: str, \n", + " seqrepo_version: str\n", + " } \n", + " 'vep_globals': struct {\n", + " vep_version: str, \n", + " vep_help: str, \n", + " vep_config: str, \n", + " gencode_version: str, \n", + " mane_select_version: str\n", + " } \n", + " 'frequency_README': str \n", + " 'date': str \n", + " 'version': str \n", + "----------------------------------------\n", + "Row fields:\n", + " 'locus': locus \n", + " 'alleles': array \n", + " 'freq': array \n", + " 'grpmax': struct {\n", + " gnomad: struct {\n", + " AC: int32, \n", + " AF: float64, \n", + " AN: int32, \n", + " homozygote_count: int64, \n", + " gen_anc: str\n", + " }, \n", + " non_ukb: struct {\n", + " AC: int32, \n", + " AF: float64, \n", + " AN: int32, \n", + " homozygote_count: int64, \n", + " gen_anc: str\n", + " }\n", + " } \n", + " 'faf': array \n", + " 'fafmax': struct {\n", + " gnomad: struct {\n", + " faf95_max: float64, \n", + " faf95_max_gen_anc: str, \n", + " faf99_max: float64, \n", + " faf99_max_gen_anc: str\n", + " }, \n", + " non_ukb: struct {\n", + " faf95_max: float64, \n", + " faf95_max_gen_anc: str, \n", + " faf99_max: float64, \n", + " faf99_max_gen_anc: str\n", + " }\n", + " } \n", + " 'a_index': int32 \n", + " 'was_split': bool \n", + " 'rsid': set \n", + " 'filters': set \n", + " 'info': struct {\n", + " FS: float64, \n", + " MQ: float64, \n", + " MQRankSum: float64, \n", + " QUALapprox: int64, \n", + " QD: float64, \n", + " ReadPosRankSum: float64, \n", + " SB: array, \n", + " SOR: float64, \n", + " VarDP: int32, \n", + " AS_FS: float64, \n", + " AS_MQ: float64, \n", + " AS_MQRankSum: float64, \n", + " AS_pab_max: float64, \n", + " AS_QUALapprox: int64, \n", + " AS_QD: float64, \n", + " AS_ReadPosRankSum: float64, \n", + " AS_SB_TABLE: array, \n", + " AS_SOR: float64, \n", + " AS_VarDP: int32, \n", + " singleton: bool, \n", + " transmitted_singleton: bool, \n", + " sibling_singleton: bool, \n", + " omni: bool, \n", + " mills: bool, \n", + " monoallelic: bool, \n", + " only_het: bool, \n", + " AS_VQSLOD: float64, \n", + " inbreeding_coeff: float64, \n", + " vrs: struct {\n", + " VRS_Allele_IDs: array, \n", + " VRS_Starts: array, \n", + " VRS_Ends: array, \n", + " VRS_States: array\n", + " }\n", + " } \n", + " 'vep': struct {\n", + " allele_string: str, \n", + " end: int32, \n", + " id: str, \n", + " input: str, \n", + " intergenic_consequences: array, \n", + " impact: str, \n", + " variant_allele: str\n", + " }>, \n", + " most_severe_consequence: str, \n", + " motif_feature_consequences: array, \n", + " high_inf_pos: str, \n", + " impact: str, \n", + " motif_feature_id: str, \n", + " motif_name: str, \n", + " motif_pos: int32, \n", + " motif_score_change: float64, \n", + " transcription_factors: array, \n", + " strand: int32, \n", + " variant_allele: str\n", + " }>, \n", + " regulatory_feature_consequences: array, \n", + " impact: str, \n", + " regulatory_feature_id: str, \n", + " variant_allele: str\n", + " }>, \n", + " seq_region_name: str, \n", + " start: int32, \n", + " strand: int32, \n", + " transcript_consequences: array, \n", + " distance: int32, \n", + " domains: array, \n", + " exon: str, \n", + " flags: str, \n", + " gene_id: str, \n", + " gene_pheno: int32, \n", + " gene_symbol: str, \n", + " gene_symbol_source: str, \n", + " hgnc_id: str, \n", + " hgvsc: str, \n", + " hgvsp: str, \n", + " hgvs_offset: int32, \n", + " impact: str, \n", + " intron: str, \n", + " lof: str, \n", + " lof_flags: str, \n", + " lof_filter: str, \n", + " lof_info: str, \n", + " mane_select: str, \n", + " mane_plus_clinical: str, \n", + " mirna: array, \n", + " protein_end: int32, \n", + " protein_start: int32, \n", + " protein_id: str, \n", + " source: str, \n", + " strand: int32, \n", + " transcript_id: str, \n", + " tsl: int32, \n", + " uniprot_isoform: array, \n", + " variant_allele: str\n", + " }>, \n", + " variant_class: str\n", + " } \n", + " 'vqsr_results': struct {\n", + " AS_VQSLOD: float64, \n", + " AS_culprit: str, \n", + " positive_train_site: bool, \n", + " negative_train_site: bool\n", + " } \n", + " 'region_flags': struct {\n", + " non_par: bool, \n", + " lcr: bool, \n", + " segdup: bool, \n", + " fail_interval_qc: bool, \n", + " outside_ukb_capture_region: bool, \n", + " outside_broad_capture_region: bool\n", + " } \n", + " 'allele_info': struct {\n", + " variant_type: str, \n", + " n_alt_alleles: int32, \n", + " has_star: bool, \n", + " allele_type: str, \n", + " was_mixed: bool\n", + " } \n", + " 'histograms': struct {\n", + " qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " raw_qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " age_hists: struct {\n", + " age_hist_het: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " age_hist_hom: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }\n", + " } \n", + " 'in_silico_predictors': struct {\n", + " cadd: struct {\n", + " phred: float32, \n", + " raw_score: float32\n", + " }, \n", + " revel_max: float64, \n", + " spliceai_ds_max: float32, \n", + " pangolin_largest_ds: float64, \n", + " phylop: float64, \n", + " sift_max: float64, \n", + " polyphen_max: float64\n", + " } \n", + "----------------------------------------\n", + "Key: ['locus', 'alleles']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "a071f738b2c888e", + "metadata": { + "tags": [] + }, + "source": [ + "#### Show the first 5 variants in the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "222de580c305d72a", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
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bin_edges
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bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
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showing top 5 rows

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[(0,0.00e+00,4,0),(1,1.59e-06,628784,0),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n", + "| [(0,0.00e+00,4,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n", + "| [(0,0.00e+00,32,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0)... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------+------------------+------------------+\n", + "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n", + "+------------------+------------------+------------------+\n", + "| int32 | float64 | int32 |\n", + "+------------------+------------------+------------------+\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "+------------------+------------------+------------------+\n", + "\n", + "+--------------------------------+-----------------------+-------------------+\n", + "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n", + "+--------------------------------+-----------------------+-------------------+\n", + "| int64 | str | int32 |\n", + "+--------------------------------+-----------------------+-------------------+\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "+--------------------------------+-----------------------+-------------------+\n", + "\n", + "+-------------------+-------------------+---------------------------------+\n", + "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n", + "+-------------------+-------------------+---------------------------------+\n", + "| float64 | int32 | int64 |\n", + "+-------------------+-------------------+---------------------------------+\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + 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NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+-------------------------+---------------------------------+\n", + "\n", + "+--------------------------+----------------------------------+\n", + "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n", + "+--------------------------+----------------------------------+\n", + "| float64 | str |\n", + "+--------------------------+----------------------------------+\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+--------------------------+----------------------------------+\n", + "\n", + "+--------------------------+----------------------------------+---------+\n", + "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n", + "+--------------------------+----------------------------------+---------+\n", + "| float64 | str | int32 |\n", + "+--------------------------+----------------------------------+---------+\n", + "| NA | NA | 1 |\n", + "| NA 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[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| histograms.qual_hists.dp_hist_all.bin_freq |\n", + "+--------------------------------------------+\n", + "| array |\n", + "+--------------------------------------------+\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,14,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "+--------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| 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[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "+--------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| histograms.qual_hists.dp_hist_alt.n_smaller |\n", + "+---------------------------------------------+\n", + "| int64 |\n", + "+---------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+---------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| histograms.qual_hists.dp_hist_alt.n_larger |\n", + "+--------------------------------------------+\n", + "| int64 |\n", + "+--------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + 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|\n", + "| 0 | 1.52e+01 |\n", + "| 0 | 4.42e+00 |\n", + "+--------------------------------------------+---------------------------------+\n", + "\n", + "+-------------------------------------+--------------------------------+\n", + "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n", + "+-------------------------------------+--------------------------------+\n", + "| float32 | float64 |\n", + "+-------------------------------------+--------------------------------+\n", + "| 1.08e+00 | NA |\n", + "| 1.54e+00 | NA |\n", + "| 7.07e-01 | NA |\n", + "| 1.41e+00 | NA |\n", + "| 3.11e-01 | NA |\n", + "+-------------------------------------+--------------------------------+\n", + "\n", + "+--------------------------------------+\n", + "| in_silico_predictors.spliceai_ds_max |\n", + "+--------------------------------------+\n", + "| float32 |\n", + "+--------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+--------------------------------------+\n", + "\n", + "+------------------------------------------+-----------------------------+\n", + "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n", + "+------------------------------------------+-----------------------------+\n", + "| float64 | float64 |\n", + "+------------------------------------------+-----------------------------+\n", + "| -1.10e-01 | 1.09e+00 |\n", + "| -7.00e-02 | 6.55e+00 |\n", + "| -9.00e-02 | -4.41e+00 |\n", + "| -4.00e-02 | 6.01e+00 |\n", + "| -8.00e-02 | 1.38e+00 |\n", + "+------------------------------------------+-----------------------------+\n", + "\n", + "+-------------------------------+-----------------------------------+\n", + "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n", + "+-------------------------------+-----------------------------------+\n", + "| float64 | float64 |\n", + "+-------------------------------+-----------------------------------+\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+-------------------------------+-----------------------------------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "b7a158a3-f21a-4f87-9596-1f918156d713", + "metadata": { + "tags": [] + }, + "source": [ + "### v4.1 genomes Hail Table" + ] + }, + { + "cell_type": "markdown", + "id": "30f86500-afc5-419e-ae2e-f944dc461fee", + "metadata": {}, + "source": [ + "#### Load the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "62ca9934-20dd-437e-898b-86a056e2606e", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:59.197890Z", + "start_time": "2024-12-06T20:08:50.859108Z" + } + }, + "outputs": [], + "source": [ + "ht = get_gnomad_release(data_type='genomes', version='4.1')" + ] + }, + { + "cell_type": "markdown", + "id": "9cf4b782-f289-47b6-9123-d08ca761b074", + "metadata": {}, + "source": [ + "#### Print the schema of the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "09de90df-0b03-4a54-817c-c8a0606026f6", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:00.032524Z", + "start_time": "2024-12-06T20:09:00.029271Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "Global fields:\n", + " 'freq_meta': array> \n", + " 'freq_index_dict': dict \n", + " 'freq_meta_sample_count': array \n", + " 'faf_meta': array> \n", + " 'faf_index_dict': dict \n", + " 'age_distribution': struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int32, \n", + " n_larger: int32\n", + " } \n", + " 'filtering_model': struct {\n", + " filter_name: str, \n", + " score_name: str, \n", + " snv_cutoff: struct {\n", + " bin: int32, \n", + " min_score: float64\n", + " }, \n", + " indel_cutoff: struct {\n", + " bin: int32, \n", + " min_score: float64\n", + " }, \n", + " snv_training_variables: array, \n", + " indel_training_variables: array\n", + " } \n", + " 'inbreeding_coeff_cutoff': float64 \n", + " 'tool_versions': struct {\n", + " cadd_version: str, \n", + " revel_version: str, \n", + " spliceai_version: str, \n", + " pangolin_version: array, \n", + " phylop_version: str, \n", + " dbsnp_version: str, \n", + " sift_version: str, \n", + " polyphen_version: str\n", + " } \n", + " 'vrs_versions': struct {\n", + " vrs_schema_version: str, \n", + " vrs_python_version: str, \n", + " seqrepo_version: str\n", + " } \n", + " 'vep_globals': struct {\n", + " vep_version: str, \n", + " vep_help: str, \n", + " vep_config: str, \n", + " gencode_version: str, \n", + " mane_select_version: str\n", + " } \n", + " 'frequency_README': str \n", + " 'date': str \n", + " 'version': str \n", + "----------------------------------------\n", + "Row fields:\n", + " 'locus': locus \n", + " 'alleles': array \n", + " 'freq': array \n", + " 'grpmax': struct {\n", + " AC: int32, \n", + " AF: float64, \n", + " AN: int32, \n", + " homozygote_count: int32, \n", + " gen_anc: str\n", + " } \n", + " 'faf': array \n", + " 'fafmax': struct {\n", + " faf95_max: float64, \n", + " faf95_max_gen_anc: str, \n", + " faf99_max: float64, \n", + " faf99_max_gen_anc: str\n", + " } \n", + " 'a_index': int32 \n", + " 'was_split': bool \n", + " 'rsid': set \n", + " 'filters': set \n", + " 'info': struct {\n", + " FS: float64, \n", + " MQ: float64, \n", + " MQRankSum: float64, \n", + " QUALapprox: int64, \n", + " QD: float32, \n", + " ReadPosRankSum: float64, \n", + " SB: array, \n", + " SOR: float64, \n", + " VarDP: int32, \n", + " AS_FS: float64, \n", + " AS_MQ: float64, \n", + " AS_MQRankSum: float64, \n", + " AS_pab_max: float64, \n", + " AS_QUALapprox: int64, \n", + " AS_QD: float32, \n", + " AS_ReadPosRankSum: float64, \n", + " AS_SB_TABLE: array, \n", + " AS_SOR: float64, \n", + " AS_VarDP: int32, \n", + " singleton: bool, \n", + " transmitted_singleton: bool, \n", + " omni: bool, \n", + " mills: bool, \n", + " monoallelic: bool, \n", + " only_het: bool, \n", + " AS_VQSLOD: float64, \n", + " inbreeding_coeff: float64, \n", + " vrs: struct {\n", + " VRS_Allele_IDs: array, \n", + " VRS_Starts: array, \n", + " VRS_Ends: array, \n", + " VRS_States: array\n", + " }\n", + " } \n", + " 'vep': struct {\n", + " allele_string: str, \n", + " end: int32, \n", + " id: str, \n", + " input: str, \n", + " intergenic_consequences: array, \n", + " impact: str, \n", + " variant_allele: str\n", + " }>, \n", + " most_severe_consequence: str, \n", + " motif_feature_consequences: array, \n", + " high_inf_pos: str, \n", + " impact: str, \n", + " motif_feature_id: str, \n", + " motif_name: str, \n", + " motif_pos: int32, \n", + " motif_score_change: float64, \n", + " transcription_factors: array, \n", + " strand: int32, \n", + " variant_allele: str\n", + " }>, \n", + " regulatory_feature_consequences: array, \n", + " impact: str, \n", + " regulatory_feature_id: str, \n", + " variant_allele: str\n", + " }>, \n", + " seq_region_name: str, \n", + " start: int32, \n", + " strand: int32, \n", + " transcript_consequences: array, \n", + " distance: int32, \n", + " domains: array, \n", + " exon: str, \n", + " flags: str, \n", + " gene_id: str, \n", + " gene_pheno: int32, \n", + " gene_symbol: str, \n", + " gene_symbol_source: str, \n", + " hgnc_id: str, \n", + " hgvsc: str, \n", + " hgvsp: str, \n", + " hgvs_offset: int32, \n", + " impact: str, \n", + " intron: str, \n", + " lof: str, \n", + " lof_flags: str, \n", + " lof_filter: str, \n", + " lof_info: str, \n", + " mane_select: str, \n", + " mane_plus_clinical: str, \n", + " mirna: array, \n", + " protein_end: int32, \n", + " protein_start: int32, \n", + " protein_id: str, \n", + " source: str, \n", + " strand: int32, \n", + " transcript_id: str, \n", + " tsl: int32, \n", + " uniprot_isoform: array, \n", + " variant_allele: str\n", + " }>, \n", + " variant_class: str\n", + " } \n", + " 'vqsr_results': struct {\n", + " AS_VQSLOD: float64, \n", + " AS_culprit: str, \n", + " positive_train_site: bool, \n", + " negative_train_site: bool\n", + " } \n", + " 'region_flags': struct {\n", + " non_par: bool, \n", + " lcr: bool, \n", + " segdup: bool\n", + " } \n", + " 'allele_info': struct {\n", + " allele_type: str, \n", + " n_alt_alleles: int32, \n", + " variant_type: str, \n", + " was_mixed: bool\n", + " } \n", + " 'histograms': struct {\n", + " qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " raw_qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " age_hists: struct {\n", + " age_hist_het: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " age_hist_hom: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }\n", + " } \n", + " 'in_silico_predictors': struct {\n", + " cadd: struct {\n", + " phred: float32, \n", + " raw_score: float32\n", + " }, \n", + " revel_max: float64, \n", + " spliceai_ds_max: float32, \n", + " pangolin_largest_ds: float64, \n", + " phylop: float64, \n", + " sift_max: float64, \n", + " polyphen_max: float64\n", + " } \n", + "----------------------------------------\n", + "Key: ['locus', 'alleles']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "ed916197-3b0e-45dc-bacd-a13cb66d70ee", + "metadata": { + "tags": [] + }, + "source": [ + "#### Show the first 5 variants in the Hail Table" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "00b0ea2f-5685-4bae-886a-b9ea31866818", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
histograms
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
grpmax
fafmax
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
allele_type
n_alt_alleles
variant_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>int32float64int32int32strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strint32boolset<str>set<str>float64float64float64int64float32float64array<int32>float64int32float64float64float64float64int64float32float64array<int32>float64int32boolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolstrint32strboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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639542312"}{"AC0","AS_VQSR"}7.30e+003.48e+016.70e-02962.74e+00-1.07e+00[21,6,4,4]9.60e-02355.10e+003.51e+01-5.72e-016.87e-01772.96e+00-1.38e+00[21,6,3,3]9.64e-0226FalseNANANAFalseFalse-4.57e+00-1.65e-05["ga4gh:VA.oTAtTrgYxm81O9fu6Mrhfo1t3eHsgg4L","ga4gh:VA.Y283OnlLjyi1T1IT_JzvW255rC6YJsW6"][10030,10030][10031,10031]["T","C"]"T/C"10031".""chr1\t10031\t.\tT\tC\t.\t.\tGT"NA"upstream_gene_variant"NANA"chr1"100311[(1,NA,NA,"transcribed_unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1979,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000450305",NA,NA,"C"),(1,NA,NA,"processed_transcript",NA,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1838,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000456328",1,NA,"C"),(1,NA,NA,"unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4373,NA,NA,NA,"ENSG00000227232",NA,"WASH7P","HGNC","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",-1,"ENST00000488147",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4331,NA,NA,NA,"653635",NA,"WASH7P","EntrezGene","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",-1,"NR_024540.1",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1843,NA,NA,NA,"100287102",NA,"DDX11L1","EntrezGene","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",1,"NR_046018.2",NA,NA,"C")]"SNV"-4.57e+00"AS_QD"FalseFalseFalseTrueTrue"snv"2"multi-snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911,665,439,351]0527[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,1476,1023,741,500,418]0744[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]008.97e+007.57e-01NANANANANANA
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showing top 5 rows

\n" + ], + "text/plain": [ + "+---------------+------------+\n", + "| locus | alleles |\n", + "+---------------+------------+\n", + "| locus | array |\n", + "+---------------+------------+\n", + "| chr1:10031 | [\"T\",\"C\"] |\n", + "| chr1:10037 | [\"T\",\"C\"] |\n", + "| chr1:10043 | [\"T\",\"C\"] |\n", + "| chr1:10055 | [\"T\",\"C\"] |\n", + "| chr1:10057 | [\"A\",\"C\"] |\n", + "+---------------+------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| freq |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e... |\n", + "| [(2,2.60e-05,76882,0),(4,3.15e-05,126902,0),(0,0.00e+00,34670,0),(1,1.80e... |\n", + "| [(1,1.17e-05,85634,0),(1,8.24e-06,121430,0),(0,0.00e+00,39236,0),(0,0.00e... |\n", + "| [(1,1.06e-05,94224,0),(5,4.64e-05,107704,0),(0,0.00e+00,44382,0),(1,1.80e... |\n", + "| [(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+-----------+-----------+-----------+-------------------------+----------------+\n", + "| grpmax.AC | grpmax.AF | grpmax.AN | grpmax.homozygote_count | grpmax.gen_anc |\n", + "+-----------+-----------+-----------+-------------------------+----------------+\n", + "| int32 | float64 | int32 | int32 | str |\n", + "+-----------+-----------+-----------+-------------------------+----------------+\n", + "| NA | NA | NA | NA | NA |\n", + "| 1 | 4.07e-04 | 2456 | 0 | \"eas\" |\n", + "| 1 | 4.39e-05 | 22760 | 0 | \"afr\" |\n", + "| NA | NA | NA | NA | NA |\n", + "| 2 | 3.78e-05 | 52912 | 0 | \"nfe\" |\n", + "+-----------+-----------+-----------+-------------------------+----------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| faf |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n", + "| [(4.32e-06,1.62e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n", + "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n", + "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n", + "| [(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------+--------------------------+------------------+\n", + "| fafmax.faf95_max | fafmax.faf95_max_gen_anc | fafmax.faf99_max |\n", + "+------------------+--------------------------+------------------+\n", + "| float64 | str | float64 |\n", + "+------------------+--------------------------+------------------+\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| 6.27e-06 | \"nfe\" | 2.35e-06 |\n", + "+------------------+--------------------------+------------------+\n", + "\n", + "+--------------------------+---------+-----------+------------------+\n", + "| fafmax.faf99_max_gen_anc | a_index | was_split | rsid |\n", + "+--------------------------+---------+-----------+------------------+\n", + "| str | int32 | bool | set |\n", + "+--------------------------+---------+-----------+------------------+\n", + "| NA | 2 | True | {\"rs1639542312\"} |\n", + "| NA | 1 | False | {\"rs1639542418\"} |\n", + "| NA | 1 | False | NA |\n", + "| NA | 2 | True | {\"rs892501864\"} |\n", + "| \"nfe\" | 1 | True | {\"rs1570391741\"} |\n", + 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info.ReadPosRankSum | info.SB | info.SOR | info.VarDP |\n", + "+----------+---------------------+---------------+----------+------------+\n", + "| float32 | float64 | array | float64 | int32 |\n", + "+----------+---------------------+---------------+----------+------------+\n", + "| 2.74e+00 | -1.07e+00 | [21,6,4,4] | 9.60e-02 | 35 |\n", + "| 2.20e+00 | -4.80e-01 | [49,12,13,8] | 1.51e-01 | 82 |\n", + "| 2.77e+00 | -8.96e-01 | [25,0,5,5] | 1.00e-03 | 35 |\n", + "| 2.12e+00 | -1.16e+00 | [51,29,15,9] | 6.16e-01 | 104 |\n", + "| 1.79e+00 | -6.84e-01 | [97,29,17,20] | 3.75e-01 | 163 |\n", + "+----------+---------------------+---------------+----------+------------+\n", + "\n", + "+------------+------------+-------------------+-----------------+\n", + "| info.AS_FS | info.AS_MQ | info.AS_MQRankSum | info.AS_pab_max |\n", + "+------------+------------+-------------------+-----------------+\n", + "| float64 | float64 | float64 | float64 |\n", + 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[51,29,7,8] |\n", + "| 264 | 2.06e+00 | -6.84e-01 | [97,29,13,19] |\n", + "+--------------------+------------+------------------------+------------------+\n", + "\n", + "+-------------+---------------+----------------+----------------------------+\n", + "| info.AS_SOR | info.AS_VarDP | info.singleton | info.transmitted_singleton |\n", + "+-------------+---------------+----------------+----------------------------+\n", + "| float64 | int32 | bool | bool |\n", + "+-------------+---------------+----------------+----------------------------+\n", + "| 9.64e-02 | 26 | False | NA |\n", + "| 1.51e-01 | 82 | False | NA |\n", + "| 1.48e-03 | 35 | True | False |\n", + "| 4.69e-01 | 75 | False | NA |\n", + "| 7.58e-01 | 128 | False | NA |\n", + "+-------------+---------------+----------------+----------------------------+\n", + "\n", + "+-----------+------------+------------------+---------------+----------------+\n", + "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n", + "+-----------+------------+------------------+---------------+----------------+\n", + "| bool | bool | bool | bool | float64 |\n", + "+-----------+------------+------------------+---------------+----------------+\n", + "| NA | NA | False | False | -4.57e+00 |\n", + "| NA | NA | False | False | -3.18e+00 |\n", + "| NA | NA | False | False | -5.79e+00 |\n", + "| NA | NA | False | False | -3.72e+00 |\n", + "| NA | NA | False | False | -3.31e+00 |\n", + "+-----------+------------+------------------+---------------+----------------+\n", + "\n", + "+-----------------------+\n", + "| info.inbreeding_coeff |\n", + "+-----------------------+\n", + "| float64 |\n", + "+-----------------------+\n", + "| -1.65e-05 |\n", + "| -3.15e-05 |\n", + "| -8.24e-06 |\n", + "| -4.64e-05 |\n", + "| -2.41e-05 |\n", + "+-----------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| info.vrs.VRS_Allele_IDs |\n", + 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"+---------------------+-------------------+---------------------+\n", + "| [10030,10030] | [10031,10031] | [\"T\",\"C\"] |\n", + "| [10036,10036] | [10037,10037] | [\"T\",\"C\"] |\n", + "| [10042,10042] | [10043,10043] | [\"T\",\"C\"] |\n", + "| [10054,10054] | [10055,10055] | [\"T\",\"C\"] |\n", + "| [10056,10056] | [10057,10057] | [\"A\",\"C\"] |\n", + "+---------------------+-------------------+---------------------+\n", + "\n", + "+-------------------+---------+--------+---------------------------+\n", + "| vep.allele_string | vep.end | vep.id | vep.input |\n", + "+-------------------+---------+--------+---------------------------+\n", + "| str | int32 | str | str |\n", + "+-------------------+---------+--------+---------------------------+\n", + "| \"T/C\" | 10031 | \".\" | \"chr1\t10031\t.\tT\tC\t.\t.\tGT\" |\n", + "| \"T/C\" | 10037 | \".\" | \"chr1\t10037\t.\tT\tC\t.\t.\tGT\" |\n", + "| \"T/C\" | 10043 | \".\" | \"chr1\t10043\t.\tT\tC\t.\t.\tGT\" |\n", + "| \"T/C\" | 10055 | \".\" | \"chr1\t10055\t.\tT\tC\t.\t.\tGT\" |\n", + "| \"A/C\" | 10057 | \".\" | \"chr1\t10057\t.\tA\tC\t.\t.\tGT\" |\n", + "+-------------------+---------+--------+---------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| vep.intergenic_consequences |\n", + "+------------------------------------------------------------------------------+\n", + "| array, impact: st... |\n", + "+------------------------------------------------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+-----------------------------+\n", + "| vep.most_severe_consequence |\n", + "+-----------------------------+\n", + "| str |\n", + "+-----------------------------+\n", + "| \"upstream_gene_variant\" |\n", + "| \"upstream_gene_variant\" |\n", + "| \"upstream_gene_variant\" |\n", + "| \"upstream_gene_variant\" |\n", + "| \"upstream_gene_variant\" |\n", + "+-----------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| vep.motif_feature_consequences |\n", + "+------------------------------------------------------------------------------+\n", + "| array, high_inf_p... |\n", + "+------------------------------------------------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| vep.regulatory_feature_consequences |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_all.bin_freq |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2] |\n", + "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15] |\n", + "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9] |\n", + "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n", + "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_all.n_smaller |\n", + "+---------------------------------------------+\n", + "| int64 |\n", + "+---------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+---------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_all.n_larger |\n", + "+--------------------------------------------+\n", + "| int64 |\n", + 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"+------------------------------------------------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| histograms.qual_hists.dp_hist_all.bin_freq |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n", + "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n", + "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n", + "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n", + "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + 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[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0] |\n", + "+--------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_alt.n_smaller |\n", + "+---------------------------------------------+\n", + "| int64 |\n", + "+---------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+---------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_alt.n_larger |\n", + "+--------------------------------------------+\n", + "| int64 |\n", + "+--------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+--------------------------------------------+\n", + "\n", + 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"+--------------------------------------------+\n", + "| array |\n", + "+--------------------------------------------+\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "+--------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| histograms.qual_hists.dp_hist_alt.n_smaller |\n", + "+---------------------------------------------+\n", + "| int64 |\n", + "+---------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+---------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| histograms.qual_hists.dp_hist_alt.n_larger |\n", + "+--------------------------------------------+\n", + "| int64 |\n", + 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in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n", + "+-------------------------------------+--------------------------------+\n", + "| float32 | float64 |\n", + "+-------------------------------------+--------------------------------+\n", + "| 7.57e-01 | NA |\n", + "| 7.49e-01 | NA |\n", + "| 7.48e-01 | NA |\n", + "| 7.46e-01 | NA |\n", + "| 7.09e-01 | NA |\n", + "+-------------------------------------+--------------------------------+\n", + "\n", + "+--------------------------------------+\n", + "| in_silico_predictors.spliceai_ds_max |\n", + "+--------------------------------------+\n", + "| float32 |\n", + "+--------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+--------------------------------------+\n", + "\n", + "+------------------------------------------+-----------------------------+\n", + "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n", + "+------------------------------------------+-----------------------------+\n", + "| float64 | float64 |\n", + "+------------------------------------------+-----------------------------+\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+------------------------------------------+-----------------------------+\n", + "\n", + "+-------------------------------+-----------------------------------+\n", + "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n", + "+-------------------------------+-----------------------------------+\n", + "| float64 | float64 |\n", + "+-------------------------------+-----------------------------------+\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+-------------------------------+-----------------------------------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "22e6f759-a0ee-4e9c-8ca4-eb154cb08763", + "metadata": { + "tags": [] + }, + "source": [ + "### v4.1 Joint Frequency Hail Table" + ] + }, + { + "cell_type": "markdown", + "id": "0a569b77-d3d2-45a4-803a-1214c77e46f2", + "metadata": {}, + "source": [ + "The joint frequency Hail table includes frequency for the exomes, genomes, and the exomes+genomes. We have also added statistics for the combined exomes and genomes frequencies, more details on these stats can be found on our [Help](https://gnomad.broadinstitute.org/help/combined-freq-stats) page." + ] + }, + { + "cell_type": "markdown", + "id": "46d4fc43-609d-4a16-8a0a-ab1e870b5d3d", + "metadata": {}, + "source": [ + "#### Load the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c1c1fbb0-4ef9-4892-bd91-aae9985317a7", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:59.197890Z", + "start_time": "2024-12-06T20:08:50.859108Z" + } + }, + "outputs": [], + "source": [ + "ht = get_gnomad_release(data_type='joint', version='4.1')" + ] + }, + { + "cell_type": "markdown", + "id": "163df47b-70de-4e1e-91be-a65c90cf2db5", + "metadata": {}, + "source": [ + "#### Print the schema of the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "750c0111-4566-4b86-8c08-18c504ff1a79", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:00.032524Z", + "start_time": "2024-12-06T20:09:00.029271Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "Global fields:\n", + " 'exomes_globals': struct {\n", + " freq_meta: array>, \n", + " freq_index_dict: dict, \n", + " freq_meta_sample_count: array, \n", + " faf_meta: array>, \n", + " faf_index_dict: dict, \n", + " age_distribution: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int32, \n", + " n_larger: int32\n", + " }\n", + " } \n", + " 'genomes_globals': struct {\n", + " freq_meta: array>, \n", + " freq_index_dict: dict, \n", + " freq_meta_sample_count: array, \n", + " faf_meta: array>, \n", + " faf_index_dict: dict, \n", + " age_distribution: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int32, \n", + " n_larger: int32\n", + " }\n", + " } \n", + " 'joint_globals': struct {\n", + " freq_meta: array>, \n", + " freq_index_dict: dict, \n", + " faf_meta: array>, \n", + " faf_index_dict: dict, \n", + " freq_meta_sample_count: array, \n", + " age_distribution: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int32, \n", + " n_larger: int32\n", + " }\n", + " } \n", + "----------------------------------------\n", + "Row fields:\n", + " 'locus': locus \n", + " 'alleles': array \n", + " 'region_flags': struct {\n", + " fail_interval_qc: bool, \n", + " outside_broad_capture_region: bool, \n", + " outside_ukb_capture_region: bool, \n", + " outside_broad_calling_region: bool, \n", + " outside_ukb_calling_region: bool, \n", + " not_called_in_exomes: bool, \n", + " not_called_in_genomes: bool\n", + " } \n", + " 'exomes': struct {\n", + " filters: set, \n", + " freq: array, \n", + " faf: array, \n", + " grpmax: struct {\n", + " AC: int32, \n", + " AF: float64, \n", + " AN: int32, \n", + " homozygote_count: int64, \n", + " gen_anc: str\n", + " }, \n", + " fafmax: struct {\n", + " faf95_max: float64, \n", + " faf95_max_gen_anc: str, \n", + " faf99_max: float64, \n", + " faf99_max_gen_anc: str\n", + " }, \n", + " histograms: struct {\n", + " qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " raw_qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " age_hists: struct {\n", + " age_hist_het: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " age_hist_hom: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }\n", + " }\n", + " } \n", + " 'genomes': struct {\n", + " filters: set, \n", + " freq: array, \n", + " faf: array, \n", + " grpmax: struct {\n", + " AC: int32, \n", + " AF: float64, \n", + " AN: int32, \n", + " homozygote_count: int32, \n", + " gen_anc: str\n", + " }, \n", + " fafmax: struct {\n", + " faf95_max: float64, \n", + " faf95_max_gen_anc: str, \n", + " faf99_max: float64, \n", + " faf99_max_gen_anc: str\n", + " }, \n", + " histograms: struct {\n", + " qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " raw_qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " age_hists: struct {\n", + " age_hist_het: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " age_hist_hom: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }\n", + " }\n", + " } \n", + " 'joint': struct {\n", + " freq: array, \n", + " faf: array, \n", + " fafmax: struct {\n", + " faf95_max: float64, \n", + " faf95_max_gen_anc: str, \n", + " faf99_max: float64, \n", + " faf99_max_gen_anc: str\n", + " }, \n", + " grpmax: struct {\n", + " AC: int32, \n", + " AF: float64, \n", + " AN: int32, \n", + " homozygote_count: int32, \n", + " gen_anc: str\n", + " }, \n", + " histograms: struct {\n", + " qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " raw_qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " age_hists: struct {\n", + " age_hist_het: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " age_hist_hom: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }\n", + " }\n", + " } \n", + " 'freq_comparison_stats': struct {\n", + " contingency_table_test: array, \n", + " cochran_mantel_haenszel_test: struct {\n", + " p_value: float64, \n", + " chisq: float64\n", + " }, \n", + " stat_union: struct {\n", + " p_value: float64, \n", + " stat_test_name: str, \n", + " gen_ancs: array\n", + " }\n", + " } \n", + "----------------------------------------\n", + "Key: ['locus', 'alleles']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "d6843db4-9e8f-42f4-9178-fc945f61a827", + "metadata": { + "tags": [] + }, + "source": [ + "#### Show the first 5 variants in the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "477ed281-4ee9-4799-b5b6-e6a0c528fa9a", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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gen_ancs
locus<GRCh38>array<str>boolboolboolboolboolboolboolset<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>int32float64int32int64strfloat64strfloat64strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64set<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>int32float64int32int32strfloat64strfloat64strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>float64strfloat64strint32float64int32int32strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<struct{p_value: float64, odds_ratio: float64}>float64float64float64strarray<str>
chr1:10031["T","C"]NATrueTrueTrueTrueTrueFalseNA[(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA)]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA{"AC0","AS_VQSR"}[(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e+00,4326,0),(0,0.00e+00,192,0),(0,0.00e+00,782,0),(0,0.00e+00,352,0),(0,0.00e+00,1550,0),(0,0.00e+00,14642,0),(0,0.00e+00,1712,0),(0,0.00e+00,1120,0),(0,0.00e+00,6420,0),(0,0.00e+00,29308,0),(0,0.00e+00,27334,0),(0,0.00e+00,14998,0),(0,0.00e+00,1060,0),(0,0.00e+00,102,0),(0,0.00e+00,402,0),(0,0.00e+00,154,0),(0,0.00e+00,814,0),(0,0.00e+00,7978,0),(0,0.00e+00,770,0),(0,0.00e+00,260,0),(0,0.00e+00,2770,0),(0,0.00e+00,10548,0),(0,0.00e+00,3266,0),(0,0.00e+00,90,0),(0,0.00e+00,380,0),(0,0.00e+00,198,0),(0,0.00e+00,736,0),(0,0.00e+00,6664,0),(0,0.00e+00,942,0),(0,0.00e+00,860,0),(0,0.00e+00,3650,0)][(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]NANANANANANANANANA[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911,665,439,351]0527[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,1476,1023,741,500,418]0744[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e+00,4326,0),(0,0.00e+00,192,0),(0,0.00e+00,782,0),(0,0.00e+00,352,0),(0,0.00e+00,1550,0),(0,0.00e+00,14642,0),(0,0.00e+00,1712,0),(0,0.00e+00,1120,0),(0,0.00e+00,6420,0),(0,0.00e+00,29308,0),(0,0.00e+00,27334,0),(0,0.00e+00,14998,0),(0,0.00e+00,1060,0),(0,0.00e+00,102,0),(0,0.00e+00,402,0),(0,0.00e+00,154,0),(0,0.00e+00,814,0),(0,0.00e+00,7978,0),(0,0.00e+00,770,0),(0,0.00e+00,260,0),(0,0.00e+00,2770,0),(0,0.00e+00,10548,0),(0,0.00e+00,3266,0),(0,0.00e+00,90,0),(0,0.00e+00,380,0),(0,0.00e+00,198,0),(0,0.00e+00,736,0),(0,0.00e+00,6664,0),(0,0.00e+00,942,0),(0,0.00e+00,860,0),(0,0.00e+00,3650,0)][(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,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showing top 5 rows

\n" + ], + "text/plain": [ + "+---------------+------------+-------------------------------+\n", + "| locus | alleles | region_flags.fail_interval_qc |\n", + "+---------------+------------+-------------------------------+\n", + "| locus | array | bool |\n", + "+---------------+------------+-------------------------------+\n", + "| chr1:10031 | [\"T\",\"C\"] | NA |\n", + "| chr1:10037 | [\"T\",\"C\"] | NA |\n", + "| chr1:10043 | [\"T\",\"C\"] | NA |\n", + "| chr1:10055 | [\"T\",\"C\"] | NA |\n", + "| chr1:10057 | [\"A\",\"C\"] | NA |\n", + "+---------------+------------+-------------------------------+\n", + "\n", + "+-------------------------------------------+\n", + "| region_flags.outside_broad_capture_region |\n", + "+-------------------------------------------+\n", + "| bool |\n", + "+-------------------------------------------+\n", + "| True |\n", + "| True |\n", + "| True |\n", + "| True |\n", + "| True |\n", + "+-------------------------------------------+\n", + "\n", + "+-----------------------------------------+\n", + "| region_flags.outside_ukb_capture_region |\n", + "+-----------------------------------------+\n", + "| bool |\n", + "+-----------------------------------------+\n", + "| True |\n", + "| True |\n", + "| True |\n", + "| True |\n", + "| True |\n", + "+-----------------------------------------+\n", + "\n", + "+-------------------------------------------+\n", + "| region_flags.outside_broad_calling_region |\n", + "+-------------------------------------------+\n", + "| bool |\n", + "+-------------------------------------------+\n", + "| True |\n", + "| True |\n", + "| True |\n", + "| True |\n", + "| True |\n", + "+-------------------------------------------+\n", + "\n", + "+-----------------------------------------+-----------------------------------+\n", + "| region_flags.outside_ukb_calling_region | region_flags.not_called_in_exomes |\n", + "+-----------------------------------------+-----------------------------------+\n", + "| bool | bool |\n", + "+-----------------------------------------+-----------------------------------+\n", + "| True | True |\n", + "| True | True |\n", + "| True | True |\n", + "| True | True |\n", + "| True | True |\n", + "+-----------------------------------------+-----------------------------------+\n", + "\n", + "+------------------------------------+----------------+\n", + "| region_flags.not_called_in_genomes | exomes.filters |\n", + "+------------------------------------+----------------+\n", + "| bool | set |\n", + "+------------------------------------+----------------+\n", + "| False | NA |\n", + "| False | NA |\n", + "| False | NA |\n", + "| False | NA |\n", + "| False | NA |\n", + "+------------------------------------+----------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| exomes.freq |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n", + "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n", + "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n", + "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n", + "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+-----------------------------------------------+------------------+\n", + "| exomes.faf | exomes.grpmax.AC |\n", + "+-----------------------------------------------+------------------+\n", + "| array | int32 |\n", + "+-----------------------------------------------+------------------+\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+-----------------------------------------------+------------------+\n", + "\n", + "+------------------+------------------+--------------------------------+\n", + "| exomes.grpmax.AF | exomes.grpmax.AN | exomes.grpmax.homozygote_count |\n", + "+------------------+------------------+--------------------------------+\n", + "| float64 | int32 | int64 |\n", + "+------------------+------------------+--------------------------------+\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "+------------------+------------------+--------------------------------+\n", + "\n", + "+-----------------------+-------------------------+\n", + "| exomes.grpmax.gen_anc | exomes.fafmax.faf95_max |\n", + "+-----------------------+-------------------------+\n", + "| str | float64 |\n", + "+-----------------------+-------------------------+\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+-----------------------+-------------------------+\n", + "\n", + "+---------------------------------+-------------------------+\n", + "| exomes.fafmax.faf95_max_gen_anc | exomes.fafmax.faf99_max |\n", + "+---------------------------------+-------------------------+\n", + "| str | float64 |\n", + "+---------------------------------+-------------------------+\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+---------------------------------+-------------------------+\n", + "\n", + "+---------------------------------+\n", + "| exomes.fafmax.faf99_max_gen_anc |\n", + "+---------------------------------+\n", + "| str |\n", + "+---------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+---------------------------------+\n", + "\n", + "+----------------------------------------------------+\n", + "| exomes.histograms.qual_hists.gq_hist_all.bin_edges |\n", + 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"+------------------------------------------+\n", + "| float64 |\n", + "+------------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+------------------------------------------+\n", + "\n", + "+-------------------------------------------------+\n", + "| freq_comparison_stats.stat_union.stat_test_name |\n", + "+-------------------------------------------------+\n", + "| str |\n", + "+-------------------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+-------------------------------------------------+\n", + "\n", + "+-------------------------------------------+\n", + "| freq_comparison_stats.stat_union.gen_ancs |\n", + "+-------------------------------------------+\n", + "| array |\n", + "+-------------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+-------------------------------------------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "354d7a5e-07a2-4f33-a830-970877cd4d63", + "metadata": { + "tags": [] + }, + "source": [ + "## All sites allele numbers\n", + "\n", + "As part of gnomAD v4.1, we [released](https://gnomad.broadinstitute.org/data#v4-all-sites-allele-number) allele number across all callable sites in the gnomAD exomes and genomes. For more information, see our [v4.1 blog post](https://gnomad.broadinstitute.org/news/2024-04-gnomad-v4-1/#allele-numbers-across-all-possible-sites)." + ] + }, + { + "cell_type": "markdown", + "id": "81008401-eec4-4e95-9709-4781db066f7f", + "metadata": { + "tags": [] + }, + "source": [ + "### Exomes all sites allele number Hail Table" + ] + }, + { + "cell_type": "markdown", + "id": "f7f1c013-a013-4fde-a7e6-fcb18d8d8a5c", + "metadata": {}, + "source": [ + "#### Load the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3b7c75d0-1eec-4b92-883e-410337b09c92", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:59.197890Z", + "start_time": "2024-12-06T20:08:50.859108Z" + } + }, + "outputs": [], + "source": [ + "ht = get_gnomad_release(data_type='exomes', version='4.1', dataset=\"all_sites_an\")" + ] + }, + { + "cell_type": "markdown", + "id": "6868a2d1-6e62-492a-8086-822c910e8608", + "metadata": {}, + "source": [ + "#### Print the schema of the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f9c6d73b-7683-47fe-bf7d-2f5bef1d23d3", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:00.032524Z", + "start_time": "2024-12-06T20:09:00.029271Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "Global fields:\n", + " 'strata_meta': array> \n", + " 'strata_sample_count': array \n", + "----------------------------------------\n", + "Row fields:\n", + " 'locus': locus \n", + " 'AN': array \n", + " 'outside_broad_capture_region': bool \n", + " 'outside_ukb_capture_region': bool \n", + " 'outside_broad_calling_region': bool \n", + " 'outside_ukb_calling_region': bool \n", + "----------------------------------------\n", + "Key: ['locus']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "626f20d9-43c1-4687-9b05-01d53115c168", + "metadata": { + "tags": [] + }, + "source": [ + "#### Show the first 5 variants in the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ee361058-54ae-4793-951b-1e0a6df6f685", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
locus
AN
outside_broad_capture_region
outside_ukb_capture_region
outside_broad_calling_region
outside_ukb_calling_region
locus<GRCh38>array<int64>boolboolboolbool
chr1:11719[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11720[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11721[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11722[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11723[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue

showing top 5 rows

\n" + ], + "text/plain": [ + "+---------------+\n", + "| locus |\n", + "+---------------+\n", + "| locus |\n", + "+---------------+\n", + "| chr1:11719 |\n", + "| chr1:11720 |\n", + "| chr1:11721 |\n", + "| chr1:11722 |\n", + "| chr1:11723 |\n", + "+---------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| AN |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n", + "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n", + "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n", + "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n", + "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------------------+----------------------------+\n", + "| outside_broad_capture_region | outside_ukb_capture_region |\n", + "+------------------------------+----------------------------+\n", + "| bool | bool |\n", + "+------------------------------+----------------------------+\n", + "| True | True |\n", + "| True | True |\n", + "| True | True |\n", + "| True | True |\n", + "| True | True |\n", + "+------------------------------+----------------------------+\n", + "\n", + "+------------------------------+----------------------------+\n", + "| outside_broad_calling_region | outside_ukb_calling_region |\n", + "+------------------------------+----------------------------+\n", + "| bool | bool |\n", + "+------------------------------+----------------------------+\n", + "| False | True |\n", + "| False | True |\n", + "| False | True |\n", + "| False | True |\n", + "| False | True |\n", + "+------------------------------+----------------------------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "dc4a4f23-d754-4e31-8e59-f62f9be65942", + "metadata": { + "tags": [] + }, + "source": [ + "### Genomes all sites allele number Hail Table" + ] + }, + { + "cell_type": "markdown", + "id": "65cb8d93-c5ef-409b-9c51-7c282a63bdc2", + "metadata": {}, + "source": [ + "#### Load the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0bb38926-f803-4be5-852c-782023b387bb", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:59.197890Z", + "start_time": "2024-12-06T20:08:50.859108Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "ht = get_gnomad_release(data_type='genomes', version='4.1', dataset=\"all_sites_an\")" + ] + }, + { + "cell_type": "markdown", + "id": "7d5c2549-151c-4b99-bac3-23fd9024f114", + "metadata": {}, + "source": [ + "#### Print the schema of the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "64d2c64c-b533-433a-89e6-72d473bd6464", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:00.032524Z", + "start_time": "2024-12-06T20:09:00.029271Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "Global fields:\n", + " 'strata_meta': array> \n", + " 'strata_sample_count': array \n", + "----------------------------------------\n", + "Row fields:\n", + " 'locus': locus \n", + " 'AN': array \n", + "----------------------------------------\n", + "Key: ['locus']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "140f66aa-83d4-4752-abcf-c674bf208194", + "metadata": { + "tags": [] + }, + "source": [ + "#### Show the first 5 variants in the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e2271a6e-16f6-48ca-805f-735e17a8f711", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
locus
AN
locus<GRCh38>array<int64>
chr1:10001[16,18232,0,0,4,0,0,6,0,6,0,0,8,8,0,0,0,0,4,0,0,0,0,0,2,4,0,0,2,4,0,0,0,0]
chr1:10002[78,32090,12,0,22,2,0,26,0,14,2,0,30,48,8,4,0,0,4,18,2,0,0,0,12,14,0,0,2,12,2,0,0,0]
chr1:10003[200,38154,48,0,48,6,4,28,6,48,6,6,102,98,38,10,0,0,8,40,6,0,0,4,14,14,2,4,26,22,6,0,2,4]
chr1:10004[948,62380,202,0,248,18,12,172,10,258,18,10,400,548,112,90,0,0,66,182,14,4,2,10,70,102,4,6,118,140,10,8,4,6]
chr1:10005[1774,70720,356,6,444,30,18,288,18,552,32,30,782,992,206,150,2,4,134,310,18,12,6,12,116,172,6,12,268,284,16,16,10,20]

showing top 5 rows

\n" + ], + "text/plain": [ + "+---------------+\n", + "| locus |\n", + "+---------------+\n", + "| locus |\n", + "+---------------+\n", + "| chr1:10001 |\n", + "| chr1:10002 |\n", + "| chr1:10003 |\n", + "| chr1:10004 |\n", + "| chr1:10005 |\n", + "+---------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| AN |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [16,18232,0,0,4,0,0,6,0,6,0,0,8,8,0,0,0,0,4,0,0,0,0,0,2,4,0,0,2,4,0,0,0,0] |\n", + "| [78,32090,12,0,22,2,0,26,0,14,2,0,30,48,8,4,0,0,4,18,2,0,0,0,12,14,0,0,2,... |\n", + "| [200,38154,48,0,48,6,4,28,6,48,6,6,102,98,38,10,0,0,8,40,6,0,0,4,14,14,2,... |\n", + "| [948,62380,202,0,248,18,12,172,10,258,18,10,400,548,112,90,0,0,66,182,14,... |\n", + "| [1774,70720,356,6,444,30,18,288,18,552,32,30,782,992,206,150,2,4,134,310,... |\n", + "+------------------------------------------------------------------------------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "1bf9f31f-34ff-4385-a80e-985cbb0acfe8", + "metadata": { + "tags": [] + }, + "source": [ + "## Coverage\n" + ] + }, + { + "cell_type": "markdown", + "id": "de70c319-787b-4d6c-9058-255a1137d81f", + "metadata": { + "tags": [] + }, + "source": [ + "### Exomes coverage Hail Table" + ] + }, + { + "cell_type": "markdown", + "id": "3278430c-4279-4d89-85e7-276184ec42b8", + "metadata": {}, + "source": [ + "#### Load the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "7f005af0-df6d-4a0a-a2e0-9cb4ff6782c2", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:59.197890Z", + "start_time": "2024-12-06T20:08:50.859108Z" + } + }, + "outputs": [], + "source": [ + "ht = get_gnomad_release(data_type='exomes', version='4.0', dataset=\"coverage\")" + ] + }, + { + "cell_type": "markdown", + "id": "128e58ce-c219-472a-88be-6babc2ba5a15", + "metadata": {}, + "source": [ + "#### Print the schema of the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6058bf38-4ce9-4d37-89a7-d11af0e4d9f1", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:00.032524Z", + "start_time": "2024-12-06T20:09:00.029271Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "Global fields:\n", + " None\n", + "----------------------------------------\n", + "Row fields:\n", + " 'locus': locus \n", + " 'mean': float64 \n", + " 'median_approx': int32 \n", + " 'total_DP': int64 \n", + " 'over_1': float64 \n", + " 'over_5': float64 \n", + " 'over_10': float64 \n", + " 'over_15': float64 \n", + " 'over_20': float64 \n", + " 'over_25': float64 \n", + " 'over_30': float64 \n", + " 'over_50': float64 \n", + " 'over_100': float64 \n", + "----------------------------------------\n", + "Key: ['locus']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "5969ab0c-7cee-4061-8740-8b82366ae806", + "metadata": { + "tags": [] + }, + "source": [ + "#### Show the first 5 variants in the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "49fcf64b-1a91-4052-bb98-b329a0e8031b", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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chr1:118221.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118231.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00

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chr1:100022.10e+0118[0,10,41,108,209,339,482,570,581,575,483,390,702,689,639,556,587,534,516,435,417,366,346,320,437,415,405,359,333,308,283,266,272,248,218,231,241,184,176,162,138,119,127,137,118,63,82,87,66,66,46,33,39,43,22,25,26,19,19,11,7,6,7,5,3,5,2,4,2,6,2,3,2,0,1,1,1,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]2.20e-012.15e-011.79e-011.39e-011.02e-017.60e-025.06e-024.83e-032.79e-05
chr1:100032.44e+0123[0,6,16,62,100,162,233,289,294,346,342,310,537,612,664,649,661,711,723,658,649,590,548,486,679,656,640,571,533,491,439,398,412,404,349,383,360,298,263,242,207,182,186,194,159,118,123,116,96,96,67,59,61,64,34,33,34,31,30,15,12,11,13,10,7,7,3,7,5,10,3,3,5,0,2,2,1,0,0,2,1,1,1,4,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,4]2.62e-012.59e-012.41e-012.07e-011.59e-011.18e-017.77e-027.61e-035.58e-05
chr1:100042.43e+0123[0,4,21,70,101,178,270,396,417,515,550,486,864,1003,1098,1138,1183,1270,1242,1181,1162,1083,966,845,1149,1088,1047,922,857,804,725,645,633,658,525,610,537,451,411,369,343,285,290,260,235,184,190,174,151,152,96,83,96,91,52,52,56,43,47,30,20,19,22,16,13,9,9,10,10,14,8,7,8,0,5,3,3,2,1,2,1,1,1,5,3,3,2,1,0,2,0,0,1,0,1,0,1,1,0,0,12]4.27e-014.24e-013.99e-013.43e-012.60e-011.87e-011.21e-011.20e-021.67e-04
chr1:100052.45e+0123[0,4,22,67,98,177,257,381,423,511,580,560,1013,1158,1257,1314,1338,1472,1401,1341,1300,1266,1105,966,1288,1243,1198,1068,976,905,842,725,728,740,600,678,613,515,464,414,396,338,324,300,268,213,210,198,175,165,113,100,108,102,61,58,61,50,53,35,22,22,27,22,15,11,12,10,13,14,10,8,9,1,6,4,6,5,3,3,4,2,2,5,3,7,3,1,0,2,0,1,2,2,2,0,1,1,0,0,17]4.83e-014.80e-014.56e-013.92e-012.96e-012.14e-011.38e-011.42e-022.37e-04

showing top 5 rows

\n" + ], + "text/plain": [ + "+---------------+----------+--------+\n", + "| locus | mean | median |\n", + "+---------------+----------+--------+\n", + "| locus | float64 | int32 |\n", + "+---------------+----------+--------+\n", + "| chr1:10001 | 1.93e+01 | 16 |\n", + "| chr1:10002 | 2.10e+01 | 18 |\n", + "| chr1:10003 | 2.44e+01 | 23 |\n", + "| chr1:10004 | 2.43e+01 | 23 |\n", + "| chr1:10005 | 2.45e+01 | 23 |\n", + "+---------------+----------+--------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| count_array |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [0,20,65,141,226,328,386,442,415,385,292,198,362,367,337,273,289,266,241,... |\n", + "| [0,10,41,108,209,339,482,570,581,575,483,390,702,689,639,556,587,534,516,... |\n", + "| [0,6,16,62,100,162,233,289,294,346,342,310,537,612,664,649,661,711,723,65... |\n", + "| [0,4,21,70,101,178,270,396,417,515,550,486,864,1003,1098,1138,1183,1270,1... |\n", + "| [0,4,22,67,98,177,257,381,423,511,580,560,1013,1158,1257,1314,1338,1472,1... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+----------+----------+----------+----------+----------+----------+----------+\n", + "| over_1 | over_5 | over_10 | over_15 | over_20 | over_25 | over_30 |\n", + "+----------+----------+----------+----------+----------+----------+----------+\n", + "| float32 | float32 | float32 | float32 | float32 | float32 | float32 |\n", + "+----------+----------+----------+----------+----------+----------+----------+\n", + "| 1.25e-01 | 1.19e-01 | 9.12e-02 | 6.95e-02 | 5.15e-02 | 3.88e-02 | 2.62e-02 |\n", + "| 2.20e-01 | 2.15e-01 | 1.79e-01 | 1.39e-01 | 1.02e-01 | 7.60e-02 | 5.06e-02 |\n", + "| 2.62e-01 | 2.59e-01 | 2.41e-01 | 2.07e-01 | 1.59e-01 | 1.18e-01 | 7.77e-02 |\n", + "| 4.27e-01 | 4.24e-01 | 3.99e-01 | 3.43e-01 | 2.60e-01 | 1.87e-01 | 1.21e-01 |\n", + "| 4.83e-01 | 4.80e-01 | 4.56e-01 | 3.92e-01 | 2.96e-01 | 2.14e-01 | 1.38e-01 |\n", + "+----------+----------+----------+----------+----------+----------+----------+\n", + "\n", + "+----------+----------+\n", + "| over_50 | over_100 |\n", + "+----------+----------+\n", + "| float32 | float32 |\n", + "+----------+----------+\n", + "| 2.27e-03 | 0.00e+00 |\n", + "| 4.83e-03 | 2.79e-05 |\n", + "| 7.61e-03 | 5.58e-05 |\n", + "| 1.20e-02 | 1.67e-04 |\n", + "| 1.42e-02 | 2.37e-04 |\n", + "+----------+----------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht.show(5)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + }, + "toc": { + "base_numbering": 1, + "nav_menu": { + "height": "613.99px", + "width": "526.312px" + }, + "number_sections": false, + "sideBar": true, + "skip_h1_title": true, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "202.438px" + }, + "toc_section_display": true, + "toc_window_display": true + }, + "toc-autonumbering": false, + "toc-showcode": false, + "toc-showmarkdowntxt": true, + "toc-showtags": false, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb b/gnomad_toolbox/notebooks/toolbox_for_gnomad_users.ipynb similarity index 72% rename from gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb rename to gnomad_toolbox/notebooks/toolbox_for_gnomad_users.ipynb index 0e081fe..326a2a3 100644 --- a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb +++ b/gnomad_toolbox/notebooks/toolbox_for_gnomad_users.ipynb @@ -23,19 +23,17 @@ "If you want to import your own data to use other gnomAD notebooks, such as for ancestry inference (https://github.com/broadinstitute/gnomad_qc/blob/main/gnomad_qc/example_notebooks/ancestry_classification_using_gnomad_rf.ipynb), you may use Hail's `import_vcf` functions." ] }, - { - "cell_type": "markdown", - "id": "ff73954c", - "metadata": {}, - "source": [] - }, { "cell_type": "code", - "execution_count": 1, "id": "e77d32b1", "metadata": { - "scrolled": true + "scrolled": true, + "ExecuteTime": { + "end_time": "2024-12-06T18:02:57.909455Z", + "start_time": "2024-12-06T18:02:56.316003Z" + } }, + "source": "import hail as hl", "outputs": [ { "data": { @@ -50,7 +48,7 @@ " \n", "
\n", " \n", - " Loading BokehJS ...\n", + " Loading BokehJS ...\n", "
\n" ] }, @@ -59,340 +57,14 @@ }, { "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - "const JS_MIME_TYPE = 'application/javascript';\n", - " const HTML_MIME_TYPE = 'text/html';\n", - " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " const CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " const script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " function drop(id) {\n", - " const view = Bokeh.index.get_by_id(id)\n", - " if (view != null) {\n", - " view.model.document.clear()\n", - " Bokeh.index.delete(view)\n", - " }\n", - " }\n", - "\n", - " const cell = handle.cell;\n", - "\n", - " const id = cell.output_area._bokeh_element_id;\n", - " const server_id = cell.output_area._bokeh_server_id;\n", - "\n", - " // Clean up Bokeh references\n", - " if (id != null) {\n", - " drop(id)\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd_clean, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " const id = msg.content.text.trim()\n", - " drop(id)\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd_destroy);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " const output_area = handle.output_area;\n", - " const output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " const bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " const script_attrs = bk_div.children[0].attributes;\n", - " for (let i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " const toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? 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\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"c9c511bf-e817-434d-8072-f4db620005e8\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"c9c511bf-e817-434d-8072-f4db620005e8\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n function drop(id) {\n const view = Bokeh.index.get_by_id(id)\n if (view != null) {\n view.model.document.clear()\n Bokeh.index.delete(view)\n }\n }\n\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n\n // Clean up Bokeh references\n if (id != null) {\n drop(id)\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim()\n drop(id)\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"e603302b-40d4-4f8c-aa3c-1000266b48fa\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"e603302b-40d4-4f8c-aa3c-1000266b48fa\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"e603302b-40d4-4f8c-aa3c-1000266b48fa\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"e603302b-40d4-4f8c-aa3c-1000266b48fa\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/miniconda3/lib/python3.11/site-packages/hailtop/aiocloud/aiogoogle/user_config.py:43: UserWarning:\n", - "\n", - "Reading spark-defaults.conf to determine GCS requester pays configuration. This is deprecated. Please use `hailctl config set gcs_requester_pays/project` and `hailctl config set gcs_requester_pays/buckets`.\n", - "\n", - "Setting default log level to \"WARN\".\n", - "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SPARKMONITOR_LISTENER: Started SparkListener for Jupyter Notebook\n", - "SPARKMONITOR_LISTENER: Port obtained from environment: 51311\n", - "SPARKMONITOR_LISTENER: Application Started: application_1730470703538_0002 ...Start Time: 1730485367380\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Running on Apache Spark version 3.5.0\n", - "SparkUI available at http://qh1-m.c.broad-mpg-gnomad.internal:39033\n", - "Welcome to\n", - " __ __ <>__\n", - " / /_/ /__ __/ /\n", - " / __ / _ `/ / /\n", - " /_/ /_/\\_,_/_/_/ version 0.2.133-4c60fddb171a\n", - "LOGGING: writing to /test_toolbox.log\n" - ] } ], - "source": [ - "import hail as hl\n", - "\n", - "hl.init(\n", - " log=\"/test_toolbox.log\",\n", - " tmp_dir=\"gs://gnomad-tmp-30day\",\n", - " )" - ] + "execution_count": 1 }, { "cell_type": "markdown", @@ -404,17 +76,16 @@ }, { "cell_type": "code", - "execution_count": 3, "id": "e69953f7", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T18:04:56.165634Z", + "start_time": "2024-12-06T18:04:55.603516Z" + } + }, + "source": "from gnomad_toolbox.load_data import get_gnomad_release", "outputs": [], - "source": [ - "from gnomad_toolbox.modules.filter_variant import get_variant_count, filter_by_interval, filter_by_gene_symbol, filter_by_csqs\n", - "from gnomad_toolbox.modules.import_data import get_ht_by_datatype_and_version\n", - "from gnomad_toolbox.modules.filter_variant import \n", - "from gnomad_toolbox.modules.extract_freq import extract_callstats_for_1anc_1variant, extract_callstats_for_multiple_ancs\n", - "from gnomad.resources.grch38.gnomad import coverage" - ] + "execution_count": 3 }, { "cell_type": "markdown", @@ -433,18 +104,1604 @@ "| genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 |\n", "| joint | 4.1 | N/A |\n", "\n", - "We use gnomAD v4.1 exomes to demonstrate for examples below. " + "We use gnomAD v4.1 exomes to demonstrate for examples below." ] }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Loading gnomAD v4.1 exomes sites Hail Table\n", + "id": "d1a4ae8933ba6421" + }, { "cell_type": "code", - "execution_count": 30, "id": "100cf576", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T18:08:08.225100Z", + "start_time": "2024-12-06T18:07:55.852971Z" + } + }, + "source": "ht = get_gnomad_release(data_type='exomes', version='4.1')", + "outputs": [ + { + "data": { + "text/plain": [ + "\u001B[?25l" + ], + "text/html": [ + "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
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+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "4720d6aa643c489bb768ba33f92dbc45"
+      }
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\n",
+       "\u001B[?25h"
+      ],
+      "text/html": [
+       "
\n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+    {
+     "data": {
+      "text/plain": [
+       "\u001B[?25l"
+      ],
+      "text/html": [
+       "
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+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
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+     "data": {
+      "text/plain": [
+       "\n",
+       "\u001B[?25h"
+      ],
+      "text/html": [
+       "
\n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 5
+  },
+  {
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')"
-   ]
+   "cell_type": "markdown",
+   "source": "## Print the schema of the Hail Table\n",
+   "id": "77d7a05e31c1f37a"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T18:14:53.139250Z",
+     "start_time": "2024-12-06T18:14:53.136478Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "ht.describe()",
+   "id": "95c14f2c8cc3e699",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'freq_meta': array> \n",
+      "    'freq_index_dict': dict \n",
+      "    'freq_meta_sample_count': array \n",
+      "    'faf_meta': array> \n",
+      "    'faf_index_dict': dict \n",
+      "    'age_distribution': struct {\n",
+      "        bin_edges: array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int32, \n",
+      "        n_larger: int32\n",
+      "    } \n",
+      "    'downsamplings': dict> \n",
+      "    'filtering_model': struct {\n",
+      "        filter_name: str, \n",
+      "        score_name: str, \n",
+      "        snv_cutoff: struct {\n",
+      "            bin: int32, \n",
+      "            min_score: float64\n",
+      "        }, \n",
+      "        indel_cutoff: struct {\n",
+      "            bin: int32, \n",
+      "            min_score: float64\n",
+      "        }, \n",
+      "        snv_training_variables: array, \n",
+      "        indel_training_variables: array\n",
+      "    } \n",
+      "    'inbreeding_coeff_cutoff': float64 \n",
+      "    'interval_qc_parameters': struct {\n",
+      "        per_platform: bool, \n",
+      "        all_platforms: bool, \n",
+      "        high_qual_cutoffs: dict>, \n",
+      "        min_platform_size: int32\n",
+      "    } \n",
+      "    'tool_versions': struct {\n",
+      "        cadd_version: str, \n",
+      "        revel_version: str, \n",
+      "        spliceai_version: str, \n",
+      "        pangolin_version: array, \n",
+      "        phylop_version: str, \n",
+      "        dbsnp_version: str, \n",
+      "        sift_version: str, \n",
+      "        polyphen_version: str\n",
+      "    } \n",
+      "    'vrs_versions': struct {\n",
+      "        vrs_schema_version: str, \n",
+      "        vrs_python_version: str, \n",
+      "        seqrepo_version: str\n",
+      "    } \n",
+      "    'vep_globals': struct {\n",
+      "        vep_version: str, \n",
+      "        vep_help: str, \n",
+      "        vep_config: str, \n",
+      "        gencode_version: str, \n",
+      "        mane_select_version: str\n",
+      "    } \n",
+      "    'frequency_README': str \n",
+      "    'date': str \n",
+      "    'version': str \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'alleles': array \n",
+      "    'freq': array \n",
+      "    'grpmax': struct {\n",
+      "        gnomad: struct {\n",
+      "            AC: int32, \n",
+      "            AF: float64, \n",
+      "            AN: int32, \n",
+      "            homozygote_count: int64, \n",
+      "            gen_anc: str\n",
+      "        }, \n",
+      "        non_ukb: struct {\n",
+      "            AC: int32, \n",
+      "            AF: float64, \n",
+      "            AN: int32, \n",
+      "            homozygote_count: int64, \n",
+      "            gen_anc: str\n",
+      "        }\n",
+      "    } \n",
+      "    'faf': array \n",
+      "    'fafmax': struct {\n",
+      "        gnomad: struct {\n",
+      "            faf95_max: float64, \n",
+      "            faf95_max_gen_anc: str, \n",
+      "            faf99_max: float64, \n",
+      "            faf99_max_gen_anc: str\n",
+      "        }, \n",
+      "        non_ukb: struct {\n",
+      "            faf95_max: float64, \n",
+      "            faf95_max_gen_anc: str, \n",
+      "            faf99_max: float64, \n",
+      "            faf99_max_gen_anc: str\n",
+      "        }\n",
+      "    } \n",
+      "    'a_index': int32 \n",
+      "    'was_split': bool \n",
+      "    'rsid': set \n",
+      "    'filters': set \n",
+      "    'info': struct {\n",
+      "        FS: float64, \n",
+      "        MQ: float64, \n",
+      "        MQRankSum: float64, \n",
+      "        QUALapprox: int64, \n",
+      "        QD: float64, \n",
+      "        ReadPosRankSum: float64, \n",
+      "        SB: array, \n",
+      "        SOR: float64, \n",
+      "        VarDP: int32, \n",
+      "        AS_FS: float64, \n",
+      "        AS_MQ: float64, \n",
+      "        AS_MQRankSum: float64, \n",
+      "        AS_pab_max: float64, \n",
+      "        AS_QUALapprox: int64, \n",
+      "        AS_QD: float64, \n",
+      "        AS_ReadPosRankSum: float64, \n",
+      "        AS_SB_TABLE: array, \n",
+      "        AS_SOR: float64, \n",
+      "        AS_VarDP: int32, \n",
+      "        singleton: bool, \n",
+      "        transmitted_singleton: bool, \n",
+      "        sibling_singleton: bool, \n",
+      "        omni: bool, \n",
+      "        mills: bool, \n",
+      "        monoallelic: bool, \n",
+      "        only_het: bool, \n",
+      "        AS_VQSLOD: float64, \n",
+      "        inbreeding_coeff: float64, \n",
+      "        vrs: struct {\n",
+      "            VRS_Allele_IDs: array, \n",
+      "            VRS_Starts: array, \n",
+      "            VRS_Ends: array, \n",
+      "            VRS_States: array\n",
+      "        }\n",
+      "    } \n",
+      "    'vep': struct {\n",
+      "        allele_string: str, \n",
+      "        end: int32, \n",
+      "        id: str, \n",
+      "        input: str, \n",
+      "        intergenic_consequences: array, \n",
+      "            impact: str, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        most_severe_consequence: str, \n",
+      "        motif_feature_consequences: array, \n",
+      "            high_inf_pos: str, \n",
+      "            impact: str, \n",
+      "            motif_feature_id: str, \n",
+      "            motif_name: str, \n",
+      "            motif_pos: int32, \n",
+      "            motif_score_change: float64, \n",
+      "            transcription_factors: array, \n",
+      "            strand: int32, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        regulatory_feature_consequences: array, \n",
+      "            impact: str, \n",
+      "            regulatory_feature_id: str, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        seq_region_name: str, \n",
+      "        start: int32, \n",
+      "        strand: int32, \n",
+      "        transcript_consequences: array, \n",
+      "            distance: int32, \n",
+      "            domains: array, \n",
+      "            exon: str, \n",
+      "            flags: str, \n",
+      "            gene_id: str, \n",
+      "            gene_pheno: int32, \n",
+      "            gene_symbol: str, \n",
+      "            gene_symbol_source: str, \n",
+      "            hgnc_id: str, \n",
+      "            hgvsc: str, \n",
+      "            hgvsp: str, \n",
+      "            hgvs_offset: int32, \n",
+      "            impact: str, \n",
+      "            intron: str, \n",
+      "            lof: str, \n",
+      "            lof_flags: str, \n",
+      "            lof_filter: str, \n",
+      "            lof_info: str, \n",
+      "            mane_select: str, \n",
+      "            mane_plus_clinical: str, \n",
+      "            mirna: array, \n",
+      "            protein_end: int32, \n",
+      "            protein_start: int32, \n",
+      "            protein_id: str, \n",
+      "            source: str, \n",
+      "            strand: int32, \n",
+      "            transcript_id: str, \n",
+      "            tsl: int32, \n",
+      "            uniprot_isoform: array, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        variant_class: str\n",
+      "    } \n",
+      "    'vqsr_results': struct {\n",
+      "        AS_VQSLOD: float64, \n",
+      "        AS_culprit: str, \n",
+      "        positive_train_site: bool, \n",
+      "        negative_train_site: bool\n",
+      "    } \n",
+      "    'region_flags': struct {\n",
+      "        non_par: bool, \n",
+      "        lcr: bool, \n",
+      "        segdup: bool, \n",
+      "        fail_interval_qc: bool, \n",
+      "        outside_ukb_capture_region: bool, \n",
+      "        outside_broad_capture_region: bool\n",
+      "    } \n",
+      "    'allele_info': struct {\n",
+      "        variant_type: str, \n",
+      "        n_alt_alleles: int32, \n",
+      "        has_star: bool, \n",
+      "        allele_type: str, \n",
+      "        was_mixed: bool\n",
+      "    } \n",
+      "    'histograms': struct {\n",
+      "        qual_hists: struct {\n",
+      "            gq_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            gq_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            ab_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }, \n",
+      "        raw_qual_hists: struct {\n",
+      "            gq_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            gq_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            ab_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }, \n",
+      "        age_hists: struct {\n",
+      "            age_hist_het: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            age_hist_hom: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }\n",
+      "    } \n",
+      "    'in_silico_predictors': struct {\n",
+      "        cadd: struct {\n",
+      "            phred: float32, \n",
+      "            raw_score: float32\n",
+      "        }, \n",
+      "        revel_max: float64, \n",
+      "        spliceai_ds_max: float32, \n",
+      "        pangolin_largest_ds: float64, \n",
+      "        phylop: float64, \n",
+      "        sift_max: float64, \n",
+      "        polyphen_max: float64\n",
+      "    } \n",
+      "----------------------------------------\n",
+      "Key: ['locus', 'alleles']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Show the first 5 variants in the Hail Table\n",
+   "id": "a071f738b2c888e"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T18:16:28.722532Z",
+     "start_time": "2024-12-06T18:16:00.055352Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "ht.show(5)",
+   "id": "222de580c305d72a",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[?25l"
+      ],
+      "text/html": [
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "a239765de31846aab6def7bc857e326e"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\n",
+       "\u001B[?25h"
+      ],
+      "text/html": [
+       "
\n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "+---------------+----------------+\n",
+       "| locus         | alleles        |\n",
+       "+---------------+----------------+\n",
+       "| locus | array     |\n",
+       "+---------------+----------------+\n",
+       "| chr1:11994    | [\"T\",\"C\"]      |\n",
+       "| chr1:12016    | [\"G\",\"A\"]      |\n",
+       "| chr1:12060    | [\"CTGGAG\",\"C\"] |\n",
+       "| chr1:12074    | [\"T\",\"C\"]      |\n",
+       "| chr1:12102    | [\"G\",\"A\"]      |\n",
+       "+---------------+----------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0,NA,0,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA,... |\n",
+       "| [(0,NA,0,0),(6,9.54e-06,628784,2),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA,... |\n",
+       "| [(0,0.00e+00,4,0),(1,1.59e-06,628784,0),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n",
+       "| [(0,0.00e+00,4,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n",
+       "| [(0,0.00e+00,32,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0)... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|               NA |               NA |               NA |\n",
+       "|               NA |               NA |               NA |\n",
+       "|               NA |               NA |               NA |\n",
+       "|               NA |               NA |               NA |\n",
+       "|               NA |               NA |               NA |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+----------+-------------------+---------+----------+\n",
+       "| was_split | rsid     | filters           | info.FS |  info.MQ |\n",
+       "+-----------+----------+-------------------+---------+----------+\n",
+       "|      bool | set | set          | float64 |  float64 |\n",
+       "+-----------+----------+-------------------+---------+----------+\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.50e+01 |\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.50e+01 |\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.60e+01 |\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.56e+01 |\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.45e+01 |\n",
+       "+-----------+----------+-------------------+---------+----------+\n",
+       "\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|        float64 |           int64 |  float64 |             float64 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|             NA |              60 | 3.00e+01 |                  NA |\n",
+       "|       0.00e+00 |             262 | 2.18e+01 |            6.74e-01 |\n",
+       "|       6.74e-01 |             123 | 3.08e+01 |           -1.15e+00 |\n",
+       "|       4.31e-01 |              99 | 1.24e+01 |           -2.53e-01 |\n",
+       "|             NA |              90 | 2.25e+01 |                  NA |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "\n",
+       "+--------------+----------+------------+------------+------------+\n",
+       "| info.SB      | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+--------------+----------+------------+------------+------------+\n",
+       "| array |  float64 |      int32 |    float64 |    float64 |\n",
+       "+--------------+----------+------------+------------+------------+\n",
+       "| [0,0,2,0]    | 2.30e+00 |          2 |         NA |   2.50e+01 |\n",
+       "| [2,0,10,0]   | 2.67e+00 |         12 |         NA |   2.50e+01 |\n",
+       "| [1,0,3,0]    | 1.61e+00 |          4 |         NA |   2.60e+01 |\n",
+       "| [4,0,4,0]    | 6.93e-01 |          8 |         NA |   2.56e+01 |\n",
+       "| [0,0,4,0]    | 3.26e+00 |          4 |         NA |   2.45e+01 |\n",
+       "+--------------+----------+------------+------------+------------+\n",
+       "\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|           float64 |         float64 |              int64 |    float64 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|                NA |              NA |                 60 |   3.00e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |                262 |   2.18e+01 |\n",
+       "|          6.74e-01 |        6.25e-01 |                123 |   3.08e+01 |\n",
+       "|          4.31e-01 |        6.87e-01 |                 99 |   1.24e+01 |\n",
+       "|                NA |              NA |                 90 |   2.25e+01 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE | info.AS_SOR | info.AS_VarDP |\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "|                float64 | array     |     float64 |         int32 |\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "|                     NA | [0,0,2,0]        |    2.30e+00 |             2 |\n",
+       "|               6.74e-01 | [2,0,10,0]       |    2.67e+00 |            12 |\n",
+       "|              -1.15e+00 | [1,0,3,0]        |    1.61e+00 |             4 |\n",
+       "|              -2.53e-01 | [4,0,4,0]        |    6.93e-01 |             8 |\n",
+       "|                     NA | [0,0,4,0]        |    3.26e+00 |             4 |\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|           bool |                       bool |                   bool |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|           True |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|      bool |       bool |             bool |          bool |        float64 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|     False |      False |            False |         False |      -5.25e+00 |\n",
+       "|     False |      False |            False |         False |      -2.75e+00 |\n",
+       "|     False |      False |            False |         False |      -2.22e+00 |\n",
+       "|     False |      False |            False |         False |      -2.18e+00 |\n",
+       "|     False |      False |            False |         False |      -2.86e+00 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "\n",
+       "+-----------------------+\n",
+       "| info.inbreeding_coeff |\n",
+       "+-----------------------+\n",
+       "|               float64 |\n",
+       "+-----------------------+\n",
+       "|              1.00e+00 |\n",
+       "|              6.67e-01 |\n",
+       "|             -1.59e-06 |\n",
+       "|              1.00e+00 |\n",
+       "|              1.00e+00 |\n",
+       "+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.ps4-9woXy7o4rS39i8hDK_cUPBa-UcyP\",\"ga4gh:VA.nHlWYJXgiuvXrLAxQs... |\n",
+       "| [\"ga4gh:VA.VFxwcI4knOzk6SHzS2qowyDGnkG3mfEH\",\"ga4gh:VA.ctgP7qNjQAGjI2eTDo... |\n",
+       "| [\"ga4gh:VA.P573ZtUtAaRcceE7NLanEyynSefvcAPL\",\"ga4gh:VA.CHmk9uDiHW2LIHndZW... |\n",
+       "| [\"ga4gh:VA.neBeBT28ISe_1-yKPFsYxntP2jz1pj7E\",\"ga4gh:VA.1RPHSwBHNUwoECJ9VV... |\n",
+       "| [\"ga4gh:VA.ZXRhPWtip8HseOMWpCnaja0-ATo8hLE1\",\"ga4gh:VA.Qi95g6E8nt6DaqTGpH... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts | info.vrs.VRS_Ends | info.vrs.VRS_States |\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "| array        | array      | array          |\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "| [11993,11993]       | [11994,11994]     | [\"T\",\"C\"]           |\n",
+       "| [12015,12015]       | [12016,12016]     | [\"G\",\"A\"]           |\n",
+       "| [12059,12060]       | [12065,12071]     | [\"CTGGAG\",\"TGGAGT\"] |\n",
+       "| [12073,12073]       | [12074,12074]     | [\"T\",\"C\"]           |\n",
+       "| [12101,12101]       | [12102,12102]     | [\"G\",\"A\"]           |\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "\n",
+       "+-------------------+---------+--------+--------------------------------+\n",
+       "| vep.allele_string | vep.end | vep.id | vep.input                      |\n",
+       "+-------------------+---------+--------+--------------------------------+\n",
+       "| str               |   int32 | str    | str                            |\n",
+       "+-------------------+---------+--------+--------------------------------+\n",
+       "| \"T/C\"             |   11994 | \".\"    | \"chr1\t11994\t.\tT\tC\t.\t.\tGT\"      |\n",
+       "| \"G/A\"             |   12016 | \".\"    | \"chr1\t12016\t.\tG\tA\t.\t.\tGT\"      |\n",
+       "| \"TGGAG/-\"         |   12065 | \".\"    | \"chr1\t12060\t.\tCTGGAG\tC\t.\t.\tGT\" |\n",
+       "| \"T/C\"             |   12074 | \".\"    | \"chr1\t12074\t.\tT\tC\t.\t.\tGT\"      |\n",
+       "| \"G/A\"             |   12102 | \".\"    | \"chr1\t12102\t.\tG\tA\t.\t.\tGT\"      |\n",
+       "+-------------------+---------+--------+--------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| vep.most_severe_consequence          |\n",
+       "+--------------------------------------+\n",
+       "| str                                  |\n",
+       "+--------------------------------------+\n",
+       "| \"non_coding_transcript_exon_variant\" |\n",
+       "| \"non_coding_transcript_exon_variant\" |\n",
+       "| \"splice_donor_5th_base_variant\"      |\n",
+       "| \"non_coding_transcript_exon_variant\" |\n",
+       "| \"non_coding_transcript_exon_variant\" |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,2,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,14,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq   |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [314391,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [314388,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [314380,0,0,0,11,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0]  |\n",
+       "| [314369,1,0,0,20,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [314323,1,0,0,54,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq     |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                       |\n",
+       "+----------------------------------------------------+\n",
+       "| [308845,5547,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [308845,5547,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [308833,5557,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [308823,5567,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [308789,5585,16,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0]      |\n",
+       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        1.26e+01 |\n",
+       "|                                          0 |                        1.60e+01 |\n",
+       "|                                          0 |                        8.52e+00 |\n",
+       "|                                          0 |                        1.52e+01 |\n",
+       "|                                          0 |                        4.42e+00 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            1.08e+00 |                             NA |\n",
+       "|                            1.54e+00 |                             NA |\n",
+       "|                            7.07e-01 |                             NA |\n",
+       "|                            1.41e+00 |                             NA |\n",
+       "|                            3.11e-01 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                -1.10e-01 |                    1.09e+00 |\n",
+       "|                                -7.00e-02 |                    6.55e+00 |\n",
+       "|                                -9.00e-02 |                   -4.41e+00 |\n",
+       "|                                -4.00e-02 |                    6.01e+00 |\n",
+       "|                                -8.00e-02 |                    1.38e+00 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ],
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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showing top 5 rows
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 9
   },
   {
    "cell_type": "markdown",
diff --git a/requirements.txt b/requirements.txt
index 2900720..32706b5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,10 @@
 # We're using the main branch of gnomad_method on github rather than the pip version
 git+https://github.com/broadinstitute/gnomad_methods@main
 hail
+jupyter
+jupyter_contrib_nbextensions
+jupyter_nbextensions_configurator
+jupyterlab
+nodejs
+npm
+jupyter_bokeh

From 095a234b196f57ead2a0c6cb8d2ac5bbb102fc55 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Mon, 9 Dec 2024 10:50:03 -0700
Subject: [PATCH 25/33] - Restructure files - Add a description of the repo
 structure to the README - Add some potential requirements - Update the
 documentation and make sure it works - Create a notebook specific to loading
 gnomAD release data and just showing what each dataset looks like.

---
 gnomad_toolbox/{modules => analysis}/__init__.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename gnomad_toolbox/{modules => analysis}/__init__.py (100%)

diff --git a/gnomad_toolbox/modules/__init__.py b/gnomad_toolbox/analysis/__init__.py
similarity index 100%
rename from gnomad_toolbox/modules/__init__.py
rename to gnomad_toolbox/analysis/__init__.py

From 5fe3010853ccc1ec9cf730ade73c86ed508b151e Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Mon, 9 Dec 2024 14:58:29 -0700
Subject: [PATCH 26/33] - Modifications to support setting a default data_type
 and version - Addition of variant.py to store some functions that can also be
 used by frequency based filtering - Changes to frequency filtering to use new
 default settings if not passed a ht

---
 README.md                                     |   2 +-
 gnomad_toolbox/filtering/frequency.py         | 131 ++++++++-------
 gnomad_toolbox/filtering/interval.py          |  84 ---------
 gnomad_toolbox/filtering/variant.py           | 159 ++++++++++++++++++
 gnomad_toolbox/load_data.py                   | 159 ++++++++++++++----
 .../notebooks/intro_to_release_data.ipynb     |  22 +--
 6 files changed, 373 insertions(+), 184 deletions(-)
 delete mode 100644 gnomad_toolbox/filtering/interval.py
 create mode 100644 gnomad_toolbox/filtering/variant.py

diff --git a/README.md b/README.md
index 52bdd94..76ef6da 100644
--- a/README.md
+++ b/README.md
@@ -12,8 +12,8 @@ ggnomad_toolbox/
 │   ├── constraint.py    # Functions to filter constraint metrics (e.g., observed/expected ratios).
 │   ├── coverage.py      # Functions to filter variants or regions based on coverage thresholds.
 │   ├── frequency.py     # Functions to filter variants by allele frequency thresholds.
-│   ├── interval.py      # Functions to filter variants within specified genomic intervals and genes.
 │   ├── pext.py          # Functions to filter variants using predicted expression (pext) scores.
+|   ├── variant.py       # Functions to filter to a specific variant or set of variants.
 │   ├── vep.py           # Functions to filter variants based on VEP (Variant Effect Predictor) annotations.
 │
 ├── analysis/
diff --git a/gnomad_toolbox/filtering/frequency.py b/gnomad_toolbox/filtering/frequency.py
index 1c028a3..2adf1c9 100644
--- a/gnomad_toolbox/filtering/frequency.py
+++ b/gnomad_toolbox/filtering/frequency.py
@@ -5,35 +5,28 @@
 import hail as hl
 from gnomad.utils.filtering import filter_arrays_by_meta
 
+from gnomad_toolbox.filtering.variant import get_single_variant
+from gnomad_toolbox.load_data import _get_gnomad_release
 
-def extract_callstats_for_1anc_1variant(
-    ht: hl.Table, gen_anc: str, contig: str, position: int, alleles: List[str]
+
+def get_callstats_for_multiple_ancestries(
+    gen_ancs: List[str],
+    **kwargs,
 ) -> hl.Table:
     """
-    Extract callstats for a specific ancestry group and single variant.
+    Extract callstats for specified ancestry groups.
 
-    :param ht: Input Hail Table with variant data.
-    :param gen_anc: Genetic ancestry group (e.g., 'afr', 'nfe').
-    :param contig: Chromosome of the variant.
-    :param position: Variant position.
-    :param alleles: List of alleles for the variant (e.g., ['A', 'T']).
-    :return: Filtered Table with callstats for the specified group.
+    :param gen_ancs: List of genetic ancestry groups (e.g., 'afr', 'amr', 'asj', 'eas',
+        'fin', 'nfe', 'oth', 'sas').
+    :param kwargs: Keyword arguments to pass to _get_gnomad_release.
+    :return: Table with callstats for the given ancestry groups and variant.
     """
-    # Filter to the variant of interest
-    ht = ht.filter(
-        (ht.locus.contig == contig)
-        & (ht.locus.position == position)
-        & (ht.alleles == alleles)
-    )
-
-    # Check if the variant exists
-    if ht.count() == 0:
-        hl.utils.warning(
-            f"No variant found at {contig}:{position} with alleles {alleles}"
-        )
+    # Load the Hail Table if not provided
+    ht = _get_gnomad_release(dataset="variant", **kwargs)
 
-    # Format gen_anc to lowercase and filter arrays by metadata
-    items_to_filter = {"gen_anc": [gen_anc.lower()], "group": ["adj"]}
+    # Format gen_ancs to lowercase and filter arrays by metadata.
+    gen_ancs = [gen_anc.lower() for gen_anc in gen_ancs]
+    items_to_filter = {"gen_anc": gen_ancs, "group": ["adj"]}
     freq_meta, array_exprs = filter_arrays_by_meta(
         ht.freq_meta,
         {
@@ -45,51 +38,75 @@ def extract_callstats_for_1anc_1variant(
         combine_operator="and",
         exact_match=True,
     )
-    # Select frequency for ancestry group
     ht = ht.select(
-        **{
-            gen_anc: array_exprs["freq"][i]
-            for i, gen_anc in enumerate([gen_anc.lower()])
-        }
+        "filters",
+        **{gen_anc: array_exprs["freq"][i] for i, gen_anc in enumerate(gen_ancs)},
     )
-    ht = ht.annotate_globals(
+
+    # Select a subset of the globals.
+    ht = ht.select_globals(
+        "date",
+        "version",
         freq_meta=freq_meta,
         freq_meta_sample_count=array_exprs["freq_meta_sample_count"],
     )
+
     return ht
 
 
-def extract_callstats_for_multiple_ancs(
-    ht: hl.Table,
-    gen_ancs: List[str],
+def get_callstats_for_single_ancestry(
+    gen_anc: str,
+    **kwargs,
 ) -> hl.Table:
     """
-    Extract callstats for multiple genetic ancestry groups.
+    Extract callstats for a specific ancestry group and single variant.
 
-    :param ht: Input Table.
-    :param gen_ancs: List of Ancestry Groups (e.g., 'afr', 'amr', 'asj', 'eas', 'fin', 'nfe',
-        'oth', 'sas').
-    :return: Table with callstats for the given groups.
+    :param gen_anc: Genetic ancestry group (e.g., 'afr', 'amr', 'asj', 'eas', 'fin',
+        'nfe', 'oth', 'sas').
+    :param kwargs: Keyword arguments to pass to _get_gnomad_release.
+    :return: Table with callstats for the given ancestry group.
     """
-    # Format the gen_ancs to lowercase if they're fed in as uppercase
-    gen_ancs = [gen_anc.lower() for gen_anc in gen_ancs]
-    items_to_filter = {"gen_anc": gen_ancs, "group": ["adj"]}
-    freq_meta, array_exprs = filter_arrays_by_meta(
-        ht.freq_meta,
-        {
-            **{a: ht[a] for a in ["freq"]},
-            "freq_meta_sample_count": ht.index_globals().freq_meta_sample_count,
-        },
-        items_to_filter=items_to_filter,
-        keep=True,
-        combine_operator="and",
-        exact_match=True,
-    )
-    ht = ht.select(
-        **{gen_anc: array_exprs["freq"][i] for i, gen_anc in enumerate(gen_ancs)}
-    )
-    ht = ht.annotate_globals(
-        freq_meta=freq_meta,
-        freq_meta_sample_count=array_exprs["freq_meta_sample_count"],
+    ht = get_callstats_for_multiple_ancestries([gen_anc], **kwargs)
+
+    # Select a subset of the globals.
+    ht = ht.select_globals(
+        "date",
+        "version",
+        sample_count=ht.freq_meta_sample_count[0],
     )
+
     return ht
+
+
+def get_single_variant_callstats_for_multiple_ancestries(
+    gen_ancs: List[str],
+    **kwargs,
+) -> hl.Table:
+    """
+    Extract callstats for specified ancestry groups and a single variant.
+
+    :param gen_ancs: List of genetic ancestry groups (e.g., 'afr', 'amr', 'asj', 'eas',
+        'fin', 'nfe', 'oth', 'sas').
+    :param kwargs: Keyword arguments to pass to get_single_variant.
+    :return: Table with callstats for the given ancestry groups and variant.
+    """
+    ht = get_single_variant(**kwargs)
+
+    return get_callstats_for_multiple_ancestries(gen_ancs, ht=ht)
+
+
+def get_single_variant_callstats_for_single_ancestry(
+    gen_anc: str,
+    **kwargs,
+) -> hl.Table:
+    """
+    Extract callstats for a specific ancestry group and single variant.
+
+    :param gen_anc: Genetic ancestry group (e.g., 'afr', 'amr', 'asj', 'eas', 'fin',
+        'nfe', 'oth', 'sas').
+    :param kwargs: Keyword arguments to pass to get_single_variant.
+    :return: Table with callstats for the given ancestry group.
+    """
+    ht = get_single_variant(**kwargs)
+
+    return get_callstats_for_single_ancestry(gen_anc, ht=ht)
diff --git a/gnomad_toolbox/filtering/interval.py b/gnomad_toolbox/filtering/interval.py
deleted file mode 100644
index 0a3b810..0000000
--- a/gnomad_toolbox/filtering/interval.py
+++ /dev/null
@@ -1,84 +0,0 @@
-"""Functions to filter the gnmoAD sites HT by interval."""
-
-import hail as hl
-
-
-def filter_by_interval(ht: hl.Table, interval: str) -> hl.Table:
-    """
-    Filter variants by interval.
-
-    :param ht: Input Table.
-    :param interval: Interval string. Format has to be "chr:start-end", e.g., "1:1000-2000".
-    :return: Table with variants in the interval.
-    """
-    if ht.locus.dtype.reference_genome.name == "GRCh38":
-        interval = "chr" + interval
-    ht = hl.filter_intervals(
-        ht,
-        [
-            hl.parse_locus_interval(
-                interval,
-                reference_genome=(
-                    "GRCh38"
-                    if ht.locus.dtype.reference_genome.name == "GRCh38"
-                    else "GRCh37"
-                ),
-            )
-        ],
-    )
-    return ht
-
-
-def filter_by_gene_symbol(ht: hl.Table, gene: str) -> hl.Table:
-    """
-    Filter variants in a gene.
-
-    .. note::
-           This function is to match the number of variants that you will get in the
-           gnomAD browser, which only focus on variants in "CDS" regions plus 75bp
-           up- and downstream. This is not the same as filtering by gene symbol with
-           our `filter_vep_transcript_csqs` function, which will include all variants.
-
-    :param ht: Input Table.
-    :param gene: Gene symbol.
-    :return: Table with variants in the gene.
-    """
-    # Make gene symbol uppercase
-    gene = gene.upper()
-
-    if ht.locus.dtype.reference_genome.name == "GRCh37":
-        gene_ht = hl.read_table(
-            "gs://gcp-public-data--gnomad/resources/grch37/browser/gnomad"
-            ".genes.GRCh37.GENCODEv19.ht"
-        )
-    else:
-        gene_ht = hl.read_table(
-            "gs://gcp-public-data--gnomad/resources/grch38/browser/gnomad"
-            ".genes.GRCh38.GENCODEv39.ht"
-        )
-
-    gene_ht = gene_ht.annotate(
-        cds_intervals=hl.array(
-            gene_ht.exons.filter(lambda exon: exon.feature_type == "CDS")
-        ).map(
-            lambda exon: hl.locus_interval(
-                hl.if_else(
-                    gene_ht.interval.start.dtype.reference_genome.name == "GRCh38",
-                    "chr" + gene_ht.chrom,
-                    gene_ht.chrom,
-                ),
-                exon.start - 75,
-                exon.stop + 75,
-                reference_genome=gene_ht.interval.start.dtype.reference_genome,
-                includes_end=True,
-            )
-        )
-    )
-
-    intervals = gene_ht.filter(gene_ht.gencode_symbol == gene).cds_intervals.collect()[
-        0
-    ]
-
-    ht = hl.filter_intervals(ht, intervals)
-
-    return ht
diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
new file mode 100644
index 0000000..e6a1a9e
--- /dev/null
+++ b/gnomad_toolbox/filtering/variant.py
@@ -0,0 +1,159 @@
+"""Functions to filter the gnomAD sites HT to a specific set of variants."""
+
+from typing import Optional
+
+import hail as hl
+from gnomad.utils.reference_genome import get_reference_genome
+
+from gnomad_toolbox.load_data import _get_gnomad_release
+
+
+def get_single_variant(
+    variant: Optional[str] = None,
+    contig: Optional[str] = None,
+    position: Optional[int] = None,
+    ref: Optional[str] = None,
+    alt: Optional[str] = None,
+    **kwargs,
+) -> hl.Table:
+    """
+    Get a single variant from the gnomAD HT.
+
+    .. note::
+
+        One of `variant` or all of `contig`, `position`, `ref`, and `alt` must be
+        provided. If `variant` is provided, `contig`, `position`, `ref`, and `alt` are
+        ignored.
+
+    :param variant: Variant string in the format "chr12-235245-A-C" or
+        "chr12:235245:A:C". If provided, `contig`, `position`, `ref`, and `alt` are
+        ignored.
+    :param contig: Chromosome of the variant. Required if `variant` is not provided.
+    :param position: Variant position. Required if `variant` is not provided.
+    :param ref: Reference allele. Required if `variant` is not provided.
+    :param alt: Alternate allele. Required if `variant` is not provided.
+    :param kwargs: Additional arguments to pass to `_get_gnomad_release`.
+    :return: Table with the single variant.
+    """
+    if not variant and not all([contig, position, ref, alt]):
+        raise ValueError(
+            "Either `variant` must be provided or all of `contig`, `position`, `ref`, "
+            "and `alt`."
+        )
+
+    # Load the Hail Table if not provided
+    ht = _get_gnomad_release(dataset="variant", **kwargs)
+
+    # Determine the reference genome build for the ht.
+    build = get_reference_genome(ht.locus).name
+
+    # TODO: Move this to gnomad_methods.
+    # Parse the variant string if provided.
+    try:
+        if variant and ":" not in variant:
+            contig, position, ref, alt = variant.split("-")
+        if all([contig, position, ref, alt]):
+            variant = f"{contig}:{position}:{ref}:{alt}"
+        variant = hl.eval(hl.parse_variant(variant, reference_genome=build))
+    except ValueError:
+        raise ValueError(
+            f"Invalid variant format: {variant}. Expected format: chr12-235245-A-C "
+            f"or chr12:235245:A:C"
+        )
+
+    # Filter to the Locus of the variant of interest.
+    ht = hl.filter_intervals(
+        ht, [hl.interval(variant.locus, variant.locus, includes_end=True)]
+    )
+
+    # Filter to the variant of interest.
+    ht = ht.filter(ht.alleles == variant.alleles)
+
+    # Check if the variant exists.
+    if ht.count() == 0:
+        hl.utils.warning(
+            f"No variant found at {variant.locus} with alleles {variant.alleles}"
+        )
+
+    return ht
+
+
+def filter_by_interval(ht: hl.Table, interval: str) -> hl.Table:
+    """
+    Filter variants by interval.
+
+    :param ht: Input Table.
+    :param interval: Interval string. Format has to be "chr:start-end", e.g., "1:1000-2000".
+    :return: Table with variants in the interval.
+    """
+    if ht.locus.dtype.reference_genome.name == "GRCh38":
+        interval = "chr" + interval
+    ht = hl.filter_intervals(
+        ht,
+        [
+            hl.parse_locus_interval(
+                interval,
+                reference_genome=(
+                    "GRCh38"
+                    if ht.locus.dtype.reference_genome.name == "GRCh38"
+                    else "GRCh37"
+                ),
+            )
+        ],
+    )
+    return ht
+
+
+def filter_by_gene_symbol(ht: hl.Table, gene: str) -> hl.Table:
+    """
+    Filter variants in a gene.
+
+    .. note::
+           This function is to match the number of variants that you will get in the
+           gnomAD browser, which only focus on variants in "CDS" regions plus 75bp
+           up- and downstream. This is not the same as filtering by gene symbol with
+           our `filter_vep_transcript_csqs` function, which will include all variants.
+
+    :param ht: Input Table.
+    :param gene: Gene symbol.
+    :return: Table with variants in the gene.
+    """
+    # Make gene symbol uppercase
+    gene = gene.upper()
+
+    if ht.locus.dtype.reference_genome.name == "GRCh37":
+        gene_ht = hl.read_table(
+            "gs://gcp-public-data--gnomad/resources/grch37/browser/gnomad"
+            ".genes.GRCh37.GENCODEv19.ht"
+        )
+    else:
+        gene_ht = hl.read_table(
+            "gs://gcp-public-data--gnomad/resources/grch38/browser/gnomad"
+            ".genes.GRCh38.GENCODEv39.ht"
+        )
+
+    gene_ht = gene_ht.annotate(
+        cds_intervals=hl.array(
+            gene_ht.exons.filter(lambda exon: exon.feature_type == "CDS")
+        ).map(
+            lambda exon: hl.locus_interval(
+                hl.if_else(
+                    gene_ht.interval.start.dtype.reference_genome.name == "GRCh38",
+                    "chr" + gene_ht.chrom,
+                    gene_ht.chrom,
+                ),
+                exon.start - 75,
+                exon.stop + 75,
+                reference_genome=gene_ht.interval.start.dtype.reference_genome,
+                includes_end=True,
+            )
+        )
+    )
+
+    intervals = gene_ht.filter(gene_ht.gencode_symbol == gene).cds_intervals.collect()[
+        0
+    ]
+
+    ht = hl.filter_intervals(ht, intervals)
+
+    return ht
diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index b40dc10..5c39aff 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -1,5 +1,8 @@
 """Functions to import gnomAD data."""
 
+import functools
+from typing import Optional
+
 import gnomad.resources.grch37.gnomad as grch37_gnomad
 import gnomad.resources.grch38.gnomad as grch38_gnomad
 import hail as hl
@@ -32,50 +35,106 @@
 RELEASES = {
     dataset: {
         data_type: {
-            build: getattr(res, release_global, None)
+            build: (
+                None
+                if release_global.get(data_type) is None
+                else getattr(res, release_global.get(data_type), None)
+            )
             for build, res in GNOMAD_BY_BUILD.items()
         }
-        for data_type, release_global in data_types.items()
+        for data_type in DATA_TYPES
     }
-    for dataset, data_types in RELEASES_GLOBAL.items()
+    for dataset, release_global in RELEASES_GLOBAL.items()
 }
 
 
-def get_gnomad_release(
-    data_type: str = "exomes",
-    version: str = grch38_gnomad.CURRENT_EXOME_RELEASE,
+class GnomADSession:
+    """Class to manage the default data type and version for a gnomAD session."""
+
+    def __init__(self) -> None:
+        """
+        Initialize a gnomAD session.
+
+        The default data type is exomes and the default version is the current exome
+        release.
+
+        :return: None.
+        """
+        self.data_type = "exomes"
+        self.version = grch38_gnomad.CURRENT_EXOME_RELEASE
+
+    def set_default_data(
+        self,
+        data_type: Optional[str] = None,
+        version: Optional[str] = None,
+    ) -> None:
+        """
+        Set default data type and version.
+
+        :param data_type: Data type (exomes, genomes, or joint).
+        :param version: gnomAD version.
+        :return: None.
+        """
+        data_type = data_type or self.data_type
+        version = version or self.version
+
+        # Validate data type.
+        if data_type and data_type not in DATA_TYPES:
+            raise ValueError(
+                f"Data type {data_type} is invalid. Choose from 'exomes', 'genomes', "
+                f"or 'joint'."
+            )
+
+        # Get all possible versions.
+        possible_versions = functools.reduce(
+            lambda x, y: (x or []) + (y or []),
+            [
+                ds[dt][r]
+                for ds in RELEASES.values()
+                for dt in ([data_type] if data_type else DATA_TYPES)
+                for r in GNOMAD_BY_BUILD
+            ],
+        )
+
+        # Check version availability.
+        if version not in possible_versions:
+            raise ValueError(
+                f"Version {version} is not available"
+                f"{'' if data_type else f' for {data_type}'}. "
+            )
+
+        self.data_type = data_type
+        self.version = version
+
+
+# Global gnomad session object
+gnomad_session = GnomADSession()
+
+
+def _get_gnomad_release(
+    ht: hl.Table = None,
     dataset: str = "variant",
+    data_type: str = None,
+    version: str = None,
 ) -> hl.Table:
     """
-    Get gnomAD HT by dataset, data type,  and version.
-
-    .. table:: Available versions for each dataset and data type are (as of 2024-10-29)
-        :widths: auto
+    Get gnomAD HT using a Hail Table, specific parameters, or session defaults.
 
-        +--------------+-----------------+----------------------------------+----------------------+
-        | Dataset      | Data Type       | GRCh38 Versions                  | GRCh37 Versions      |
-        +==============+=================+==================================+======================+
-        | variant      | exomes          | 4.0, 4.1                         | 2.1, 2.1.1           |
-        |              +-----------------+----------------------------------+----------------------+
-        |              | genomes         | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1           |
-        |              +-----------------+----------------------------------+----------------------+
-        |              | joint           | 4.1                              | N/A                  |
-        +--------------+-----------------+----------------------------------+----------------------+
-        | coverage     | exomes          | 4.0                              | 2.1                  |
-        |              +-----------------+----------------------------------+----------------------+
-        |              | genomes         | 3.0.1                            | 2.1                  |
-        +--------------+-----------------+----------------------------------+----------------------+
-        | all_sites_an | exomes          | 4.1                              | N/A                  |
-        |              +-----------------+----------------------------------+----------------------+
-        |              | genomes         | 4.1                              | N/A                  |
-        +--------------+-----------------+----------------------------------+----------------------+
-
-    :param data_type: Data type (exomes, genomes, or joint). Default is "exomes".
-    :param version: gnomAD version. Default is the current exome release.
+    :param ht: Pre-loaded Hail Table. If provided, other parameters are ignored.
     :param dataset: Dataset type. One of "variant", "all_sites_an", "coverage". Default
-        is "variant".
+        is variant.
+    :param data_type: Data type (exomes, genomes, or joint). Default is session value.
+    :param version: gnomAD version. Default is session value.
     :return: Hail Table for requested dataset, data type, and version.
     """
+    # If a pre-loaded Hail Table is provided, return it directly.
+    if ht is not None:
+        return ht
+
+    # Use session defaults if parameters are not provided.
+    data_type = data_type or gnomad_session.data_type
+    version = version or gnomad_session.version
+
     # Get all releases for the given dataset.
     releases = RELEASES.get(dataset)
 
@@ -109,3 +168,41 @@ def get_gnomad_release(
             f"Available versions: GRCh38 - {data_type_releases['GRCh38']}, "
             f"GRCh37 - {data_type_releases['GRCh37']}."
         )
+
+
+def get_gnomad_release(
+    dataset: str = "variant",
+    data_type: Optional[str] = None,
+    version: Optional[str] = None,
+) -> hl.Table:
+    """
+    Get gnomAD HT by dataset, data type,  and version.
+
+    .. table:: Available versions for each dataset and data type are (as of 2024-10-29)
+        :widths: auto
+
+        +--------------+-----------------+----------------------------------+----------------------+
+        | Dataset      | Data Type       | GRCh38 Versions                  | GRCh37 Versions      |
+        +==============+=================+==================================+======================+
+        | variant      | exomes          | 4.0, 4.1                         | 2.1, 2.1.1           |
+        |              +-----------------+----------------------------------+----------------------+
+        |              | genomes         | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1           |
+        |              +-----------------+----------------------------------+----------------------+
+        |              | joint           | 4.1                              | N/A                  |
+        +--------------+-----------------+----------------------------------+----------------------+
+        | coverage     | exomes          | 4.0                              | 2.1                  |
+        |              +-----------------+----------------------------------+----------------------+
+        |              | genomes         | 3.0.1                            | 2.1                  |
+        +--------------+-----------------+----------------------------------+----------------------+
+        | all_sites_an | exomes          | 4.1                              | N/A                  |
+        |              +-----------------+----------------------------------+----------------------+
+        |              | genomes         | 4.1                              | N/A                  |
+        +--------------+-----------------+----------------------------------+----------------------+
+
+    :param data_type: Data type (exomes, genomes, or joint). Default is "exomes".
+    :param version: gnomAD version. Default is the current exome release.
+    :param dataset: Dataset type. One of "variant", "all_sites_an", "coverage". Default
+        is "variant".
+    :return: Hail Table for requested dataset, data type, and version.
+    """
+    return _get_gnomad_release(dataset=dataset, data_type=data_type, version=version)
diff --git a/gnomad_toolbox/notebooks/intro_to_release_data.ipynb b/gnomad_toolbox/notebooks/intro_to_release_data.ipynb
index c84c0d7..1e49925 100644
--- a/gnomad_toolbox/notebooks/intro_to_release_data.ipynb
+++ b/gnomad_toolbox/notebooks/intro_to_release_data.ipynb
@@ -86,7 +86,7 @@
        "    \n",
        "    
\n",
        "        \n",
-       "        Loading BokehJS ...\n",
+       "        Loading BokehJS ...\n",
        "    
\n"
       ]
      },
@@ -258,7 +258,7 @@
        "     \"\"}};\n",
        "\n",
        "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"e0412447-897b-4b15-8731-38893ba83dd5\");\n",
+       "    const el = document.getElementById(\"a8702bf8-b019-4dc5-b5ff-b8a2fe2976da\");\n",
        "    if (el != null) {\n",
        "      el.textContent = \"BokehJS is loading...\";\n",
        "    }\n",
@@ -364,7 +364,7 @@
        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "      root._bokeh_failed_load = true;\n",
        "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"e0412447-897b-4b15-8731-38893ba83dd5\")).parents('.cell').data().cell;\n",
+       "      const cell = $(document.getElementById(\"a8702bf8-b019-4dc5-b5ff-b8a2fe2976da\")).parents('.cell').data().cell;\n",
        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "    }\n",
        "  }\n",
@@ -380,7 +380,7 @@
        "  }\n",
        "}(window));"
       ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"e0412447-897b-4b15-8731-38893ba83dd5\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"e0412447-897b-4b15-8731-38893ba83dd5\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"a8702bf8-b019-4dc5-b5ff-b8a2fe2976da\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"a8702bf8-b019-4dc5-b5ff-b8a2fe2976da\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -406,7 +406,7 @@
       "    / /_/ /__  __/ /\n",
       "   / __  / _ `/ / /\n",
       "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
-      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241208-2218-0.2.132-678e1f52b999.log\n"
+      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241209-1355-0.2.132-678e1f52b999.log\n"
      ]
     }
    ],
@@ -6254,7 +6254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "7f005af0-df6d-4a0a-a2e0-9cb4ff6782c2",
    "metadata": {
     "ExecuteTime": {
@@ -6277,7 +6277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "6058bf38-4ce9-4d37-89a7-d11af0e4d9f1",
    "metadata": {
     "ExecuteTime": {
@@ -6331,7 +6331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "49fcf64b-1a91-4052-bb98-b329a0e8031b",
    "metadata": {
     "ExecuteTime": {
@@ -6407,7 +6407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 21,
    "id": "a8d0be07-c35d-425a-b554-c86034e367fc",
    "metadata": {
     "ExecuteTime": {
@@ -6431,7 +6431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 22,
    "id": "18afb20f-7429-4fe2-a6c5-73a22dcbdb76",
    "metadata": {
     "ExecuteTime": {
@@ -6485,7 +6485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 23,
    "id": "b27cb655-3abb-4501-bcc9-3f634db64591",
    "metadata": {
     "ExecuteTime": {

From 5330ea6a68009c11681f232f43403599c55d5f10 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 10 Dec 2024 22:38:45 -0700
Subject: [PATCH 27/33] More clean-up of notebooks and functions

---
 README.md                                     |    1 +
 gnomad_toolbox/analysis/general.py            |  124 +-
 gnomad_toolbox/filtering/frequency.py         |  100 +-
 gnomad_toolbox/filtering/variant.py           |  139 +-
 gnomad_toolbox/filtering/vep.py               |   39 +-
 gnomad_toolbox/load_data.py                   |    2 +
 .../notebooks/intro_to_release_data.ipynb     | 6622 -----------------
 .../notebooks/toolbox_for_gnomad_users.ipynb  | 3778 ----------
 8 files changed, 266 insertions(+), 10539 deletions(-)
 delete mode 100644 gnomad_toolbox/notebooks/intro_to_release_data.ipynb
 delete mode 100644 gnomad_toolbox/notebooks/toolbox_for_gnomad_users.ipynb

diff --git a/README.md b/README.md
index 76ef6da..08eecb8 100644
--- a/README.md
+++ b/README.md
@@ -24,6 +24,7 @@ ggnomad_toolbox/
 │   ├── intro_to_release_data.ipynb    # Jupyter notebook introducing the loading of gnomAD release data.
 ```
 
+# TODO: Add fully detailed info about how to install and open the notebooks.
 ## Getting started
 ### Install
 pip install -r requirements.txt
diff --git a/gnomad_toolbox/analysis/general.py b/gnomad_toolbox/analysis/general.py
index 7334f43..bcdde4a 100644
--- a/gnomad_toolbox/analysis/general.py
+++ b/gnomad_toolbox/analysis/general.py
@@ -1,42 +1,126 @@
 """Set of general functions for gnomAD analysis."""
 
+from typing import Dict, List, Optional, Tuple, Union
+
 import hail as hl
 
+from gnomad_toolbox.load_data import _get_gnomad_release
+
+
+# TODO: Modify this function in gnomad_methods.
+def freq_bin_expr(
+    freq_expr: Union[hl.expr.StructExpression, hl.expr.ArrayExpression],
+    index: int = 0,
+    ac_cutoffs: Optional[List[Union[int, Tuple[int, str]]]] = [
+        (0, "AC0"),
+        (1, "singleton"),
+        (2, "doubleton"),
+    ],
+    af_cutoffs: Optional[List[Union[float, Tuple[float, str]]]] = [
+        (1e-4, "0.01%"),
+        (1e-3, "0.1%"),
+        (1e-2, "1%"),
+        (1e-1, "10%"),
+    ],
+    upper_af: Optional[Union[float, Tuple[float, str]]] = (0.95, "95%"),
+) -> hl.expr.StringExpression:
+    """
+    Return frequency string annotations based on input AC or AF.
+
+    .. note::
+
+        - Default index is 0 because function assumes freq_expr was calculated with
+          `annotate_freq`.
+        - Frequency index 0 from `annotate_freq` is frequency for all pops calculated
+          on adj genotypes only.
+
+    :param freq_expr: Array of structs containing frequency information.
+    :param index: Which index of freq_expr to use for annotation. Default is 0.
+    :param ac_cutoffs:
+    :return: StringExpression containing bin name based on input AC or AF.
+    """
+    if isinstance(freq_expr, hl.expr.ArrayExpression):
+        freq_expr = freq_expr[index]
+
+    if ac_cutoffs and isinstance(ac_cutoffs[0], int):
+        ac_cutoffs = [(c, f"AC{c}") for c in ac_cutoffs]
+
+    if af_cutoffs and isinstance(af_cutoffs[0], float):
+        af_cutoffs = [(c, f"{c*100}%") for c in af_cutoffs]
 
-def get_variant_count(
-    ht: hl.Table,
-    afs: list[float] = [0.01, 0.001],
+    if isinstance(upper_af, float):
+        upper_af = (upper_af, f"{upper_af*100}%")
+
+    freq_bin_expr = hl.case().when(hl.is_missing(freq_expr.AC), "Missing")
+    prev_af = None
+    for ac, name in sorted(ac_cutoffs):
+        freq_bin_expr = freq_bin_expr.when(freq_expr.AC == ac, name)
+        prev_af = name
+
+    for af, name in sorted(af_cutoffs):
+        prev_af = "<" if prev_af is None else f"{prev_af} - "
+        freq_bin_expr = freq_bin_expr.when(freq_expr.AF < af, f"{prev_af}{name}")
+        prev_af = name
+
+    if upper_af:
+        freq_bin_expr = freq_bin_expr.when(
+            freq_expr.AF > upper_af[0], f">{upper_af[1]}"
+        )
+        default_af = "<" if prev_af is None else f"{prev_af} - "
+        default_af = f"{default_af}{upper_af[1]}"
+    else:
+        default_af = f">{prev_af}"
+
+    return freq_bin_expr.default(default_af)
+
+
+def get_variant_count_by_freq_bin(
+    af_cutoffs: List[float] = [0.001, 0.01],
     singletons: bool = False,
     doubletons: bool = False,
-) -> dict:
+    pass_only: bool = True,
+    **kwargs,
+) -> Dict[str, int]:
     """
-    Count variants with frequency <1%, <0.1%, and singletons (AC == 1).
+    Count variants by frequency bin.
+
+    By default, this function counts PASS variants that are AC0, AF < 0.01%, and
+    AF 0.01% - 0.1%.
+
+    The function can also include counts of singletons and doubletons, with or
+    without passing filters.
 
     .. note::
 
-        This function works for gnomAD exomes and genomes datasets, not yet for gnomAD
-        joint dataset, since the HT schema is slightly different.
+        This function works for gnomAD exomes and genomes data types, not yet for gnomAD
+        joint data type, since the HT schema is slightly different.
 
-    :param ht: Input Table.
-    :param afs: List of allele frequencies cutoffs.
+    :param af_cutoffs: List of allele frequencies cutoffs.
     :param singletons: Include singletons.
     :param doubletons: Include doubletons.
+    :param pass_only: Include only PASS variants.
+    :param kwargs: Keyword arguments to pass to _get_gnomad_release. Includes
+        'ht', 'data_type', and 'version'.
     :return: Dictionary with counts.
     """
-    counts = {}
+    # Load the Hail Table if not provided
+    ht = _get_gnomad_release(dataset="variant", **kwargs)
 
     # Filter to PASS variants.
-    ht = ht.filter(hl.len(ht.filters) == 0)
+    if pass_only:
+        ht = ht.filter(hl.len(ht.filters) == 0)
+
+    # Initialize allele count cutoffs with AC0.
+    ac_cutoffs = [(0, "AC0")]
+
     if singletons:
-        n_singletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 1))
-        counts["number of singletons"] = n_singletons
+        ac_cutoffs.append((1, "singletons"))
+
     if doubletons:
-        n_doubletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 2))
-        counts["number of doubletons"] = n_doubletons
+        ac_cutoffs.append((2, "doubletons"))
 
-    for af in afs:
-        n_variants = ht.aggregate(hl.agg.count_where(ht.freq[0].AF < af))
-        counts[f"number of variants with AF < {af}"] = n_variants
+    freq_expr = freq_bin_expr(
+        ht.freq, ac_cutoffs=ac_cutoffs, af_cutoffs=af_cutoffs, upper_af=None
+    )
 
-    # Count variants with frequency <1%, <0.1%, and singletons (AC == 1).
-    return counts
+    return ht.aggregate(hl.agg.counter(freq_expr))
diff --git a/gnomad_toolbox/filtering/frequency.py b/gnomad_toolbox/filtering/frequency.py
index 2adf1c9..c5a008b 100644
--- a/gnomad_toolbox/filtering/frequency.py
+++ b/gnomad_toolbox/filtering/frequency.py
@@ -1,6 +1,6 @@
 """Functions for filtering the gnomAD sites HT frequency data."""
 
-from typing import List
+from typing import List, Union
 
 import hail as hl
 from gnomad.utils.filtering import filter_arrays_by_meta
@@ -9,24 +9,34 @@
 from gnomad_toolbox.load_data import _get_gnomad_release
 
 
-def get_callstats_for_multiple_ancestries(
-    gen_ancs: List[str],
+def get_ancestry_callstats(
+    gen_ancs: Union[str, List[str]],
     **kwargs,
 ) -> hl.Table:
     """
-    Extract callstats for specified ancestry groups.
+    Extract callstats for specified ancestry group(s).
 
-    :param gen_ancs: List of genetic ancestry groups (e.g., 'afr', 'amr', 'asj', 'eas',
-        'fin', 'nfe', 'oth', 'sas').
+    :param gen_ancs: Genetic ancestry group(s) (e.g., 'afr', 'amr', 'asj', 'eas',
+        'fin', 'nfe', 'oth', 'sas'). Can be a single ancestry group or a list of
+        ancestry groups.
     :param kwargs: Keyword arguments to pass to _get_gnomad_release.
     :return: Table with callstats for the given ancestry groups and variant.
     """
     # Load the Hail Table if not provided
     ht = _get_gnomad_release(dataset="variant", **kwargs)
 
+    # Check if gen_ancs is a single ancestry group.
+    one_anc = isinstance(gen_ancs, str)
+
+    if one_anc:
+        gen_ancs = [gen_ancs]
+
     # Format gen_ancs to lowercase and filter arrays by metadata.
     gen_ancs = [gen_anc.lower() for gen_anc in gen_ancs]
-    items_to_filter = {"gen_anc": gen_ancs, "group": ["adj"]}
+    gen_anc_label = (
+        "gen_anc" if any(["gen_anc" in m for m in hl.eval(ht.freq_meta)]) else "pop"
+    )
+    items_to_filter = {gen_anc_label: gen_ancs, "group": ["adj"]}
     freq_meta, array_exprs = filter_arrays_by_meta(
         ht.freq_meta,
         {
@@ -40,73 +50,41 @@ def get_callstats_for_multiple_ancestries(
     )
     ht = ht.select(
         "filters",
-        **{gen_anc: array_exprs["freq"][i] for i, gen_anc in enumerate(gen_ancs)},
-    )
-
-    # Select a subset of the globals.
-    ht = ht.select_globals(
-        "date",
-        "version",
-        freq_meta=freq_meta,
-        freq_meta_sample_count=array_exprs["freq_meta_sample_count"],
+        **{
+            m[gen_anc_label]: array_exprs["freq"][i]
+            for i, m in enumerate(hl.eval(freq_meta))
+        },
     )
 
-    return ht
-
-
-def get_callstats_for_single_ancestry(
-    gen_anc: str,
-    **kwargs,
-) -> hl.Table:
-    """
-    Extract callstats for a specific ancestry group and single variant.
-
-    :param gen_anc: Genetic ancestry group (e.g., 'afr', 'amr', 'asj', 'eas', 'fin',
-        'nfe', 'oth', 'sas').
-    :param kwargs: Keyword arguments to pass to _get_gnomad_release.
-    :return: Table with callstats for the given ancestry group.
-    """
-    ht = get_callstats_for_multiple_ancestries([gen_anc], **kwargs)
-
     # Select a subset of the globals.
-    ht = ht.select_globals(
-        "date",
-        "version",
-        sample_count=ht.freq_meta_sample_count[0],
-    )
+    sample_count = array_exprs["freq_meta_sample_count"]
+    if one_anc:
+        sample_count = sample_count[0]
+    else:
+        sample_count = hl.struct(
+            **{
+                m[gen_anc_label]: sample_count[i]
+                for i, m in enumerate(hl.eval(freq_meta))
+            }
+        )
+    ht = ht.select_globals("date", "version", sample_count=sample_count)
 
     return ht
 
 
-def get_single_variant_callstats_for_multiple_ancestries(
-    gen_ancs: List[str],
+def get_single_variant_ancestry_callstats(
+    gen_ancs: Union[str, List[str]],
     **kwargs,
 ) -> hl.Table:
     """
-    Extract callstats for specified ancestry groups and a single variant.
+    Extract callstats for specified ancestry group(s) and a single variant.
 
-    :param gen_ancs: List of genetic ancestry groups (e.g., 'afr', 'amr', 'asj', 'eas',
-        'fin', 'nfe', 'oth', 'sas').
+    :param gen_ancs: Genetic ancestry group(s) (e.g., 'afr', 'amr', 'asj', 'eas',
+        'fin', 'nfe', 'oth', 'sas'). Can be a single ancestry group or a list of
+        ancestry groups.
     :param kwargs: Keyword arguments to pass to get_single_variant.
     :return: Table with callstats for the given ancestry groups and variant.
     """
     ht = get_single_variant(**kwargs)
 
-    return get_callstats_for_multiple_ancestries(gen_ancs, ht=ht)
-
-
-def get_single_variant_callstats_for_single_ancestry(
-    gen_anc: str,
-    **kwargs,
-) -> hl.Table:
-    """
-    Extract callstats for a specific ancestry group and single variant.
-
-    :param gen_anc: Genetic ancestry group (e.g., 'afr', 'amr', 'asj', 'eas', 'fin',
-        'nfe', 'oth', 'sas').
-    :param kwargs: Keyword arguments to pass to get_single_variant.
-    :return: Table with callstats for the given ancestry group.
-    """
-    ht = get_single_variant(**kwargs)
-
-    return get_callstats_for_single_ancestry(gen_anc, ht=ht)
+    return get_ancestry_callstats(gen_ancs, ht=ht)
diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
index e6a1a9e..ec00bc7 100644
--- a/gnomad_toolbox/filtering/variant.py
+++ b/gnomad_toolbox/filtering/variant.py
@@ -1,6 +1,6 @@
 """Functions to filter the gnomAD sites HT to a specific set of variants."""
 
-from typing import Optional
+from typing import Optional, Union
 
 import hail as hl
 from gnomad.utils.reference_genome import get_reference_genome
@@ -78,81 +78,110 @@ def get_single_variant(
     return ht
 
 
-def filter_by_interval(ht: hl.Table, interval: str) -> hl.Table:
+def filter_by_intervals(
+    intervals: Union[str, list[str]],
+    **kwargs,
+) -> hl.Table:
     """
-    Filter variants by interval.
+    Filter variants by interval(s).
 
-    :param ht: Input Table.
-    :param interval: Interval string. Format has to be "chr:start-end", e.g., "1:1000-2000".
-    :return: Table with variants in the interval.
+    :param intervals: Interval string or list of interval strings. The interval string
+        format has to be "contig:start-end", e.g.,"1:1000-2000" (GRCh37) or
+        "chr1:1000-2000" (GRCh38).
+    :param kwargs: Arguments to pass to `_get_gnomad_release`.
+    :return: Table with variants in the interval(s).
     """
-    if ht.locus.dtype.reference_genome.name == "GRCh38":
-        interval = "chr" + interval
+    # Load the Hail Table if not provided
+    ht = _get_gnomad_release(dataset="variant", **kwargs)
+
+    # Determine the reference genome build for the ht.
+    build = get_reference_genome(ht.locus).name
+
+    if isinstance(intervals, str):
+        intervals = [intervals]
+
+    if build == "GRCh38" and any([not i.startswith("chr") for i in intervals]):
+        raise ValueError("Interval must start with 'chr' for GRCh38 reference genome.")
+
     ht = hl.filter_intervals(
-        ht,
-        [
-            hl.parse_locus_interval(
-                interval,
-                reference_genome=(
-                    "GRCh38"
-                    if ht.locus.dtype.reference_genome.name == "GRCh38"
-                    else "GRCh37"
-                ),
-            )
-        ],
+        ht, [hl.parse_locus_interval(i, reference_genome=build) for i in intervals]
     )
+
     return ht
 
 
-def filter_by_gene_symbol(ht: hl.Table, gene: str) -> hl.Table:
+def filter_by_gene_symbol(gene: str, exon_padding_bp: int = 75, **kwargs) -> hl.Table:
     """
     Filter variants in a gene.
 
     .. note::
+
            This function is to match the number of variants that you will get in the
-           gnomAD browser, which only focus on variants in "CDS" regions plus 75bp
-           up- and downstream. This is not the same as filtering by gene symbol with
-           our `filter_vep_transcript_csqs` function, which will include all variants.
+           gnomAD browser, which only focus on variants in "CDS" regions plus
+           75bp (default of `exon_padding_bp`) up- and downstream.
 
-    :param ht: Input Table.
     :param gene: Gene symbol.
+    :param exon_padding_bp: Number of base pairs to pad the CDS intervals. Default is
+        75bp.
+    :param kwargs: Arguments to pass to `_get_gnomad_release`.
     :return: Table with variants in the gene.
     """
+    # Load the Hail Table if not provided
+    ht = _get_gnomad_release(dataset="variant", **kwargs)
+
+    # Determine the reference genome build for the ht.
+    build = get_reference_genome(ht.locus).name
+
     # Make gene symbol uppercase
     gene = gene.upper()
 
-    if ht.locus.dtype.reference_genome.name == "GRCh37":
-        gene_ht = hl.read_table(
-            "gs://gcp-public-data--gnomad/resources/grch37/browser/gnomad"
-            ".genes.GRCh37.GENCODEv19.ht"
-        )
-    else:
-        gene_ht = hl.read_table(
-            "gs://gcp-public-data--gnomad/resources/grch38/browser/gnomad"
-            ".genes.GRCh38.GENCODEv39.ht"
-        )
-
-    gene_ht = gene_ht.annotate(
-        cds_intervals=hl.array(
-            gene_ht.exons.filter(lambda exon: exon.feature_type == "CDS")
-        ).map(
-            lambda exon: hl.locus_interval(
-                hl.if_else(
-                    gene_ht.interval.start.dtype.reference_genome.name == "GRCh38",
-                    "chr" + gene_ht.chrom,
-                    gene_ht.chrom,
-                ),
-                exon.start - 75,
-                exon.stop + 75,
-                reference_genome=gene_ht.interval.start.dtype.reference_genome,
-                includes_end=True,
-            )
-        )
+    # TODO: Create a resource for this in gnomad_methods (is it different from our
+    #  current gencode resources?
+    # gene_ht = hl.read_table(
+    #    f"gs://gcp-public-data--gnomad/resources/{build.lower()}/browser/gnomad"
+    #    f".genes.{build}.GENCODEv{'19' if build == 'GRCh37' else '39'}.ht"
+    # )
+
+    # TODO: This actually takes a while to run locally for a single gene. Is there a
+    #  way to speed this up?
+
+    # Filter to the gene of interest.
+    # gene_ht = gene_ht.filter(gene_ht.gencode_symbol == gene)
+
+    # Get the CDS intervals for the gene.
+    # chrom_expr = hl.if_else(build == "GRCh38", "chr" + gene_ht.chrom, gene_ht.chrom)
+    # intervals = gene_ht.aggregate(
+    #    hl.agg.explode(
+    #        lambda exon: hl.agg.collect(
+    #            hl.locus_interval(
+    #                chrom_expr,
+    #                exon.start - 75,
+    #                exon.stop + 75,
+    #                reference_genome=build,
+    #                includes_end=True,
+    #            )
+    #        ),
+    #        gene_ht.exons.filter(lambda exon: exon.feature_type == "CDS"),
+    #    )
+    # )
+
+    # TODO: Consider this alternative approach to get the intervals from gencode. That
+    #  is not too bad time wise
+
+    from gnomad.resources.grch38.reference_data import gencode
+
+    gencode_ht = gencode.ht()
+    gencode_ht = gencode_ht.filter(
+        (gencode_ht.gene_name == gene) & ((gencode_ht.feature == "CDS"))
     )
-
-    intervals = gene_ht.filter(gene_ht.gencode_symbol == gene).cds_intervals.collect()[
-        0
-    ]
+    intervals = hl.locus_interval(
+        gencode_ht.interval.start.contig,
+        gencode_ht.interval.start.position - exon_padding_bp,
+        gencode_ht.interval.end.position + exon_padding_bp,
+        includes_start=gencode_ht.interval.includes_start,
+        includes_end=gencode_ht.interval.includes_end,
+        reference_genome=build,
+    ).collect()
 
     ht = hl.filter_intervals(ht, intervals)
 
diff --git a/gnomad_toolbox/filtering/vep.py b/gnomad_toolbox/filtering/vep.py
index f851598..a8b8793 100644
--- a/gnomad_toolbox/filtering/vep.py
+++ b/gnomad_toolbox/filtering/vep.py
@@ -5,19 +5,30 @@
 import hail as hl
 from gnomad.utils.vep import CSQ_CODING, LOF_CSQ_SET
 
+from gnomad_toolbox.load_data import _get_gnomad_release
+
+# TODO: I haven't looked over this function yet. Is there anything in gnomad_methods
+#  that could be used here? If not, is there anything here that should be moved to
+#  gnomad_methods?
+
 
 def filter_by_csqs(
-    ht: hl.Table, csqs: list[str], pass_filters: bool = True
+    csqs: list[str],
+    pass_filters: bool = True,
+    **kwargs,
 ) -> hl.Table:
     """
-    Filter variants by consequences.
+    Filter variants by VEP transcript consequences.
 
-    :param ht: Input Table.
     :param csqs: List of consequences to filter by. It can be specified as the
          categories on the browser: pLoF, Missense / Inframe indel, Synonymous, Other.
     :param pass_filters: Boolean if the variants pass the filters.
+    :param kwargs: Arguments to pass to _get_gnomad_release.
     :return: Table with variants with the specified consequences.
     """
+    # Load the Hail Table if not provided
+    ht = _get_gnomad_release(dataset="variant", **kwargs)
+
     missense_inframe = ["missense_variant", "inframe_insertion", "inframe_deletion"]
 
     filter_expr = []
@@ -62,3 +73,25 @@ def filter_by_csqs(
         ht = ht.filter(hl.len(ht.filters) == 0)
 
     return ht
+
+
+# TODO: The following was in one of the notebooks, and I think we should add a wrapper
+#  around this function to make it much simpler instead of using it in the notebook.
+
+# Filter to LOFTEE high-confidence variants for certain genes
+
+# In this example, we are filtering to variants in ASH1L that are LOFTEE high-confidence
+# (with no flags) in the MANE select transcript.
+
+# from gnomad.utils.vep import filter_vep_transcript_csqs
+# ht = get_gnomad_release(data_type='exomes', version='4.1')
+# ht = filter_vep_transcript_csqs(
+#    ht,
+#    synonymous=False,
+#    mane_select=True,
+#    genes=["ASH1L"],
+#    match_by_gene_symbol=True,
+#    additional_filtering_criteria=[lambda x: (x.lof == "HC") & hl.is_missing(x.lof_flags)],
+# )
+# ht.show()
+# ht.count()
diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index 5c39aff..d591bed 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -48,6 +48,8 @@
 }
 
 
+# TODO: Are there other things we want to store in a session? Like a PASS variants only
+#  variable?
 class GnomADSession:
     """Class to manage the default data type and version for a gnomAD session."""
 
diff --git a/gnomad_toolbox/notebooks/intro_to_release_data.ipynb b/gnomad_toolbox/notebooks/intro_to_release_data.ipynb
deleted file mode 100644
index 1e49925..0000000
--- a/gnomad_toolbox/notebooks/intro_to_release_data.ipynb
+++ /dev/null
@@ -1,6622 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "853c94b9",
-   "metadata": {},
-   "source": [
-    "# Introduction to gnomAD Hail release files\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5cf83cfe-0fce-40ae-add7-c9f2c20c1e85",
-   "metadata": {},
-   "source": [
-    "In this notebook we will explore all of the available [gnomAD v4 release files](https://gnomad.broadinstitute.org/data#v4) that are in Hail formats."
-   ]
-  },
-  {
-   "attachments": {
-    "afcd4ecc-4e90-464b-91c7-9cd80b0e92ba.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "88ddfe98-92b3-4c9c-906d-84d4835d2bf1",
-   "metadata": {},
-   "source": [
-    "![image.png](attachment:afcd4ecc-4e90-464b-91c7-9cd80b0e92ba.png)"
-   ]
-  },
-  {
-   "attachments": {
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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "f97431a4-78a0-473f-99a0-67eeefb8bbc6",
-   "metadata": {},
-   "source": [
-    "![image.png](attachment:2f957256-5d08-40e1-b77c-5faa4f771fb2.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e609a46",
-   "metadata": {
-    "tags": [],
-    "toc": true
-   },
-   "source": [
-    "
Table of Contents
\n",
-    "
"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e713032",
-   "metadata": {},
-   "source": [
-    "## Import modules"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "e69953f7",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:40.044647Z",
-     "start_time": "2024-12-06T20:08:37.758061Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "    \n",
-       "    
\n",
-       "        \n",
-       "        Loading BokehJS ...\n",
-       "    
\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "(function(root) {\n",
-       "  function now() {\n",
-       "    return new Date();\n",
-       "  }\n",
-       "\n",
-       "  const force = true;\n",
-       "\n",
-       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
-       "    root._bokeh_onload_callbacks = [];\n",
-       "    root._bokeh_is_loading = undefined;\n",
-       "  }\n",
-       "\n",
-       "const JS_MIME_TYPE = 'application/javascript';\n",
-       "  const HTML_MIME_TYPE = 'text/html';\n",
-       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
-       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
-       "\n",
-       "  /**\n",
-       "   * Render data to the DOM node\n",
-       "   */\n",
-       "  function render(props, node) {\n",
-       "    const script = document.createElement(\"script\");\n",
-       "    node.appendChild(script);\n",
-       "  }\n",
-       "\n",
-       "  /**\n",
-       "   * Handle when an output is cleared or removed\n",
-       "   */\n",
-       "  function handleClearOutput(event, handle) {\n",
-       "    function drop(id) {\n",
-       "      const view = Bokeh.index.get_by_id(id)\n",
-       "      if (view != null) {\n",
-       "        view.model.document.clear()\n",
-       "        Bokeh.index.delete(view)\n",
-       "      }\n",
-       "    }\n",
-       "\n",
-       "    const cell = handle.cell;\n",
-       "\n",
-       "    const id = cell.output_area._bokeh_element_id;\n",
-       "    const server_id = cell.output_area._bokeh_server_id;\n",
-       "\n",
-       "    // Clean up Bokeh references\n",
-       "    if (id != null) {\n",
-       "      drop(id)\n",
-       "    }\n",
-       "\n",
-       "    if (server_id !== undefined) {\n",
-       "      // Clean up Bokeh references\n",
-       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
-       "      cell.notebook.kernel.execute(cmd_clean, {\n",
-       "        iopub: {\n",
-       "          output: function(msg) {\n",
-       "            const id = msg.content.text.trim()\n",
-       "            drop(id)\n",
-       "          }\n",
-       "        }\n",
-       "      });\n",
-       "      // Destroy server and session\n",
-       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
-       "      cell.notebook.kernel.execute(cmd_destroy);\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  /**\n",
-       "   * Handle when a new output is added\n",
-       "   */\n",
-       "  function handleAddOutput(event, handle) {\n",
-       "    const output_area = handle.output_area;\n",
-       "    const output = handle.output;\n",
-       "\n",
-       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
-       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
-       "      return\n",
-       "    }\n",
-       "\n",
-       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
-       "\n",
-       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
-       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
-       "      // store reference to embed id on output_area\n",
-       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
-       "    }\n",
-       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
-       "      const bk_div = document.createElement(\"div\");\n",
-       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
-       "      const script_attrs = bk_div.children[0].attributes;\n",
-       "      for (let i = 0; i < script_attrs.length; i++) {\n",
-       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
-       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
-       "      }\n",
-       "      // store reference to server id on output_area\n",
-       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  function register_renderer(events, OutputArea) {\n",
-       "\n",
-       "    function append_mime(data, metadata, element) {\n",
-       "      // create a DOM node to render to\n",
-       "      const toinsert = this.create_output_subarea(\n",
-       "        metadata,\n",
-       "        CLASS_NAME,\n",
-       "        EXEC_MIME_TYPE\n",
-       "      );\n",
-       "      this.keyboard_manager.register_events(toinsert);\n",
-       "      // Render to node\n",
-       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
-       "      render(props, toinsert[toinsert.length - 1]);\n",
-       "      element.append(toinsert);\n",
-       "      return toinsert\n",
-       "    }\n",
-       "\n",
-       "    /* Handle when an output is cleared or removed */\n",
-       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
-       "    events.on('delete.Cell', handleClearOutput);\n",
-       "\n",
-       "    /* Handle when a new output is added */\n",
-       "    events.on('output_added.OutputArea', handleAddOutput);\n",
-       "\n",
-       "    /**\n",
-       "     * Register the mime type and append_mime function with output_area\n",
-       "     */\n",
-       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
-       "      /* Is output safe? */\n",
-       "      safe: true,\n",
-       "      /* Index of renderer in `output_area.display_order` */\n",
-       "      index: 0\n",
-       "    });\n",
-       "  }\n",
-       "\n",
-       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
-       "  if (root.Jupyter !== undefined) {\n",
-       "    const events = require('base/js/events');\n",
-       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
-       "\n",
-       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
-       "      register_renderer(events, OutputArea);\n",
-       "    }\n",
-       "  }\n",
-       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
-       "    root._bokeh_timeout = Date.now() + 5000;\n",
-       "    root._bokeh_failed_load = false;\n",
-       "  }\n",
-       "\n",
-       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
-       "     \"
\\n\"+\n",
-       "     \"
\\n\"+\n",
-       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
-       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
-       "     \"
\\n\"+\n",
-       "     \"
    \\n\"+\n",
-       "     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n",
-       "     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n",
-       "     \"
\\n\"+\n",
-       "     \"\\n\"+\n",
-       "     \"from bokeh.resources import INLINE\\n\"+\n",
-       "     \"output_notebook(resources=INLINE)\\n\"+\n",
-       "     \"\\n\"+\n",
-       "     \"
\"}};\n",
-       "\n",
-       "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"a8702bf8-b019-4dc5-b5ff-b8a2fe2976da\");\n",
-       "    if (el != null) {\n",
-       "      el.textContent = \"BokehJS is loading...\";\n",
-       "    }\n",
-       "    if (root.Bokeh !== undefined) {\n",
-       "      if (el != null) {\n",
-       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
-       "      }\n",
-       "    } else if (Date.now() < root._bokeh_timeout) {\n",
-       "      setTimeout(display_loaded, 100)\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  function run_callbacks() {\n",
-       "    try {\n",
-       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
-       "        if (callback != null)\n",
-       "          callback();\n",
-       "      });\n",
-       "    } finally {\n",
-       "      delete root._bokeh_onload_callbacks\n",
-       "    }\n",
-       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
-       "  }\n",
-       "\n",
-       "  function load_libs(css_urls, js_urls, callback) {\n",
-       "    if (css_urls == null) css_urls = [];\n",
-       "    if (js_urls == null) js_urls = [];\n",
-       "\n",
-       "    root._bokeh_onload_callbacks.push(callback);\n",
-       "    if (root._bokeh_is_loading > 0) {\n",
-       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
-       "      return null;\n",
-       "    }\n",
-       "    if (js_urls == null || js_urls.length === 0) {\n",
-       "      run_callbacks();\n",
-       "      return null;\n",
-       "    }\n",
-       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
-       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
-       "\n",
-       "    function on_load() {\n",
-       "      root._bokeh_is_loading--;\n",
-       "      if (root._bokeh_is_loading === 0) {\n",
-       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
-       "        run_callbacks()\n",
-       "      }\n",
-       "    }\n",
-       "\n",
-       "    function on_error(url) {\n",
-       "      console.error(\"failed to load \" + url);\n",
-       "    }\n",
-       "\n",
-       "    for (let i = 0; i < css_urls.length; i++) {\n",
-       "      const url = css_urls[i];\n",
-       "      const element = document.createElement(\"link\");\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error.bind(null, url);\n",
-       "      element.rel = \"stylesheet\";\n",
-       "      element.type = \"text/css\";\n",
-       "      element.href = url;\n",
-       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
-       "      document.body.appendChild(element);\n",
-       "    }\n",
-       "\n",
-       "    for (let i = 0; i < js_urls.length; i++) {\n",
-       "      const url = js_urls[i];\n",
-       "      const element = document.createElement('script');\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error.bind(null, url);\n",
-       "      element.async = false;\n",
-       "      element.src = url;\n",
-       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
-       "      document.head.appendChild(element);\n",
-       "    }\n",
-       "  };\n",
-       "\n",
-       "  function inject_raw_css(css) {\n",
-       "    const element = document.createElement(\"style\");\n",
-       "    element.appendChild(document.createTextNode(css));\n",
-       "    document.body.appendChild(element);\n",
-       "  }\n",
-       "\n",
-       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n",
-       "  const css_urls = [];\n",
-       "\n",
-       "  const inline_js = [    function(Bokeh) {\n",
-       "      Bokeh.set_log_level(\"info\");\n",
-       "    },\n",
-       "function(Bokeh) {\n",
-       "    }\n",
-       "  ];\n",
-       "\n",
-       "  function run_inline_js() {\n",
-       "    if (root.Bokeh !== undefined || force === true) {\n",
-       "          for (let i = 0; i < inline_js.length; i++) {\n",
-       "      inline_js[i].call(root, root.Bokeh);\n",
-       "    }\n",
-       "if (force === true) {\n",
-       "        display_loaded();\n",
-       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
-       "      setTimeout(run_inline_js, 100);\n",
-       "    } else if (!root._bokeh_failed_load) {\n",
-       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
-       "      root._bokeh_failed_load = true;\n",
-       "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"a8702bf8-b019-4dc5-b5ff-b8a2fe2976da\")).parents('.cell').data().cell;\n",
-       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  if (root._bokeh_is_loading === 0) {\n",
-       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
-       "    run_inline_js();\n",
-       "  } else {\n",
-       "    load_libs(css_urls, js_urls, function() {\n",
-       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
-       "      run_inline_js();\n",
-       "    });\n",
-       "  }\n",
-       "}(window));"
-      ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"a8702bf8-b019-4dc5-b5ff-b8a2fe2976da\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"a8702bf8-b019-4dc5-b5ff-b8a2fe2976da\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import hail as hl\n",
-    "from gnomad_toolbox.load_data import  get_gnomad_release"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "8649f215-0afc-4f66-920a-53b707f41c4a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Welcome to\n",
-      "     __  __     <>__\n",
-      "    / /_/ /__  __/ /\n",
-      "   / __  / _ `/ / /\n",
-      "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
-      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241209-1355-0.2.132-678e1f52b999.log\n"
-     ]
-    }
-   ],
-   "source": [
-    "hl.init(backend=\"local\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5335a135",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Variant data\n",
-    "\n",
-    "Available versions for each data type and reference build are (as of 2024-10-29):\n",
-    "\n",
-    "| Data Type       | GRCh38 Versions                  | GRCh37 Versions      |\n",
-    "|-----------------|----------------------------------|----------------------|\n",
-    "| exomes          | 4.0, 4.1                         | 2.1, 2.1.1           |\n",
-    "| genomes         | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1           |\n",
-    "| joint           | 4.1                              | N/A                  |\n",
-    "\n",
-    "For a description of all fields the the HT, see the [Help/FAQ](https://gnomad.broadinstitute.org/help/v4-hts) page."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1a4ae8933ba6421",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### v4.1 exomes Hail Table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "318a034c-ac84-4147-9f25-e5e8783e9b91",
-   "metadata": {},
-   "source": [
-    "#### Load the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "100cf576",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:59.197890Z",
-     "start_time": "2024-12-06T20:08:50.859108Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ht = get_gnomad_release(data_type='exomes', version='4.1')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77d7a05e31c1f37a",
-   "metadata": {},
-   "source": [
-    "#### Print the schema of the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "95c14f2c8cc3e699",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:00.032524Z",
-     "start_time": "2024-12-06T20:09:00.029271Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    'freq_meta': array> \n",
-      "    'freq_index_dict': dict \n",
-      "    'freq_meta_sample_count': array \n",
-      "    'faf_meta': array> \n",
-      "    'faf_index_dict': dict \n",
-      "    'age_distribution': struct {\n",
-      "        bin_edges: array, \n",
-      "        bin_freq: array, \n",
-      "        n_smaller: int32, \n",
-      "        n_larger: int32\n",
-      "    } \n",
-      "    'downsamplings': dict> \n",
-      "    'filtering_model': struct {\n",
-      "        filter_name: str, \n",
-      "        score_name: str, \n",
-      "        snv_cutoff: struct {\n",
-      "            bin: int32, \n",
-      "            min_score: float64\n",
-      "        }, \n",
-      "        indel_cutoff: struct {\n",
-      "            bin: int32, \n",
-      "            min_score: float64\n",
-      "        }, \n",
-      "        snv_training_variables: array, \n",
-      "        indel_training_variables: array\n",
-      "    } \n",
-      "    'inbreeding_coeff_cutoff': float64 \n",
-      "    'interval_qc_parameters': struct {\n",
-      "        per_platform: bool, \n",
-      "        all_platforms: bool, \n",
-      "        high_qual_cutoffs: dict>, \n",
-      "        min_platform_size: int32\n",
-      "    } \n",
-      "    'tool_versions': struct {\n",
-      "        cadd_version: str, \n",
-      "        revel_version: str, \n",
-      "        spliceai_version: str, \n",
-      "        pangolin_version: array, \n",
-      "        phylop_version: str, \n",
-      "        dbsnp_version: str, \n",
-      "        sift_version: str, \n",
-      "        polyphen_version: str\n",
-      "    } \n",
-      "    'vrs_versions': struct {\n",
-      "        vrs_schema_version: str, \n",
-      "        vrs_python_version: str, \n",
-      "        seqrepo_version: str\n",
-      "    } \n",
-      "    'vep_globals': struct {\n",
-      "        vep_version: str, \n",
-      "        vep_help: str, \n",
-      "        vep_config: str, \n",
-      "        gencode_version: str, \n",
-      "        mane_select_version: str\n",
-      "    } \n",
-      "    'frequency_README': str \n",
-      "    'date': str \n",
-      "    'version': str \n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'alleles': array \n",
-      "    'freq': array \n",
-      "    'grpmax': struct {\n",
-      "        gnomad: struct {\n",
-      "            AC: int32, \n",
-      "            AF: float64, \n",
-      "            AN: int32, \n",
-      "            homozygote_count: int64, \n",
-      "            gen_anc: str\n",
-      "        }, \n",
-      "        non_ukb: struct {\n",
-      "            AC: int32, \n",
-      "            AF: float64, \n",
-      "            AN: int32, \n",
-      "            homozygote_count: int64, \n",
-      "            gen_anc: str\n",
-      "        }\n",
-      "    } \n",
-      "    'faf': array \n",
-      "    'fafmax': struct {\n",
-      "        gnomad: struct {\n",
-      "            faf95_max: float64, \n",
-      "            faf95_max_gen_anc: str, \n",
-      "            faf99_max: float64, \n",
-      "            faf99_max_gen_anc: str\n",
-      "        }, \n",
-      "        non_ukb: struct {\n",
-      "            faf95_max: float64, \n",
-      "            faf95_max_gen_anc: str, \n",
-      "            faf99_max: float64, \n",
-      "            faf99_max_gen_anc: str\n",
-      "        }\n",
-      "    } \n",
-      "    'a_index': int32 \n",
-      "    'was_split': bool \n",
-      "    'rsid': set \n",
-      "    'filters': set \n",
-      "    'info': struct {\n",
-      "        FS: float64, \n",
-      "        MQ: float64, \n",
-      "        MQRankSum: float64, \n",
-      "        QUALapprox: int64, \n",
-      "        QD: float64, \n",
-      "        ReadPosRankSum: float64, \n",
-      "        SB: array, \n",
-      "        SOR: float64, \n",
-      "        VarDP: int32, \n",
-      "        AS_FS: float64, \n",
-      "        AS_MQ: float64, \n",
-      "        AS_MQRankSum: float64, \n",
-      "        AS_pab_max: float64, \n",
-      "        AS_QUALapprox: int64, \n",
-      "        AS_QD: float64, \n",
-      "        AS_ReadPosRankSum: float64, \n",
-      "        AS_SB_TABLE: array, \n",
-      "        AS_SOR: float64, \n",
-      "        AS_VarDP: int32, \n",
-      "        singleton: bool, \n",
-      "        transmitted_singleton: bool, \n",
-      "        sibling_singleton: bool, \n",
-      "        omni: bool, \n",
-      "        mills: bool, \n",
-      "        monoallelic: bool, \n",
-      "        only_het: bool, \n",
-      "        AS_VQSLOD: float64, \n",
-      "        inbreeding_coeff: float64, \n",
-      "        vrs: struct {\n",
-      "            VRS_Allele_IDs: array, \n",
-      "            VRS_Starts: array, \n",
-      "            VRS_Ends: array, \n",
-      "            VRS_States: array\n",
-      "        }\n",
-      "    } \n",
-      "    'vep': struct {\n",
-      "        allele_string: str, \n",
-      "        end: int32, \n",
-      "        id: str, \n",
-      "        input: str, \n",
-      "        intergenic_consequences: array, \n",
-      "            impact: str, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        most_severe_consequence: str, \n",
-      "        motif_feature_consequences: array, \n",
-      "            high_inf_pos: str, \n",
-      "            impact: str, \n",
-      "            motif_feature_id: str, \n",
-      "            motif_name: str, \n",
-      "            motif_pos: int32, \n",
-      "            motif_score_change: float64, \n",
-      "            transcription_factors: array, \n",
-      "            strand: int32, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        regulatory_feature_consequences: array, \n",
-      "            impact: str, \n",
-      "            regulatory_feature_id: str, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        seq_region_name: str, \n",
-      "        start: int32, \n",
-      "        strand: int32, \n",
-      "        transcript_consequences: array, \n",
-      "            distance: int32, \n",
-      "            domains: array, \n",
-      "            exon: str, \n",
-      "            flags: str, \n",
-      "            gene_id: str, \n",
-      "            gene_pheno: int32, \n",
-      "            gene_symbol: str, \n",
-      "            gene_symbol_source: str, \n",
-      "            hgnc_id: str, \n",
-      "            hgvsc: str, \n",
-      "            hgvsp: str, \n",
-      "            hgvs_offset: int32, \n",
-      "            impact: str, \n",
-      "            intron: str, \n",
-      "            lof: str, \n",
-      "            lof_flags: str, \n",
-      "            lof_filter: str, \n",
-      "            lof_info: str, \n",
-      "            mane_select: str, \n",
-      "            mane_plus_clinical: str, \n",
-      "            mirna: array, \n",
-      "            protein_end: int32, \n",
-      "            protein_start: int32, \n",
-      "            protein_id: str, \n",
-      "            source: str, \n",
-      "            strand: int32, \n",
-      "            transcript_id: str, \n",
-      "            tsl: int32, \n",
-      "            uniprot_isoform: array, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        variant_class: str\n",
-      "    } \n",
-      "    'vqsr_results': struct {\n",
-      "        AS_VQSLOD: float64, \n",
-      "        AS_culprit: str, \n",
-      "        positive_train_site: bool, \n",
-      "        negative_train_site: bool\n",
-      "    } \n",
-      "    'region_flags': struct {\n",
-      "        non_par: bool, \n",
-      "        lcr: bool, \n",
-      "        segdup: bool, \n",
-      "        fail_interval_qc: bool, \n",
-      "        outside_ukb_capture_region: bool, \n",
-      "        outside_broad_capture_region: bool\n",
-      "    } \n",
-      "    'allele_info': struct {\n",
-      "        variant_type: str, \n",
-      "        n_alt_alleles: int32, \n",
-      "        has_star: bool, \n",
-      "        allele_type: str, \n",
-      "        was_mixed: bool\n",
-      "    } \n",
-      "    'histograms': struct {\n",
-      "        qual_hists: struct {\n",
-      "            gq_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            gq_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            ab_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }, \n",
-      "        raw_qual_hists: struct {\n",
-      "            gq_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            gq_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            ab_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }, \n",
-      "        age_hists: struct {\n",
-      "            age_hist_het: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            age_hist_hom: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }\n",
-      "    } \n",
-      "    'in_silico_predictors': struct {\n",
-      "        cadd: struct {\n",
-      "            phred: float32, \n",
-      "            raw_score: float32\n",
-      "        }, \n",
-      "        revel_max: float64, \n",
-      "        spliceai_ds_max: float32, \n",
-      "        pangolin_largest_ds: float64, \n",
-      "        phylop: float64, \n",
-      "        sift_max: float64, \n",
-      "        polyphen_max: float64\n",
-      "    } \n",
-      "----------------------------------------\n",
-      "Key: ['locus', 'alleles']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a071f738b2c888e",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "#### Show the first 5 variants in the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "222de580c305d72a",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:20.949958Z",
-     "start_time": "2024-12-06T20:09:05.622171Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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cessed_transcript",NA,NA,234,234,NA,NA,NA,["non_coding_transcript_exon_variant"],NA,NA,"1/3",NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102","ENST00000456328.2:n.234G>A",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000456328",1,NA,"A"),(1,NA,NA,"unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],2302,NA,NA,NA,"ENSG00000227232",NA,"WASH7P","HGNC","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",-1,"ENST00000488147",NA,NA,"A"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],2260,NA,NA,NA,"653635",NA,"WASH7P","EntrezGene","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",-1,"NR_024540.1",NA,NA,"A"),(1,NA,NA,"transcribed_pseudogene",1,NA,229,229,NA,NA,NA,["non_coding_transcript_exon_variant"],NA,NA,"1/3",NA,"100287102",NA,"DDX11L1","EntrezGene","HGNC:37102","NR_046018.2:n.229G>A",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",1,"NR_046018.2",NA,NA,"A")]"SNV"-2.86e+00"AS_MQ"FalseFalseFalseFalseTrueNATrueFalse"snv"1False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,14,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][314323,1,0,0,54,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][308789,5585,16,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]004.42e+003.11e-01NANA-8.00e-021.38e+00NANA
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+---------------+----------------+\n",
-       "| locus         | alleles        |\n",
-       "+---------------+----------------+\n",
-       "| locus | array     |\n",
-       "+---------------+----------------+\n",
-       "| chr1:11994    | [\"T\",\"C\"]      |\n",
-       "| chr1:12016    | [\"G\",\"A\"]      |\n",
-       "| chr1:12060    | [\"CTGGAG\",\"C\"] |\n",
-       "| chr1:12074    | [\"T\",\"C\"]      |\n",
-       "| chr1:12102    | [\"G\",\"A\"]      |\n",
-       "+---------------+----------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| freq                                                                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0,NA,0,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA,... |\n",
-       "| [(0,NA,0,0),(6,9.54e-06,628784,2),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA,... |\n",
-       "| [(0,0.00e+00,4,0),(1,1.59e-06,628784,0),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n",
-       "| [(0,0.00e+00,4,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n",
-       "| [(0,0.00e+00,32,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0)... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------+------------------+------------------+\n",
-       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
-       "+------------------+------------------+------------------+\n",
-       "|            int32 |          float64 |            int32 |\n",
-       "+------------------+------------------+------------------+\n",
-       "|               NA |               NA |               NA |\n",
-       "|               NA |               NA |               NA |\n",
-       "|               NA |               NA |               NA |\n",
-       "|               NA |               NA |               NA |\n",
-       "|               NA |               NA |               NA |\n",
-       "+------------------+------------------+------------------+\n",
-       "\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                          int64 | str                   |             int32 |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|           float64 |             int32 |                           int64 |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|                NA |                NA |                              NA |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|                NA |                NA |                              NA |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "\n",
-       "+------------------------+\n",
-       "| grpmax.non_ukb.gen_anc |\n",
-       "+------------------------+\n",
-       "| str                    |\n",
-       "+------------------------+\n",
-       "| NA                     |\n",
-       "| NA                     |\n",
-       "| NA                     |\n",
-       "| NA                     |\n",
-       "| NA                     |\n",
-       "+------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| faf                                                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+\n",
-       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                  float64 | str                              |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "+--------------------------+----------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                  float64 | str                              |   int32 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "\n",
-       "+-----------+----------+-------------------+---------+----------+\n",
-       "| was_split | rsid     | filters           | info.FS |  info.MQ |\n",
-       "+-----------+----------+-------------------+---------+----------+\n",
-       "|      bool | set | set          | float64 |  float64 |\n",
-       "+-----------+----------+-------------------+---------+----------+\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.50e+01 |\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.50e+01 |\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.60e+01 |\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.56e+01 |\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.45e+01 |\n",
-       "+-----------+----------+-------------------+---------+----------+\n",
-       "\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|        float64 |           int64 |  float64 |             float64 |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|             NA |              60 | 3.00e+01 |                  NA |\n",
-       "|       0.00e+00 |             262 | 2.18e+01 |            6.74e-01 |\n",
-       "|       6.74e-01 |             123 | 3.08e+01 |           -1.15e+00 |\n",
-       "|       4.31e-01 |              99 | 1.24e+01 |           -2.53e-01 |\n",
-       "|             NA |              90 | 2.25e+01 |                  NA |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "\n",
-       "+--------------+----------+------------+------------+------------+\n",
-       "| info.SB      | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
-       "+--------------+----------+------------+------------+------------+\n",
-       "| array |  float64 |      int32 |    float64 |    float64 |\n",
-       "+--------------+----------+------------+------------+------------+\n",
-       "| [0,0,2,0]    | 2.30e+00 |          2 |         NA |   2.50e+01 |\n",
-       "| [2,0,10,0]   | 2.67e+00 |         12 |         NA |   2.50e+01 |\n",
-       "| [1,0,3,0]    | 1.61e+00 |          4 |         NA |   2.60e+01 |\n",
-       "| [4,0,4,0]    | 6.93e-01 |          8 |         NA |   2.56e+01 |\n",
-       "| [0,0,4,0]    | 3.26e+00 |          4 |         NA |   2.45e+01 |\n",
-       "+--------------+----------+------------+------------+------------+\n",
-       "\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|           float64 |         float64 |              int64 |    float64 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|                NA |              NA |                 60 |   3.00e+01 |\n",
-       "|          0.00e+00 |        1.00e+00 |                262 |   2.18e+01 |\n",
-       "|          6.74e-01 |        6.25e-01 |                123 |   3.08e+01 |\n",
-       "|          4.31e-01 |        6.87e-01 |                 99 |   1.24e+01 |\n",
-       "|                NA |              NA |                 90 |   2.25e+01 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "\n",
-       "+------------------------+------------------+-------------+---------------+\n",
-       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE | info.AS_SOR | info.AS_VarDP |\n",
-       "+------------------------+------------------+-------------+---------------+\n",
-       "|                float64 | array     |     float64 |         int32 |\n",
-       "+------------------------+------------------+-------------+---------------+\n",
-       "|                     NA | [0,0,2,0]        |    2.30e+00 |             2 |\n",
-       "|               6.74e-01 | [2,0,10,0]       |    2.67e+00 |            12 |\n",
-       "|              -1.15e+00 | [1,0,3,0]        |    1.61e+00 |             4 |\n",
-       "|              -2.53e-01 | [4,0,4,0]        |    6.93e-01 |             8 |\n",
-       "|                     NA | [0,0,4,0]        |    3.26e+00 |             4 |\n",
-       "+------------------------+------------------+-------------+---------------+\n",
-       "\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "|           bool |                       bool |                   bool |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "|          False |                         NA |                     NA |\n",
-       "|          False |                         NA |                     NA |\n",
-       "|           True |                         NA |                     NA |\n",
-       "|          False |                         NA |                     NA |\n",
-       "|          False |                         NA |                     NA |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|      bool |       bool |             bool |          bool |        float64 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|     False |      False |            False |         False |      -5.25e+00 |\n",
-       "|     False |      False |            False |         False |      -2.75e+00 |\n",
-       "|     False |      False |            False |         False |      -2.22e+00 |\n",
-       "|     False |      False |            False |         False |      -2.18e+00 |\n",
-       "|     False |      False |            False |         False |      -2.86e+00 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "\n",
-       "+-----------------------+\n",
-       "| info.inbreeding_coeff |\n",
-       "+-----------------------+\n",
-       "|               float64 |\n",
-       "+-----------------------+\n",
-       "|              1.00e+00 |\n",
-       "|              6.67e-01 |\n",
-       "|             -1.59e-06 |\n",
-       "|              1.00e+00 |\n",
-       "|              1.00e+00 |\n",
-       "+-----------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| info.vrs.VRS_Allele_IDs                                                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [\"ga4gh:VA.ps4-9woXy7o4rS39i8hDK_cUPBa-UcyP\",\"ga4gh:VA.nHlWYJXgiuvXrLAxQs... |\n",
-       "| [\"ga4gh:VA.VFxwcI4knOzk6SHzS2qowyDGnkG3mfEH\",\"ga4gh:VA.ctgP7qNjQAGjI2eTDo... |\n",
-       "| [\"ga4gh:VA.P573ZtUtAaRcceE7NLanEyynSefvcAPL\",\"ga4gh:VA.CHmk9uDiHW2LIHndZW... |\n",
-       "| [\"ga4gh:VA.neBeBT28ISe_1-yKPFsYxntP2jz1pj7E\",\"ga4gh:VA.1RPHSwBHNUwoECJ9VV... |\n",
-       "| [\"ga4gh:VA.ZXRhPWtip8HseOMWpCnaja0-ATo8hLE1\",\"ga4gh:VA.Qi95g6E8nt6DaqTGpH... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "| info.vrs.VRS_Starts | info.vrs.VRS_Ends | info.vrs.VRS_States |\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "| array        | array      | array          |\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "| [11993,11993]       | [11994,11994]     | [\"T\",\"C\"]           |\n",
-       "| [12015,12015]       | [12016,12016]     | [\"G\",\"A\"]           |\n",
-       "| [12059,12060]       | [12065,12071]     | [\"CTGGAG\",\"TGGAGT\"] |\n",
-       "| [12073,12073]       | [12074,12074]     | [\"T\",\"C\"]           |\n",
-       "| [12101,12101]       | [12102,12102]     | [\"G\",\"A\"]           |\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "\n",
-       "+-------------------+---------+--------+--------------------------------+\n",
-       "| vep.allele_string | vep.end | vep.id | vep.input                      |\n",
-       "+-------------------+---------+--------+--------------------------------+\n",
-       "| str               |   int32 | str    | str                            |\n",
-       "+-------------------+---------+--------+--------------------------------+\n",
-       "| \"T/C\"             |   11994 | \".\"    | \"chr1\t11994\t.\tT\tC\t.\t.\tGT\"      |\n",
-       "| \"G/A\"             |   12016 | \".\"    | \"chr1\t12016\t.\tG\tA\t.\t.\tGT\"      |\n",
-       "| \"TGGAG/-\"         |   12065 | \".\"    | \"chr1\t12060\t.\tCTGGAG\tC\t.\t.\tGT\" |\n",
-       "| \"T/C\"             |   12074 | \".\"    | \"chr1\t12074\t.\tT\tC\t.\t.\tGT\"      |\n",
-       "| \"G/A\"             |   12102 | \".\"    | \"chr1\t12102\t.\tG\tA\t.\t.\tGT\"      |\n",
-       "+-------------------+---------+--------+--------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.intergenic_consequences                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, impact: st... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| vep.most_severe_consequence          |\n",
-       "+--------------------------------------+\n",
-       "| str                                  |\n",
-       "+--------------------------------------+\n",
-       "| \"non_coding_transcript_exon_variant\" |\n",
-       "| \"non_coding_transcript_exon_variant\" |\n",
-       "| \"splice_donor_5th_base_variant\"      |\n",
-       "| \"non_coding_transcript_exon_variant\" |\n",
-       "| \"non_coding_transcript_exon_variant\" |\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.motif_feature_consequences                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, high_inf_p... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.regulatory_feature_consequences                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,2,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,14,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_freq   |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [314391,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [314388,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [314380,0,0,0,11,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0]  |\n",
-       "| [314369,1,0,0,20,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [314323,1,0,0,54,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_freq     |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                       |\n",
-       "+----------------------------------------------------+\n",
-       "| [308845,5547,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [308845,5547,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [308833,5557,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [308823,5567,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [308789,5585,16,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0]      |\n",
-       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                      int64 |                         float32 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                          0 |                        1.26e+01 |\n",
-       "|                                          0 |                        1.60e+01 |\n",
-       "|                                          0 |                        8.52e+00 |\n",
-       "|                                          0 |                        1.52e+01 |\n",
-       "|                                          0 |                        4.42e+00 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                             float32 |                        float64 |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                            1.08e+00 |                             NA |\n",
-       "|                            1.54e+00 |                             NA |\n",
-       "|                            7.07e-01 |                             NA |\n",
-       "|                            1.41e+00 |                             NA |\n",
-       "|                            3.11e-01 |                             NA |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| in_silico_predictors.spliceai_ds_max |\n",
-       "+--------------------------------------+\n",
-       "|                              float32 |\n",
-       "+--------------------------------------+\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                  float64 |                     float64 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                -1.10e-01 |                    1.09e+00 |\n",
-       "|                                -7.00e-02 |                    6.55e+00 |\n",
-       "|                                -9.00e-02 |                   -4.41e+00 |\n",
-       "|                                -4.00e-02 |                    6.01e+00 |\n",
-       "|                                -8.00e-02 |                    1.38e+00 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                       float64 |                           float64 |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht.show(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b7a158a3-f21a-4f87-9596-1f918156d713",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### v4.1 genomes Hail Table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "30f86500-afc5-419e-ae2e-f944dc461fee",
-   "metadata": {},
-   "source": [
-    "#### Load the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "62ca9934-20dd-437e-898b-86a056e2606e",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:59.197890Z",
-     "start_time": "2024-12-06T20:08:50.859108Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ht = get_gnomad_release(data_type='genomes', version='4.1')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9cf4b782-f289-47b6-9123-d08ca761b074",
-   "metadata": {},
-   "source": [
-    "#### Print the schema of the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "09de90df-0b03-4a54-817c-c8a0606026f6",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:00.032524Z",
-     "start_time": "2024-12-06T20:09:00.029271Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    'freq_meta': array> \n",
-      "    'freq_index_dict': dict \n",
-      "    'freq_meta_sample_count': array \n",
-      "    'faf_meta': array> \n",
-      "    'faf_index_dict': dict \n",
-      "    'age_distribution': struct {\n",
-      "        bin_edges: array, \n",
-      "        bin_freq: array, \n",
-      "        n_smaller: int32, \n",
-      "        n_larger: int32\n",
-      "    } \n",
-      "    'filtering_model': struct {\n",
-      "        filter_name: str, \n",
-      "        score_name: str, \n",
-      "        snv_cutoff: struct {\n",
-      "            bin: int32, \n",
-      "            min_score: float64\n",
-      "        }, \n",
-      "        indel_cutoff: struct {\n",
-      "            bin: int32, \n",
-      "            min_score: float64\n",
-      "        }, \n",
-      "        snv_training_variables: array, \n",
-      "        indel_training_variables: array\n",
-      "    } \n",
-      "    'inbreeding_coeff_cutoff': float64 \n",
-      "    'tool_versions': struct {\n",
-      "        cadd_version: str, \n",
-      "        revel_version: str, \n",
-      "        spliceai_version: str, \n",
-      "        pangolin_version: array, \n",
-      "        phylop_version: str, \n",
-      "        dbsnp_version: str, \n",
-      "        sift_version: str, \n",
-      "        polyphen_version: str\n",
-      "    } \n",
-      "    'vrs_versions': struct {\n",
-      "        vrs_schema_version: str, \n",
-      "        vrs_python_version: str, \n",
-      "        seqrepo_version: str\n",
-      "    } \n",
-      "    'vep_globals': struct {\n",
-      "        vep_version: str, \n",
-      "        vep_help: str, \n",
-      "        vep_config: str, \n",
-      "        gencode_version: str, \n",
-      "        mane_select_version: str\n",
-      "    } \n",
-      "    'frequency_README': str \n",
-      "    'date': str \n",
-      "    'version': str \n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'alleles': array \n",
-      "    'freq': array \n",
-      "    'grpmax': struct {\n",
-      "        AC: int32, \n",
-      "        AF: float64, \n",
-      "        AN: int32, \n",
-      "        homozygote_count: int32, \n",
-      "        gen_anc: str\n",
-      "    } \n",
-      "    'faf': array \n",
-      "    'fafmax': struct {\n",
-      "        faf95_max: float64, \n",
-      "        faf95_max_gen_anc: str, \n",
-      "        faf99_max: float64, \n",
-      "        faf99_max_gen_anc: str\n",
-      "    } \n",
-      "    'a_index': int32 \n",
-      "    'was_split': bool \n",
-      "    'rsid': set \n",
-      "    'filters': set \n",
-      "    'info': struct {\n",
-      "        FS: float64, \n",
-      "        MQ: float64, \n",
-      "        MQRankSum: float64, \n",
-      "        QUALapprox: int64, \n",
-      "        QD: float32, \n",
-      "        ReadPosRankSum: float64, \n",
-      "        SB: array, \n",
-      "        SOR: float64, \n",
-      "        VarDP: int32, \n",
-      "        AS_FS: float64, \n",
-      "        AS_MQ: float64, \n",
-      "        AS_MQRankSum: float64, \n",
-      "        AS_pab_max: float64, \n",
-      "        AS_QUALapprox: int64, \n",
-      "        AS_QD: float32, \n",
-      "        AS_ReadPosRankSum: float64, \n",
-      "        AS_SB_TABLE: array, \n",
-      "        AS_SOR: float64, \n",
-      "        AS_VarDP: int32, \n",
-      "        singleton: bool, \n",
-      "        transmitted_singleton: bool, \n",
-      "        omni: bool, \n",
-      "        mills: bool, \n",
-      "        monoallelic: bool, \n",
-      "        only_het: bool, \n",
-      "        AS_VQSLOD: float64, \n",
-      "        inbreeding_coeff: float64, \n",
-      "        vrs: struct {\n",
-      "            VRS_Allele_IDs: array, \n",
-      "            VRS_Starts: array, \n",
-      "            VRS_Ends: array, \n",
-      "            VRS_States: array\n",
-      "        }\n",
-      "    } \n",
-      "    'vep': struct {\n",
-      "        allele_string: str, \n",
-      "        end: int32, \n",
-      "        id: str, \n",
-      "        input: str, \n",
-      "        intergenic_consequences: array, \n",
-      "            impact: str, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        most_severe_consequence: str, \n",
-      "        motif_feature_consequences: array, \n",
-      "            high_inf_pos: str, \n",
-      "            impact: str, \n",
-      "            motif_feature_id: str, \n",
-      "            motif_name: str, \n",
-      "            motif_pos: int32, \n",
-      "            motif_score_change: float64, \n",
-      "            transcription_factors: array, \n",
-      "            strand: int32, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        regulatory_feature_consequences: array, \n",
-      "            impact: str, \n",
-      "            regulatory_feature_id: str, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        seq_region_name: str, \n",
-      "        start: int32, \n",
-      "        strand: int32, \n",
-      "        transcript_consequences: array, \n",
-      "            distance: int32, \n",
-      "            domains: array, \n",
-      "            exon: str, \n",
-      "            flags: str, \n",
-      "            gene_id: str, \n",
-      "            gene_pheno: int32, \n",
-      "            gene_symbol: str, \n",
-      "            gene_symbol_source: str, \n",
-      "            hgnc_id: str, \n",
-      "            hgvsc: str, \n",
-      "            hgvsp: str, \n",
-      "            hgvs_offset: int32, \n",
-      "            impact: str, \n",
-      "            intron: str, \n",
-      "            lof: str, \n",
-      "            lof_flags: str, \n",
-      "            lof_filter: str, \n",
-      "            lof_info: str, \n",
-      "            mane_select: str, \n",
-      "            mane_plus_clinical: str, \n",
-      "            mirna: array, \n",
-      "            protein_end: int32, \n",
-      "            protein_start: int32, \n",
-      "            protein_id: str, \n",
-      "            source: str, \n",
-      "            strand: int32, \n",
-      "            transcript_id: str, \n",
-      "            tsl: int32, \n",
-      "            uniprot_isoform: array, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        variant_class: str\n",
-      "    } \n",
-      "    'vqsr_results': struct {\n",
-      "        AS_VQSLOD: float64, \n",
-      "        AS_culprit: str, \n",
-      "        positive_train_site: bool, \n",
-      "        negative_train_site: bool\n",
-      "    } \n",
-      "    'region_flags': struct {\n",
-      "        non_par: bool, \n",
-      "        lcr: bool, \n",
-      "        segdup: bool\n",
-      "    } \n",
-      "    'allele_info': struct {\n",
-      "        allele_type: str, \n",
-      "        n_alt_alleles: int32, \n",
-      "        variant_type: str, \n",
-      "        was_mixed: bool\n",
-      "    } \n",
-      "    'histograms': struct {\n",
-      "        qual_hists: struct {\n",
-      "            gq_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            gq_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            ab_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }, \n",
-      "        raw_qual_hists: struct {\n",
-      "            gq_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            gq_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            ab_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }, \n",
-      "        age_hists: struct {\n",
-      "            age_hist_het: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            age_hist_hom: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }\n",
-      "    } \n",
-      "    'in_silico_predictors': struct {\n",
-      "        cadd: struct {\n",
-      "            phred: float32, \n",
-      "            raw_score: float32\n",
-      "        }, \n",
-      "        revel_max: float64, \n",
-      "        spliceai_ds_max: float32, \n",
-      "        pangolin_largest_ds: float64, \n",
-      "        phylop: float64, \n",
-      "        sift_max: float64, \n",
-      "        polyphen_max: float64\n",
-      "    } \n",
-      "----------------------------------------\n",
-      "Key: ['locus', 'alleles']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed916197-3b0e-45dc-bacd-a13cb66d70ee",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "#### Show the first 5 variants in the Hail Table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "00b0ea2f-5685-4bae-886a-b9ea31866818",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:20.949958Z",
-     "start_time": "2024-12-06T20:09:05.622171Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
histograms
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
grpmax
fafmax
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
allele_type
n_alt_alleles
variant_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>int32float64int32int32strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strint32boolset<str>set<str>float64float64float64int64float32float64array<int32>float64int32float64float64float64float64int64float32float64array<int32>float64int32boolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolstrint32strboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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639542312"}{"AC0","AS_VQSR"}7.30e+003.48e+016.70e-02962.74e+00-1.07e+00[21,6,4,4]9.60e-02355.10e+003.51e+01-5.72e-016.87e-01772.96e+00-1.38e+00[21,6,3,3]9.64e-0226FalseNANANAFalseFalse-4.57e+00-1.65e-05["ga4gh:VA.oTAtTrgYxm81O9fu6Mrhfo1t3eHsgg4L","ga4gh:VA.Y283OnlLjyi1T1IT_JzvW255rC6YJsW6"][10030,10030][10031,10031]["T","C"]"T/C"10031".""chr1\t10031\t.\tT\tC\t.\t.\tGT"NA"upstream_gene_variant"NANA"chr1"100311[(1,NA,NA,"transcribed_unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1979,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000450305",NA,NA,"C"),(1,NA,NA,"processed_transcript",NA,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1838,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000456328",1,NA,"C"),(1,NA,NA,"unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4373,NA,NA,NA,"ENSG00000227232",NA,"WASH7P","HGNC","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",-1,"ENST00000488147",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4331,NA,NA,NA,"653635",NA,"WASH7P","EntrezGene","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",-1,"NR_024540.1",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1843,NA,NA,NA,"100287102",NA,"DDX11L1","EntrezGene","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",1,"NR_046018.2",NA,NA,"C")]"SNV"-4.57e+00"AS_QD"FalseFalseFalseTrueTrue"snv"2"multi-snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911,665,439,351]0527[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,1476,1023,741,500,418]0744[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]008.97e+007.57e-01NANANANANANA
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[(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]6.27e-06"nfe"2.35e-06"nfe"1True{"rs1570391741"}{"AS_VQSR"}3.30e+013.60e+017.88e-012921.79e+00-6.84e-01[97,29,17,20]3.75e-011633.79e+013.61e+017.88e-011.00e+002642.06e+00-6.84e-01[97,29,13,19]7.58e-01128FalseNANANAFalseFalse-3.31e+00-2.41e-05["ga4gh:VA.TV0UyS-jCvsVkEOMbipUnzTc4hYuVNTg","ga4gh:VA.33zGTKcHyj5wWpTfc3OfD8-UlPQ-YOfx"][10056,10056][10057,10057]["A","C"]"A/C"10057".""chr1\t10057\t.\tA\tC\t.\t.\tGT"NA"upstream_gene_variant"NANA"chr1"100571[(1,NA,NA,"transcribed_unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1953,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000450305",NA,NA,"C"),(1,NA,NA,"processed_transcript",NA,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1812,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000456328",1,NA,"C"),(1,NA,NA,"unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4347,NA,NA,NA,"ENSG00000227232",NA,"WASH7P","HGNC","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",-1,"ENST00000488147",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4305,NA,NA,NA,"653635",NA,"WASH7P","EntrezGene","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",-1,"NR_024540.1",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1817,NA,NA,NA,"100287102",NA,"DDX11L1","EntrezGene","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",1,"NR_046018.2",NA,NA,"C")]"SNV"-3.31e+00"AS_FS"FalseFalseFalseTrueTrue"snv"2"multi-snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2503,1787,1521]03622[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,3518,2603,1850,1572]03743[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]008.53e+007.09e-01NANANANANANA
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+---------------+------------+\n",
-       "| locus         | alleles    |\n",
-       "+---------------+------------+\n",
-       "| locus | array |\n",
-       "+---------------+------------+\n",
-       "| chr1:10031    | [\"T\",\"C\"]  |\n",
-       "| chr1:10037    | [\"T\",\"C\"]  |\n",
-       "| chr1:10043    | [\"T\",\"C\"]  |\n",
-       "| chr1:10055    | [\"T\",\"C\"]  |\n",
-       "| chr1:10057    | [\"A\",\"C\"]  |\n",
-       "+---------------+------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| freq                                                                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e... |\n",
-       "| [(2,2.60e-05,76882,0),(4,3.15e-05,126902,0),(0,0.00e+00,34670,0),(1,1.80e... |\n",
-       "| [(1,1.17e-05,85634,0),(1,8.24e-06,121430,0),(0,0.00e+00,39236,0),(0,0.00e... |\n",
-       "| [(1,1.06e-05,94224,0),(5,4.64e-05,107704,0),(0,0.00e+00,44382,0),(1,1.80e... |\n",
-       "| [(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------+-----------+-----------+-------------------------+----------------+\n",
-       "| grpmax.AC | grpmax.AF | grpmax.AN | grpmax.homozygote_count | grpmax.gen_anc |\n",
-       "+-----------+-----------+-----------+-------------------------+----------------+\n",
-       "|     int32 |   float64 |     int32 |                   int32 | str            |\n",
-       "+-----------+-----------+-----------+-------------------------+----------------+\n",
-       "|        NA |        NA |        NA |                      NA | NA             |\n",
-       "|         1 |  4.07e-04 |      2456 |                       0 | \"eas\"          |\n",
-       "|         1 |  4.39e-05 |     22760 |                       0 | \"afr\"          |\n",
-       "|        NA |        NA |        NA |                      NA | NA             |\n",
-       "|         2 |  3.78e-05 |     52912 |                       0 | \"nfe\"          |\n",
-       "+-----------+-----------+-----------+-------------------------+----------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| faf                                                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(4.32e-06,1.62e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------+--------------------------+------------------+\n",
-       "| fafmax.faf95_max | fafmax.faf95_max_gen_anc | fafmax.faf99_max |\n",
-       "+------------------+--------------------------+------------------+\n",
-       "|          float64 | str                      |          float64 |\n",
-       "+------------------+--------------------------+------------------+\n",
-       "|               NA | NA                       |               NA |\n",
-       "|               NA | NA                       |               NA |\n",
-       "|               NA | NA                       |               NA |\n",
-       "|               NA | NA                       |               NA |\n",
-       "|         6.27e-06 | \"nfe\"                    |         2.35e-06 |\n",
-       "+------------------+--------------------------+------------------+\n",
-       "\n",
-       "+--------------------------+---------+-----------+------------------+\n",
-       "| fafmax.faf99_max_gen_anc | a_index | was_split | rsid             |\n",
-       "+--------------------------+---------+-----------+------------------+\n",
-       "| str                      |   int32 |      bool | set         |\n",
-       "+--------------------------+---------+-----------+------------------+\n",
-       "| NA                       |       2 |      True | {\"rs1639542312\"} |\n",
-       "| NA                       |       1 |     False | {\"rs1639542418\"} |\n",
-       "| NA                       |       1 |     False | NA               |\n",
-       "| NA                       |       2 |      True | {\"rs892501864\"}  |\n",
-       "| \"nfe\"                    |       1 |      True | {\"rs1570391741\"} |\n",
-       "+--------------------------+---------+-----------+------------------+\n",
-       "\n",
-       "+-------------------+----------+----------+----------------+-----------------+\n",
-       "| filters           |  info.FS |  info.MQ | info.MQRankSum | info.QUALapprox |\n",
-       "+-------------------+----------+----------+----------------+-----------------+\n",
-       "| set          |  float64 |  float64 |        float64 |           int64 |\n",
-       "+-------------------+----------+----------+----------------+-----------------+\n",
-       "| {\"AC0\",\"AS_VQSR\"} | 7.30e+00 | 3.48e+01 |       6.70e-02 |              96 |\n",
-       "| {\"AS_VQSR\"}       | 8.58e+00 | 3.83e+01 |       1.37e+00 |             180 |\n",
-       "| {\"AS_VQSR\"}       | 3.11e+01 | 3.52e+01 |       1.23e+00 |              97 |\n",
-       "| {\"AS_VQSR\"}       | 0.00e+00 | 3.55e+01 |       1.07e-01 |             220 |\n",
-       "| {\"AS_VQSR\"}       | 3.30e+01 | 3.60e+01 |       7.88e-01 |             292 |\n",
-       "+-------------------+----------+----------+----------------+-----------------+\n",
-       "\n",
-       "+----------+---------------------+---------------+----------+------------+\n",
-       "|  info.QD | info.ReadPosRankSum | info.SB       | info.SOR | info.VarDP |\n",
-       "+----------+---------------------+---------------+----------+------------+\n",
-       "|  float32 |             float64 | array  |  float64 |      int32 |\n",
-       "+----------+---------------------+---------------+----------+------------+\n",
-       "| 2.74e+00 |           -1.07e+00 | [21,6,4,4]    | 9.60e-02 |         35 |\n",
-       "| 2.20e+00 |           -4.80e-01 | [49,12,13,8]  | 1.51e-01 |         82 |\n",
-       "| 2.77e+00 |           -8.96e-01 | [25,0,5,5]    | 1.00e-03 |         35 |\n",
-       "| 2.12e+00 |           -1.16e+00 | [51,29,15,9]  | 6.16e-01 |        104 |\n",
-       "| 1.79e+00 |           -6.84e-01 | [97,29,17,20] | 3.75e-01 |        163 |\n",
-       "+----------+---------------------+---------------+----------+------------+\n",
-       "\n",
-       "+------------+------------+-------------------+-----------------+\n",
-       "| info.AS_FS | info.AS_MQ | info.AS_MQRankSum | info.AS_pab_max |\n",
-       "+------------+------------+-------------------+-----------------+\n",
-       "|    float64 |    float64 |           float64 |         float64 |\n",
-       "+------------+------------+-------------------+-----------------+\n",
-       "|   5.10e+00 |   3.51e+01 |         -5.72e-01 |        6.87e-01 |\n",
-       "|   8.58e+00 |   3.83e+01 |          1.37e+00 |        1.00e+00 |\n",
-       "|   3.11e+01 |   3.52e+01 |          1.23e+00 |        1.00e+00 |\n",
-       "|   5.94e+00 |   3.48e+01 |          7.15e-01 |        2.27e-01 |\n",
-       "|   3.79e+01 |   3.61e+01 |          7.88e-01 |        1.00e+00 |\n",
-       "+------------+------------+-------------------+-----------------+\n",
-       "\n",
-       "+--------------------+------------+------------------------+------------------+\n",
-       "| info.AS_QUALapprox | info.AS_QD | info.AS_ReadPosRankSum | info.AS_SB_TABLE |\n",
-       "+--------------------+------------+------------------------+------------------+\n",
-       "|              int64 |    float32 |                float64 | array     |\n",
-       "+--------------------+------------+------------------------+------------------+\n",
-       "|                 77 |   2.96e+00 |              -1.38e+00 | [21,6,3,3]       |\n",
-       "|                180 |   2.20e+00 |              -4.80e-01 | [49,12,13,8]     |\n",
-       "|                 97 |   2.77e+00 |              -8.96e-01 | [25,0,5,5]       |\n",
-       "|                 91 |   1.21e+00 |              -1.16e+00 | [51,29,7,8]      |\n",
-       "|                264 |   2.06e+00 |              -6.84e-01 | [97,29,13,19]    |\n",
-       "+--------------------+------------+------------------------+------------------+\n",
-       "\n",
-       "+-------------+---------------+----------------+----------------------------+\n",
-       "| info.AS_SOR | info.AS_VarDP | info.singleton | info.transmitted_singleton |\n",
-       "+-------------+---------------+----------------+----------------------------+\n",
-       "|     float64 |         int32 |           bool |                       bool |\n",
-       "+-------------+---------------+----------------+----------------------------+\n",
-       "|    9.64e-02 |            26 |          False |                         NA |\n",
-       "|    1.51e-01 |            82 |          False |                         NA |\n",
-       "|    1.48e-03 |            35 |           True |                      False |\n",
-       "|    4.69e-01 |            75 |          False |                         NA |\n",
-       "|    7.58e-01 |           128 |          False |                         NA |\n",
-       "+-------------+---------------+----------------+----------------------------+\n",
-       "\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|      bool |       bool |             bool |          bool |        float64 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|        NA |         NA |            False |         False |      -4.57e+00 |\n",
-       "|        NA |         NA |            False |         False |      -3.18e+00 |\n",
-       "|        NA |         NA |            False |         False |      -5.79e+00 |\n",
-       "|        NA |         NA |            False |         False |      -3.72e+00 |\n",
-       "|        NA |         NA |            False |         False |      -3.31e+00 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "\n",
-       "+-----------------------+\n",
-       "| info.inbreeding_coeff |\n",
-       "+-----------------------+\n",
-       "|               float64 |\n",
-       "+-----------------------+\n",
-       "|             -1.65e-05 |\n",
-       "|             -3.15e-05 |\n",
-       "|             -8.24e-06 |\n",
-       "|             -4.64e-05 |\n",
-       "|             -2.41e-05 |\n",
-       "+-----------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| info.vrs.VRS_Allele_IDs                                                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [\"ga4gh:VA.oTAtTrgYxm81O9fu6Mrhfo1t3eHsgg4L\",\"ga4gh:VA.Y283OnlLjyi1T1IT_J... |\n",
-       "| [\"ga4gh:VA.6pIoPAYDsphCprRcyrititenuEWlZaxV\",\"ga4gh:VA.iHyQ1mh_iQX6WcqXcx... |\n",
-       "| [\"ga4gh:VA.HdrkFJS16zwJPcGSKX9___fJcdqSATlQ\",\"ga4gh:VA.LSHuFx03sqw8WJGN2-... |\n",
-       "| [\"ga4gh:VA.xis4Nhtveh7q75mtkipQAUqZfWUfboWB\",\"ga4gh:VA.Ciou33oFTNEilAEegb... |\n",
-       "| [\"ga4gh:VA.TV0UyS-jCvsVkEOMbipUnzTc4hYuVNTg\",\"ga4gh:VA.33zGTKcHyj5wWpTfc3... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "| info.vrs.VRS_Starts | info.vrs.VRS_Ends | info.vrs.VRS_States |\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "| array        | array      | array          |\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "| [10030,10030]       | [10031,10031]     | [\"T\",\"C\"]           |\n",
-       "| [10036,10036]       | [10037,10037]     | [\"T\",\"C\"]           |\n",
-       "| [10042,10042]       | [10043,10043]     | [\"T\",\"C\"]           |\n",
-       "| [10054,10054]       | [10055,10055]     | [\"T\",\"C\"]           |\n",
-       "| [10056,10056]       | [10057,10057]     | [\"A\",\"C\"]           |\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "\n",
-       "+-------------------+---------+--------+---------------------------+\n",
-       "| vep.allele_string | vep.end | vep.id | vep.input                 |\n",
-       "+-------------------+---------+--------+---------------------------+\n",
-       "| str               |   int32 | str    | str                       |\n",
-       "+-------------------+---------+--------+---------------------------+\n",
-       "| \"T/C\"             |   10031 | \".\"    | \"chr1\t10031\t.\tT\tC\t.\t.\tGT\" |\n",
-       "| \"T/C\"             |   10037 | \".\"    | \"chr1\t10037\t.\tT\tC\t.\t.\tGT\" |\n",
-       "| \"T/C\"             |   10043 | \".\"    | \"chr1\t10043\t.\tT\tC\t.\t.\tGT\" |\n",
-       "| \"T/C\"             |   10055 | \".\"    | \"chr1\t10055\t.\tT\tC\t.\t.\tGT\" |\n",
-       "| \"A/C\"             |   10057 | \".\"    | \"chr1\t10057\t.\tA\tC\t.\t.\tGT\" |\n",
-       "+-------------------+---------+--------+---------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.intergenic_consequences                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, impact: st... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------+\n",
-       "| vep.most_severe_consequence |\n",
-       "+-----------------------------+\n",
-       "| str                         |\n",
-       "+-----------------------------+\n",
-       "| \"upstream_gene_variant\"     |\n",
-       "| \"upstream_gene_variant\"     |\n",
-       "| \"upstream_gene_variant\"     |\n",
-       "| \"upstream_gene_variant\"     |\n",
-       "| \"upstream_gene_variant\"     |\n",
-       "+-----------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.motif_feature_consequences                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, high_inf_p... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.regulatory_feature_consequences                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.bin_freq                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]                |\n",
-       "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15]          |\n",
-       "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9]           |\n",
-       "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n",
-       "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n",
-       "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n",
-       "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n",
-       "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n",
-       "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                        527 |\n",
-       "|                                        888 |\n",
-       "|                                       1232 |\n",
-       "|                                       2440 |\n",
-       "|                                       3622 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_freq                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]   |\n",
-       "| [10121,5441,5610,3838,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,... |\n",
-       "| [6578,3782,4202,3336,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5... |\n",
-       "| [1429,1382,1901,2026,25677,6108,5433,3528,1753,1702,1130,485,518,282,119,... |\n",
-       "| [995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,14... |\n",
-       "| [7,37,269,903,1948,4127,5519,7075,7322,6972,6095,5249,4374,3624,2893,2106... |\n",
-       "| [2,24,149,466,1201,2614,4035,5568,6331,6725,6267,5855,5101,4268,3465,2554... |\n",
-       "| [1,9,42,122,294,787,1537,2717,3751,4972,5404,5762,5875,5161,4526,3415,252... |\n",
-       "| [2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,351... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                            744 |\n",
-       "|                                           1130 |\n",
-       "|                                           1487 |\n",
-       "|                                           2622 |\n",
-       "|                                           3743 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,1,0,0,1,0,0,0,0,0]                      |\n",
-       "| [0,0,1,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,1,0,1,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                      int64 |                         float32 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                          0 |                        8.97e+00 |\n",
-       "|                                          0 |                        8.90e+00 |\n",
-       "|                                          0 |                        8.90e+00 |\n",
-       "|                                          0 |                        8.88e+00 |\n",
-       "|                                          0 |                        8.53e+00 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                             float32 |                        float64 |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                            7.57e-01 |                             NA |\n",
-       "|                            7.49e-01 |                             NA |\n",
-       "|                            7.48e-01 |                             NA |\n",
-       "|                            7.46e-01 |                             NA |\n",
-       "|                            7.09e-01 |                             NA |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| in_silico_predictors.spliceai_ds_max |\n",
-       "+--------------------------------------+\n",
-       "|                              float32 |\n",
-       "+--------------------------------------+\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                  float64 |                     float64 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                       NA |                          NA |\n",
-       "|                                       NA |                          NA |\n",
-       "|                                       NA |                          NA |\n",
-       "|                                       NA |                          NA |\n",
-       "|                                       NA |                          NA |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                       float64 |                           float64 |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht.show(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22e6f759-a0ee-4e9c-8ca4-eb154cb08763",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### v4.1 Joint Frequency Hail Table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0a569b77-d3d2-45a4-803a-1214c77e46f2",
-   "metadata": {},
-   "source": [
-    "The joint frequency Hail table includes frequency for the exomes, genomes, and the exomes+genomes. We have also added statistics for the combined exomes and genomes frequencies, more details on these stats can be found on our [Help](https://gnomad.broadinstitute.org/help/combined-freq-stats) page."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "46d4fc43-609d-4a16-8a0a-ab1e870b5d3d",
-   "metadata": {},
-   "source": [
-    "#### Load the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "c1c1fbb0-4ef9-4892-bd91-aae9985317a7",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:59.197890Z",
-     "start_time": "2024-12-06T20:08:50.859108Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ht = get_gnomad_release(data_type='joint', version='4.1')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "163df47b-70de-4e1e-91be-a65c90cf2db5",
-   "metadata": {},
-   "source": [
-    "#### Print the schema of the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "750c0111-4566-4b86-8c08-18c504ff1a79",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:00.032524Z",
-     "start_time": "2024-12-06T20:09:00.029271Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    'exomes_globals': struct {\n",
-      "        freq_meta: array>, \n",
-      "        freq_index_dict: dict, \n",
-      "        freq_meta_sample_count: array, \n",
-      "        faf_meta: array>, \n",
-      "        faf_index_dict: dict, \n",
-      "        age_distribution: struct {\n",
-      "            bin_edges: array, \n",
-      "            bin_freq: array, \n",
-      "            n_smaller: int32, \n",
-      "            n_larger: int32\n",
-      "        }\n",
-      "    } \n",
-      "    'genomes_globals': struct {\n",
-      "        freq_meta: array>, \n",
-      "        freq_index_dict: dict, \n",
-      "        freq_meta_sample_count: array, \n",
-      "        faf_meta: array>, \n",
-      "        faf_index_dict: dict, \n",
-      "        age_distribution: struct {\n",
-      "            bin_edges: array, \n",
-      "            bin_freq: array, \n",
-      "            n_smaller: int32, \n",
-      "            n_larger: int32\n",
-      "        }\n",
-      "    } \n",
-      "    'joint_globals': struct {\n",
-      "        freq_meta: array>, \n",
-      "        freq_index_dict: dict, \n",
-      "        faf_meta: array>, \n",
-      "        faf_index_dict: dict, \n",
-      "        freq_meta_sample_count: array, \n",
-      "        age_distribution: struct {\n",
-      "            bin_edges: array, \n",
-      "            bin_freq: array, \n",
-      "            n_smaller: int32, \n",
-      "            n_larger: int32\n",
-      "        }\n",
-      "    } \n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'alleles': array \n",
-      "    'region_flags': struct {\n",
-      "        fail_interval_qc: bool, \n",
-      "        outside_broad_capture_region: bool, \n",
-      "        outside_ukb_capture_region: bool, \n",
-      "        outside_broad_calling_region: bool, \n",
-      "        outside_ukb_calling_region: bool, \n",
-      "        not_called_in_exomes: bool, \n",
-      "        not_called_in_genomes: bool\n",
-      "    } \n",
-      "    'exomes': struct {\n",
-      "        filters: set, \n",
-      "        freq: array, \n",
-      "        faf: array, \n",
-      "        grpmax: struct {\n",
-      "            AC: int32, \n",
-      "            AF: float64, \n",
-      "            AN: int32, \n",
-      "            homozygote_count: int64, \n",
-      "            gen_anc: str\n",
-      "        }, \n",
-      "        fafmax: struct {\n",
-      "            faf95_max: float64, \n",
-      "            faf95_max_gen_anc: str, \n",
-      "            faf99_max: float64, \n",
-      "            faf99_max_gen_anc: str\n",
-      "        }, \n",
-      "        histograms: struct {\n",
-      "            qual_hists: struct {\n",
-      "                gq_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                gq_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                ab_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            raw_qual_hists: struct {\n",
-      "                gq_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                gq_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                ab_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            age_hists: struct {\n",
-      "                age_hist_het: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                age_hist_hom: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }\n",
-      "        }\n",
-      "    } \n",
-      "    'genomes': struct {\n",
-      "        filters: set, \n",
-      "        freq: array, \n",
-      "        faf: array, \n",
-      "        grpmax: struct {\n",
-      "            AC: int32, \n",
-      "            AF: float64, \n",
-      "            AN: int32, \n",
-      "            homozygote_count: int32, \n",
-      "            gen_anc: str\n",
-      "        }, \n",
-      "        fafmax: struct {\n",
-      "            faf95_max: float64, \n",
-      "            faf95_max_gen_anc: str, \n",
-      "            faf99_max: float64, \n",
-      "            faf99_max_gen_anc: str\n",
-      "        }, \n",
-      "        histograms: struct {\n",
-      "            qual_hists: struct {\n",
-      "                gq_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                gq_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                ab_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            raw_qual_hists: struct {\n",
-      "                gq_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                gq_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                ab_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            age_hists: struct {\n",
-      "                age_hist_het: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                age_hist_hom: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }\n",
-      "        }\n",
-      "    } \n",
-      "    'joint': struct {\n",
-      "        freq: array, \n",
-      "        faf: array, \n",
-      "        fafmax: struct {\n",
-      "            faf95_max: float64, \n",
-      "            faf95_max_gen_anc: str, \n",
-      "            faf99_max: float64, \n",
-      "            faf99_max_gen_anc: str\n",
-      "        }, \n",
-      "        grpmax: struct {\n",
-      "            AC: int32, \n",
-      "            AF: float64, \n",
-      "            AN: int32, \n",
-      "            homozygote_count: int32, \n",
-      "            gen_anc: str\n",
-      "        }, \n",
-      "        histograms: struct {\n",
-      "            qual_hists: struct {\n",
-      "                gq_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                gq_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                ab_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            raw_qual_hists: struct {\n",
-      "                gq_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                gq_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                ab_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            age_hists: struct {\n",
-      "                age_hist_het: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                age_hist_hom: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }\n",
-      "        }\n",
-      "    } \n",
-      "    'freq_comparison_stats': struct {\n",
-      "        contingency_table_test: array, \n",
-      "        cochran_mantel_haenszel_test: struct {\n",
-      "            p_value: float64, \n",
-      "            chisq: float64\n",
-      "        }, \n",
-      "        stat_union: struct {\n",
-      "            p_value: float64, \n",
-      "            stat_test_name: str, \n",
-      "            gen_ancs: array\n",
-      "        }\n",
-      "    } \n",
-      "----------------------------------------\n",
-      "Key: ['locus', 'alleles']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6843db4-9e8f-42f4-9178-fc945f61a827",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "#### Show the first 5 variants in the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "477ed281-4ee9-4799-b5b6-e6a0c528fa9a",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:20.949958Z",
-     "start_time": "2024-12-06T20:09:05.622171Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
exomes
genomes
joint
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stat_union
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faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
AC
AF
AN
homozygote_count
gen_anc
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
contingency_table_test
p_value
chisq
p_value
stat_test_name
gen_ancs
locus<GRCh38>array<str>boolboolboolboolboolboolboolset<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>int32float64int32int64strfloat64strfloat64strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64set<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>int32float64int32int32strfloat64strfloat64strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>float64strfloat64strint32float64int32int32strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<struct{p_value: float64, odds_ratio: float64}>float64float64float64strarray<str>
chr1:10031["T","C"]NATrueTrueTrueTrueTrueFalseNA[(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA)]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA{"AC0","AS_VQSR"}[(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e+00,4326,0),(0,0.00e+00,192,0),(0,0.00e+00,782,0),(0,0.00e+00,352,0),(0,0.00e+00,1550,0),(0,0.00e+00,14642,0),(0,0.00e+00,1712,0),(0,0.00e+00,1120,0),(0,0.00e+00,6420,0),(0,0.00e+00,29308,0),(0,0.00e+00,27334,0),(0,0.00e+00,14998,0),(0,0.00e+00,1060,0),(0,0.00e+00,102,0),(0,0.00e+00,402,0),(0,0.00e+00,154,0),(0,0.00e+00,814,0),(0,0.00e+00,7978,0),(0,0.00e+00,770,0),(0,0.00e+00,260,0),(0,0.00e+00,2770,0),(0,0.00e+00,10548,0),(0,0.00e+00,3266,0),(0,0.00e+00,90,0),(0,0.00e+00,380,0),(0,0.00e+00,198,0),(0,0.00e+00,736,0),(0,0.00e+00,6664,0),(0,0.00e+00,942,0),(0,0.00e+00,860,0),(0,0.00e+00,3650,0)][(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]NANANANANANANANANA[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911,665,439,351]0527[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,1476,1023,741,500,418]0744[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e+00,4326,0),(0,0.00e+00,192,0),(0,0.00e+00,782,0),(0,0.00e+00,352,0),(0,0.00e+00,1550,0),(0,0.00e+00,14642,0),(0,0.00e+00,1712,0),(0,0.00e+00,1120,0),(0,0.00e+00,6420,0),(0,0.00e+00,29308,0),(0,0.00e+00,27334,0),(0,0.00e+00,14998,0),(0,0.00e+00,1060,0),(0,0.00e+00,102,0),(0,0.00e+00,402,0),(0,0.00e+00,154,0),(0,0.00e+00,814,0),(0,0.00e+00,7978,0),(0,0.00e+00,770,0),(0,0.00e+00,260,0),(0,0.00e+00,2770,0),(0,0.00e+00,10548,0),(0,0.00e+00,3266,0),(0,0.00e+00,90,0),(0,0.00e+00,380,0),(0,0.00e+00,198,0),(0,0.00e+00,736,0),(0,0.00e+00,6664,0),(0,0.00e+00,942,0),(0,0.00e+00,860,0),(0,0.00e+00,3650,0)][(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,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chr1:10057["A","C"]NATrueTrueTrueTrueTrueFalseNA[(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA)]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA{"AS_VQSR"}[(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00e+00,6886,0),(0,0.00e+00,266,0),(0,0.00e+00,1504,0),(0,0.00e+00,742,0),(0,0.00e+00,2768,0),(1,3.25e-05,30736,0),(0,0.00e+00,3850,0),(0,0.00e+00,3424,0),(0,0.00e+00,10448,0),(2,3.37e-05,59380,0),(1,1.85e-05,54156,0),(2,6.43e-05,31106,0),(0,0.00e+00,1472,0),(0,0.00e+00,132,0),(0,0.00e+00,764,0),(0,0.00e+00,378,0),(0,0.00e+00,1532,0),(0,0.00e+00,16598,0),(0,0.00e+00,1670,0),(0,0.00e+00,784,0),(0,0.00e+00,4944,0),(0,0.00e+00,21806,0),(0,0.00e+00,5414,0),(0,0.00e+00,134,0),(0,0.00e+00,740,0),(0,0.00e+00,364,0),(0,0.00e+00,1236,0),(1,7.07e-05,14138,0),(0,0.00e+00,2180,0),(0,0.00e+00,2640,0),(0,0.00e+00,5504,0)][(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]23.78e-05529120"nfe"6.27e-06"nfe"2.35e-06"nfe"[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2503,1787,1521]03622[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,3518,2603,1850,1572]03743[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00e+00,6886,0),(0,0.00e+00,266,0),(0,0.00e+00,1504,0),(0,0.00e+00,742,0),(0,0.00e+00,2768,0),(1,3.25e-05,30736,0),(0,0.00e+00,3850,0),(0,0.00e+00,3424,0),(0,0.00e+00,10448,0),(2,3.37e-05,59380,0),(1,1.85e-05,54156,0),(2,6.43e-05,31106,0),(0,0.00e+00,1472,0),(0,0.00e+00,132,0),(0,0.00e+00,764,0),(0,0.00e+00,378,0),(0,0.00e+00,1532,0),(0,0.00e+00,16598,0),(0,0.00e+00,1670,0),(0,0.00e+00,784,0),(0,0.00e+00,4944,0),(0,0.00e+00,21806,0),(0,0.00e+00,5414,0),(0,0.00e+00,134,0),(0,0.00e+00,740,0),(0,0.00e+00,364,0),(0,0.00e+00,1236,0),(1,7.07e-05,14138,0),(0,0.00e+00,2180,0),(0,0.00e+00,2640,0),(0,0.00e+00,5504,0)][(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]6.27e-06"nfe"2.35e-06"nfe"23.78e-05529120"nfe"[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2503,1787,1521]03622[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,3518,2603,1850,1572]03743[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]NANANANANA
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+---------------+------------+-------------------------------+\n",
-       "| locus         | alleles    | region_flags.fail_interval_qc |\n",
-       "+---------------+------------+-------------------------------+\n",
-       "| locus | array |                          bool |\n",
-       "+---------------+------------+-------------------------------+\n",
-       "| chr1:10031    | [\"T\",\"C\"]  |                            NA |\n",
-       "| chr1:10037    | [\"T\",\"C\"]  |                            NA |\n",
-       "| chr1:10043    | [\"T\",\"C\"]  |                            NA |\n",
-       "| chr1:10055    | [\"T\",\"C\"]  |                            NA |\n",
-       "| chr1:10057    | [\"A\",\"C\"]  |                            NA |\n",
-       "+---------------+------------+-------------------------------+\n",
-       "\n",
-       "+-------------------------------------------+\n",
-       "| region_flags.outside_broad_capture_region |\n",
-       "+-------------------------------------------+\n",
-       "|                                      bool |\n",
-       "+-------------------------------------------+\n",
-       "|                                      True |\n",
-       "|                                      True |\n",
-       "|                                      True |\n",
-       "|                                      True |\n",
-       "|                                      True |\n",
-       "+-------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------+\n",
-       "| region_flags.outside_ukb_capture_region |\n",
-       "+-----------------------------------------+\n",
-       "|                                    bool |\n",
-       "+-----------------------------------------+\n",
-       "|                                    True |\n",
-       "|                                    True |\n",
-       "|                                    True |\n",
-       "|                                    True |\n",
-       "|                                    True |\n",
-       "+-----------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------+\n",
-       "| region_flags.outside_broad_calling_region |\n",
-       "+-------------------------------------------+\n",
-       "|                                      bool |\n",
-       "+-------------------------------------------+\n",
-       "|                                      True |\n",
-       "|                                      True |\n",
-       "|                                      True |\n",
-       "|                                      True |\n",
-       "|                                      True |\n",
-       "+-------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------+-----------------------------------+\n",
-       "| region_flags.outside_ukb_calling_region | region_flags.not_called_in_exomes |\n",
-       "+-----------------------------------------+-----------------------------------+\n",
-       "|                                    bool |                              bool |\n",
-       "+-----------------------------------------+-----------------------------------+\n",
-       "|                                    True |                              True |\n",
-       "|                                    True |                              True |\n",
-       "|                                    True |                              True |\n",
-       "|                                    True |                              True |\n",
-       "|                                    True |                              True |\n",
-       "+-----------------------------------------+-----------------------------------+\n",
-       "\n",
-       "+------------------------------------+----------------+\n",
-       "| region_flags.not_called_in_genomes | exomes.filters |\n",
-       "+------------------------------------+----------------+\n",
-       "|                               bool | set       |\n",
-       "+------------------------------------+----------------+\n",
-       "|                              False | NA             |\n",
-       "|                              False | NA             |\n",
-       "|                              False | NA             |\n",
-       "|                              False | NA             |\n",
-       "|                              False | NA             |\n",
-       "+------------------------------------+----------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| exomes.freq                                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
-       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
-       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
-       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
-       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------+------------------+\n",
-       "| exomes.faf                                    | exomes.grpmax.AC |\n",
-       "+-----------------------------------------------+------------------+\n",
-       "| array |            int32 |\n",
-       "+-----------------------------------------------+------------------+\n",
-       "| NA                                            |               NA |\n",
-       "| NA                                            |               NA |\n",
-       "| NA                                            |               NA |\n",
-       "| NA                                            |               NA |\n",
-       "| NA                                            |               NA |\n",
-       "+-----------------------------------------------+------------------+\n",
-       "\n",
-       "+------------------+------------------+--------------------------------+\n",
-       "| exomes.grpmax.AF | exomes.grpmax.AN | exomes.grpmax.homozygote_count |\n",
-       "+------------------+------------------+--------------------------------+\n",
-       "|          float64 |            int32 |                          int64 |\n",
-       "+------------------+------------------+--------------------------------+\n",
-       "|               NA |               NA |                             NA |\n",
-       "|               NA |               NA |                             NA |\n",
-       "|               NA |               NA |                             NA |\n",
-       "|               NA |               NA |                             NA |\n",
-       "|               NA |               NA |                             NA |\n",
-       "+------------------+------------------+--------------------------------+\n",
-       "\n",
-       "+-----------------------+-------------------------+\n",
-       "| exomes.grpmax.gen_anc | exomes.fafmax.faf95_max |\n",
-       "+-----------------------+-------------------------+\n",
-       "| str                   |                 float64 |\n",
-       "+-----------------------+-------------------------+\n",
-       "| NA                    |                      NA |\n",
-       "| NA                    |                      NA |\n",
-       "| NA                    |                      NA |\n",
-       "| NA                    |                      NA |\n",
-       "| NA                    |                      NA |\n",
-       "+-----------------------+-------------------------+\n",
-       "\n",
-       "+---------------------------------+-------------------------+\n",
-       "| exomes.fafmax.faf95_max_gen_anc | exomes.fafmax.faf99_max |\n",
-       "+---------------------------------+-------------------------+\n",
-       "| str                             |                 float64 |\n",
-       "+---------------------------------+-------------------------+\n",
-       "| NA                              |                      NA |\n",
-       "| NA                              |                      NA |\n",
-       "| NA                              |                      NA |\n",
-       "| NA                              |                      NA |\n",
-       "| NA                              |                      NA |\n",
-       "+---------------------------------+-------------------------+\n",
-       "\n",
-       "+---------------------------------+\n",
-       "| exomes.fafmax.faf99_max_gen_anc |\n",
-       "+---------------------------------+\n",
-       "| str                             |\n",
-       "+---------------------------------+\n",
-       "| NA                              |\n",
-       "| NA                              |\n",
-       "| NA                              |\n",
-       "| NA                              |\n",
-       "| NA                              |\n",
-       "+---------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.gq_hist_all.bin_edges |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+----------------------------------------------------+\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.gq_hist_all.bin_freq |\n",
-       "+---------------------------------------------------+\n",
-       "| array                                      |\n",
-       "+---------------------------------------------------+\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.dp_hist_all.bin_edges |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+----------------------------------------------------+\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.dp_hist_all.bin_freq |\n",
-       "+---------------------------------------------------+\n",
-       "| array                                      |\n",
-       "+---------------------------------------------------+\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.gq_hist_alt.bin_edges |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+----------------------------------------------------+\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+---------------------------------------------------+\n",
-       "| array                                      |\n",
-       "+---------------------------------------------------+\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.dp_hist_alt.bin_edges |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+----------------------------------------------------+\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+---------------------------------------------------+\n",
-       "| array                                      |\n",
-       "+---------------------------------------------------+\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.ab_hist_alt.bin_edges |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+----------------------------------------------------+\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.ab_hist_alt.bin_freq |\n",
-       "+---------------------------------------------------+\n",
-       "| array                                      |\n",
-       "+---------------------------------------------------+\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.gq_hist_all.bin_edges |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+--------------------------------------------------------+\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.gq_hist_all.bin_freq |\n",
-       "+-------------------------------------------------------+\n",
-       "| array                                          |\n",
-       "+-------------------------------------------------------+\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.dp_hist_all.bin_edges |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+--------------------------------------------------------+\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.dp_hist_all.bin_freq |\n",
-       "+-------------------------------------------------------+\n",
-       "| array                                          |\n",
-       "+-------------------------------------------------------+\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.gq_hist_alt.bin_edges |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+--------------------------------------------------------+\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+-------------------------------------------------------+\n",
-       "| array                                          |\n",
-       "+-------------------------------------------------------+\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.dp_hist_alt.bin_edges |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+--------------------------------------------------------+\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+-------------------------------------------------------+\n",
-       "| array                                          |\n",
-       "+-------------------------------------------------------+\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.ab_hist_alt.bin_edges |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+--------------------------------------------------------+\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+-------------------------------------------------------+\n",
-       "| array                                          |\n",
-       "+-------------------------------------------------------+\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.age_hists.age_hist_het.bin_edges |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+----------------------------------------------------+\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+---------------------------------------------------+\n",
-       "| array                                      |\n",
-       "+---------------------------------------------------+\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.age_hists.age_hist_het.n_larger |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.age_hists.age_hist_hom.bin_edges |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+----------------------------------------------------+\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+---------------------------------------------------+\n",
-       "| array                                      |\n",
-       "+---------------------------------------------------+\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+-------------------+\n",
-       "| exomes.histograms.age_hists.age_hist_hom.n_larger | genomes.filters   |\n",
-       "+---------------------------------------------------+-------------------+\n",
-       "|                                             int64 | set          |\n",
-       "+---------------------------------------------------+-------------------+\n",
-       "|                                                NA | {\"AC0\",\"AS_VQSR\"} |\n",
-       "|                                                NA | {\"AS_VQSR\"}       |\n",
-       "|                                                NA | {\"AS_VQSR\"}       |\n",
-       "|                                                NA | {\"AS_VQSR\"}       |\n",
-       "|                                                NA | {\"AS_VQSR\"}       |\n",
-       "+---------------------------------------------------+-------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.freq                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e... |\n",
-       "| [(2,2.60e-05,76882,0),(4,3.15e-05,126902,0),(0,0.00e+00,34670,0),(1,1.80e... |\n",
-       "| [(1,1.17e-05,85634,0),(1,8.24e-06,121430,0),(0,0.00e+00,39236,0),(0,0.00e... |\n",
-       "| [(1,1.06e-05,94224,0),(5,4.64e-05,107704,0),(0,0.00e+00,44382,0),(1,1.80e... |\n",
-       "| [(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.faf                                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(4.32e-06,1.62e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------+-------------------+-------------------+\n",
-       "| genomes.grpmax.AC | genomes.grpmax.AF | genomes.grpmax.AN |\n",
-       "+-------------------+-------------------+-------------------+\n",
-       "|             int32 |           float64 |             int32 |\n",
-       "+-------------------+-------------------+-------------------+\n",
-       "|                NA |                NA |                NA |\n",
-       "|                 1 |          4.07e-04 |              2456 |\n",
-       "|                 1 |          4.39e-05 |             22760 |\n",
-       "|                NA |                NA |                NA |\n",
-       "|                 2 |          3.78e-05 |             52912 |\n",
-       "+-------------------+-------------------+-------------------+\n",
-       "\n",
-       "+---------------------------------+------------------------+\n",
-       "| genomes.grpmax.homozygote_count | genomes.grpmax.gen_anc |\n",
-       "+---------------------------------+------------------------+\n",
-       "|                           int32 | str                    |\n",
-       "+---------------------------------+------------------------+\n",
-       "|                              NA | NA                     |\n",
-       "|                               0 | \"eas\"                  |\n",
-       "|                               0 | \"afr\"                  |\n",
-       "|                              NA | NA                     |\n",
-       "|                               0 | \"nfe\"                  |\n",
-       "+---------------------------------+------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+\n",
-       "| genomes.fafmax.faf95_max | genomes.fafmax.faf95_max_gen_anc |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                  float64 | str                              |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                 6.27e-06 | \"nfe\"                            |\n",
-       "+--------------------------+----------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+\n",
-       "| genomes.fafmax.faf99_max | genomes.fafmax.faf99_max_gen_anc |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                  float64 | str                              |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                 2.35e-06 | \"nfe\"                            |\n",
-       "+--------------------------+----------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.gq_hist_all.bin_edges                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.gq_hist_all.bin_freq                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]                |\n",
-       "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15]          |\n",
-       "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9]           |\n",
-       "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n",
-       "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                               int64 |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "+-----------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.dp_hist_all.bin_edges                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.dp_hist_all.bin_freq                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n",
-       "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n",
-       "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n",
-       "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n",
-       "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                               int64 |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "+-----------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                527 |\n",
-       "|                                                888 |\n",
-       "|                                               1232 |\n",
-       "|                                               2440 |\n",
-       "|                                               3622 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.gq_hist_alt.bin_edges                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                       |\n",
-       "+----------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]          |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                               int64 |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "+-----------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.dp_hist_alt.bin_edges                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                       |\n",
-       "+----------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                               int64 |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "+-----------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.ab_hist_alt.bin_edges                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.ab_hist_alt.bin_freq |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                       |\n",
-       "+----------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                               int64 |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "+-----------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.gq_hist_all.bin_edges                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.gq_hist_all.bin_freq                       |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]   |\n",
-       "| [10121,5441,5610,3838,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,... |\n",
-       "| [6578,3782,4202,3336,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5... |\n",
-       "| [1429,1382,1901,2026,25677,6108,5433,3528,1753,1702,1130,485,518,282,119,... |\n",
-       "| [995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.dp_hist_all.bin_edges                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.dp_hist_all.bin_freq                       |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,14... |\n",
-       "| [7,37,269,903,1948,4127,5519,7075,7322,6972,6095,5249,4374,3624,2893,2106... |\n",
-       "| [2,24,149,466,1201,2614,4035,5568,6331,6725,6267,5855,5101,4268,3465,2554... |\n",
-       "| [1,9,42,122,294,787,1537,2717,3751,4972,5404,5762,5875,5161,4526,3415,252... |\n",
-       "| [2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,351... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                    744 |\n",
-       "|                                                   1130 |\n",
-       "|                                                   1487 |\n",
-       "|                                                   2622 |\n",
-       "|                                                   3743 |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.gq_hist_alt.bin_edges                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                           |\n",
-       "+--------------------------------------------------------+\n",
-       "| [0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]              |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.dp_hist_alt.bin_edges                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                           |\n",
-       "+--------------------------------------------------------+\n",
-       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.ab_hist_alt.bin_edges                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                           |\n",
-       "+--------------------------------------------------------+\n",
-       "| [0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.age_hists.age_hist_het.bin_edges                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                       |\n",
-       "+----------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
-       "| [0,1,0,0,1,0,0,0,0,0]                              |\n",
-       "| [0,0,1,0,0,0,0,0,0,0]                              |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
-       "| [0,0,1,0,1,0,0,0,0,0]                              |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------+\n",
-       "| genomes.histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                               int64 |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "+-----------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.age_hists.age_hist_het.n_larger |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.age_hists.age_hist_hom.bin_edges                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                       |\n",
-       "+----------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------+\n",
-       "| genomes.histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                               int64 |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "+-----------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.age_hists.age_hist_hom.n_larger |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.freq                                                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e... |\n",
-       "| [(2,2.60e-05,76882,0),(4,3.15e-05,126902,0),(0,0.00e+00,34670,0),(1,1.80e... |\n",
-       "| [(1,1.17e-05,85634,0),(1,8.24e-06,121430,0),(0,0.00e+00,39236,0),(0,0.00e... |\n",
-       "| [(1,1.06e-05,94224,0),(5,4.64e-05,107704,0),(0,0.00e+00,44382,0),(1,1.80e... |\n",
-       "| [(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.faf                                                                    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(4.32e-06,1.62e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------+--------------------------------+\n",
-       "| joint.fafmax.faf95_max | joint.fafmax.faf95_max_gen_anc |\n",
-       "+------------------------+--------------------------------+\n",
-       "|                float64 | str                            |\n",
-       "+------------------------+--------------------------------+\n",
-       "|                     NA | NA                             |\n",
-       "|                     NA | NA                             |\n",
-       "|                     NA | NA                             |\n",
-       "|                     NA | NA                             |\n",
-       "|               6.27e-06 | \"nfe\"                          |\n",
-       "+------------------------+--------------------------------+\n",
-       "\n",
-       "+------------------------+--------------------------------+-----------------+\n",
-       "| joint.fafmax.faf99_max | joint.fafmax.faf99_max_gen_anc | joint.grpmax.AC |\n",
-       "+------------------------+--------------------------------+-----------------+\n",
-       "|                float64 | str                            |           int32 |\n",
-       "+------------------------+--------------------------------+-----------------+\n",
-       "|                     NA | NA                             |              NA |\n",
-       "|                     NA | NA                             |               1 |\n",
-       "|                     NA | NA                             |               1 |\n",
-       "|                     NA | NA                             |              NA |\n",
-       "|               2.35e-06 | \"nfe\"                          |               2 |\n",
-       "+------------------------+--------------------------------+-----------------+\n",
-       "\n",
-       "+-----------------+-----------------+-------------------------------+\n",
-       "| joint.grpmax.AF | joint.grpmax.AN | joint.grpmax.homozygote_count |\n",
-       "+-----------------+-----------------+-------------------------------+\n",
-       "|         float64 |           int32 |                         int32 |\n",
-       "+-----------------+-----------------+-------------------------------+\n",
-       "|              NA |              NA |                            NA |\n",
-       "|        4.07e-04 |            2456 |                             0 |\n",
-       "|        4.39e-05 |           22760 |                             0 |\n",
-       "|              NA |              NA |                            NA |\n",
-       "|        3.78e-05 |           52912 |                             0 |\n",
-       "+-----------------+-----------------+-------------------------------+\n",
-       "\n",
-       "+----------------------+\n",
-       "| joint.grpmax.gen_anc |\n",
-       "+----------------------+\n",
-       "| str                  |\n",
-       "+----------------------+\n",
-       "| NA                   |\n",
-       "| \"eas\"                |\n",
-       "| \"afr\"                |\n",
-       "| NA                   |\n",
-       "| \"nfe\"                |\n",
-       "+----------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_all.bin_edges                            |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_all.bin_freq                             |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]                |\n",
-       "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15]          |\n",
-       "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9]           |\n",
-       "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n",
-       "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_all.bin_edges                            |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_all.bin_freq                             |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n",
-       "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n",
-       "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n",
-       "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n",
-       "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                              527 |\n",
-       "|                                              888 |\n",
-       "|                                             1232 |\n",
-       "|                                             2440 |\n",
-       "|                                             3622 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_alt.bin_edges                            |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]        |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_alt.bin_edges                            |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.ab_hist_alt.bin_edges                            |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.ab_hist_alt.bin_freq |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_all.bin_edges                        |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_all.bin_freq                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]   |\n",
-       "| [10121,5441,5610,3838,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,... |\n",
-       "| [6578,3782,4202,3336,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5... |\n",
-       "| [1429,1382,1901,2026,25677,6108,5433,3528,1753,1702,1130,485,518,282,119,... |\n",
-       "| [995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                int64 |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_all.bin_edges                        |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_all.bin_freq                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,14... |\n",
-       "| [7,37,269,903,1948,4127,5519,7075,7322,6972,6095,5249,4374,3624,2893,2106... |\n",
-       "| [2,24,149,466,1201,2614,4035,5568,6331,6725,6267,5855,5101,4268,3465,2554... |\n",
-       "| [1,9,42,122,294,787,1537,2717,3751,4972,5404,5762,5875,5161,4526,3415,252... |\n",
-       "| [2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,351... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                int64 |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                  744 |\n",
-       "|                                                 1130 |\n",
-       "|                                                 1487 |\n",
-       "|                                                 2622 |\n",
-       "|                                                 3743 |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_alt.bin_edges                        |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+------------------------------------------------------+\n",
-       "| [0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]            |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                int64 |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_alt.bin_edges                        |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+------------------------------------------------------+\n",
-       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                int64 |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.ab_hist_alt.bin_edges                        |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+------------------------------------------------------+\n",
-       "| [0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                int64 |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_het.bin_edges                            |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "| [0,1,0,0,1,0,0,0,0,0]                            |\n",
-       "| [0,0,1,0,0,0,0,0,0,0]                            |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "| [0,0,1,0,1,0,0,0,0,0]                            |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_het.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_hom.bin_edges                            |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_hom.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| freq_comparison_stats.contingency_table_test                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
-       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
-       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
-       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
-       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------+\n",
-       "| freq_comparison_stats.cochran_mantel_haenszel_test.p_value |\n",
-       "+------------------------------------------------------------+\n",
-       "|                                                    float64 |\n",
-       "+------------------------------------------------------------+\n",
-       "|                                                         NA |\n",
-       "|                                                         NA |\n",
-       "|                                                         NA |\n",
-       "|                                                         NA |\n",
-       "|                                                         NA |\n",
-       "+------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------------+\n",
-       "| freq_comparison_stats.cochran_mantel_haenszel_test.chisq |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                  float64 |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "+----------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+\n",
-       "| freq_comparison_stats.stat_union.p_value |\n",
-       "+------------------------------------------+\n",
-       "|                                  float64 |\n",
-       "+------------------------------------------+\n",
-       "|                                       NA |\n",
-       "|                                       NA |\n",
-       "|                                       NA |\n",
-       "|                                       NA |\n",
-       "|                                       NA |\n",
-       "+------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| freq_comparison_stats.stat_union.stat_test_name |\n",
-       "+-------------------------------------------------+\n",
-       "| str                                             |\n",
-       "+-------------------------------------------------+\n",
-       "| NA                                              |\n",
-       "| NA                                              |\n",
-       "| NA                                              |\n",
-       "| NA                                              |\n",
-       "| NA                                              |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------+\n",
-       "| freq_comparison_stats.stat_union.gen_ancs |\n",
-       "+-------------------------------------------+\n",
-       "| array                                |\n",
-       "+-------------------------------------------+\n",
-       "| NA                                        |\n",
-       "| NA                                        |\n",
-       "| NA                                        |\n",
-       "| NA                                        |\n",
-       "| NA                                        |\n",
-       "+-------------------------------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht.show(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "354d7a5e-07a2-4f33-a830-970877cd4d63",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## All sites allele numbers\n",
-    "\n",
-    "As part of gnomAD v4.1, we [released](https://gnomad.broadinstitute.org/data#v4-all-sites-allele-number) allele number across all callable sites in the gnomAD exomes and genomes. For more information, see our [v4.1 blog post](https://gnomad.broadinstitute.org/news/2024-04-gnomad-v4-1/#allele-numbers-across-all-possible-sites)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "81008401-eec4-4e95-9709-4781db066f7f",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Exomes all sites allele number Hail Table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f7f1c013-a013-4fde-a7e6-fcb18d8d8a5c",
-   "metadata": {},
-   "source": [
-    "#### Load the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "3b7c75d0-1eec-4b92-883e-410337b09c92",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:59.197890Z",
-     "start_time": "2024-12-06T20:08:50.859108Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ht = get_gnomad_release(data_type='exomes', version='4.1', dataset=\"all_sites_an\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6868a2d1-6e62-492a-8086-822c910e8608",
-   "metadata": {},
-   "source": [
-    "#### Print the schema of the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "f9c6d73b-7683-47fe-bf7d-2f5bef1d23d3",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:00.032524Z",
-     "start_time": "2024-12-06T20:09:00.029271Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    'strata_meta': array> \n",
-      "    'strata_sample_count': array \n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'AN': array \n",
-      "    'outside_broad_capture_region': bool \n",
-      "    'outside_ukb_capture_region': bool \n",
-      "    'outside_broad_calling_region': bool \n",
-      "    'outside_ukb_calling_region': bool \n",
-      "----------------------------------------\n",
-      "Key: ['locus']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "626f20d9-43c1-4687-9b05-01d53115c168",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "#### Show the first 5 variants in the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "ee361058-54ae-4793-951b-1e0a6df6f685",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:20.949958Z",
-     "start_time": "2024-12-06T20:09:05.622171Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
locus
AN
outside_broad_capture_region
outside_ukb_capture_region
outside_broad_calling_region
outside_ukb_calling_region
locus<GRCh38>array<int64>boolboolboolbool
chr1:11719[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11720[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11721[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11722[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11723[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+---------------+\n",
-       "| locus         |\n",
-       "+---------------+\n",
-       "| locus |\n",
-       "+---------------+\n",
-       "| chr1:11719    |\n",
-       "| chr1:11720    |\n",
-       "| chr1:11721    |\n",
-       "| chr1:11722    |\n",
-       "| chr1:11723    |\n",
-       "+---------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| AN                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
-       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
-       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
-       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
-       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------+----------------------------+\n",
-       "| outside_broad_capture_region | outside_ukb_capture_region |\n",
-       "+------------------------------+----------------------------+\n",
-       "|                         bool |                       bool |\n",
-       "+------------------------------+----------------------------+\n",
-       "|                         True |                       True |\n",
-       "|                         True |                       True |\n",
-       "|                         True |                       True |\n",
-       "|                         True |                       True |\n",
-       "|                         True |                       True |\n",
-       "+------------------------------+----------------------------+\n",
-       "\n",
-       "+------------------------------+----------------------------+\n",
-       "| outside_broad_calling_region | outside_ukb_calling_region |\n",
-       "+------------------------------+----------------------------+\n",
-       "|                         bool |                       bool |\n",
-       "+------------------------------+----------------------------+\n",
-       "|                        False |                       True |\n",
-       "|                        False |                       True |\n",
-       "|                        False |                       True |\n",
-       "|                        False |                       True |\n",
-       "|                        False |                       True |\n",
-       "+------------------------------+----------------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht.show(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc4a4f23-d754-4e31-8e59-f62f9be65942",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Genomes all sites allele number Hail Table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "65cb8d93-c5ef-409b-9c51-7c282a63bdc2",
-   "metadata": {},
-   "source": [
-    "#### Load the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "0bb38926-f803-4be5-852c-782023b387bb",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:59.197890Z",
-     "start_time": "2024-12-06T20:08:50.859108Z"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "ht = get_gnomad_release(data_type='genomes', version='4.1', dataset=\"all_sites_an\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7d5c2549-151c-4b99-bac3-23fd9024f114",
-   "metadata": {},
-   "source": [
-    "#### Print the schema of the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "64d2c64c-b533-433a-89e6-72d473bd6464",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:00.032524Z",
-     "start_time": "2024-12-06T20:09:00.029271Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    'strata_meta': array> \n",
-      "    'strata_sample_count': array \n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'AN': array \n",
-      "----------------------------------------\n",
-      "Key: ['locus']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "140f66aa-83d4-4752-abcf-c674bf208194",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "#### Show the first 5 variants in the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "e2271a6e-16f6-48ca-805f-735e17a8f711",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:20.949958Z",
-     "start_time": "2024-12-06T20:09:05.622171Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
locus
AN
locus<GRCh38>array<int64>
chr1:10001[16,18232,0,0,4,0,0,6,0,6,0,0,8,8,0,0,0,0,4,0,0,0,0,0,2,4,0,0,2,4,0,0,0,0]
chr1:10002[78,32090,12,0,22,2,0,26,0,14,2,0,30,48,8,4,0,0,4,18,2,0,0,0,12,14,0,0,2,12,2,0,0,0]
chr1:10003[200,38154,48,0,48,6,4,28,6,48,6,6,102,98,38,10,0,0,8,40,6,0,0,4,14,14,2,4,26,22,6,0,2,4]
chr1:10004[948,62380,202,0,248,18,12,172,10,258,18,10,400,548,112,90,0,0,66,182,14,4,2,10,70,102,4,6,118,140,10,8,4,6]
chr1:10005[1774,70720,356,6,444,30,18,288,18,552,32,30,782,992,206,150,2,4,134,310,18,12,6,12,116,172,6,12,268,284,16,16,10,20]
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+---------------+\n",
-       "| locus         |\n",
-       "+---------------+\n",
-       "| locus |\n",
-       "+---------------+\n",
-       "| chr1:10001    |\n",
-       "| chr1:10002    |\n",
-       "| chr1:10003    |\n",
-       "| chr1:10004    |\n",
-       "| chr1:10005    |\n",
-       "+---------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| AN                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [16,18232,0,0,4,0,0,6,0,6,0,0,8,8,0,0,0,0,4,0,0,0,0,0,2,4,0,0,2,4,0,0,0,0]   |\n",
-       "| [78,32090,12,0,22,2,0,26,0,14,2,0,30,48,8,4,0,0,4,18,2,0,0,0,12,14,0,0,2,... |\n",
-       "| [200,38154,48,0,48,6,4,28,6,48,6,6,102,98,38,10,0,0,8,40,6,0,0,4,14,14,2,... |\n",
-       "| [948,62380,202,0,248,18,12,172,10,258,18,10,400,548,112,90,0,0,66,182,14,... |\n",
-       "| [1774,70720,356,6,444,30,18,288,18,552,32,30,782,992,206,150,2,4,134,310,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht.show(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1bf9f31f-34ff-4385-a80e-985cbb0acfe8",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Coverage\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "de70c319-787b-4d6c-9058-255a1137d81f",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Exomes coverage Hail Table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3278430c-4279-4d89-85e7-276184ec42b8",
-   "metadata": {},
-   "source": [
-    "#### Load the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "7f005af0-df6d-4a0a-a2e0-9cb4ff6782c2",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:59.197890Z",
-     "start_time": "2024-12-06T20:08:50.859108Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ht = get_gnomad_release(data_type='exomes', version='4.0', dataset=\"coverage\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "128e58ce-c219-472a-88be-6babc2ba5a15",
-   "metadata": {},
-   "source": [
-    "#### Print the schema of the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "6058bf38-4ce9-4d37-89a7-d11af0e4d9f1",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:00.032524Z",
-     "start_time": "2024-12-06T20:09:00.029271Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    None\n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'mean': float64 \n",
-      "    'median_approx': int32 \n",
-      "    'total_DP': int64 \n",
-      "    'over_1': float64 \n",
-      "    'over_5': float64 \n",
-      "    'over_10': float64 \n",
-      "    'over_15': float64 \n",
-      "    'over_20': float64 \n",
-      "    'over_25': float64 \n",
-      "    'over_30': float64 \n",
-      "    'over_50': float64 \n",
-      "    'over_100': float64 \n",
-      "----------------------------------------\n",
-      "Key: ['locus']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5969ab0c-7cee-4061-8740-8b82366ae806",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "#### Show the first 5 variants in the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "49fcf64b-1a91-4052-bb98-b329a0e8031b",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:20.949958Z",
-     "start_time": "2024-12-06T20:09:05.622171Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
locus
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>float64int32int64float64float64float64float64float64float64float64float64float64
chr1:118191.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118201.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118211.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118221.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118231.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+---------------+----------+---------------+----------+----------+----------+\n",
-       "| locus         |     mean | median_approx | total_DP |   over_1 |   over_5 |\n",
-       "+---------------+----------+---------------+----------+----------+----------+\n",
-       "| locus |  float64 |         int32 |    int64 |  float64 |  float64 |\n",
-       "+---------------+----------+---------------+----------+----------+----------+\n",
-       "| chr1:11819    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
-       "| chr1:11820    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
-       "| chr1:11821    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
-       "| chr1:11822    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
-       "| chr1:11823    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
-       "+---------------+----------+---------------+----------+----------+----------+\n",
-       "\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "|  over_10 |  over_15 |  over_20 |  over_25 |  over_30 |  over_50 | over_100 |\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "|  float64 |  float64 |  float64 |  float64 |  float64 |  float64 |  float64 |\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
-       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
-       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
-       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
-       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht.show(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "19b2e9be-48fd-4859-af3d-a0775656d24c",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Genomes coverage Hail Table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5918f8e0-a723-439a-a11f-558d5ed13be8",
-   "metadata": {},
-   "source": [
-    "#### Load the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "a8d0be07-c35d-425a-b554-c86034e367fc",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:59.197890Z",
-     "start_time": "2024-12-06T20:08:50.859108Z"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "ht = get_gnomad_release(data_type='genomes', version='3.0', dataset=\"coverage\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d129b898-d642-44d1-8243-66a9cca8d1b1",
-   "metadata": {},
-   "source": [
-    "#### Print the schema of the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "18afb20f-7429-4fe2-a6c5-73a22dcbdb76",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:00.032524Z",
-     "start_time": "2024-12-06T20:09:00.029271Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    None\n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'mean': float64 \n",
-      "    'median': int32 \n",
-      "    'count_array': array \n",
-      "    'over_1': float32 \n",
-      "    'over_5': float32 \n",
-      "    'over_10': float32 \n",
-      "    'over_15': float32 \n",
-      "    'over_20': float32 \n",
-      "    'over_25': float32 \n",
-      "    'over_30': float32 \n",
-      "    'over_50': float32 \n",
-      "    'over_100': float32 \n",
-      "----------------------------------------\n",
-      "Key: ['locus']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "05d8e22c-93d6-4ecf-b9e7-711945268c82",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "#### Show the first 5 variants in the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "b27cb655-3abb-4501-bcc9-3f634db64591",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:20.949958Z",
-     "start_time": "2024-12-06T20:09:05.622171Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
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locus
mean
median
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over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>float64int32array<int32>float32float32float32float32float32float32float32float32float32
chr1:100011.93e+0116[0,20,65,141,226,328,386,442,415,385,292,198,362,367,337,273,289,266,241,226,191,169,168,151,227,194,179,190,177,166,143,131,150,130,125,121,136,94,93,83,67,60,68,68,59,33,39,39,39,38,20,18,21,25,10,9,16,8,7,6,2,3,2,4,1,2,2,2,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]1.25e-011.19e-019.12e-026.95e-025.15e-023.88e-022.62e-022.27e-030.00e+00
chr1:100022.10e+0118[0,10,41,108,209,339,482,570,581,575,483,390,702,689,639,556,587,534,516,435,417,366,346,320,437,415,405,359,333,308,283,266,272,248,218,231,241,184,176,162,138,119,127,137,118,63,82,87,66,66,46,33,39,43,22,25,26,19,19,11,7,6,7,5,3,5,2,4,2,6,2,3,2,0,1,1,1,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]2.20e-012.15e-011.79e-011.39e-011.02e-017.60e-025.06e-024.83e-032.79e-05
chr1:100032.44e+0123[0,6,16,62,100,162,233,289,294,346,342,310,537,612,664,649,661,711,723,658,649,590,548,486,679,656,640,571,533,491,439,398,412,404,349,383,360,298,263,242,207,182,186,194,159,118,123,116,96,96,67,59,61,64,34,33,34,31,30,15,12,11,13,10,7,7,3,7,5,10,3,3,5,0,2,2,1,0,0,2,1,1,1,4,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,4]2.62e-012.59e-012.41e-012.07e-011.59e-011.18e-017.77e-027.61e-035.58e-05
chr1:100042.43e+0123[0,4,21,70,101,178,270,396,417,515,550,486,864,1003,1098,1138,1183,1270,1242,1181,1162,1083,966,845,1149,1088,1047,922,857,804,725,645,633,658,525,610,537,451,411,369,343,285,290,260,235,184,190,174,151,152,96,83,96,91,52,52,56,43,47,30,20,19,22,16,13,9,9,10,10,14,8,7,8,0,5,3,3,2,1,2,1,1,1,5,3,3,2,1,0,2,0,0,1,0,1,0,1,1,0,0,12]4.27e-014.24e-013.99e-013.43e-012.60e-011.87e-011.21e-011.20e-021.67e-04
chr1:100052.45e+0123[0,4,22,67,98,177,257,381,423,511,580,560,1013,1158,1257,1314,1338,1472,1401,1341,1300,1266,1105,966,1288,1243,1198,1068,976,905,842,725,728,740,600,678,613,515,464,414,396,338,324,300,268,213,210,198,175,165,113,100,108,102,61,58,61,50,53,35,22,22,27,22,15,11,12,10,13,14,10,8,9,1,6,4,6,5,3,3,4,2,2,5,3,7,3,1,0,2,0,1,2,2,2,0,1,1,0,0,17]4.83e-014.80e-014.56e-013.92e-012.96e-012.14e-011.38e-011.42e-022.37e-04
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+---------------+----------+--------+\n",
-       "| locus         |     mean | median |\n",
-       "+---------------+----------+--------+\n",
-       "| locus |  float64 |  int32 |\n",
-       "+---------------+----------+--------+\n",
-       "| chr1:10001    | 1.93e+01 |     16 |\n",
-       "| chr1:10002    | 2.10e+01 |     18 |\n",
-       "| chr1:10003    | 2.44e+01 |     23 |\n",
-       "| chr1:10004    | 2.43e+01 |     23 |\n",
-       "| chr1:10005    | 2.45e+01 |     23 |\n",
-       "+---------------+----------+--------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| count_array                                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,20,65,141,226,328,386,442,415,385,292,198,362,367,337,273,289,266,241,... |\n",
-       "| [0,10,41,108,209,339,482,570,581,575,483,390,702,689,639,556,587,534,516,... |\n",
-       "| [0,6,16,62,100,162,233,289,294,346,342,310,537,612,664,649,661,711,723,65... |\n",
-       "| [0,4,21,70,101,178,270,396,417,515,550,486,864,1003,1098,1138,1183,1270,1... |\n",
-       "| [0,4,22,67,98,177,257,381,423,511,580,560,1013,1158,1257,1314,1338,1472,1... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "|   over_1 |   over_5 |  over_10 |  over_15 |  over_20 |  over_25 |  over_30 |\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "|  float32 |  float32 |  float32 |  float32 |  float32 |  float32 |  float32 |\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "| 1.25e-01 | 1.19e-01 | 9.12e-02 | 6.95e-02 | 5.15e-02 | 3.88e-02 | 2.62e-02 |\n",
-       "| 2.20e-01 | 2.15e-01 | 1.79e-01 | 1.39e-01 | 1.02e-01 | 7.60e-02 | 5.06e-02 |\n",
-       "| 2.62e-01 | 2.59e-01 | 2.41e-01 | 2.07e-01 | 1.59e-01 | 1.18e-01 | 7.77e-02 |\n",
-       "| 4.27e-01 | 4.24e-01 | 3.99e-01 | 3.43e-01 | 2.60e-01 | 1.87e-01 | 1.21e-01 |\n",
-       "| 4.83e-01 | 4.80e-01 | 4.56e-01 | 3.92e-01 | 2.96e-01 | 2.14e-01 | 1.38e-01 |\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "\n",
-       "+----------+----------+\n",
-       "|  over_50 | over_100 |\n",
-       "+----------+----------+\n",
-       "|  float32 |  float32 |\n",
-       "+----------+----------+\n",
-       "| 2.27e-03 | 0.00e+00 |\n",
-       "| 4.83e-03 | 2.79e-05 |\n",
-       "| 7.61e-03 | 5.58e-05 |\n",
-       "| 1.20e-02 | 1.67e-04 |\n",
-       "| 1.42e-02 | 2.37e-04 |\n",
-       "+----------+----------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht.show(5)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {
-    "height": "613.99px",
-    "width": "526.312px"
-   },
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": true,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "202.438px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "toc-autonumbering": false,
-  "toc-showcode": false,
-  "toc-showmarkdowntxt": true,
-  "toc-showtags": false,
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/gnomad_toolbox/notebooks/toolbox_for_gnomad_users.ipynb b/gnomad_toolbox/notebooks/toolbox_for_gnomad_users.ipynb
deleted file mode 100644
index 326a2a3..0000000
--- a/gnomad_toolbox/notebooks/toolbox_for_gnomad_users.ipynb
+++ /dev/null
@@ -1,3778 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "8e609a46",
-   "metadata": {
-    "toc": true
-   },
-   "source": [
-    "
Table of Contents
\n",
-    "
"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "853c94b9",
-   "metadata": {},
-   "source": [
-    "# README\n",
-    "\n",
-    "This toolbox is meant to use Hail tables of gnomAD releases on cloud computing, if you want to query variants for gene(s), you should use gnomAD API (https://gnomad.broadinstitute.org/api).\n",
-    "\n",
-    "If you want to import your own data to use other gnomAD notebooks, such as for ancestry inference (https://github.com/broadinstitute/gnomad_qc/blob/main/gnomad_qc/example_notebooks/ancestry_classification_using_gnomad_rf.ipynb), you may use Hail's `import_vcf` functions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "id": "e77d32b1",
-   "metadata": {
-    "scrolled": true,
-    "ExecuteTime": {
-     "end_time": "2024-12-06T18:02:57.909455Z",
-     "start_time": "2024-12-06T18:02:56.316003Z"
-    }
-   },
-   "source": "import hail as hl",
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "    \n",
-       "    
\n",
-       "        \n",
-       "        Loading BokehJS ...\n",
-       "    
\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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*/\n      safe: true,\n      /* Index of renderer in `output_area.display_order` */\n      index: 0\n    });\n  }\n\n  // register the mime type if in Jupyter Notebook environment and previously unregistered\n  if (root.Jupyter !== undefined) {\n    const events = require('base/js/events');\n    const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  }\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"e603302b-40d4-4f8c-aa3c-1000266b48fa\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"e603302b-40d4-4f8c-aa3c-1000266b48fa\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"e603302b-40d4-4f8c-aa3c-1000266b48fa\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"e603302b-40d4-4f8c-aa3c-1000266b48fa\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 1
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e713032",
-   "metadata": {},
-   "source": [
-    "# Import modules"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "id": "e69953f7",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T18:04:56.165634Z",
-     "start_time": "2024-12-06T18:04:55.603516Z"
-    }
-   },
-   "source": "from gnomad_toolbox.load_data import  get_gnomad_release",
-   "outputs": [],
-   "execution_count": 3
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5335a135",
-   "metadata": {},
-   "source": [
-    "# Import data\n",
-    "\n",
-    "You can choose which version of gnomAD release you want to look at, here we listed the available version per data type per reference build. \n",
-    "\n",
-    "Available versions for each data type are (as of 2024-10-29):\n",
-    "\n",
-    "| Data Type       | GRCh38 Versions                  | GRCh37 Versions      |\n",
-    "|-----------------|----------------------------------|----------------------|\n",
-    "| exomes          | 4.0, 4.1                         | 2.1, 2.1.1           |\n",
-    "| genomes         | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1           |\n",
-    "| joint           | 4.1                              | N/A                  |\n",
-    "\n",
-    "We use gnomAD v4.1 exomes to demonstrate for examples below."
-   ]
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "## Loading gnomAD v4.1 exomes sites Hail Table\n",
-   "id": "d1a4ae8933ba6421"
-  },
-  {
-   "cell_type": "code",
-   "id": "100cf576",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T18:08:08.225100Z",
-     "start_time": "2024-12-06T18:07:55.852971Z"
-    }
-   },
-   "source": "ht = get_gnomad_release(data_type='exomes', version='4.1')",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[?25l"
-      ],
-      "text/html": [
-       "
\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Output()"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "4720d6aa643c489bb768ba33f92dbc45"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\n",
-       "\u001B[?25h"
-      ],
-      "text/html": [
-       "
\n",
-       "
\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[?25l"
-      ],
-      "text/html": [
-       "
\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Output()"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "782f39c8b7904334873519d3c7da2b37"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\n",
-       "\u001B[?25h"
-      ],
-      "text/html": [
-       "
\n",
-       "
\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 5
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "## Print the schema of the Hail Table\n",
-   "id": "77d7a05e31c1f37a"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T18:14:53.139250Z",
-     "start_time": "2024-12-06T18:14:53.136478Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "ht.describe()",
-   "id": "95c14f2c8cc3e699",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    'freq_meta': array> \n",
-      "    'freq_index_dict': dict \n",
-      "    'freq_meta_sample_count': array \n",
-      "    'faf_meta': array> \n",
-      "    'faf_index_dict': dict \n",
-      "    'age_distribution': struct {\n",
-      "        bin_edges: array, \n",
-      "        bin_freq: array, \n",
-      "        n_smaller: int32, \n",
-      "        n_larger: int32\n",
-      "    } \n",
-      "    'downsamplings': dict> \n",
-      "    'filtering_model': struct {\n",
-      "        filter_name: str, \n",
-      "        score_name: str, \n",
-      "        snv_cutoff: struct {\n",
-      "            bin: int32, \n",
-      "            min_score: float64\n",
-      "        }, \n",
-      "        indel_cutoff: struct {\n",
-      "            bin: int32, \n",
-      "            min_score: float64\n",
-      "        }, \n",
-      "        snv_training_variables: array, \n",
-      "        indel_training_variables: array\n",
-      "    } \n",
-      "    'inbreeding_coeff_cutoff': float64 \n",
-      "    'interval_qc_parameters': struct {\n",
-      "        per_platform: bool, \n",
-      "        all_platforms: bool, \n",
-      "        high_qual_cutoffs: dict>, \n",
-      "        min_platform_size: int32\n",
-      "    } \n",
-      "    'tool_versions': struct {\n",
-      "        cadd_version: str, \n",
-      "        revel_version: str, \n",
-      "        spliceai_version: str, \n",
-      "        pangolin_version: array, \n",
-      "        phylop_version: str, \n",
-      "        dbsnp_version: str, \n",
-      "        sift_version: str, \n",
-      "        polyphen_version: str\n",
-      "    } \n",
-      "    'vrs_versions': struct {\n",
-      "        vrs_schema_version: str, \n",
-      "        vrs_python_version: str, \n",
-      "        seqrepo_version: str\n",
-      "    } \n",
-      "    'vep_globals': struct {\n",
-      "        vep_version: str, \n",
-      "        vep_help: str, \n",
-      "        vep_config: str, \n",
-      "        gencode_version: str, \n",
-      "        mane_select_version: str\n",
-      "    } \n",
-      "    'frequency_README': str \n",
-      "    'date': str \n",
-      "    'version': str \n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'alleles': array \n",
-      "    'freq': array \n",
-      "    'grpmax': struct {\n",
-      "        gnomad: struct {\n",
-      "            AC: int32, \n",
-      "            AF: float64, \n",
-      "            AN: int32, \n",
-      "            homozygote_count: int64, \n",
-      "            gen_anc: str\n",
-      "        }, \n",
-      "        non_ukb: struct {\n",
-      "            AC: int32, \n",
-      "            AF: float64, \n",
-      "            AN: int32, \n",
-      "            homozygote_count: int64, \n",
-      "            gen_anc: str\n",
-      "        }\n",
-      "    } \n",
-      "    'faf': array \n",
-      "    'fafmax': struct {\n",
-      "        gnomad: struct {\n",
-      "            faf95_max: float64, \n",
-      "            faf95_max_gen_anc: str, \n",
-      "            faf99_max: float64, \n",
-      "            faf99_max_gen_anc: str\n",
-      "        }, \n",
-      "        non_ukb: struct {\n",
-      "            faf95_max: float64, \n",
-      "            faf95_max_gen_anc: str, \n",
-      "            faf99_max: float64, \n",
-      "            faf99_max_gen_anc: str\n",
-      "        }\n",
-      "    } \n",
-      "    'a_index': int32 \n",
-      "    'was_split': bool \n",
-      "    'rsid': set \n",
-      "    'filters': set \n",
-      "    'info': struct {\n",
-      "        FS: float64, \n",
-      "        MQ: float64, \n",
-      "        MQRankSum: float64, \n",
-      "        QUALapprox: int64, \n",
-      "        QD: float64, \n",
-      "        ReadPosRankSum: float64, \n",
-      "        SB: array, \n",
-      "        SOR: float64, \n",
-      "        VarDP: int32, \n",
-      "        AS_FS: float64, \n",
-      "        AS_MQ: float64, \n",
-      "        AS_MQRankSum: float64, \n",
-      "        AS_pab_max: float64, \n",
-      "        AS_QUALapprox: int64, \n",
-      "        AS_QD: float64, \n",
-      "        AS_ReadPosRankSum: float64, \n",
-      "        AS_SB_TABLE: array, \n",
-      "        AS_SOR: float64, \n",
-      "        AS_VarDP: int32, \n",
-      "        singleton: bool, \n",
-      "        transmitted_singleton: bool, \n",
-      "        sibling_singleton: bool, \n",
-      "        omni: bool, \n",
-      "        mills: bool, \n",
-      "        monoallelic: bool, \n",
-      "        only_het: bool, \n",
-      "        AS_VQSLOD: float64, \n",
-      "        inbreeding_coeff: float64, \n",
-      "        vrs: struct {\n",
-      "            VRS_Allele_IDs: array, \n",
-      "            VRS_Starts: array, \n",
-      "            VRS_Ends: array, \n",
-      "            VRS_States: array\n",
-      "        }\n",
-      "    } \n",
-      "    'vep': struct {\n",
-      "        allele_string: str, \n",
-      "        end: int32, \n",
-      "        id: str, \n",
-      "        input: str, \n",
-      "        intergenic_consequences: array, \n",
-      "            impact: str, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        most_severe_consequence: str, \n",
-      "        motif_feature_consequences: array, \n",
-      "            high_inf_pos: str, \n",
-      "            impact: str, \n",
-      "            motif_feature_id: str, \n",
-      "            motif_name: str, \n",
-      "            motif_pos: int32, \n",
-      "            motif_score_change: float64, \n",
-      "            transcription_factors: array, \n",
-      "            strand: int32, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        regulatory_feature_consequences: array, \n",
-      "            impact: str, \n",
-      "            regulatory_feature_id: str, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        seq_region_name: str, \n",
-      "        start: int32, \n",
-      "        strand: int32, \n",
-      "        transcript_consequences: array, \n",
-      "            distance: int32, \n",
-      "            domains: array, \n",
-      "            exon: str, \n",
-      "            flags: str, \n",
-      "            gene_id: str, \n",
-      "            gene_pheno: int32, \n",
-      "            gene_symbol: str, \n",
-      "            gene_symbol_source: str, \n",
-      "            hgnc_id: str, \n",
-      "            hgvsc: str, \n",
-      "            hgvsp: str, \n",
-      "            hgvs_offset: int32, \n",
-      "            impact: str, \n",
-      "            intron: str, \n",
-      "            lof: str, \n",
-      "            lof_flags: str, \n",
-      "            lof_filter: str, \n",
-      "            lof_info: str, \n",
-      "            mane_select: str, \n",
-      "            mane_plus_clinical: str, \n",
-      "            mirna: array, \n",
-      "            protein_end: int32, \n",
-      "            protein_start: int32, \n",
-      "            protein_id: str, \n",
-      "            source: str, \n",
-      "            strand: int32, \n",
-      "            transcript_id: str, \n",
-      "            tsl: int32, \n",
-      "            uniprot_isoform: array, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        variant_class: str\n",
-      "    } \n",
-      "    'vqsr_results': struct {\n",
-      "        AS_VQSLOD: float64, \n",
-      "        AS_culprit: str, \n",
-      "        positive_train_site: bool, \n",
-      "        negative_train_site: bool\n",
-      "    } \n",
-      "    'region_flags': struct {\n",
-      "        non_par: bool, \n",
-      "        lcr: bool, \n",
-      "        segdup: bool, \n",
-      "        fail_interval_qc: bool, \n",
-      "        outside_ukb_capture_region: bool, \n",
-      "        outside_broad_capture_region: bool\n",
-      "    } \n",
-      "    'allele_info': struct {\n",
-      "        variant_type: str, \n",
-      "        n_alt_alleles: int32, \n",
-      "        has_star: bool, \n",
-      "        allele_type: str, \n",
-      "        was_mixed: bool\n",
-      "    } \n",
-      "    'histograms': struct {\n",
-      "        qual_hists: struct {\n",
-      "            gq_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            gq_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            ab_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }, \n",
-      "        raw_qual_hists: struct {\n",
-      "            gq_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            gq_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            ab_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }, \n",
-      "        age_hists: struct {\n",
-      "            age_hist_het: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            age_hist_hom: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }\n",
-      "    } \n",
-      "    'in_silico_predictors': struct {\n",
-      "        cadd: struct {\n",
-      "            phred: float32, \n",
-      "            raw_score: float32\n",
-      "        }, \n",
-      "        revel_max: float64, \n",
-      "        spliceai_ds_max: float32, \n",
-      "        pangolin_largest_ds: float64, \n",
-      "        phylop: float64, \n",
-      "        sift_max: float64, \n",
-      "        polyphen_max: float64\n",
-      "    } \n",
-      "----------------------------------------\n",
-      "Key: ['locus', 'alleles']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "execution_count": 8
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "## Show the first 5 variants in the Hail Table\n",
-   "id": "a071f738b2c888e"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T18:16:28.722532Z",
-     "start_time": "2024-12-06T18:16:00.055352Z"
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-   "cell_type": "code",
-   "source": "ht.show(5)",
-   "id": "222de580c305d72a",
-   "outputs": [
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-      "text/plain": [
-       "+---------------+----------------+\n",
-       "| locus         | alleles        |\n",
-       "+---------------+----------------+\n",
-       "| locus | array     |\n",
-       "+---------------+----------------+\n",
-       "| chr1:11994    | [\"T\",\"C\"]      |\n",
-       "| chr1:12016    | [\"G\",\"A\"]      |\n",
-       "| chr1:12060    | [\"CTGGAG\",\"C\"] |\n",
-       "| chr1:12074    | [\"T\",\"C\"]      |\n",
-       "| chr1:12102    | [\"G\",\"A\"]      |\n",
-       "+---------------+----------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| freq                                                                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0,NA,0,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA,... |\n",
-       "| [(0,NA,0,0),(6,9.54e-06,628784,2),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA,... |\n",
-       "| [(0,0.00e+00,4,0),(1,1.59e-06,628784,0),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n",
-       "| [(0,0.00e+00,4,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n",
-       "| [(0,0.00e+00,32,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0)... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------+------------------+------------------+\n",
-       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
-       "+------------------+------------------+------------------+\n",
-       "|            int32 |          float64 |            int32 |\n",
-       "+------------------+------------------+------------------+\n",
-       "|               NA |               NA |               NA |\n",
-       "|               NA |               NA |               NA |\n",
-       "|               NA |               NA |               NA |\n",
-       "|               NA |               NA |               NA |\n",
-       "|               NA |               NA |               NA |\n",
-       "+------------------+------------------+------------------+\n",
-       "\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                          int64 | str                   |             int32 |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|           float64 |             int32 |                           int64 |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|                NA |                NA |                              NA |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|                NA |                NA |                              NA |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "\n",
-       "+------------------------+\n",
-       "| grpmax.non_ukb.gen_anc |\n",
-       "+------------------------+\n",
-       "| str                    |\n",
-       "+------------------------+\n",
-       "| NA                     |\n",
-       "| NA                     |\n",
-       "| NA                     |\n",
-       "| NA                     |\n",
-       "| NA                     |\n",
-       "+------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| faf                                                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+\n",
-       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                  float64 | str                              |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "+--------------------------+----------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                  float64 | str                              |   int32 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "\n",
-       "+-----------+----------+-------------------+---------+----------+\n",
-       "| was_split | rsid     | filters           | info.FS |  info.MQ |\n",
-       "+-----------+----------+-------------------+---------+----------+\n",
-       "|      bool | set | set          | float64 |  float64 |\n",
-       "+-----------+----------+-------------------+---------+----------+\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.50e+01 |\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.50e+01 |\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.60e+01 |\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.56e+01 |\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.45e+01 |\n",
-       "+-----------+----------+-------------------+---------+----------+\n",
-       "\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|        float64 |           int64 |  float64 |             float64 |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|             NA |              60 | 3.00e+01 |                  NA |\n",
-       "|       0.00e+00 |             262 | 2.18e+01 |            6.74e-01 |\n",
-       "|       6.74e-01 |             123 | 3.08e+01 |           -1.15e+00 |\n",
-       "|       4.31e-01 |              99 | 1.24e+01 |           -2.53e-01 |\n",
-       "|             NA |              90 | 2.25e+01 |                  NA |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "\n",
-       "+--------------+----------+------------+------------+------------+\n",
-       "| info.SB      | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
-       "+--------------+----------+------------+------------+------------+\n",
-       "| array |  float64 |      int32 |    float64 |    float64 |\n",
-       "+--------------+----------+------------+------------+------------+\n",
-       "| [0,0,2,0]    | 2.30e+00 |          2 |         NA |   2.50e+01 |\n",
-       "| [2,0,10,0]   | 2.67e+00 |         12 |         NA |   2.50e+01 |\n",
-       "| [1,0,3,0]    | 1.61e+00 |          4 |         NA |   2.60e+01 |\n",
-       "| [4,0,4,0]    | 6.93e-01 |          8 |         NA |   2.56e+01 |\n",
-       "| [0,0,4,0]    | 3.26e+00 |          4 |         NA |   2.45e+01 |\n",
-       "+--------------+----------+------------+------------+------------+\n",
-       "\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|           float64 |         float64 |              int64 |    float64 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|                NA |              NA |                 60 |   3.00e+01 |\n",
-       "|          0.00e+00 |        1.00e+00 |                262 |   2.18e+01 |\n",
-       "|          6.74e-01 |        6.25e-01 |                123 |   3.08e+01 |\n",
-       "|          4.31e-01 |        6.87e-01 |                 99 |   1.24e+01 |\n",
-       "|                NA |              NA |                 90 |   2.25e+01 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "\n",
-       "+------------------------+------------------+-------------+---------------+\n",
-       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE | info.AS_SOR | info.AS_VarDP |\n",
-       "+------------------------+------------------+-------------+---------------+\n",
-       "|                float64 | array     |     float64 |         int32 |\n",
-       "+------------------------+------------------+-------------+---------------+\n",
-       "|                     NA | [0,0,2,0]        |    2.30e+00 |             2 |\n",
-       "|               6.74e-01 | [2,0,10,0]       |    2.67e+00 |            12 |\n",
-       "|              -1.15e+00 | [1,0,3,0]        |    1.61e+00 |             4 |\n",
-       "|              -2.53e-01 | [4,0,4,0]        |    6.93e-01 |             8 |\n",
-       "|                     NA | [0,0,4,0]        |    3.26e+00 |             4 |\n",
-       "+------------------------+------------------+-------------+---------------+\n",
-       "\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "|           bool |                       bool |                   bool |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "|          False |                         NA |                     NA |\n",
-       "|          False |                         NA |                     NA |\n",
-       "|           True |                         NA |                     NA |\n",
-       "|          False |                         NA |                     NA |\n",
-       "|          False |                         NA |                     NA |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|      bool |       bool |             bool |          bool |        float64 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|     False |      False |            False |         False |      -5.25e+00 |\n",
-       "|     False |      False |            False |         False |      -2.75e+00 |\n",
-       "|     False |      False |            False |         False |      -2.22e+00 |\n",
-       "|     False |      False |            False |         False |      -2.18e+00 |\n",
-       "|     False |      False |            False |         False |      -2.86e+00 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "\n",
-       "+-----------------------+\n",
-       "| info.inbreeding_coeff |\n",
-       "+-----------------------+\n",
-       "|               float64 |\n",
-       "+-----------------------+\n",
-       "|              1.00e+00 |\n",
-       "|              6.67e-01 |\n",
-       "|             -1.59e-06 |\n",
-       "|              1.00e+00 |\n",
-       "|              1.00e+00 |\n",
-       "+-----------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| info.vrs.VRS_Allele_IDs                                                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [\"ga4gh:VA.ps4-9woXy7o4rS39i8hDK_cUPBa-UcyP\",\"ga4gh:VA.nHlWYJXgiuvXrLAxQs... |\n",
-       "| [\"ga4gh:VA.VFxwcI4knOzk6SHzS2qowyDGnkG3mfEH\",\"ga4gh:VA.ctgP7qNjQAGjI2eTDo... |\n",
-       "| [\"ga4gh:VA.P573ZtUtAaRcceE7NLanEyynSefvcAPL\",\"ga4gh:VA.CHmk9uDiHW2LIHndZW... |\n",
-       "| [\"ga4gh:VA.neBeBT28ISe_1-yKPFsYxntP2jz1pj7E\",\"ga4gh:VA.1RPHSwBHNUwoECJ9VV... |\n",
-       "| [\"ga4gh:VA.ZXRhPWtip8HseOMWpCnaja0-ATo8hLE1\",\"ga4gh:VA.Qi95g6E8nt6DaqTGpH... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "| info.vrs.VRS_Starts | info.vrs.VRS_Ends | info.vrs.VRS_States |\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "| array        | array      | array          |\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "| [11993,11993]       | [11994,11994]     | [\"T\",\"C\"]           |\n",
-       "| [12015,12015]       | [12016,12016]     | [\"G\",\"A\"]           |\n",
-       "| [12059,12060]       | [12065,12071]     | [\"CTGGAG\",\"TGGAGT\"] |\n",
-       "| [12073,12073]       | [12074,12074]     | [\"T\",\"C\"]           |\n",
-       "| [12101,12101]       | [12102,12102]     | [\"G\",\"A\"]           |\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "\n",
-       "+-------------------+---------+--------+--------------------------------+\n",
-       "| vep.allele_string | vep.end | vep.id | vep.input                      |\n",
-       "+-------------------+---------+--------+--------------------------------+\n",
-       "| str               |   int32 | str    | str                            |\n",
-       "+-------------------+---------+--------+--------------------------------+\n",
-       "| \"T/C\"             |   11994 | \".\"    | \"chr1\t11994\t.\tT\tC\t.\t.\tGT\"      |\n",
-       "| \"G/A\"             |   12016 | \".\"    | \"chr1\t12016\t.\tG\tA\t.\t.\tGT\"      |\n",
-       "| \"TGGAG/-\"         |   12065 | \".\"    | \"chr1\t12060\t.\tCTGGAG\tC\t.\t.\tGT\" |\n",
-       "| \"T/C\"             |   12074 | \".\"    | \"chr1\t12074\t.\tT\tC\t.\t.\tGT\"      |\n",
-       "| \"G/A\"             |   12102 | \".\"    | \"chr1\t12102\t.\tG\tA\t.\t.\tGT\"      |\n",
-       "+-------------------+---------+--------+--------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.intergenic_consequences                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, impact: st... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| vep.most_severe_consequence          |\n",
-       "+--------------------------------------+\n",
-       "| str                                  |\n",
-       "+--------------------------------------+\n",
-       "| \"non_coding_transcript_exon_variant\" |\n",
-       "| \"non_coding_transcript_exon_variant\" |\n",
-       "| \"splice_donor_5th_base_variant\"      |\n",
-       "| \"non_coding_transcript_exon_variant\" |\n",
-       "| \"non_coding_transcript_exon_variant\" |\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.motif_feature_consequences                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, high_inf_p... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.regulatory_feature_consequences                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,2,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,14,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_freq   |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [314391,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [314388,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [314380,0,0,0,11,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0]  |\n",
-       "| [314369,1,0,0,20,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [314323,1,0,0,54,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_freq     |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                       |\n",
-       "+----------------------------------------------------+\n",
-       "| [308845,5547,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [308845,5547,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [308833,5557,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [308823,5567,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [308789,5585,16,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0]      |\n",
-       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                      int64 |                         float32 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                          0 |                        1.26e+01 |\n",
-       "|                                          0 |                        1.60e+01 |\n",
-       "|                                          0 |                        8.52e+00 |\n",
-       "|                                          0 |                        1.52e+01 |\n",
-       "|                                          0 |                        4.42e+00 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                             float32 |                        float64 |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                            1.08e+00 |                             NA |\n",
-       "|                            1.54e+00 |                             NA |\n",
-       "|                            7.07e-01 |                             NA |\n",
-       "|                            1.41e+00 |                             NA |\n",
-       "|                            3.11e-01 |                             NA |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| in_silico_predictors.spliceai_ds_max |\n",
-       "+--------------------------------------+\n",
-       "|                              float32 |\n",
-       "+--------------------------------------+\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                  float64 |                     float64 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                -1.10e-01 |                    1.09e+00 |\n",
-       "|                                -7.00e-02 |                    6.55e+00 |\n",
-       "|                                -9.00e-02 |                   -4.41e+00 |\n",
-       "|                                -4.00e-02 |                    6.01e+00 |\n",
-       "|                                -8.00e-02 |                    1.38e+00 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                       float64 |                           float64 |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "showing top 5 rows"
-      ],
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
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AF
AN
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gen_anc
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AF
AN
homozygote_count
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faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
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revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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showing top 5 rows
\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 9
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1ba4bfaf",
-   "metadata": {},
-   "source": [
-    "# Get variant count"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "df28f17d",
-   "metadata": {},
-   "source": [
-    "## Get variant count by AF for a release\n",
-    "\n",
-    "**Note: this will take long if your notebook is NOT using multiple nodes.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "c276fb7e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Stage 1:====================================================>(8782 + 7) / 8789]\r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'number of variants with AF < 0.01': 68398090, 'number of variants with AF < 0.001': 67709028}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(get_variant_count(ht))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "c0243c4b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Stage 5:====================================================>(8781 + 8) / 8789]\r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'number of singletons': 34047562, 'number of doubletons': 10161819, 'number of variants with AF < 0.01': 68398090, 'number of variants with AF < 0.001': 67709028}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "[Stage 5:====================================================>(8786 + 3) / 8789]\r"
-     ]
-    }
-   ],
-   "source": [
-    "print(get_variant_count(ht, singletons=True, doubletons=True))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec659eeb",
-   "metadata": {},
-   "source": [
-    "## Get variant count by AF for coding variants"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "d65b0ea8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Stage 21:===================================================>(8784 + 5) / 8789]\r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'number of variants with AF < 0.01': 23762097, 'number of variants with AF < 0.001': 23643787, 'number of variants with AF < 0.0005': 23569893}\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n",
-    "\n",
-    "ht = filter_by_csqs(ht, ['coding'])\n",
-    "\n",
-    "print(get_variant_count(ht, afs=[0.01, 0.001, 0.0005]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f07ca88f",
-   "metadata": {},
-   "source": [
-    "## Get variant count by VEP consequence"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "b515bfc0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18231426"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# total number of missense variant in exomes data\n",
-    "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n",
-    "\n",
-    "ht.filter(\n",
-    "    hl.any(\n",
-    "        hl.map(\n",
-    "            lambda x: (x.consequence_terms.contains(\"missense_variant\")),\n",
-    "            ht.vep.transcript_consequences,\n",
-    "        )\n",
-    "    )\n",
-    ").count()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "725f9a57",
-   "metadata": {},
-   "source": [
-    "## Get variant count by AF for a gene\n",
-    "\n",
-    "**Note: This isn't necessarily equal to the number of variants annotated to this gene by VEP.**\n",
-    "\n",
-    "Here we show two ways that you can load a variant table on the gnomAD browser, one is the [gene page](https://gnomad.broadinstitute.org/gene/ENSG00000149295?dataset=gnomad_r4) (filtered to MANE Select transcript of that gene, and only variants located in or within 75 base pairs of a coding exon (CDS)), the other is the [region view](https://gnomad.broadinstitute.org/region/11-113409605-113475691?dataset=gnomad_r4). We use *DRD2* gene as an example. "
-   ]
-  },
-  {
-   "attachments": {
-    "Screenshot%202024-11-01%20at%209.31.00%E2%80%AFPM.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "164a07b7",
-   "metadata": {},
-   "source": [
-    "### On region view (the interval of a gene)\n",
-    "\n",
-    "This is for 'DRD2' gene. \n",
-    "![Screenshot%202024-11-01%20at%209.31.00%E2%80%AFPM.png](attachment:Screenshot%202024-11-01%20at%209.31.00%E2%80%AFPM.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "9f8e1ba4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The total number of variants in this gene interval is:  8126\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Stage 37:=============================>                            (1 + 1) / 2]\r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'number of singletons': 1390, 'number of doubletons': 384, 'number of variants with AF < 0.01': 2711, 'number of variants with AF < 0.001': 2662}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "[Stage 38:=============================>                            (1 + 1) / 2]\r"
-     ]
-    }
-   ],
-   "source": [
-    "# Filter to interval, e.g. for DRD2.\n",
-    "gene_interval = \"11:113409605-113475691\"\n",
-    "\n",
-    "gene_ht = filter_by_interval(ht, gene_interval)\n",
-    "\n",
-    "# Filter the exome release Hail Table to the ASH1L gene interval.\n",
-    "print(\"The total number of variants in this gene interval is: \", gene_ht.count())\n",
-    "\n",
-    "print(get_variant_count(gene_ht, singletons=True, doubletons=True))"
-   ]
-  },
-  {
-   "attachments": {
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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "ac393319",
-   "metadata": {},
-   "source": [
-    "### On gene page\n",
-    "\n",
-    "To get the number of variants shown on the gene page, if you click 'all' and check 'exomes', 'SNVs', 'Indels', and 'Filtered variants' as this: \n",
-    "\n",
-    "![Screenshot%202024-11-01%20at%209.28.50%E2%80%AFPM.png](attachment:Screenshot%202024-11-01%20at%209.28.50%E2%80%AFPM.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "e6bf7236",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Stage 26:>                                                         (0 + 1) / 1]\r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "1764"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# gene_symbol can be upper or lower case\n",
-    "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n",
-    "\n",
-    "drd2 = filter_by_gene_symbol(ht, 'drd2')\n",
-    "drd2.count()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7bff63bb",
-   "metadata": {},
-   "source": [
-    "# Filter to variants by VEP annotations\n",
-    "\n",
-    "You can get the variant table either by gene_symbol or gene interval, we recommen you to get by gene_symbol because it's already filtered to MANE Select transcript of a gene. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "700582e4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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,"1"),("ENSP_mappings","5aer"),("ENSP_mappings","5aer"),("ENSP_mappings","6cm4"),("ENSP_mappings","6luq"),("ENSP_mappings","6vms"),("ENSP_mappings","7dfp"),("ENSP_mappings","7jvr"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000542968.5:c.1323C>A","ENSP00000442172.1:p.Leu441=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,441,441,"ENSP00000442172","Ensembl",-1,"ENST00000542968",1,["P14416-1"],"T"),(1,"L","A1","protein_coding",NA,NA,1353,1353,1320,1320,"ctC/ctA",["synonymous_variant"],NA,[("Gene3D","1"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000544518.5:c.1320C>A","ENSP00000441068.1:p.Leu440=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,440,440,"ENSP00000441068","Ensembl",-1,"ENST00000544518",1,NA,"T"),(1,NA,NA,"lncRNA",1,NA,NA,NA,NA,NA,NA,["intron_variant","non_coding_transcript_variant"],NA,NA,NA,NA,"ENSG00000256757",NA,NA,NA,NA,"ENST00000546284.1:n.245-811G>T",NA,NA,"MODIFIER","2/3",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000546284",3,NA,"T"),(1,"L",NA,"protein_coding",1,NA,1677,1677,1323,1323,"ctC/ctA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.1323C>A","NP_000786.1:p.Leu441=",NA,"LOW",NA,NA,NA,NA,NA,"ENST00000362072.8",NA,NA,441,441,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"T"),(1,"L",NA,"protein_coding",NA,NA,1590,1590,1236,1236,"ctC/ctA",["synonymous_variant"],NA,NA,"7/7",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.1236C>A","NP_057658.2:p.Leu412=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,412,412,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"T"),(1,"L",NA,"protein_coding",NA,NA,1405,1405,1323,1323,"ctC/ctA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.1323C>A","XP_016872785.1:p.Leu441=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,441,441,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"T")]"SNV"5.00e+00"AS_MQ"FalseFalseFalseFalseFalseTrueFalseFalse"multi-snv"2False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,2955,2914,2974,2923,2564,2680]07685[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,2955,2914,2974,2923,2564,2680]07685[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,1,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]002.96e+001.86e-01NA0.00e+003.00e-02-2.55e-01NANA
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+-----------------+------------+\n",
-       "| locus           | alleles    |\n",
-       "+-----------------+------------+\n",
-       "| locus   | array |\n",
-       "+-----------------+------------+\n",
-       "| chr11:113410731 | [\"C\",\"A\"]  |\n",
-       "| chr11:113410731 | [\"C\",\"T\"]  |\n",
-       "| chr11:113410735 | [\"G\",\"A\"]  |\n",
-       "| chr11:113410736 | [\"G\",\"A\"]  |\n",
-       "| chr11:113410736 | [\"G\",\"T\"]  |\n",
-       "+-----------------+------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| freq                                                                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(2,1.37e-06,1461866,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(2,4.... |\n",
-       "| [(1,6.84e-07,1461866,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
-       "| [(75,5.13e-05,1461876,0),(75,5.13e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
-       "| [(15,1.03e-05,1461882,0),(15,1.03e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
-       "| [(1,6.84e-07,1461882,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------+------------------+------------------+\n",
-       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
-       "+------------------+------------------+------------------+\n",
-       "|            int32 |          float64 |            int32 |\n",
-       "+------------------+------------------+------------------+\n",
-       "|                2 |         4.47e-05 |            44724 |\n",
-       "|                1 |         8.99e-07 |          1112010 |\n",
-       "|                8 |         7.19e-06 |          1112004 |\n",
-       "|               15 |         1.35e-05 |          1112010 |\n",
-       "|                1 |         8.99e-07 |          1112010 |\n",
-       "+------------------+------------------+------------------+\n",
-       "\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                          int64 | str                   |             int32 |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                              0 | \"amr\"                 |                 2 |\n",
-       "|                              0 | \"nfe\"                 |                NA |\n",
-       "|                              0 | \"nfe\"                 |                 2 |\n",
-       "|                              0 | \"nfe\"                 |                 1 |\n",
-       "|                              0 | \"nfe\"                 |                NA |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|           float64 |             int32 |                           int64 |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|          4.57e-05 |             43740 |                               0 |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|          5.71e-06 |            350102 |                               0 |\n",
-       "|          2.86e-06 |            350106 |                               0 |\n",
-       "|                NA |                NA |                              NA |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "\n",
-       "+------------------------+\n",
-       "| grpmax.non_ukb.gen_anc |\n",
-       "+------------------------+\n",
-       "| str                    |\n",
-       "+------------------------+\n",
-       "| \"amr\"                  |\n",
-       "| NA                     |\n",
-       "| \"nfe\"                  |\n",
-       "| \"nfe\"                  |\n",
-       "| NA                     |\n",
-       "+------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| faf                                                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(7.41e-06,2.77e-06),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(4.19e-05,3.82e-05),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(6.16e-06,4.89e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                7.41e-06 | \"amr\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "|                3.09e-06 | \"nfe\"                           |\n",
-       "|                8.10e-06 | \"nfe\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                2.77e-06 | \"amr\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "|                2.24e-06 | \"nfe\"                           |\n",
-       "|                6.42e-06 | \"nfe\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+\n",
-       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                  float64 | str                              |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                 7.58e-06 | \"amr\"                            |\n",
-       "|                       NA | NA                               |\n",
-       "|                 9.50e-07 | \"nfe\"                            |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "+--------------------------+----------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                  float64 | str                              |   int32 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                 2.84e-06 | \"amr\"                            |       1 |\n",
-       "|                       NA | NA                               |       2 |\n",
-       "|                 3.60e-07 | \"nfe\"                            |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       2 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "| was_split | rsid             | filters  |  info.FS |  info.MQ |\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "|      bool | set         | set |  float64 |  float64 |\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "|      True | {\"rs140938110\"}  | {}       | 5.56e+00 | 6.00e+01 |\n",
-       "|      True | NA               | {}       | 5.56e+00 | 6.00e+01 |\n",
-       "|      True | {\"rs201801648\"}  | {}       | 0.00e+00 | 6.00e+01 |\n",
-       "|      True | {\"rs1159504221\"} | {}       | 5.49e-01 | 6.00e+01 |\n",
-       "|      True | NA               | {}       | 5.49e-01 | 6.00e+01 |\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|        float64 |           int64 |  float64 |             float64 |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
-       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
-       "|       0.00e+00 |          127501 | 1.23e+01 |            3.70e-02 |\n",
-       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
-       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "\n",
-       "+-----------------------+----------+------------+------------+------------+\n",
-       "| info.SB               | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
-       "+-----------------------+----------+------------+------------+------------+\n",
-       "| array          |  float64 |      int32 |    float64 |    float64 |\n",
-       "+-----------------------+----------+------------+------------+------------+\n",
-       "| [134,83,109,52]       | 9.81e-01 |        378 |   1.31e+01 |   6.00e+01 |\n",
-       "| [134,83,109,52]       | 9.81e-01 |        378 |   0.00e+00 |   6.00e+01 |\n",
-       "| [3240,2211,2993,1962] | 7.34e-01 |      10404 |   0.00e+00 |   6.00e+01 |\n",
-       "| [386,195,316,166]     | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
-       "| [386,195,316,166]     | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
-       "+-----------------------+----------+------------+------------+------------+\n",
-       "\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|           float64 |         float64 |              int64 |    float64 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|          0.00e+00 |        2.94e-01 |               1895 |   1.06e+01 |\n",
-       "|          0.00e+00 |        9.04e-01 |               1967 |   9.88e+00 |\n",
-       "|          0.00e+00 |        1.00e+00 |             127480 |   1.23e+01 |\n",
-       "|          0.00e+00 |        9.11e-01 |              11899 |   1.17e+01 |\n",
-       "|          0.00e+00 |        3.28e-02 |                408 |   8.16e+00 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "\n",
-       "+------------------------+-----------------------+-------------+---------------+\n",
-       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE      | info.AS_SOR | info.AS_VarDP |\n",
-       "+------------------------+-----------------------+-------------+---------------+\n",
-       "|                float64 | array          |     float64 |         int32 |\n",
-       "+------------------------+-----------------------+-------------+---------------+\n",
-       "|               2.52e-01 | [134,83,56,19]        |    1.42e+00 |           179 |\n",
-       "|              -5.01e-01 | [134,83,53,33]        |    6.81e-01 |           199 |\n",
-       "|               1.60e-02 | [3240,2211,2991,1961] |    7.34e-01 |         10383 |\n",
-       "|               9.70e-02 | [386,195,304,161]     |    6.47e-01 |          1013 |\n",
-       "|              -9.23e-01 | [386,195,12,5]        |    7.90e-01 |            50 |\n",
-       "+------------------------+-----------------------+-------------+---------------+\n",
-       "\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "|           bool |                       bool |                   bool |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "|          False |                         NA |                     NA |\n",
-       "|           True |                      False |                  False |\n",
-       "|          False |                         NA |                     NA |\n",
-       "|          False |                         NA |                     NA |\n",
-       "|           True |                      False |                  False |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|      bool |       bool |             bool |          bool |        float64 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|     False |      False |            False |         False |       5.27e+00 |\n",
-       "|     False |      False |            False |         False |       5.40e+00 |\n",
-       "|     False |      False |            False |         False |       8.85e+00 |\n",
-       "|     False |      False |            False |         False |       7.50e+00 |\n",
-       "|     False |      False |            False |         False |       5.00e+00 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "\n",
-       "+-----------------------+\n",
-       "| info.inbreeding_coeff |\n",
-       "+-----------------------+\n",
-       "|               float64 |\n",
-       "+-----------------------+\n",
-       "|             -1.37e-06 |\n",
-       "|             -6.84e-07 |\n",
-       "|             -5.13e-05 |\n",
-       "|             -1.03e-05 |\n",
-       "|             -6.84e-07 |\n",
-       "+-----------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| info.vrs.VRS_Allele_IDs                                                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.ZonDIhdwyghm7SuWkX... |\n",
-       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.Hp20SlFHUdM7jlWmVV... |\n",
-       "| [\"ga4gh:VA.SBSoboxxiC0V-mhjjHg2geRlf3I0Ib2d\",\"ga4gh:VA.GzUD_86k_EDbtk-R58... |\n",
-       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.OXPDvbuulFZ9CSk4Xt... |\n",
-       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.4qhkDG1qrfqiD2TeOA... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| array          | array          | array          |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"A\"]           |\n",
-       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"T\"]           |\n",
-       "| [113410734,113410734] | [113410735,113410735] | [\"G\",\"A\"]           |\n",
-       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"A\"]           |\n",
-       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"T\"]           |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "| str               |     int32 | str    | str                            |\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "| \"C/A\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tA\t.\t.\tGT\" |\n",
-       "| \"C/T\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tT\t.\t.\tGT\" |\n",
-       "| \"G/A\"             | 113410735 | \".\"    | \"chr11\t113410735\t.\tG\tA\t.\t.\tGT\" |\n",
-       "| \"G/A\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tA\t.\t.\tGT\" |\n",
-       "| \"G/T\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tT\t.\t.\tGT\" |\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.intergenic_consequences                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, impact: st... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------+\n",
-       "| vep.most_severe_consequence |\n",
-       "+-----------------------------+\n",
-       "| str                         |\n",
-       "+-----------------------------+\n",
-       "| \"missense_variant\"          |\n",
-       "| \"missense_variant\"          |\n",
-       "| \"missense_variant\"          |\n",
-       "| \"synonymous_variant\"        |\n",
-       "| \"synonymous_variant\"        |\n",
-       "+-----------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.motif_feature_consequences                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, high_inf_p... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.regulatory_feature_consequences                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.bin_freq              |\n",
-       "+---------------------------------------------------------+\n",
-       "| array                                            |\n",
-       "+---------------------------------------------------------+\n",
-       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
-       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
-       "| [0,0,0,0,12,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
-       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
-       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
-       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
-       "| [0,0,65,332,11308,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
-       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
-       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                       7715 |\n",
-       "|                                       7715 |\n",
-       "|                                       7717 |\n",
-       "|                                       7685 |\n",
-       "|                                       7685 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75] |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15] |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]  |\n",
-       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          1 |\n",
-       "|                                          0 |\n",
-       "|                                         32 |\n",
-       "|                                          1 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_freq   |\n",
-       "+----------------------------------------------+\n",
-       "| array                                 |\n",
-       "+----------------------------------------------+\n",
-       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]    |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]    |\n",
-       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0] |\n",
-       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]    |\n",
-       "+----------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_freq          |\n",
-       "+---------------------------------------------------------+\n",
-       "| array                                            |\n",
-       "+---------------------------------------------------------+\n",
-       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
-       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
-       "| [6,0,1,0,14,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
-       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
-       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
-       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
-       "| [0,2,67,335,11310,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
-       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
-       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                           7715 |\n",
-       "|                                           7715 |\n",
-       "|                                           7717 |\n",
-       "|                                           7685 |\n",
-       "|                                           7685 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75]     |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15]     |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]      |\n",
-       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              1 |\n",
-       "|                                              0 |\n",
-       "|                                             32 |\n",
-       "|                                              1 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]     |\n",
-       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
-       "| [1,0,0,2,3,3,8,4,0,0]                      |\n",
-       "| [0,0,1,2,4,2,4,1,0,0]                      |\n",
-       "| [0,0,0,0,0,1,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           1 |\n",
-       "|                                           0 |\n",
-       "|                                           3 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          1 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                      int64 |                         float32 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                          0 |                        2.77e+01 |\n",
-       "|                                          0 |                        2.75e+01 |\n",
-       "|                                          0 |                        2.28e+01 |\n",
-       "|                                          0 |                        3.44e+00 |\n",
-       "|                                          0 |                        2.96e+00 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                             float32 |                        float64 |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                            4.10e+00 |                       6.02e-01 |\n",
-       "|                            4.07e+00 |                       7.19e-01 |\n",
-       "|                            2.69e+00 |                       2.67e-01 |\n",
-       "|                            2.28e-01 |                             NA |\n",
-       "|                            1.86e-01 |                             NA |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| in_silico_predictors.spliceai_ds_max |\n",
-       "+--------------------------------------+\n",
-       "|                              float32 |\n",
-       "+--------------------------------------+\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                  float64 |                     float64 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                 0.00e+00 |                    8.78e+00 |\n",
-       "|                                 2.00e-02 |                    8.78e+00 |\n",
-       "|                                 1.00e-02 |                    8.67e+00 |\n",
-       "|                                 1.00e-02 |                   -2.55e-01 |\n",
-       "|                                 3.00e-02 |                   -2.55e-01 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                       float64 |                           float64 |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                      0.00e+00 |                          9.89e-01 |\n",
-       "|                      0.00e+00 |                          9.89e-01 |\n",
-       "|                      1.30e-01 |                          1.18e-01 |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# lof, missense, synonymous variants passing filters\n",
-    "variants_of_interest = filter_by_csqs(drd2,['lof','missense','synonymous'])\n",
-    "variants_of_interest.show(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "9887fdb0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "[Stage 27:>                                                         (0 + 1) / 1]\r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "17"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# number of lof variants passing filters\n",
-    "filter_by_csqs(drd2,['lof']).count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "86596aaf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "[Stage 28:>                                                         (0 + 1) / 1]\r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "409"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# number of missense variants passing filters\n",
-    "filter_by_csqs(drd2,['missense']).count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "b7e4368b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "[Stage 29:>                                                         (0 + 1) / 1]\r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "238"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# number of synonymous variants passing filters\n",
-    "filter_by_csqs(drd2,['synonymous']).count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "4141ccb3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "[Stage 30:>                                                         (0 + 1) / 1]\r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "783"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# number of 'Other' variants passing filters\n",
-    "filter_by_csqs(drd2,['other']).count()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b1031947",
-   "metadata": {},
-   "source": [
-    "## Filter to 'HC' LOF variants for certain genes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "5b08a706",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO (gnomad.utils.vep 928): Filtering to canonical transcripts\n",
-      "INFO (gnomad.utils.vep 931): Filtering to MANE Select transcripts...\n",
-      "INFO (gnomad.utils.vep 934): Filtering to Ensembl transcripts...\n",
-      "INFO (gnomad.utils.vep 940): Filtering to genes of interest...\n",
-      "INFO (gnomad.utils.vep 948): Filtering to variants with additional criteria...\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Filter to variants in ASH1L that are LOFTEE high-confidence (with no flags) in the MANE select transcript.\n",
-    "ht = filter_vep_transcript_csqs(\n",
-    "    ht, \n",
-    "    synonymous=False, \n",
-    "    mane_select=True,\n",
-    "    genes=[\"ASH1L\"],\n",
-    "    match_by_gene_symbol=True,\n",
-    "    additional_filtering_criteria=[lambda x: (x.lof == \"HC\") & hl.is_missing(x.lof_flags)],\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {
-    "Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "f9b2d921",
-   "metadata": {},
-   "source": [
-    "### Filter to pLOF variants that we used to compute constraint metrics\n",
-    "pLOF variants meets the following requirements:\n",
-    "* High-confidence LOFTEE variants (without any flags),\n",
-    "* Only variants in the MANE Select transcript,\n",
-    "* PASS variants that are SNVs with MAF ≤ 0.1%, and\n",
-    "* Exome median depth ≥ 30 (**This is changing in v4 constraint?**)\n",
-    "\n",
-    "**Note: this number should match the number of observed pLOF SNVs on the gene page of gnomAD Browser.**\n",
-    "\n",
-    "![Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png](attachment:Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "6ce87a77",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Stage 4:=======================================>                   (2 + 1) / 3]\r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of variants: 18\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Stage 5:=======================================>                   (2 + 1) / 3]\r"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "
freq
coverage
locus
alleles
AC
AF
AN
homozygote_count
csq
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>array<str>int32float64int32int64array<str>float64int32int64float64float64float64float64float64float64float64float64float64
chr1:155337668["G","A"]4593.14e-0414612700["stop_gained"]2.99e+0130218875281.00e+001.00e+001.00e+009.97e-019.86e-019.32e-016.07e-018.96e-046.02e-05
chr1:155337704["G","A"]64.10e-0614617140["stop_gained"]3.02e+0130220423721.00e+001.00e+001.00e+009.99e-019.94e-019.48e-016.21e-018.39e-044.93e-05
chr1:155337735["G","C"]16.84e-0714616940["stop_gained"]3.00e+0130219569141.00e+001.00e+001.00e+009.99e-019.92e-019.37e-016.12e-018.29e-044.51e-05
chr1:155338087["A","T"]16.85e-0714596480["splice_donor_variant"]3.16e+0131231113191.00e+001.00e+009.99e-019.95e-019.89e-019.59e-017.28e-011.35e-027.59e-04
chr1:155338161["G","A"]16.84e-0714618840["stop_gained"]3.19e+0131233084341.00e+001.00e+001.00e+001.00e+001.00e+009.76e-017.42e-011.35e-027.59e-04
chr1:155349380["T","A"]16.84e-0714617680["stop_gained"]3.21e+0132234449991.00e+001.00e+001.00e+001.00e+009.99e-019.76e-017.69e-015.01e-031.85e-04
chr1:155354631["C","T"]16.88e-0714534160["splice_acceptor_variant"]3.04e+0131222097141.00e+001.00e+009.95e-019.83e-019.70e-019.28e-016.53e-014.38e-044.93e-05
chr1:155357583["A","T"]16.84e-0714616800["splice_donor_variant"]3.13e+0131228588871.00e+001.00e+001.00e+009.99e-019.97e-019.79e-017.47e-018.14e-046.29e-05
chr1:155370984["C","T"]32.06e-0614557260["splice_acceptor_variant"]3.12e+0131228021811.00e+001.00e+001.00e+009.99e-019.97e-019.54e-016.97e-011.14e-033.69e-05
chr1:155415924["C","A"]21.48e-0613520320["splice_acceptor_variant"]2.73e+0130199323751.00e+009.84e-019.41e-018.72e-018.17e-017.64e-015.70e-011.35e-031.76e-04
chr1:155478020["G","C"]16.84e-0714618880["stop_gained"]3.26e+0133238300811.00e+001.00e+001.00e+001.00e+001.00e+009.72e-017.53e-013.11e-031.40e-03
chr1:155478203["C","T"]16.84e-0714618740["stop_gained"]3.27e+0133239248711.00e+001.00e+001.00e+001.00e+001.00e+009.72e-017.55e-014.07e-032.20e-03
chr1:155478439["C","T"]16.84e-0714618900["stop_gained"]3.28e+0133239948001.00e+001.00e+001.00e+001.00e+001.00e+009.73e-017.59e-014.64e-032.58e-03
chr1:155478528["G","A"]16.84e-0714618760["stop_gained"]3.28e+0133239977761.00e+001.00e+001.00e+001.00e+001.00e+009.73e-017.59e-014.67e-032.60e-03
chr1:155479767["G","A"]16.84e-0714618580["stop_gained"]3.30e+0133241434591.00e+001.00e+001.00e+001.00e+001.00e+009.74e-017.66e-016.55e-033.89e-03
chr1:155479862["G","T"]16.84e-0714611600["stop_gained"]3.24e+0132236512031.00e+001.00e+001.00e+009.98e-019.91e-019.42e-017.13e-016.42e-033.91e-03
chr1:155521291["C","A"]16.84e-0714618620["stop_gained"]3.30e+0132241144561.00e+001.00e+001.00e+001.00e+001.00e+009.81e-017.72e-011.94e-023.68e-03
chr1:155521474["C","A"]16.84e-0714617240["stop_gained"]3.29e+0132240775511.00e+001.00e+001.00e+009.99e-019.98e-019.79e-017.69e-011.94e-023.67e-03
"
-      ],
-      "text/plain": [
-       "+----------------+------------+---------+----------+---------+\n",
-       "| locus          | alleles    | freq.AC |  freq.AF | freq.AN |\n",
-       "+----------------+------------+---------+----------+---------+\n",
-       "| locus  | array |   int32 |  float64 |   int32 |\n",
-       "+----------------+------------+---------+----------+---------+\n",
-       "| chr1:155337668 | [\"G\",\"A\"]  |     459 | 3.14e-04 | 1461270 |\n",
-       "| chr1:155337704 | [\"G\",\"A\"]  |       6 | 4.10e-06 | 1461714 |\n",
-       "| chr1:155337735 | [\"G\",\"C\"]  |       1 | 6.84e-07 | 1461694 |\n",
-       "| chr1:155338087 | [\"A\",\"T\"]  |       1 | 6.85e-07 | 1459648 |\n",
-       "| chr1:155338161 | [\"G\",\"A\"]  |       1 | 6.84e-07 | 1461884 |\n",
-       "| chr1:155349380 | [\"T\",\"A\"]  |       1 | 6.84e-07 | 1461768 |\n",
-       "| chr1:155354631 | [\"C\",\"T\"]  |       1 | 6.88e-07 | 1453416 |\n",
-       "| chr1:155357583 | [\"A\",\"T\"]  |       1 | 6.84e-07 | 1461680 |\n",
-       "| chr1:155370984 | [\"C\",\"T\"]  |       3 | 2.06e-06 | 1455726 |\n",
-       "| chr1:155415924 | [\"C\",\"A\"]  |       2 | 1.48e-06 | 1352032 |\n",
-       "| chr1:155478020 | [\"G\",\"C\"]  |       1 | 6.84e-07 | 1461888 |\n",
-       "| chr1:155478203 | [\"C\",\"T\"]  |       1 | 6.84e-07 | 1461874 |\n",
-       "| chr1:155478439 | [\"C\",\"T\"]  |       1 | 6.84e-07 | 1461890 |\n",
-       "| chr1:155478528 | [\"G\",\"A\"]  |       1 | 6.84e-07 | 1461876 |\n",
-       "| chr1:155479767 | [\"G\",\"A\"]  |       1 | 6.84e-07 | 1461858 |\n",
-       "| chr1:155479862 | [\"G\",\"T\"]  |       1 | 6.84e-07 | 1461160 |\n",
-       "| chr1:155521291 | [\"C\",\"A\"]  |       1 | 6.84e-07 | 1461862 |\n",
-       "| chr1:155521474 | [\"C\",\"A\"]  |       1 | 6.84e-07 | 1461724 |\n",
-       "+----------------+------------+---------+----------+---------+\n",
-       "\n",
-       "+-----------------------+-----------------------------+---------------+\n",
-       "| freq.homozygote_count | csq                         | coverage.mean |\n",
-       "+-----------------------+-----------------------------+---------------+\n",
-       "|                 int64 | array                  |       float64 |\n",
-       "+-----------------------+-----------------------------+---------------+\n",
-       "|                     0 | [\"stop_gained\"]             |      2.99e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.02e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.00e+01 |\n",
-       "|                     0 | [\"splice_donor_variant\"]    |      3.16e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.19e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.21e+01 |\n",
-       "|                     0 | [\"splice_acceptor_variant\"] |      3.04e+01 |\n",
-       "|                     0 | [\"splice_donor_variant\"]    |      3.13e+01 |\n",
-       "|                     0 | [\"splice_acceptor_variant\"] |      3.12e+01 |\n",
-       "|                     0 | [\"splice_acceptor_variant\"] |      2.73e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.26e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.27e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.28e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.28e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.30e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.24e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.30e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.29e+01 |\n",
-       "+-----------------------+-----------------------------+---------------+\n",
-       "\n",
-       "+------------------------+-------------------+-----------------+\n",
-       "| coverage.median_approx | coverage.total_DP | coverage.over_1 |\n",
-       "+------------------------+-------------------+-----------------+\n",
-       "|                  int32 |             int64 |         float64 |\n",
-       "+------------------------+-------------------+-----------------+\n",
-       "|                     30 |          21887528 |        1.00e+00 |\n",
-       "|                     30 |          22042372 |        1.00e+00 |\n",
-       "|                     30 |          21956914 |        1.00e+00 |\n",
-       "|                     31 |          23111319 |        1.00e+00 |\n",
-       "|                     31 |          23308434 |        1.00e+00 |\n",
-       "|                     32 |          23444999 |        1.00e+00 |\n",
-       "|                     31 |          22209714 |        1.00e+00 |\n",
-       "|                     31 |          22858887 |        1.00e+00 |\n",
-       "|                     31 |          22802181 |        1.00e+00 |\n",
-       "|                     30 |          19932375 |        1.00e+00 |\n",
-       "|                     33 |          23830081 |        1.00e+00 |\n",
-       "|                     33 |          23924871 |        1.00e+00 |\n",
-       "|                     33 |          23994800 |        1.00e+00 |\n",
-       "|                     33 |          23997776 |        1.00e+00 |\n",
-       "|                     33 |          24143459 |        1.00e+00 |\n",
-       "|                     32 |          23651203 |        1.00e+00 |\n",
-       "|                     32 |          24114456 |        1.00e+00 |\n",
-       "|                     32 |          24077551 |        1.00e+00 |\n",
-       "+------------------------+-------------------+-----------------+\n",
-       "\n",
-       "+-----------------+------------------+------------------+------------------+\n",
-       "| coverage.over_5 | coverage.over_10 | coverage.over_15 | coverage.over_20 |\n",
-       "+-----------------+------------------+------------------+------------------+\n",
-       "|         float64 |          float64 |          float64 |          float64 |\n",
-       "+-----------------+------------------+------------------+------------------+\n",
-       "|        1.00e+00 |         1.00e+00 |         9.97e-01 |         9.86e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.94e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.92e-01 |\n",
-       "|        1.00e+00 |         9.99e-01 |         9.95e-01 |         9.89e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         9.99e-01 |\n",
-       "|        1.00e+00 |         9.95e-01 |         9.83e-01 |         9.70e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.97e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.97e-01 |\n",
-       "|        9.84e-01 |         9.41e-01 |         8.72e-01 |         8.17e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         9.98e-01 |         9.91e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.98e-01 |\n",
-       "+-----------------+------------------+------------------+------------------+\n",
-       "\n",
-       "+------------------+------------------+------------------+-------------------+\n",
-       "| coverage.over_25 | coverage.over_30 | coverage.over_50 | coverage.over_100 |\n",
-       "+------------------+------------------+------------------+-------------------+\n",
-       "|          float64 |          float64 |          float64 |           float64 |\n",
-       "+------------------+------------------+------------------+-------------------+\n",
-       "|         9.32e-01 |         6.07e-01 |         8.96e-04 |          6.02e-05 |\n",
-       "|         9.48e-01 |         6.21e-01 |         8.39e-04 |          4.93e-05 |\n",
-       "|         9.37e-01 |         6.12e-01 |         8.29e-04 |          4.51e-05 |\n",
-       "|         9.59e-01 |         7.28e-01 |         1.35e-02 |          7.59e-04 |\n",
-       "|         9.76e-01 |         7.42e-01 |         1.35e-02 |          7.59e-04 |\n",
-       "|         9.76e-01 |         7.69e-01 |         5.01e-03 |          1.85e-04 |\n",
-       "|         9.28e-01 |         6.53e-01 |         4.38e-04 |          4.93e-05 |\n",
-       "|         9.79e-01 |         7.47e-01 |         8.14e-04 |          6.29e-05 |\n",
-       "|         9.54e-01 |         6.97e-01 |         1.14e-03 |          3.69e-05 |\n",
-       "|         7.64e-01 |         5.70e-01 |         1.35e-03 |          1.76e-04 |\n",
-       "|         9.72e-01 |         7.53e-01 |         3.11e-03 |          1.40e-03 |\n",
-       "|         9.72e-01 |         7.55e-01 |         4.07e-03 |          2.20e-03 |\n",
-       "|         9.73e-01 |         7.59e-01 |         4.64e-03 |          2.58e-03 |\n",
-       "|         9.73e-01 |         7.59e-01 |         4.67e-03 |          2.60e-03 |\n",
-       "|         9.74e-01 |         7.66e-01 |         6.55e-03 |          3.89e-03 |\n",
-       "|         9.42e-01 |         7.13e-01 |         6.42e-03 |          3.91e-03 |\n",
-       "|         9.81e-01 |         7.72e-01 |         1.94e-02 |          3.68e-03 |\n",
-       "|         9.79e-01 |         7.69e-01 |         1.94e-02 |          3.67e-03 |\n",
-       "+------------------+------------------+------------------+-------------------+"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "coverage_ht = coverage(\"exomes\").ht()\n",
-    "\n",
-    "#Filter to PASS SNVs with AF <= 0.1% and median exome depth ≥ 30.\n",
-    "ht = ht.filter(\n",
-    "    (hl.len(ht.filters) == 0) \n",
-    "    & (ht.allele_info.allele_type == \"snv\")\n",
-    "    & (ht.freq[0].AF <= 0.001)\n",
-    "    & (coverage_ht[ht.locus].median_approx >= 30)\n",
-    ")\n",
-    "\n",
-    "\n",
-    "print(f\"Number of variants: {ht.count()}\")\n",
-    "ht.select(\n",
-    "    freq=ht.freq[0],\n",
-    "    csq=ht.vep.transcript_consequences[0].consequence_terms,\n",
-    "    coverage=coverage_ht[ht.locus],\n",
-    ").show(-1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b104a39b",
-   "metadata": {},
-   "source": [
-    "# Get 'freq' for specific genetic ancestry groups"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "135565fe",
-   "metadata": {},
-   "source": [
-    "## Get 'freq' for multiple groups for an (gene) interval"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "4f78166f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n",
-    "\n",
-    "# Filter to interval, e.g. for ASH1L.\n",
-    "gene_interval = \"chr1:155335268-155563162\"\n",
-    "\n",
-    "# Filter the exome release Hail Table to the ASH1L gene interval.\n",
-    "ht = hl.filter_intervals(ht, [hl.parse_locus_interval(gene_interval, reference_genome=\"GRCh38\")])\n",
-    "\n",
-    "# Filter to variants with adj.AC > 0 \n",
-    "ht = ht.filter(ht.freq[0].AC>0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "ce7a1e8c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "[Stage 39:>                                                         (0 + 3) / 3]\r",
-      "\r",
-      "[Stage 39:======================================>                   (2 + 1) / 3]\r"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
afr
amr
eas
mid
nfe
sas
locus
alleles
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64
chr1:155335497["A","C"]0NA000NA0000.00e+0013800NA0000.00e+00200NA00
chr1:155335570["T","C"]0NA000NA0017.25e-0313800NA0000.00e+00200NA00
chr1:155335571["TA","T"]0NA000NA0017.25e-0313800NA0000.00e+00200NA00
chr1:155335746["G","C"]0NA000NA0000.00e+0013200NA0000.00e+00200NA00
chr1:155335855["G","A"]0NA000NA0017.25e-0313800NA0000.00e+00600NA00
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+----------------+------------+--------+---------+--------+\n",
-       "| locus          | alleles    | afr.AC |  afr.AF | afr.AN |\n",
-       "+----------------+------------+--------+---------+--------+\n",
-       "| locus  | array |  int32 | float64 |  int32 |\n",
-       "+----------------+------------+--------+---------+--------+\n",
-       "| chr1:155335497 | [\"A\",\"C\"]  |      0 |      NA |      0 |\n",
-       "| chr1:155335570 | [\"T\",\"C\"]  |      0 |      NA |      0 |\n",
-       "| chr1:155335571 | [\"TA\",\"T\"] |      0 |      NA |      0 |\n",
-       "| chr1:155335746 | [\"G\",\"C\"]  |      0 |      NA |      0 |\n",
-       "| chr1:155335855 | [\"G\",\"A\"]  |      0 |      NA |      0 |\n",
-       "+----------------+------------+--------+---------+--------+\n",
-       "\n",
-       "+----------------------+--------+---------+--------+----------------------+\n",
-       "| afr.homozygote_count | amr.AC |  amr.AF | amr.AN | amr.homozygote_count |\n",
-       "+----------------------+--------+---------+--------+----------------------+\n",
-       "|                int64 |  int32 | float64 |  int32 |                int64 |\n",
-       "+----------------------+--------+---------+--------+----------------------+\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "+----------------------+--------+---------+--------+----------------------+\n",
-       "\n",
-       "+--------+----------+--------+----------------------+--------+---------+\n",
-       "| eas.AC |   eas.AF | eas.AN | eas.homozygote_count | mid.AC |  mid.AF |\n",
-       "+--------+----------+--------+----------------------+--------+---------+\n",
-       "|  int32 |  float64 |  int32 |                int64 |  int32 | float64 |\n",
-       "+--------+----------+--------+----------------------+--------+---------+\n",
-       "|      0 | 0.00e+00 |    138 |                    0 |      0 |      NA |\n",
-       "|      1 | 7.25e-03 |    138 |                    0 |      0 |      NA |\n",
-       "|      1 | 7.25e-03 |    138 |                    0 |      0 |      NA |\n",
-       "|      0 | 0.00e+00 |    132 |                    0 |      0 |      NA |\n",
-       "|      1 | 7.25e-03 |    138 |                    0 |      0 |      NA |\n",
-       "+--------+----------+--------+----------------------+--------+---------+\n",
-       "\n",
-       "+--------+----------------------+--------+----------+--------+\n",
-       "| mid.AN | mid.homozygote_count | nfe.AC |   nfe.AF | nfe.AN |\n",
-       "+--------+----------------------+--------+----------+--------+\n",
-       "|  int32 |                int64 |  int32 |  float64 |  int32 |\n",
-       "+--------+----------------------+--------+----------+--------+\n",
-       "|      0 |                    0 |      0 | 0.00e+00 |      2 |\n",
-       "|      0 |                    0 |      0 | 0.00e+00 |      2 |\n",
-       "|      0 |                    0 |      0 | 0.00e+00 |      2 |\n",
-       "|      0 |                    0 |      0 | 0.00e+00 |      2 |\n",
-       "|      0 |                    0 |      0 | 0.00e+00 |      6 |\n",
-       "+--------+----------------------+--------+----------+--------+\n",
-       "\n",
-       "+----------------------+--------+---------+--------+----------------------+\n",
-       "| nfe.homozygote_count | sas.AC |  sas.AF | sas.AN | sas.homozygote_count |\n",
-       "+----------------------+--------+---------+--------+----------------------+\n",
-       "|                int64 |  int32 | float64 |  int32 |                int64 |\n",
-       "+----------------------+--------+---------+--------+----------------------+\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "+----------------------+--------+---------+--------+----------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht = extract_callstats_for_multiple_ancs(ht, gen_ancs=['afr', 'amr', 'eas', 'mid', 'nfe', 'sas'])\n",
-    "\n",
-    "ht.show(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fe2e98b8",
-   "metadata": {},
-   "source": [
-    "## Get 'freq' for a specific group and a specific variant"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "4846958a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "
afr
locus
alleles
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>int32float64int32int64
chr22:15528692["C","G"]6351.90e-02333806
"
-      ],
-      "text/plain": [
-       "+----------------+------------+--------+----------+--------+\n",
-       "| locus          | alleles    | afr.AC |   afr.AF | afr.AN |\n",
-       "+----------------+------------+--------+----------+--------+\n",
-       "| locus  | array |  int32 |  float64 |  int32 |\n",
-       "+----------------+------------+--------+----------+--------+\n",
-       "| chr22:15528692 | [\"C\",\"G\"]  |    635 | 1.90e-02 |  33380 |\n",
-       "+----------------+------------+--------+----------+--------+\n",
-       "\n",
-       "+----------------------+\n",
-       "| afr.homozygote_count |\n",
-       "+----------------------+\n",
-       "|                int64 |\n",
-       "+----------------------+\n",
-       "|                    6 |\n",
-       "+----------------------+"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n",
-    "\n",
-    "# When a variant exists...\n",
-    "extract_callstats_for_1anc_1variant(ht, gen_anc='AFR', contig='chr22', position=15528692, alleles=['C','G']).show(-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "9f4c689b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-11-02 02:02:23.969 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "
afr
locus
alleles
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>int32float64int32int64
"
-      ],
-      "text/plain": [
-       "+---------------+------------+--------+---------+--------+\n",
-       "| locus         | alleles    | afr.AC |  afr.AF | afr.AN |\n",
-       "+---------------+------------+--------+---------+--------+\n",
-       "| locus | array |  int32 | float64 |  int32 |\n",
-       "+---------------+------------+--------+---------+--------+\n",
-       "+---------------+------------+--------+---------+--------+\n",
-       "\n",
-       "+----------------------+\n",
-       "| afr.homozygote_count |\n",
-       "+----------------------+\n",
-       "|                int64 |\n",
-       "+----------------------+\n",
-       "+----------------------+"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# When a variant doesn't exist...\n",
-    "extract_callstats_for_1anc_1variant(ht, gen_anc='AFR', contig='chr22', position=15528692, alleles=['C','A']).show(-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "6fc82c5c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Copying gs://gnomad-qin/qin_notebooks/toolbox_for_gnomad_users.ipynb...\n",
-      "/ [1 files][747.1 KiB/747.1 KiB]                                                \n",
-      "Operation completed over 1 objects/747.1 KiB.                                    \n",
-      "[NbConvertApp] WARNING | Config option `extra_template_paths` not recognized by `EmbedHTMLExporter`.  Did you mean `template_path`?\n",
-      "[NbConvertApp] Converting notebook toolbox_for_gnomad_users.ipynb to html_embed\n",
-      "[NbConvertApp] Writing 943138 bytes to toolbox_for_gnomad_users.html\n",
-      "Copying file://toolbox_for_gnomad_users.html [Content-Type=text/html]...\n",
-      "/ [1 files][921.1 KiB/921.1 KiB]                                                \n",
-      "Operation completed over 1 objects/921.1 KiB.                                    \n"
-     ]
-    }
-   ],
-   "source": [
-    "notebook_name='toolbox_for_gnomad_users'\n",
-    "\n",
-    "#Uncomment top lines for the first time exporting on a cluster\n",
-    "#!/opt/conda/default/bin/conda create -n save-html-env --clone /opt/conda/default\n",
-    "#!/opt/conda/miniconda3/envs/save-html-env/bin/pip install \"nbconvert<6\" jinja2==3.0.3 jupyter_contrib_nbextensions\n",
-    "\n",
-    "# Download the notebook from Google Cloud Storage\n",
-    "!gsutil -u broad-mpg-gnomad cp gs://gnomad-qin/qin_notebooks/{notebook_name}.ipynb .\n",
-    "\n",
-    "# Convert the notebook to HTML with embedded resources\n",
-    "! /opt/conda/miniconda3/envs/save-html-env/bin/jupyter nbconvert \\\n",
-    "    --CodeFoldingPreprocessor.remove_folded_code=True --to html_embed \\\n",
-    "    --template \"/opt/conda/miniconda3/envs/save-html-env/lib/python3.11/site-packages/jupyter_contrib_nbextensions/templates/toc2.tpl\" \\\n",
-    "    {notebook_name}.ipynb\n",
-    "\n",
-    "# Upload the converted HTML back to Google Cloud Storage\n",
-    "!gsutil -u broad-mpg-gnomad cp {notebook_name}.html gs://gnomad-qin/qin_notebooks/{notebook_name}.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a9e4c9bb",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.7"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {
-    "height": "613.99px",
-    "width": "526.312px"
-   },
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": true,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "219.438px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From b853fc20d2a78b6d6d533b8f00ea209f13c93e7a Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 11 Dec 2024 08:40:57 -0700
Subject: [PATCH 28/33] Add notebooks to git

---
 .../notebooks/explore_release_data.ipynb      | 6622 +++++++++++++++++
 .../intro_to_filtering_variant_data.ipynb     | 6288 ++++++++++++++++
 gnomad_toolbox/notebooks/needs_a_name.ipynb   |  602 ++
 3 files changed, 13512 insertions(+)
 create mode 100644 gnomad_toolbox/notebooks/explore_release_data.ipynb
 create mode 100644 gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
 create mode 100644 gnomad_toolbox/notebooks/needs_a_name.ipynb

diff --git a/gnomad_toolbox/notebooks/explore_release_data.ipynb b/gnomad_toolbox/notebooks/explore_release_data.ipynb
new file mode 100644
index 0000000..1801596
--- /dev/null
+++ b/gnomad_toolbox/notebooks/explore_release_data.ipynb
@@ -0,0 +1,6622 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "853c94b9",
+   "metadata": {},
+   "source": [
+    "# Introduction to gnomAD Hail release files\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cf83cfe-0fce-40ae-add7-c9f2c20c1e85",
+   "metadata": {},
+   "source": [
+    "In this notebook we will explore all of the available [gnomAD v4 release files](https://gnomad.broadinstitute.org/data#v4) that are in Hail formats."
+   ]
+  },
+  {
+   "attachments": {
+    "afcd4ecc-4e90-464b-91c7-9cd80b0e92ba.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "88ddfe98-92b3-4c9c-906d-84d4835d2bf1",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:afcd4ecc-4e90-464b-91c7-9cd80b0e92ba.png)"
+   ]
+  },
+  {
+   "attachments": {
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+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "f97431a4-78a0-473f-99a0-67eeefb8bbc6",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:2f957256-5d08-40e1-b77c-5faa4f771fb2.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e609a46",
+   "metadata": {
+    "tags": [],
+    "toc": true
+   },
+   "source": [
+    "
Table of Contents
\n",
+    "
"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e713032",
+   "metadata": {},
+   "source": [
+    "## Import modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e69953f7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:40.044647Z",
+     "start_time": "2024-12-06T20:08:37.758061Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "    \n",
+       "    
\n",
+       "        \n",
+       "        Loading BokehJS ...\n",
+       "    
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function now() {\n",
+       "    return new Date();\n",
+       "  }\n",
+       "\n",
+       "  const force = true;\n",
+       "\n",
+       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
+       "    root._bokeh_onload_callbacks = [];\n",
+       "    root._bokeh_is_loading = undefined;\n",
+       "  }\n",
+       "\n",
+       "const JS_MIME_TYPE = 'application/javascript';\n",
+       "  const HTML_MIME_TYPE = 'text/html';\n",
+       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
+       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
+       "\n",
+       "  /**\n",
+       "   * Render data to the DOM node\n",
+       "   */\n",
+       "  function render(props, node) {\n",
+       "    const script = document.createElement(\"script\");\n",
+       "    node.appendChild(script);\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when an output is cleared or removed\n",
+       "   */\n",
+       "  function handleClearOutput(event, handle) {\n",
+       "    function drop(id) {\n",
+       "      const view = Bokeh.index.get_by_id(id)\n",
+       "      if (view != null) {\n",
+       "        view.model.document.clear()\n",
+       "        Bokeh.index.delete(view)\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    const cell = handle.cell;\n",
+       "\n",
+       "    const id = cell.output_area._bokeh_element_id;\n",
+       "    const server_id = cell.output_area._bokeh_server_id;\n",
+       "\n",
+       "    // Clean up Bokeh references\n",
+       "    if (id != null) {\n",
+       "      drop(id)\n",
+       "    }\n",
+       "\n",
+       "    if (server_id !== undefined) {\n",
+       "      // Clean up Bokeh references\n",
+       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
+       "      cell.notebook.kernel.execute(cmd_clean, {\n",
+       "        iopub: {\n",
+       "          output: function(msg) {\n",
+       "            const id = msg.content.text.trim()\n",
+       "            drop(id)\n",
+       "          }\n",
+       "        }\n",
+       "      });\n",
+       "      // Destroy server and session\n",
+       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
+       "      cell.notebook.kernel.execute(cmd_destroy);\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when a new output is added\n",
+       "   */\n",
+       "  function handleAddOutput(event, handle) {\n",
+       "    const output_area = handle.output_area;\n",
+       "    const output = handle.output;\n",
+       "\n",
+       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
+       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
+       "      return\n",
+       "    }\n",
+       "\n",
+       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
+       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
+       "      // store reference to embed id on output_area\n",
+       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "    }\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "      const bk_div = document.createElement(\"div\");\n",
+       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "      const script_attrs = bk_div.children[0].attributes;\n",
+       "      for (let i = 0; i < script_attrs.length; i++) {\n",
+       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
+       "      }\n",
+       "      // store reference to server id on output_area\n",
+       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function register_renderer(events, OutputArea) {\n",
+       "\n",
+       "    function append_mime(data, metadata, element) {\n",
+       "      // create a DOM node to render to\n",
+       "      const toinsert = this.create_output_subarea(\n",
+       "        metadata,\n",
+       "        CLASS_NAME,\n",
+       "        EXEC_MIME_TYPE\n",
+       "      );\n",
+       "      this.keyboard_manager.register_events(toinsert);\n",
+       "      // Render to node\n",
+       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+       "      render(props, toinsert[toinsert.length - 1]);\n",
+       "      element.append(toinsert);\n",
+       "      return toinsert\n",
+       "    }\n",
+       "\n",
+       "    /* Handle when an output is cleared or removed */\n",
+       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
+       "    events.on('delete.Cell', handleClearOutput);\n",
+       "\n",
+       "    /* Handle when a new output is added */\n",
+       "    events.on('output_added.OutputArea', handleAddOutput);\n",
+       "\n",
+       "    /**\n",
+       "     * Register the mime type and append_mime function with output_area\n",
+       "     */\n",
+       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+       "      /* Is output safe? */\n",
+       "      safe: true,\n",
+       "      /* Index of renderer in `output_area.display_order` */\n",
+       "      index: 0\n",
+       "    });\n",
+       "  }\n",
+       "\n",
+       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
+       "  if (root.Jupyter !== undefined) {\n",
+       "    const events = require('base/js/events');\n",
+       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+       "\n",
+       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+       "      register_renderer(events, OutputArea);\n",
+       "    }\n",
+       "  }\n",
+       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
+       "    root._bokeh_timeout = Date.now() + 5000;\n",
+       "    root._bokeh_failed_load = false;\n",
+       "  }\n",
+       "\n",
+       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
+       "     \"
\\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
+       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"
    \\n\"+\n",
+       "     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n",
+       "     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"\\n\"+\n",
+       "     \"from bokeh.resources import INLINE\\n\"+\n",
+       "     \"output_notebook(resources=INLINE)\\n\"+\n",
+       "     \"\\n\"+\n",
+       "     \"
\"}};\n",
+       "\n",
+       "  function display_loaded() {\n",
+       "    const el = document.getElementById(\"ac1134fa-55fa-4b47-b5c7-344dfbc63734\");\n",
+       "    if (el != null) {\n",
+       "      el.textContent = \"BokehJS is loading...\";\n",
+       "    }\n",
+       "    if (root.Bokeh !== undefined) {\n",
+       "      if (el != null) {\n",
+       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
+       "      }\n",
+       "    } else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(display_loaded, 100)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function run_callbacks() {\n",
+       "    try {\n",
+       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+       "        if (callback != null)\n",
+       "          callback();\n",
+       "      });\n",
+       "    } finally {\n",
+       "      delete root._bokeh_onload_callbacks\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
+       "  }\n",
+       "\n",
+       "  function load_libs(css_urls, js_urls, callback) {\n",
+       "    if (css_urls == null) css_urls = [];\n",
+       "    if (js_urls == null) js_urls = [];\n",
+       "\n",
+       "    root._bokeh_onload_callbacks.push(callback);\n",
+       "    if (root._bokeh_is_loading > 0) {\n",
+       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+       "      return null;\n",
+       "    }\n",
+       "    if (js_urls == null || js_urls.length === 0) {\n",
+       "      run_callbacks();\n",
+       "      return null;\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
+       "\n",
+       "    function on_load() {\n",
+       "      root._bokeh_is_loading--;\n",
+       "      if (root._bokeh_is_loading === 0) {\n",
+       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+       "        run_callbacks()\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    function on_error(url) {\n",
+       "      console.error(\"failed to load \" + url);\n",
+       "    }\n",
+       "\n",
+       "    for (let i = 0; i < css_urls.length; i++) {\n",
+       "      const url = css_urls[i];\n",
+       "      const element = document.createElement(\"link\");\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error.bind(null, url);\n",
+       "      element.rel = \"stylesheet\";\n",
+       "      element.type = \"text/css\";\n",
+       "      element.href = url;\n",
+       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+       "      document.body.appendChild(element);\n",
+       "    }\n",
+       "\n",
+       "    for (let i = 0; i < js_urls.length; i++) {\n",
+       "      const url = js_urls[i];\n",
+       "      const element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error.bind(null, url);\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "  };\n",
+       "\n",
+       "  function inject_raw_css(css) {\n",
+       "    const element = document.createElement(\"style\");\n",
+       "    element.appendChild(document.createTextNode(css));\n",
+       "    document.body.appendChild(element);\n",
+       "  }\n",
+       "\n",
+       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n",
+       "  const css_urls = [];\n",
+       "\n",
+       "  const inline_js = [    function(Bokeh) {\n",
+       "      Bokeh.set_log_level(\"info\");\n",
+       "    },\n",
+       "function(Bokeh) {\n",
+       "    }\n",
+       "  ];\n",
+       "\n",
+       "  function run_inline_js() {\n",
+       "    if (root.Bokeh !== undefined || force === true) {\n",
+       "          for (let i = 0; i < inline_js.length; i++) {\n",
+       "      inline_js[i].call(root, root.Bokeh);\n",
+       "    }\n",
+       "if (force === true) {\n",
+       "        display_loaded();\n",
+       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(run_inline_js, 100);\n",
+       "    } else if (!root._bokeh_failed_load) {\n",
+       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+       "      root._bokeh_failed_load = true;\n",
+       "    } else if (force !== true) {\n",
+       "      const cell = $(document.getElementById(\"ac1134fa-55fa-4b47-b5c7-344dfbc63734\")).parents('.cell').data().cell;\n",
+       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  if (root._bokeh_is_loading === 0) {\n",
+       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
+       "    run_inline_js();\n",
+       "  } else {\n",
+       "    load_libs(css_urls, js_urls, function() {\n",
+       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+       "      run_inline_js();\n",
+       "    });\n",
+       "  }\n",
+       "}(window));"
+      ],
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"ac1134fa-55fa-4b47-b5c7-344dfbc63734\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"ac1134fa-55fa-4b47-b5c7-344dfbc63734\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import hail as hl\n",
+    "from gnomad_toolbox.load_data import  get_gnomad_release"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8649f215-0afc-4f66-920a-53b707f41c4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Welcome to\n",
+      "     __  __     <>__\n",
+      "    / /_/ /__  __/ /\n",
+      "   / __  / _ `/ / /\n",
+      "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
+      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241210-1438-0.2.132-678e1f52b999.log\n"
+     ]
+    }
+   ],
+   "source": [
+    "hl.init(backend=\"local\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5335a135",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Variant data\n",
+    "\n",
+    "Available versions for each data type and reference build are (as of 2024-10-29):\n",
+    "\n",
+    "| Data Type       | GRCh38 Versions                  | GRCh37 Versions      |\n",
+    "|-----------------|----------------------------------|----------------------|\n",
+    "| exomes          | 4.0, 4.1                         | 2.1, 2.1.1           |\n",
+    "| genomes         | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1           |\n",
+    "| joint           | 4.1                              | N/A                  |\n",
+    "\n",
+    "For a description of all fields the the HT, see the [Help/FAQ](https://gnomad.broadinstitute.org/help/v4-hts) page."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1a4ae8933ba6421",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### v4.1 exomes Hail Table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "318a034c-ac84-4147-9f25-e5e8783e9b91",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "100cf576",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(data_type='exomes', version='4.1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77d7a05e31c1f37a",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "95c14f2c8cc3e699",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'freq_meta': array> \n",
+      "    'freq_index_dict': dict \n",
+      "    'freq_meta_sample_count': array \n",
+      "    'faf_meta': array> \n",
+      "    'faf_index_dict': dict \n",
+      "    'age_distribution': struct {\n",
+      "        bin_edges: array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int32, \n",
+      "        n_larger: int32\n",
+      "    } \n",
+      "    'downsamplings': dict> \n",
+      "    'filtering_model': struct {\n",
+      "        filter_name: str, \n",
+      "        score_name: str, \n",
+      "        snv_cutoff: struct {\n",
+      "            bin: int32, \n",
+      "            min_score: float64\n",
+      "        }, \n",
+      "        indel_cutoff: struct {\n",
+      "            bin: int32, \n",
+      "            min_score: float64\n",
+      "        }, \n",
+      "        snv_training_variables: array, \n",
+      "        indel_training_variables: array\n",
+      "    } \n",
+      "    'inbreeding_coeff_cutoff': float64 \n",
+      "    'interval_qc_parameters': struct {\n",
+      "        per_platform: bool, \n",
+      "        all_platforms: bool, \n",
+      "        high_qual_cutoffs: dict>, \n",
+      "        min_platform_size: int32\n",
+      "    } \n",
+      "    'tool_versions': struct {\n",
+      "        cadd_version: str, \n",
+      "        revel_version: str, \n",
+      "        spliceai_version: str, \n",
+      "        pangolin_version: array, \n",
+      "        phylop_version: str, \n",
+      "        dbsnp_version: str, \n",
+      "        sift_version: str, \n",
+      "        polyphen_version: str\n",
+      "    } \n",
+      "    'vrs_versions': struct {\n",
+      "        vrs_schema_version: str, \n",
+      "        vrs_python_version: str, \n",
+      "        seqrepo_version: str\n",
+      "    } \n",
+      "    'vep_globals': struct {\n",
+      "        vep_version: str, \n",
+      "        vep_help: str, \n",
+      "        vep_config: str, \n",
+      "        gencode_version: str, \n",
+      "        mane_select_version: str\n",
+      "    } \n",
+      "    'frequency_README': str \n",
+      "    'date': str \n",
+      "    'version': str \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'alleles': array \n",
+      "    'freq': array \n",
+      "    'grpmax': struct {\n",
+      "        gnomad: struct {\n",
+      "            AC: int32, \n",
+      "            AF: float64, \n",
+      "            AN: int32, \n",
+      "            homozygote_count: int64, \n",
+      "            gen_anc: str\n",
+      "        }, \n",
+      "        non_ukb: struct {\n",
+      "            AC: int32, \n",
+      "            AF: float64, \n",
+      "            AN: int32, \n",
+      "            homozygote_count: int64, \n",
+      "            gen_anc: str\n",
+      "        }\n",
+      "    } \n",
+      "    'faf': array \n",
+      "    'fafmax': struct {\n",
+      "        gnomad: struct {\n",
+      "            faf95_max: float64, \n",
+      "            faf95_max_gen_anc: str, \n",
+      "            faf99_max: float64, \n",
+      "            faf99_max_gen_anc: str\n",
+      "        }, \n",
+      "        non_ukb: struct {\n",
+      "            faf95_max: float64, \n",
+      "            faf95_max_gen_anc: str, \n",
+      "            faf99_max: float64, \n",
+      "            faf99_max_gen_anc: str\n",
+      "        }\n",
+      "    } \n",
+      "    'a_index': int32 \n",
+      "    'was_split': bool \n",
+      "    'rsid': set \n",
+      "    'filters': set \n",
+      "    'info': struct {\n",
+      "        FS: float64, \n",
+      "        MQ: float64, \n",
+      "        MQRankSum: float64, \n",
+      "        QUALapprox: int64, \n",
+      "        QD: float64, \n",
+      "        ReadPosRankSum: float64, \n",
+      "        SB: array, \n",
+      "        SOR: float64, \n",
+      "        VarDP: int32, \n",
+      "        AS_FS: float64, \n",
+      "        AS_MQ: float64, \n",
+      "        AS_MQRankSum: float64, \n",
+      "        AS_pab_max: float64, \n",
+      "        AS_QUALapprox: int64, \n",
+      "        AS_QD: float64, \n",
+      "        AS_ReadPosRankSum: float64, \n",
+      "        AS_SB_TABLE: array, \n",
+      "        AS_SOR: float64, \n",
+      "        AS_VarDP: int32, \n",
+      "        singleton: bool, \n",
+      "        transmitted_singleton: bool, \n",
+      "        sibling_singleton: bool, \n",
+      "        omni: bool, \n",
+      "        mills: bool, \n",
+      "        monoallelic: bool, \n",
+      "        only_het: bool, \n",
+      "        AS_VQSLOD: float64, \n",
+      "        inbreeding_coeff: float64, \n",
+      "        vrs: struct {\n",
+      "            VRS_Allele_IDs: array, \n",
+      "            VRS_Starts: array, \n",
+      "            VRS_Ends: array, \n",
+      "            VRS_States: array\n",
+      "        }\n",
+      "    } \n",
+      "    'vep': struct {\n",
+      "        allele_string: str, \n",
+      "        end: int32, \n",
+      "        id: str, \n",
+      "        input: str, \n",
+      "        intergenic_consequences: array, \n",
+      "            impact: str, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        most_severe_consequence: str, \n",
+      "        motif_feature_consequences: array, \n",
+      "            high_inf_pos: str, \n",
+      "            impact: str, \n",
+      "            motif_feature_id: str, \n",
+      "            motif_name: str, \n",
+      "            motif_pos: int32, \n",
+      "            motif_score_change: float64, \n",
+      "            transcription_factors: array, \n",
+      "            strand: int32, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        regulatory_feature_consequences: array, \n",
+      "            impact: str, \n",
+      "            regulatory_feature_id: str, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        seq_region_name: str, \n",
+      "        start: int32, \n",
+      "        strand: int32, \n",
+      "        transcript_consequences: array, \n",
+      "            distance: int32, \n",
+      "            domains: array, \n",
+      "            exon: str, \n",
+      "            flags: str, \n",
+      "            gene_id: str, \n",
+      "            gene_pheno: int32, \n",
+      "            gene_symbol: str, \n",
+      "            gene_symbol_source: str, \n",
+      "            hgnc_id: str, \n",
+      "            hgvsc: str, \n",
+      "            hgvsp: str, \n",
+      "            hgvs_offset: int32, \n",
+      "            impact: str, \n",
+      "            intron: str, \n",
+      "            lof: str, \n",
+      "            lof_flags: str, \n",
+      "            lof_filter: str, \n",
+      "            lof_info: str, \n",
+      "            mane_select: str, \n",
+      "            mane_plus_clinical: str, \n",
+      "            mirna: array, \n",
+      "            protein_end: int32, \n",
+      "            protein_start: int32, \n",
+      "            protein_id: str, \n",
+      "            source: str, \n",
+      "            strand: int32, \n",
+      "            transcript_id: str, \n",
+      "            tsl: int32, \n",
+      "            uniprot_isoform: array, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        variant_class: str\n",
+      "    } \n",
+      "    'vqsr_results': struct {\n",
+      "        AS_VQSLOD: float64, \n",
+      "        AS_culprit: str, \n",
+      "        positive_train_site: bool, \n",
+      "        negative_train_site: bool\n",
+      "    } \n",
+      "    'region_flags': struct {\n",
+      "        non_par: bool, \n",
+      "        lcr: bool, \n",
+      "        segdup: bool, \n",
+      "        fail_interval_qc: bool, \n",
+      "        outside_ukb_capture_region: bool, \n",
+      "        outside_broad_capture_region: bool\n",
+      "    } \n",
+      "    'allele_info': struct {\n",
+      "        variant_type: str, \n",
+      "        n_alt_alleles: int32, \n",
+      "        has_star: bool, \n",
+      "        allele_type: str, \n",
+      "        was_mixed: bool\n",
+      "    } \n",
+      "    'histograms': struct {\n",
+      "        qual_hists: struct {\n",
+      "            gq_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            gq_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            ab_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }, \n",
+      "        raw_qual_hists: struct {\n",
+      "            gq_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            gq_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            ab_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }, \n",
+      "        age_hists: struct {\n",
+      "            age_hist_het: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            age_hist_hom: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }\n",
+      "    } \n",
+      "    'in_silico_predictors': struct {\n",
+      "        cadd: struct {\n",
+      "            phred: float32, \n",
+      "            raw_score: float32\n",
+      "        }, \n",
+      "        revel_max: float64, \n",
+      "        spliceai_ds_max: float32, \n",
+      "        pangolin_largest_ds: float64, \n",
+      "        phylop: float64, \n",
+      "        sift_max: float64, \n",
+      "        polyphen_max: float64\n",
+      "    } \n",
+      "----------------------------------------\n",
+      "Key: ['locus', 'alleles']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a071f738b2c888e",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "222de580c305d72a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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cessed_transcript",NA,NA,234,234,NA,NA,NA,["non_coding_transcript_exon_variant"],NA,NA,"1/3",NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102","ENST00000456328.2:n.234G>A",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000456328",1,NA,"A"),(1,NA,NA,"unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],2302,NA,NA,NA,"ENSG00000227232",NA,"WASH7P","HGNC","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",-1,"ENST00000488147",NA,NA,"A"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],2260,NA,NA,NA,"653635",NA,"WASH7P","EntrezGene","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",-1,"NR_024540.1",NA,NA,"A"),(1,NA,NA,"transcribed_pseudogene",1,NA,229,229,NA,NA,NA,["non_coding_transcript_exon_variant"],NA,NA,"1/3",NA,"100287102",NA,"DDX11L1","EntrezGene","HGNC:37102","NR_046018.2:n.229G>A",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",1,"NR_046018.2",NA,NA,"A")]"SNV"-2.86e+00"AS_MQ"FalseFalseFalseFalseTrueNATrueFalse"snv"1False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,14,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][314323,1,0,0,54,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][308789,5585,16,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]004.42e+003.11e-01NANA-8.00e-021.38e+00NANA
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+----------------+\n",
+       "| locus         | alleles        |\n",
+       "+---------------+----------------+\n",
+       "| locus | array     |\n",
+       "+---------------+----------------+\n",
+       "| chr1:11994    | [\"T\",\"C\"]      |\n",
+       "| chr1:12016    | [\"G\",\"A\"]      |\n",
+       "| chr1:12060    | [\"CTGGAG\",\"C\"] |\n",
+       "| chr1:12074    | [\"T\",\"C\"]      |\n",
+       "| chr1:12102    | [\"G\",\"A\"]      |\n",
+       "+---------------+----------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0,NA,0,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA,... |\n",
+       "| [(0,NA,0,0),(6,9.54e-06,628784,2),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA,... |\n",
+       "| [(0,0.00e+00,4,0),(1,1.59e-06,628784,0),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n",
+       "| [(0,0.00e+00,4,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n",
+       "| [(0,0.00e+00,32,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0)... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|               NA |               NA |               NA |\n",
+       "|               NA |               NA |               NA |\n",
+       "|               NA |               NA |               NA |\n",
+       "|               NA |               NA |               NA |\n",
+       "|               NA |               NA |               NA |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+----------+-------------------+---------+----------+\n",
+       "| was_split | rsid     | filters           | info.FS |  info.MQ |\n",
+       "+-----------+----------+-------------------+---------+----------+\n",
+       "|      bool | set | set          | float64 |  float64 |\n",
+       "+-----------+----------+-------------------+---------+----------+\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.50e+01 |\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.50e+01 |\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.60e+01 |\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.56e+01 |\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.45e+01 |\n",
+       "+-----------+----------+-------------------+---------+----------+\n",
+       "\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|        float64 |           int64 |  float64 |             float64 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|             NA |              60 | 3.00e+01 |                  NA |\n",
+       "|       0.00e+00 |             262 | 2.18e+01 |            6.74e-01 |\n",
+       "|       6.74e-01 |             123 | 3.08e+01 |           -1.15e+00 |\n",
+       "|       4.31e-01 |              99 | 1.24e+01 |           -2.53e-01 |\n",
+       "|             NA |              90 | 2.25e+01 |                  NA |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "\n",
+       "+--------------+----------+------------+------------+------------+\n",
+       "| info.SB      | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+--------------+----------+------------+------------+------------+\n",
+       "| array |  float64 |      int32 |    float64 |    float64 |\n",
+       "+--------------+----------+------------+------------+------------+\n",
+       "| [0,0,2,0]    | 2.30e+00 |          2 |         NA |   2.50e+01 |\n",
+       "| [2,0,10,0]   | 2.67e+00 |         12 |         NA |   2.50e+01 |\n",
+       "| [1,0,3,0]    | 1.61e+00 |          4 |         NA |   2.60e+01 |\n",
+       "| [4,0,4,0]    | 6.93e-01 |          8 |         NA |   2.56e+01 |\n",
+       "| [0,0,4,0]    | 3.26e+00 |          4 |         NA |   2.45e+01 |\n",
+       "+--------------+----------+------------+------------+------------+\n",
+       "\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|           float64 |         float64 |              int64 |    float64 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|                NA |              NA |                 60 |   3.00e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |                262 |   2.18e+01 |\n",
+       "|          6.74e-01 |        6.25e-01 |                123 |   3.08e+01 |\n",
+       "|          4.31e-01 |        6.87e-01 |                 99 |   1.24e+01 |\n",
+       "|                NA |              NA |                 90 |   2.25e+01 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE | info.AS_SOR | info.AS_VarDP |\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "|                float64 | array     |     float64 |         int32 |\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "|                     NA | [0,0,2,0]        |    2.30e+00 |             2 |\n",
+       "|               6.74e-01 | [2,0,10,0]       |    2.67e+00 |            12 |\n",
+       "|              -1.15e+00 | [1,0,3,0]        |    1.61e+00 |             4 |\n",
+       "|              -2.53e-01 | [4,0,4,0]        |    6.93e-01 |             8 |\n",
+       "|                     NA | [0,0,4,0]        |    3.26e+00 |             4 |\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|           bool |                       bool |                   bool |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|           True |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|      bool |       bool |             bool |          bool |        float64 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|     False |      False |            False |         False |      -5.25e+00 |\n",
+       "|     False |      False |            False |         False |      -2.75e+00 |\n",
+       "|     False |      False |            False |         False |      -2.22e+00 |\n",
+       "|     False |      False |            False |         False |      -2.18e+00 |\n",
+       "|     False |      False |            False |         False |      -2.86e+00 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "\n",
+       "+-----------------------+\n",
+       "| info.inbreeding_coeff |\n",
+       "+-----------------------+\n",
+       "|               float64 |\n",
+       "+-----------------------+\n",
+       "|              1.00e+00 |\n",
+       "|              6.67e-01 |\n",
+       "|             -1.59e-06 |\n",
+       "|              1.00e+00 |\n",
+       "|              1.00e+00 |\n",
+       "+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.ps4-9woXy7o4rS39i8hDK_cUPBa-UcyP\",\"ga4gh:VA.nHlWYJXgiuvXrLAxQs... |\n",
+       "| [\"ga4gh:VA.VFxwcI4knOzk6SHzS2qowyDGnkG3mfEH\",\"ga4gh:VA.ctgP7qNjQAGjI2eTDo... |\n",
+       "| [\"ga4gh:VA.P573ZtUtAaRcceE7NLanEyynSefvcAPL\",\"ga4gh:VA.CHmk9uDiHW2LIHndZW... |\n",
+       "| [\"ga4gh:VA.neBeBT28ISe_1-yKPFsYxntP2jz1pj7E\",\"ga4gh:VA.1RPHSwBHNUwoECJ9VV... |\n",
+       "| [\"ga4gh:VA.ZXRhPWtip8HseOMWpCnaja0-ATo8hLE1\",\"ga4gh:VA.Qi95g6E8nt6DaqTGpH... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts | info.vrs.VRS_Ends | info.vrs.VRS_States |\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "| array        | array      | array          |\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "| [11993,11993]       | [11994,11994]     | [\"T\",\"C\"]           |\n",
+       "| [12015,12015]       | [12016,12016]     | [\"G\",\"A\"]           |\n",
+       "| [12059,12060]       | [12065,12071]     | [\"CTGGAG\",\"TGGAGT\"] |\n",
+       "| [12073,12073]       | [12074,12074]     | [\"T\",\"C\"]           |\n",
+       "| [12101,12101]       | [12102,12102]     | [\"G\",\"A\"]           |\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "\n",
+       "+-------------------+---------+--------+--------------------------------+\n",
+       "| vep.allele_string | vep.end | vep.id | vep.input                      |\n",
+       "+-------------------+---------+--------+--------------------------------+\n",
+       "| str               |   int32 | str    | str                            |\n",
+       "+-------------------+---------+--------+--------------------------------+\n",
+       "| \"T/C\"             |   11994 | \".\"    | \"chr1\t11994\t.\tT\tC\t.\t.\tGT\"      |\n",
+       "| \"G/A\"             |   12016 | \".\"    | \"chr1\t12016\t.\tG\tA\t.\t.\tGT\"      |\n",
+       "| \"TGGAG/-\"         |   12065 | \".\"    | \"chr1\t12060\t.\tCTGGAG\tC\t.\t.\tGT\" |\n",
+       "| \"T/C\"             |   12074 | \".\"    | \"chr1\t12074\t.\tT\tC\t.\t.\tGT\"      |\n",
+       "| \"G/A\"             |   12102 | \".\"    | \"chr1\t12102\t.\tG\tA\t.\t.\tGT\"      |\n",
+       "+-------------------+---------+--------+--------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| vep.most_severe_consequence          |\n",
+       "+--------------------------------------+\n",
+       "| str                                  |\n",
+       "+--------------------------------------+\n",
+       "| \"non_coding_transcript_exon_variant\" |\n",
+       "| \"non_coding_transcript_exon_variant\" |\n",
+       "| \"splice_donor_5th_base_variant\"      |\n",
+       "| \"non_coding_transcript_exon_variant\" |\n",
+       "| \"non_coding_transcript_exon_variant\" |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,2,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,14,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq   |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [314391,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [314388,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [314380,0,0,0,11,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0]  |\n",
+       "| [314369,1,0,0,20,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [314323,1,0,0,54,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq     |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                       |\n",
+       "+----------------------------------------------------+\n",
+       "| [308845,5547,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [308845,5547,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [308833,5557,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [308823,5567,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [308789,5585,16,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0]      |\n",
+       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        1.26e+01 |\n",
+       "|                                          0 |                        1.60e+01 |\n",
+       "|                                          0 |                        8.52e+00 |\n",
+       "|                                          0 |                        1.52e+01 |\n",
+       "|                                          0 |                        4.42e+00 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            1.08e+00 |                             NA |\n",
+       "|                            1.54e+00 |                             NA |\n",
+       "|                            7.07e-01 |                             NA |\n",
+       "|                            1.41e+00 |                             NA |\n",
+       "|                            3.11e-01 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                -1.10e-01 |                    1.09e+00 |\n",
+       "|                                -7.00e-02 |                    6.55e+00 |\n",
+       "|                                -9.00e-02 |                   -4.41e+00 |\n",
+       "|                                -4.00e-02 |                    6.01e+00 |\n",
+       "|                                -8.00e-02 |                    1.38e+00 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7a158a3-f21a-4f87-9596-1f918156d713",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### v4.1 genomes Hail Table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30f86500-afc5-419e-ae2e-f944dc461fee",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "62ca9934-20dd-437e-898b-86a056e2606e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(data_type='genomes', version='4.1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cf4b782-f289-47b6-9123-d08ca761b074",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "09de90df-0b03-4a54-817c-c8a0606026f6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'freq_meta': array> \n",
+      "    'freq_index_dict': dict \n",
+      "    'freq_meta_sample_count': array \n",
+      "    'faf_meta': array> \n",
+      "    'faf_index_dict': dict \n",
+      "    'age_distribution': struct {\n",
+      "        bin_edges: array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int32, \n",
+      "        n_larger: int32\n",
+      "    } \n",
+      "    'filtering_model': struct {\n",
+      "        filter_name: str, \n",
+      "        score_name: str, \n",
+      "        snv_cutoff: struct {\n",
+      "            bin: int32, \n",
+      "            min_score: float64\n",
+      "        }, \n",
+      "        indel_cutoff: struct {\n",
+      "            bin: int32, \n",
+      "            min_score: float64\n",
+      "        }, \n",
+      "        snv_training_variables: array, \n",
+      "        indel_training_variables: array\n",
+      "    } \n",
+      "    'inbreeding_coeff_cutoff': float64 \n",
+      "    'tool_versions': struct {\n",
+      "        cadd_version: str, \n",
+      "        revel_version: str, \n",
+      "        spliceai_version: str, \n",
+      "        pangolin_version: array, \n",
+      "        phylop_version: str, \n",
+      "        dbsnp_version: str, \n",
+      "        sift_version: str, \n",
+      "        polyphen_version: str\n",
+      "    } \n",
+      "    'vrs_versions': struct {\n",
+      "        vrs_schema_version: str, \n",
+      "        vrs_python_version: str, \n",
+      "        seqrepo_version: str\n",
+      "    } \n",
+      "    'vep_globals': struct {\n",
+      "        vep_version: str, \n",
+      "        vep_help: str, \n",
+      "        vep_config: str, \n",
+      "        gencode_version: str, \n",
+      "        mane_select_version: str\n",
+      "    } \n",
+      "    'frequency_README': str \n",
+      "    'date': str \n",
+      "    'version': str \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'alleles': array \n",
+      "    'freq': array \n",
+      "    'grpmax': struct {\n",
+      "        AC: int32, \n",
+      "        AF: float64, \n",
+      "        AN: int32, \n",
+      "        homozygote_count: int32, \n",
+      "        gen_anc: str\n",
+      "    } \n",
+      "    'faf': array \n",
+      "    'fafmax': struct {\n",
+      "        faf95_max: float64, \n",
+      "        faf95_max_gen_anc: str, \n",
+      "        faf99_max: float64, \n",
+      "        faf99_max_gen_anc: str\n",
+      "    } \n",
+      "    'a_index': int32 \n",
+      "    'was_split': bool \n",
+      "    'rsid': set \n",
+      "    'filters': set \n",
+      "    'info': struct {\n",
+      "        FS: float64, \n",
+      "        MQ: float64, \n",
+      "        MQRankSum: float64, \n",
+      "        QUALapprox: int64, \n",
+      "        QD: float32, \n",
+      "        ReadPosRankSum: float64, \n",
+      "        SB: array, \n",
+      "        SOR: float64, \n",
+      "        VarDP: int32, \n",
+      "        AS_FS: float64, \n",
+      "        AS_MQ: float64, \n",
+      "        AS_MQRankSum: float64, \n",
+      "        AS_pab_max: float64, \n",
+      "        AS_QUALapprox: int64, \n",
+      "        AS_QD: float32, \n",
+      "        AS_ReadPosRankSum: float64, \n",
+      "        AS_SB_TABLE: array, \n",
+      "        AS_SOR: float64, \n",
+      "        AS_VarDP: int32, \n",
+      "        singleton: bool, \n",
+      "        transmitted_singleton: bool, \n",
+      "        omni: bool, \n",
+      "        mills: bool, \n",
+      "        monoallelic: bool, \n",
+      "        only_het: bool, \n",
+      "        AS_VQSLOD: float64, \n",
+      "        inbreeding_coeff: float64, \n",
+      "        vrs: struct {\n",
+      "            VRS_Allele_IDs: array, \n",
+      "            VRS_Starts: array, \n",
+      "            VRS_Ends: array, \n",
+      "            VRS_States: array\n",
+      "        }\n",
+      "    } \n",
+      "    'vep': struct {\n",
+      "        allele_string: str, \n",
+      "        end: int32, \n",
+      "        id: str, \n",
+      "        input: str, \n",
+      "        intergenic_consequences: array, \n",
+      "            impact: str, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        most_severe_consequence: str, \n",
+      "        motif_feature_consequences: array, \n",
+      "            high_inf_pos: str, \n",
+      "            impact: str, \n",
+      "            motif_feature_id: str, \n",
+      "            motif_name: str, \n",
+      "            motif_pos: int32, \n",
+      "            motif_score_change: float64, \n",
+      "            transcription_factors: array, \n",
+      "            strand: int32, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        regulatory_feature_consequences: array, \n",
+      "            impact: str, \n",
+      "            regulatory_feature_id: str, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        seq_region_name: str, \n",
+      "        start: int32, \n",
+      "        strand: int32, \n",
+      "        transcript_consequences: array, \n",
+      "            distance: int32, \n",
+      "            domains: array, \n",
+      "            exon: str, \n",
+      "            flags: str, \n",
+      "            gene_id: str, \n",
+      "            gene_pheno: int32, \n",
+      "            gene_symbol: str, \n",
+      "            gene_symbol_source: str, \n",
+      "            hgnc_id: str, \n",
+      "            hgvsc: str, \n",
+      "            hgvsp: str, \n",
+      "            hgvs_offset: int32, \n",
+      "            impact: str, \n",
+      "            intron: str, \n",
+      "            lof: str, \n",
+      "            lof_flags: str, \n",
+      "            lof_filter: str, \n",
+      "            lof_info: str, \n",
+      "            mane_select: str, \n",
+      "            mane_plus_clinical: str, \n",
+      "            mirna: array, \n",
+      "            protein_end: int32, \n",
+      "            protein_start: int32, \n",
+      "            protein_id: str, \n",
+      "            source: str, \n",
+      "            strand: int32, \n",
+      "            transcript_id: str, \n",
+      "            tsl: int32, \n",
+      "            uniprot_isoform: array, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        variant_class: str\n",
+      "    } \n",
+      "    'vqsr_results': struct {\n",
+      "        AS_VQSLOD: float64, \n",
+      "        AS_culprit: str, \n",
+      "        positive_train_site: bool, \n",
+      "        negative_train_site: bool\n",
+      "    } \n",
+      "    'region_flags': struct {\n",
+      "        non_par: bool, \n",
+      "        lcr: bool, \n",
+      "        segdup: bool\n",
+      "    } \n",
+      "    'allele_info': struct {\n",
+      "        allele_type: str, \n",
+      "        n_alt_alleles: int32, \n",
+      "        variant_type: str, \n",
+      "        was_mixed: bool\n",
+      "    } \n",
+      "    'histograms': struct {\n",
+      "        qual_hists: struct {\n",
+      "            gq_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            gq_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            ab_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }, \n",
+      "        raw_qual_hists: struct {\n",
+      "            gq_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            gq_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            ab_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }, \n",
+      "        age_hists: struct {\n",
+      "            age_hist_het: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            age_hist_hom: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }\n",
+      "    } \n",
+      "    'in_silico_predictors': struct {\n",
+      "        cadd: struct {\n",
+      "            phred: float32, \n",
+      "            raw_score: float32\n",
+      "        }, \n",
+      "        revel_max: float64, \n",
+      "        spliceai_ds_max: float32, \n",
+      "        pangolin_largest_ds: float64, \n",
+      "        phylop: float64, \n",
+      "        sift_max: float64, \n",
+      "        polyphen_max: float64\n",
+      "    } \n",
+      "----------------------------------------\n",
+      "Key: ['locus', 'alleles']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed916197-3b0e-45dc-bacd-a13cb66d70ee",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "00b0ea2f-5685-4bae-886a-b9ea31866818",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
grpmax
fafmax
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
allele_type
n_alt_alleles
variant_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>int32float64int32int32strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strint32boolset<str>set<str>float64float64float64int64float32float64array<int32>float64int32float64float64float64float64int64float32float64array<int32>float64int32boolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolstrint32strboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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639542312"}{"AC0","AS_VQSR"}7.30e+003.48e+016.70e-02962.74e+00-1.07e+00[21,6,4,4]9.60e-02355.10e+003.51e+01-5.72e-016.87e-01772.96e+00-1.38e+00[21,6,3,3]9.64e-0226FalseNANANAFalseFalse-4.57e+00-1.65e-05["ga4gh:VA.oTAtTrgYxm81O9fu6Mrhfo1t3eHsgg4L","ga4gh:VA.Y283OnlLjyi1T1IT_JzvW255rC6YJsW6"][10030,10030][10031,10031]["T","C"]"T/C"10031".""chr1\t10031\t.\tT\tC\t.\t.\tGT"NA"upstream_gene_variant"NANA"chr1"100311[(1,NA,NA,"transcribed_unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1979,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000450305",NA,NA,"C"),(1,NA,NA,"processed_transcript",NA,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1838,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000456328",1,NA,"C"),(1,NA,NA,"unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4373,NA,NA,NA,"ENSG00000227232",NA,"WASH7P","HGNC","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",-1,"ENST00000488147",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4331,NA,NA,NA,"653635",NA,"WASH7P","EntrezGene","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",-1,"NR_024540.1",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1843,NA,NA,NA,"100287102",NA,"DDX11L1","EntrezGene","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",1,"NR_046018.2",NA,NA,"C")]"SNV"-4.57e+00"AS_QD"FalseFalseFalseTrueTrue"snv"2"multi-snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911,665,439,351]0527[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,1476,1023,741,500,418]0744[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]008.97e+007.57e-01NANANANANANA
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[(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]6.27e-06"nfe"2.35e-06"nfe"1True{"rs1570391741"}{"AS_VQSR"}3.30e+013.60e+017.88e-012921.79e+00-6.84e-01[97,29,17,20]3.75e-011633.79e+013.61e+017.88e-011.00e+002642.06e+00-6.84e-01[97,29,13,19]7.58e-01128FalseNANANAFalseFalse-3.31e+00-2.41e-05["ga4gh:VA.TV0UyS-jCvsVkEOMbipUnzTc4hYuVNTg","ga4gh:VA.33zGTKcHyj5wWpTfc3OfD8-UlPQ-YOfx"][10056,10056][10057,10057]["A","C"]"A/C"10057".""chr1\t10057\t.\tA\tC\t.\t.\tGT"NA"upstream_gene_variant"NANA"chr1"100571[(1,NA,NA,"transcribed_unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1953,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000450305",NA,NA,"C"),(1,NA,NA,"processed_transcript",NA,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1812,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000456328",1,NA,"C"),(1,NA,NA,"unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4347,NA,NA,NA,"ENSG00000227232",NA,"WASH7P","HGNC","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",-1,"ENST00000488147",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4305,NA,NA,NA,"653635",NA,"WASH7P","EntrezGene","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",-1,"NR_024540.1",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1817,NA,NA,NA,"100287102",NA,"DDX11L1","EntrezGene","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",1,"NR_046018.2",NA,NA,"C")]"SNV"-3.31e+00"AS_FS"FalseFalseFalseTrueTrue"snv"2"multi-snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2503,1787,1521]03622[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,3518,2603,1850,1572]03743[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]008.53e+007.09e-01NANANANANANA
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+------------+\n",
+       "| locus         | alleles    |\n",
+       "+---------------+------------+\n",
+       "| locus | array |\n",
+       "+---------------+------------+\n",
+       "| chr1:10031    | [\"T\",\"C\"]  |\n",
+       "| chr1:10037    | [\"T\",\"C\"]  |\n",
+       "| chr1:10043    | [\"T\",\"C\"]  |\n",
+       "| chr1:10055    | [\"T\",\"C\"]  |\n",
+       "| chr1:10057    | [\"A\",\"C\"]  |\n",
+       "+---------------+------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e... |\n",
+       "| [(2,2.60e-05,76882,0),(4,3.15e-05,126902,0),(0,0.00e+00,34670,0),(1,1.80e... |\n",
+       "| [(1,1.17e-05,85634,0),(1,8.24e-06,121430,0),(0,0.00e+00,39236,0),(0,0.00e... |\n",
+       "| [(1,1.06e-05,94224,0),(5,4.64e-05,107704,0),(0,0.00e+00,44382,0),(1,1.80e... |\n",
+       "| [(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------+-----------+-----------+-------------------------+----------------+\n",
+       "| grpmax.AC | grpmax.AF | grpmax.AN | grpmax.homozygote_count | grpmax.gen_anc |\n",
+       "+-----------+-----------+-----------+-------------------------+----------------+\n",
+       "|     int32 |   float64 |     int32 |                   int32 | str            |\n",
+       "+-----------+-----------+-----------+-------------------------+----------------+\n",
+       "|        NA |        NA |        NA |                      NA | NA             |\n",
+       "|         1 |  4.07e-04 |      2456 |                       0 | \"eas\"          |\n",
+       "|         1 |  4.39e-05 |     22760 |                       0 | \"afr\"          |\n",
+       "|        NA |        NA |        NA |                      NA | NA             |\n",
+       "|         2 |  3.78e-05 |     52912 |                       0 | \"nfe\"          |\n",
+       "+-----------+-----------+-----------+-------------------------+----------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(4.32e-06,1.62e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+--------------------------+------------------+\n",
+       "| fafmax.faf95_max | fafmax.faf95_max_gen_anc | fafmax.faf99_max |\n",
+       "+------------------+--------------------------+------------------+\n",
+       "|          float64 | str                      |          float64 |\n",
+       "+------------------+--------------------------+------------------+\n",
+       "|               NA | NA                       |               NA |\n",
+       "|               NA | NA                       |               NA |\n",
+       "|               NA | NA                       |               NA |\n",
+       "|               NA | NA                       |               NA |\n",
+       "|         6.27e-06 | \"nfe\"                    |         2.35e-06 |\n",
+       "+------------------+--------------------------+------------------+\n",
+       "\n",
+       "+--------------------------+---------+-----------+------------------+\n",
+       "| fafmax.faf99_max_gen_anc | a_index | was_split | rsid             |\n",
+       "+--------------------------+---------+-----------+------------------+\n",
+       "| str                      |   int32 |      bool | set         |\n",
+       "+--------------------------+---------+-----------+------------------+\n",
+       "| NA                       |       2 |      True | {\"rs1639542312\"} |\n",
+       "| NA                       |       1 |     False | {\"rs1639542418\"} |\n",
+       "| NA                       |       1 |     False | NA               |\n",
+       "| NA                       |       2 |      True | {\"rs892501864\"}  |\n",
+       "| \"nfe\"                    |       1 |      True | {\"rs1570391741\"} |\n",
+       "+--------------------------+---------+-----------+------------------+\n",
+       "\n",
+       "+-------------------+----------+----------+----------------+-----------------+\n",
+       "| filters           |  info.FS |  info.MQ | info.MQRankSum | info.QUALapprox |\n",
+       "+-------------------+----------+----------+----------------+-----------------+\n",
+       "| set          |  float64 |  float64 |        float64 |           int64 |\n",
+       "+-------------------+----------+----------+----------------+-----------------+\n",
+       "| {\"AC0\",\"AS_VQSR\"} | 7.30e+00 | 3.48e+01 |       6.70e-02 |              96 |\n",
+       "| {\"AS_VQSR\"}       | 8.58e+00 | 3.83e+01 |       1.37e+00 |             180 |\n",
+       "| {\"AS_VQSR\"}       | 3.11e+01 | 3.52e+01 |       1.23e+00 |              97 |\n",
+       "| {\"AS_VQSR\"}       | 0.00e+00 | 3.55e+01 |       1.07e-01 |             220 |\n",
+       "| {\"AS_VQSR\"}       | 3.30e+01 | 3.60e+01 |       7.88e-01 |             292 |\n",
+       "+-------------------+----------+----------+----------------+-----------------+\n",
+       "\n",
+       "+----------+---------------------+---------------+----------+------------+\n",
+       "|  info.QD | info.ReadPosRankSum | info.SB       | info.SOR | info.VarDP |\n",
+       "+----------+---------------------+---------------+----------+------------+\n",
+       "|  float32 |             float64 | array  |  float64 |      int32 |\n",
+       "+----------+---------------------+---------------+----------+------------+\n",
+       "| 2.74e+00 |           -1.07e+00 | [21,6,4,4]    | 9.60e-02 |         35 |\n",
+       "| 2.20e+00 |           -4.80e-01 | [49,12,13,8]  | 1.51e-01 |         82 |\n",
+       "| 2.77e+00 |           -8.96e-01 | [25,0,5,5]    | 1.00e-03 |         35 |\n",
+       "| 2.12e+00 |           -1.16e+00 | [51,29,15,9]  | 6.16e-01 |        104 |\n",
+       "| 1.79e+00 |           -6.84e-01 | [97,29,17,20] | 3.75e-01 |        163 |\n",
+       "+----------+---------------------+---------------+----------+------------+\n",
+       "\n",
+       "+------------+------------+-------------------+-----------------+\n",
+       "| info.AS_FS | info.AS_MQ | info.AS_MQRankSum | info.AS_pab_max |\n",
+       "+------------+------------+-------------------+-----------------+\n",
+       "|    float64 |    float64 |           float64 |         float64 |\n",
+       "+------------+------------+-------------------+-----------------+\n",
+       "|   5.10e+00 |   3.51e+01 |         -5.72e-01 |        6.87e-01 |\n",
+       "|   8.58e+00 |   3.83e+01 |          1.37e+00 |        1.00e+00 |\n",
+       "|   3.11e+01 |   3.52e+01 |          1.23e+00 |        1.00e+00 |\n",
+       "|   5.94e+00 |   3.48e+01 |          7.15e-01 |        2.27e-01 |\n",
+       "|   3.79e+01 |   3.61e+01 |          7.88e-01 |        1.00e+00 |\n",
+       "+------------+------------+-------------------+-----------------+\n",
+       "\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "| info.AS_QUALapprox | info.AS_QD | info.AS_ReadPosRankSum | info.AS_SB_TABLE |\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "|              int64 |    float32 |                float64 | array     |\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "|                 77 |   2.96e+00 |              -1.38e+00 | [21,6,3,3]       |\n",
+       "|                180 |   2.20e+00 |              -4.80e-01 | [49,12,13,8]     |\n",
+       "|                 97 |   2.77e+00 |              -8.96e-01 | [25,0,5,5]       |\n",
+       "|                 91 |   1.21e+00 |              -1.16e+00 | [51,29,7,8]      |\n",
+       "|                264 |   2.06e+00 |              -6.84e-01 | [97,29,13,19]    |\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "| info.AS_SOR | info.AS_VarDP | info.singleton | info.transmitted_singleton |\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "|     float64 |         int32 |           bool |                       bool |\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "|    9.64e-02 |            26 |          False |                         NA |\n",
+       "|    1.51e-01 |            82 |          False |                         NA |\n",
+       "|    1.48e-03 |            35 |           True |                      False |\n",
+       "|    4.69e-01 |            75 |          False |                         NA |\n",
+       "|    7.58e-01 |           128 |          False |                         NA |\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|      bool |       bool |             bool |          bool |        float64 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|        NA |         NA |            False |         False |      -4.57e+00 |\n",
+       "|        NA |         NA |            False |         False |      -3.18e+00 |\n",
+       "|        NA |         NA |            False |         False |      -5.79e+00 |\n",
+       "|        NA |         NA |            False |         False |      -3.72e+00 |\n",
+       "|        NA |         NA |            False |         False |      -3.31e+00 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "\n",
+       "+-----------------------+\n",
+       "| info.inbreeding_coeff |\n",
+       "+-----------------------+\n",
+       "|               float64 |\n",
+       "+-----------------------+\n",
+       "|             -1.65e-05 |\n",
+       "|             -3.15e-05 |\n",
+       "|             -8.24e-06 |\n",
+       "|             -4.64e-05 |\n",
+       "|             -2.41e-05 |\n",
+       "+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.oTAtTrgYxm81O9fu6Mrhfo1t3eHsgg4L\",\"ga4gh:VA.Y283OnlLjyi1T1IT_J... |\n",
+       "| [\"ga4gh:VA.6pIoPAYDsphCprRcyrititenuEWlZaxV\",\"ga4gh:VA.iHyQ1mh_iQX6WcqXcx... |\n",
+       "| [\"ga4gh:VA.HdrkFJS16zwJPcGSKX9___fJcdqSATlQ\",\"ga4gh:VA.LSHuFx03sqw8WJGN2-... |\n",
+       "| [\"ga4gh:VA.xis4Nhtveh7q75mtkipQAUqZfWUfboWB\",\"ga4gh:VA.Ciou33oFTNEilAEegb... |\n",
+       "| [\"ga4gh:VA.TV0UyS-jCvsVkEOMbipUnzTc4hYuVNTg\",\"ga4gh:VA.33zGTKcHyj5wWpTfc3... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts | info.vrs.VRS_Ends | info.vrs.VRS_States |\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "| array        | array      | array          |\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "| [10030,10030]       | [10031,10031]     | [\"T\",\"C\"]           |\n",
+       "| [10036,10036]       | [10037,10037]     | [\"T\",\"C\"]           |\n",
+       "| [10042,10042]       | [10043,10043]     | [\"T\",\"C\"]           |\n",
+       "| [10054,10054]       | [10055,10055]     | [\"T\",\"C\"]           |\n",
+       "| [10056,10056]       | [10057,10057]     | [\"A\",\"C\"]           |\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "\n",
+       "+-------------------+---------+--------+---------------------------+\n",
+       "| vep.allele_string | vep.end | vep.id | vep.input                 |\n",
+       "+-------------------+---------+--------+---------------------------+\n",
+       "| str               |   int32 | str    | str                       |\n",
+       "+-------------------+---------+--------+---------------------------+\n",
+       "| \"T/C\"             |   10031 | \".\"    | \"chr1\t10031\t.\tT\tC\t.\t.\tGT\" |\n",
+       "| \"T/C\"             |   10037 | \".\"    | \"chr1\t10037\t.\tT\tC\t.\t.\tGT\" |\n",
+       "| \"T/C\"             |   10043 | \".\"    | \"chr1\t10043\t.\tT\tC\t.\t.\tGT\" |\n",
+       "| \"T/C\"             |   10055 | \".\"    | \"chr1\t10055\t.\tT\tC\t.\t.\tGT\" |\n",
+       "| \"A/C\"             |   10057 | \".\"    | \"chr1\t10057\t.\tA\tC\t.\t.\tGT\" |\n",
+       "+-------------------+---------+--------+---------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"upstream_gene_variant\"     |\n",
+       "| \"upstream_gene_variant\"     |\n",
+       "| \"upstream_gene_variant\"     |\n",
+       "| \"upstream_gene_variant\"     |\n",
+       "| \"upstream_gene_variant\"     |\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]                |\n",
+       "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15]          |\n",
+       "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9]           |\n",
+       "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n",
+       "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n",
+       "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n",
+       "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n",
+       "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n",
+       "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                        527 |\n",
+       "|                                        888 |\n",
+       "|                                       1232 |\n",
+       "|                                       2440 |\n",
+       "|                                       3622 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]   |\n",
+       "| [10121,5441,5610,3838,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,... |\n",
+       "| [6578,3782,4202,3336,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5... |\n",
+       "| [1429,1382,1901,2026,25677,6108,5433,3528,1753,1702,1130,485,518,282,119,... |\n",
+       "| [995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,14... |\n",
+       "| [7,37,269,903,1948,4127,5519,7075,7322,6972,6095,5249,4374,3624,2893,2106... |\n",
+       "| [2,24,149,466,1201,2614,4035,5568,6331,6725,6267,5855,5101,4268,3465,2554... |\n",
+       "| [1,9,42,122,294,787,1537,2717,3751,4972,5404,5762,5875,5161,4526,3415,252... |\n",
+       "| [2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,351... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                            744 |\n",
+       "|                                           1130 |\n",
+       "|                                           1487 |\n",
+       "|                                           2622 |\n",
+       "|                                           3743 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,1,0,0,1,0,0,0,0,0]                      |\n",
+       "| [0,0,1,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,1,0,1,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        8.97e+00 |\n",
+       "|                                          0 |                        8.90e+00 |\n",
+       "|                                          0 |                        8.90e+00 |\n",
+       "|                                          0 |                        8.88e+00 |\n",
+       "|                                          0 |                        8.53e+00 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            7.57e-01 |                             NA |\n",
+       "|                            7.49e-01 |                             NA |\n",
+       "|                            7.48e-01 |                             NA |\n",
+       "|                            7.46e-01 |                             NA |\n",
+       "|                            7.09e-01 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                       NA |                          NA |\n",
+       "|                                       NA |                          NA |\n",
+       "|                                       NA |                          NA |\n",
+       "|                                       NA |                          NA |\n",
+       "|                                       NA |                          NA |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22e6f759-a0ee-4e9c-8ca4-eb154cb08763",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### v4.1 Joint Frequency Hail Table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a569b77-d3d2-45a4-803a-1214c77e46f2",
+   "metadata": {},
+   "source": [
+    "The joint frequency Hail table includes frequency for the exomes, genomes, and the exomes+genomes. We have also added statistics for the combined exomes and genomes frequencies, more details on these stats can be found on our [Help](https://gnomad.broadinstitute.org/help/combined-freq-stats) page."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46d4fc43-609d-4a16-8a0a-ab1e870b5d3d",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c1c1fbb0-4ef9-4892-bd91-aae9985317a7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(data_type='joint', version='4.1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "163df47b-70de-4e1e-91be-a65c90cf2db5",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "750c0111-4566-4b86-8c08-18c504ff1a79",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'exomes_globals': struct {\n",
+      "        freq_meta: array>, \n",
+      "        freq_index_dict: dict, \n",
+      "        freq_meta_sample_count: array, \n",
+      "        faf_meta: array>, \n",
+      "        faf_index_dict: dict, \n",
+      "        age_distribution: struct {\n",
+      "            bin_edges: array, \n",
+      "            bin_freq: array, \n",
+      "            n_smaller: int32, \n",
+      "            n_larger: int32\n",
+      "        }\n",
+      "    } \n",
+      "    'genomes_globals': struct {\n",
+      "        freq_meta: array>, \n",
+      "        freq_index_dict: dict, \n",
+      "        freq_meta_sample_count: array, \n",
+      "        faf_meta: array>, \n",
+      "        faf_index_dict: dict, \n",
+      "        age_distribution: struct {\n",
+      "            bin_edges: array, \n",
+      "            bin_freq: array, \n",
+      "            n_smaller: int32, \n",
+      "            n_larger: int32\n",
+      "        }\n",
+      "    } \n",
+      "    'joint_globals': struct {\n",
+      "        freq_meta: array>, \n",
+      "        freq_index_dict: dict, \n",
+      "        faf_meta: array>, \n",
+      "        faf_index_dict: dict, \n",
+      "        freq_meta_sample_count: array, \n",
+      "        age_distribution: struct {\n",
+      "            bin_edges: array, \n",
+      "            bin_freq: array, \n",
+      "            n_smaller: int32, \n",
+      "            n_larger: int32\n",
+      "        }\n",
+      "    } \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'alleles': array \n",
+      "    'region_flags': struct {\n",
+      "        fail_interval_qc: bool, \n",
+      "        outside_broad_capture_region: bool, \n",
+      "        outside_ukb_capture_region: bool, \n",
+      "        outside_broad_calling_region: bool, \n",
+      "        outside_ukb_calling_region: bool, \n",
+      "        not_called_in_exomes: bool, \n",
+      "        not_called_in_genomes: bool\n",
+      "    } \n",
+      "    'exomes': struct {\n",
+      "        filters: set, \n",
+      "        freq: array, \n",
+      "        faf: array, \n",
+      "        grpmax: struct {\n",
+      "            AC: int32, \n",
+      "            AF: float64, \n",
+      "            AN: int32, \n",
+      "            homozygote_count: int64, \n",
+      "            gen_anc: str\n",
+      "        }, \n",
+      "        fafmax: struct {\n",
+      "            faf95_max: float64, \n",
+      "            faf95_max_gen_anc: str, \n",
+      "            faf99_max: float64, \n",
+      "            faf99_max_gen_anc: str\n",
+      "        }, \n",
+      "        histograms: struct {\n",
+      "            qual_hists: struct {\n",
+      "                gq_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                gq_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                ab_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            raw_qual_hists: struct {\n",
+      "                gq_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                gq_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                ab_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            age_hists: struct {\n",
+      "                age_hist_het: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                age_hist_hom: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }\n",
+      "        }\n",
+      "    } \n",
+      "    'genomes': struct {\n",
+      "        filters: set, \n",
+      "        freq: array, \n",
+      "        faf: array, \n",
+      "        grpmax: struct {\n",
+      "            AC: int32, \n",
+      "            AF: float64, \n",
+      "            AN: int32, \n",
+      "            homozygote_count: int32, \n",
+      "            gen_anc: str\n",
+      "        }, \n",
+      "        fafmax: struct {\n",
+      "            faf95_max: float64, \n",
+      "            faf95_max_gen_anc: str, \n",
+      "            faf99_max: float64, \n",
+      "            faf99_max_gen_anc: str\n",
+      "        }, \n",
+      "        histograms: struct {\n",
+      "            qual_hists: struct {\n",
+      "                gq_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                gq_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                ab_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            raw_qual_hists: struct {\n",
+      "                gq_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                gq_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                ab_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            age_hists: struct {\n",
+      "                age_hist_het: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                age_hist_hom: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }\n",
+      "        }\n",
+      "    } \n",
+      "    'joint': struct {\n",
+      "        freq: array, \n",
+      "        faf: array, \n",
+      "        fafmax: struct {\n",
+      "            faf95_max: float64, \n",
+      "            faf95_max_gen_anc: str, \n",
+      "            faf99_max: float64, \n",
+      "            faf99_max_gen_anc: str\n",
+      "        }, \n",
+      "        grpmax: struct {\n",
+      "            AC: int32, \n",
+      "            AF: float64, \n",
+      "            AN: int32, \n",
+      "            homozygote_count: int32, \n",
+      "            gen_anc: str\n",
+      "        }, \n",
+      "        histograms: struct {\n",
+      "            qual_hists: struct {\n",
+      "                gq_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                gq_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                ab_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            raw_qual_hists: struct {\n",
+      "                gq_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                gq_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                ab_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            age_hists: struct {\n",
+      "                age_hist_het: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                age_hist_hom: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }\n",
+      "        }\n",
+      "    } \n",
+      "    'freq_comparison_stats': struct {\n",
+      "        contingency_table_test: array, \n",
+      "        cochran_mantel_haenszel_test: struct {\n",
+      "            p_value: float64, \n",
+      "            chisq: float64\n",
+      "        }, \n",
+      "        stat_union: struct {\n",
+      "            p_value: float64, \n",
+      "            stat_test_name: str, \n",
+      "            gen_ancs: array\n",
+      "        }\n",
+      "    } \n",
+      "----------------------------------------\n",
+      "Key: ['locus', 'alleles']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6843db4-9e8f-42f4-9178-fc945f61a827",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "477ed281-4ee9-4799-b5b6-e6a0c528fa9a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
exomes
genomes
joint
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faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
AC
AF
AN
homozygote_count
gen_anc
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
contingency_table_test
p_value
chisq
p_value
stat_test_name
gen_ancs
locus<GRCh38>array<str>boolboolboolboolboolboolboolset<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>int32float64int32int64strfloat64strfloat64strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64set<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>int32float64int32int32strfloat64strfloat64strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>float64strfloat64strint32float64int32int32strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<struct{p_value: float64, odds_ratio: float64}>float64float64float64strarray<str>
chr1:10031["T","C"]NATrueTrueTrueTrueTrueFalseNA[(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA)]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA{"AC0","AS_VQSR"}[(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e+00,4326,0),(0,0.00e+00,192,0),(0,0.00e+00,782,0),(0,0.00e+00,352,0),(0,0.00e+00,1550,0),(0,0.00e+00,14642,0),(0,0.00e+00,1712,0),(0,0.00e+00,1120,0),(0,0.00e+00,6420,0),(0,0.00e+00,29308,0),(0,0.00e+00,27334,0),(0,0.00e+00,14998,0),(0,0.00e+00,1060,0),(0,0.00e+00,102,0),(0,0.00e+00,402,0),(0,0.00e+00,154,0),(0,0.00e+00,814,0),(0,0.00e+00,7978,0),(0,0.00e+00,770,0),(0,0.00e+00,260,0),(0,0.00e+00,2770,0),(0,0.00e+00,10548,0),(0,0.00e+00,3266,0),(0,0.00e+00,90,0),(0,0.00e+00,380,0),(0,0.00e+00,198,0),(0,0.00e+00,736,0),(0,0.00e+00,6664,0),(0,0.00e+00,942,0),(0,0.00e+00,860,0),(0,0.00e+00,3650,0)][(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]NANANANANANANANANA[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911,665,439,351]0527[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,1476,1023,741,500,418]0744[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e+00,4326,0),(0,0.00e+00,192,0),(0,0.00e+00,782,0),(0,0.00e+00,352,0),(0,0.00e+00,1550,0),(0,0.00e+00,14642,0),(0,0.00e+00,1712,0),(0,0.00e+00,1120,0),(0,0.00e+00,6420,0),(0,0.00e+00,29308,0),(0,0.00e+00,27334,0),(0,0.00e+00,14998,0),(0,0.00e+00,1060,0),(0,0.00e+00,102,0),(0,0.00e+00,402,0),(0,0.00e+00,154,0),(0,0.00e+00,814,0),(0,0.00e+00,7978,0),(0,0.00e+00,770,0),(0,0.00e+00,260,0),(0,0.00e+00,2770,0),(0,0.00e+00,10548,0),(0,0.00e+00,3266,0),(0,0.00e+00,90,0),(0,0.00e+00,380,0),(0,0.00e+00,198,0),(0,0.00e+00,736,0),(0,0.00e+00,6664,0),(0,0.00e+00,942,0),(0,0.00e+00,860,0),(0,0.00e+00,3650,0)][(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,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chr1:10057["A","C"]NATrueTrueTrueTrueTrueFalseNA[(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA)]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA{"AS_VQSR"}[(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00e+00,6886,0),(0,0.00e+00,266,0),(0,0.00e+00,1504,0),(0,0.00e+00,742,0),(0,0.00e+00,2768,0),(1,3.25e-05,30736,0),(0,0.00e+00,3850,0),(0,0.00e+00,3424,0),(0,0.00e+00,10448,0),(2,3.37e-05,59380,0),(1,1.85e-05,54156,0),(2,6.43e-05,31106,0),(0,0.00e+00,1472,0),(0,0.00e+00,132,0),(0,0.00e+00,764,0),(0,0.00e+00,378,0),(0,0.00e+00,1532,0),(0,0.00e+00,16598,0),(0,0.00e+00,1670,0),(0,0.00e+00,784,0),(0,0.00e+00,4944,0),(0,0.00e+00,21806,0),(0,0.00e+00,5414,0),(0,0.00e+00,134,0),(0,0.00e+00,740,0),(0,0.00e+00,364,0),(0,0.00e+00,1236,0),(1,7.07e-05,14138,0),(0,0.00e+00,2180,0),(0,0.00e+00,2640,0),(0,0.00e+00,5504,0)][(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]23.78e-05529120"nfe"6.27e-06"nfe"2.35e-06"nfe"[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2503,1787,1521]03622[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,3518,2603,1850,1572]03743[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00e+00,6886,0),(0,0.00e+00,266,0),(0,0.00e+00,1504,0),(0,0.00e+00,742,0),(0,0.00e+00,2768,0),(1,3.25e-05,30736,0),(0,0.00e+00,3850,0),(0,0.00e+00,3424,0),(0,0.00e+00,10448,0),(2,3.37e-05,59380,0),(1,1.85e-05,54156,0),(2,6.43e-05,31106,0),(0,0.00e+00,1472,0),(0,0.00e+00,132,0),(0,0.00e+00,764,0),(0,0.00e+00,378,0),(0,0.00e+00,1532,0),(0,0.00e+00,16598,0),(0,0.00e+00,1670,0),(0,0.00e+00,784,0),(0,0.00e+00,4944,0),(0,0.00e+00,21806,0),(0,0.00e+00,5414,0),(0,0.00e+00,134,0),(0,0.00e+00,740,0),(0,0.00e+00,364,0),(0,0.00e+00,1236,0),(1,7.07e-05,14138,0),(0,0.00e+00,2180,0),(0,0.00e+00,2640,0),(0,0.00e+00,5504,0)][(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]6.27e-06"nfe"2.35e-06"nfe"23.78e-05529120"nfe"[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2503,1787,1521]03622[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,3518,2603,1850,1572]03743[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]NANANANANA
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+------------+-------------------------------+\n",
+       "| locus         | alleles    | region_flags.fail_interval_qc |\n",
+       "+---------------+------------+-------------------------------+\n",
+       "| locus | array |                          bool |\n",
+       "+---------------+------------+-------------------------------+\n",
+       "| chr1:10031    | [\"T\",\"C\"]  |                            NA |\n",
+       "| chr1:10037    | [\"T\",\"C\"]  |                            NA |\n",
+       "| chr1:10043    | [\"T\",\"C\"]  |                            NA |\n",
+       "| chr1:10055    | [\"T\",\"C\"]  |                            NA |\n",
+       "| chr1:10057    | [\"A\",\"C\"]  |                            NA |\n",
+       "+---------------+------------+-------------------------------+\n",
+       "\n",
+       "+-------------------------------------------+\n",
+       "| region_flags.outside_broad_capture_region |\n",
+       "+-------------------------------------------+\n",
+       "|                                      bool |\n",
+       "+-------------------------------------------+\n",
+       "|                                      True |\n",
+       "|                                      True |\n",
+       "|                                      True |\n",
+       "|                                      True |\n",
+       "|                                      True |\n",
+       "+-------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------+\n",
+       "| region_flags.outside_ukb_capture_region |\n",
+       "+-----------------------------------------+\n",
+       "|                                    bool |\n",
+       "+-----------------------------------------+\n",
+       "|                                    True |\n",
+       "|                                    True |\n",
+       "|                                    True |\n",
+       "|                                    True |\n",
+       "|                                    True |\n",
+       "+-----------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------+\n",
+       "| region_flags.outside_broad_calling_region |\n",
+       "+-------------------------------------------+\n",
+       "|                                      bool |\n",
+       "+-------------------------------------------+\n",
+       "|                                      True |\n",
+       "|                                      True |\n",
+       "|                                      True |\n",
+       "|                                      True |\n",
+       "|                                      True |\n",
+       "+-------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------+-----------------------------------+\n",
+       "| region_flags.outside_ukb_calling_region | region_flags.not_called_in_exomes |\n",
+       "+-----------------------------------------+-----------------------------------+\n",
+       "|                                    bool |                              bool |\n",
+       "+-----------------------------------------+-----------------------------------+\n",
+       "|                                    True |                              True |\n",
+       "|                                    True |                              True |\n",
+       "|                                    True |                              True |\n",
+       "|                                    True |                              True |\n",
+       "|                                    True |                              True |\n",
+       "+-----------------------------------------+-----------------------------------+\n",
+       "\n",
+       "+------------------------------------+----------------+\n",
+       "| region_flags.not_called_in_genomes | exomes.filters |\n",
+       "+------------------------------------+----------------+\n",
+       "|                               bool | set       |\n",
+       "+------------------------------------+----------------+\n",
+       "|                              False | NA             |\n",
+       "|                              False | NA             |\n",
+       "|                              False | NA             |\n",
+       "|                              False | NA             |\n",
+       "|                              False | NA             |\n",
+       "+------------------------------------+----------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| exomes.freq                                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
+       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
+       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
+       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
+       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------+------------------+\n",
+       "| exomes.faf                                    | exomes.grpmax.AC |\n",
+       "+-----------------------------------------------+------------------+\n",
+       "| array |            int32 |\n",
+       "+-----------------------------------------------+------------------+\n",
+       "| NA                                            |               NA |\n",
+       "| NA                                            |               NA |\n",
+       "| NA                                            |               NA |\n",
+       "| NA                                            |               NA |\n",
+       "| NA                                            |               NA |\n",
+       "+-----------------------------------------------+------------------+\n",
+       "\n",
+       "+------------------+------------------+--------------------------------+\n",
+       "| exomes.grpmax.AF | exomes.grpmax.AN | exomes.grpmax.homozygote_count |\n",
+       "+------------------+------------------+--------------------------------+\n",
+       "|          float64 |            int32 |                          int64 |\n",
+       "+------------------+------------------+--------------------------------+\n",
+       "|               NA |               NA |                             NA |\n",
+       "|               NA |               NA |                             NA |\n",
+       "|               NA |               NA |                             NA |\n",
+       "|               NA |               NA |                             NA |\n",
+       "|               NA |               NA |                             NA |\n",
+       "+------------------+------------------+--------------------------------+\n",
+       "\n",
+       "+-----------------------+-------------------------+\n",
+       "| exomes.grpmax.gen_anc | exomes.fafmax.faf95_max |\n",
+       "+-----------------------+-------------------------+\n",
+       "| str                   |                 float64 |\n",
+       "+-----------------------+-------------------------+\n",
+       "| NA                    |                      NA |\n",
+       "| NA                    |                      NA |\n",
+       "| NA                    |                      NA |\n",
+       "| NA                    |                      NA |\n",
+       "| NA                    |                      NA |\n",
+       "+-----------------------+-------------------------+\n",
+       "\n",
+       "+---------------------------------+-------------------------+\n",
+       "| exomes.fafmax.faf95_max_gen_anc | exomes.fafmax.faf99_max |\n",
+       "+---------------------------------+-------------------------+\n",
+       "| str                             |                 float64 |\n",
+       "+---------------------------------+-------------------------+\n",
+       "| NA                              |                      NA |\n",
+       "| NA                              |                      NA |\n",
+       "| NA                              |                      NA |\n",
+       "| NA                              |                      NA |\n",
+       "| NA                              |                      NA |\n",
+       "+---------------------------------+-------------------------+\n",
+       "\n",
+       "+---------------------------------+\n",
+       "| exomes.fafmax.faf99_max_gen_anc |\n",
+       "+---------------------------------+\n",
+       "| str                             |\n",
+       "+---------------------------------+\n",
+       "| NA                              |\n",
+       "| NA                              |\n",
+       "| NA                              |\n",
+       "| NA                              |\n",
+       "| NA                              |\n",
+       "+---------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.gq_hist_all.bin_edges |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+----------------------------------------------------+\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.gq_hist_all.bin_freq |\n",
+       "+---------------------------------------------------+\n",
+       "| array                                      |\n",
+       "+---------------------------------------------------+\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.dp_hist_all.bin_edges |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+----------------------------------------------------+\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.dp_hist_all.bin_freq |\n",
+       "+---------------------------------------------------+\n",
+       "| array                                      |\n",
+       "+---------------------------------------------------+\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.gq_hist_alt.bin_edges |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+----------------------------------------------------+\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+---------------------------------------------------+\n",
+       "| array                                      |\n",
+       "+---------------------------------------------------+\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.dp_hist_alt.bin_edges |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+----------------------------------------------------+\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+---------------------------------------------------+\n",
+       "| array                                      |\n",
+       "+---------------------------------------------------+\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.ab_hist_alt.bin_edges |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+----------------------------------------------------+\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.ab_hist_alt.bin_freq |\n",
+       "+---------------------------------------------------+\n",
+       "| array                                      |\n",
+       "+---------------------------------------------------+\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.gq_hist_all.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.gq_hist_all.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.dp_hist_all.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.dp_hist_all.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.gq_hist_alt.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.dp_hist_alt.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.ab_hist_alt.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.age_hists.age_hist_het.bin_edges |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+----------------------------------------------------+\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+---------------------------------------------------+\n",
+       "| array                                      |\n",
+       "+---------------------------------------------------+\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.age_hists.age_hist_het.n_larger |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.age_hists.age_hist_hom.bin_edges |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+----------------------------------------------------+\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+---------------------------------------------------+\n",
+       "| array                                      |\n",
+       "+---------------------------------------------------+\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+-------------------+\n",
+       "| exomes.histograms.age_hists.age_hist_hom.n_larger | genomes.filters   |\n",
+       "+---------------------------------------------------+-------------------+\n",
+       "|                                             int64 | set          |\n",
+       "+---------------------------------------------------+-------------------+\n",
+       "|                                                NA | {\"AC0\",\"AS_VQSR\"} |\n",
+       "|                                                NA | {\"AS_VQSR\"}       |\n",
+       "|                                                NA | {\"AS_VQSR\"}       |\n",
+       "|                                                NA | {\"AS_VQSR\"}       |\n",
+       "|                                                NA | {\"AS_VQSR\"}       |\n",
+       "+---------------------------------------------------+-------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.freq                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e... |\n",
+       "| [(2,2.60e-05,76882,0),(4,3.15e-05,126902,0),(0,0.00e+00,34670,0),(1,1.80e... |\n",
+       "| [(1,1.17e-05,85634,0),(1,8.24e-06,121430,0),(0,0.00e+00,39236,0),(0,0.00e... |\n",
+       "| [(1,1.06e-05,94224,0),(5,4.64e-05,107704,0),(0,0.00e+00,44382,0),(1,1.80e... |\n",
+       "| [(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.faf                                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(4.32e-06,1.62e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------+-------------------+-------------------+\n",
+       "| genomes.grpmax.AC | genomes.grpmax.AF | genomes.grpmax.AN |\n",
+       "+-------------------+-------------------+-------------------+\n",
+       "|             int32 |           float64 |             int32 |\n",
+       "+-------------------+-------------------+-------------------+\n",
+       "|                NA |                NA |                NA |\n",
+       "|                 1 |          4.07e-04 |              2456 |\n",
+       "|                 1 |          4.39e-05 |             22760 |\n",
+       "|                NA |                NA |                NA |\n",
+       "|                 2 |          3.78e-05 |             52912 |\n",
+       "+-------------------+-------------------+-------------------+\n",
+       "\n",
+       "+---------------------------------+------------------------+\n",
+       "| genomes.grpmax.homozygote_count | genomes.grpmax.gen_anc |\n",
+       "+---------------------------------+------------------------+\n",
+       "|                           int32 | str                    |\n",
+       "+---------------------------------+------------------------+\n",
+       "|                              NA | NA                     |\n",
+       "|                               0 | \"eas\"                  |\n",
+       "|                               0 | \"afr\"                  |\n",
+       "|                              NA | NA                     |\n",
+       "|                               0 | \"nfe\"                  |\n",
+       "+---------------------------------+------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| genomes.fafmax.faf95_max | genomes.fafmax.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                 6.27e-06 | \"nfe\"                            |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| genomes.fafmax.faf99_max | genomes.fafmax.faf99_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                 2.35e-06 | \"nfe\"                            |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.gq_hist_all.bin_edges                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.gq_hist_all.bin_freq                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]                |\n",
+       "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15]          |\n",
+       "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9]           |\n",
+       "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n",
+       "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                               int64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.dp_hist_all.bin_edges                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.dp_hist_all.bin_freq                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n",
+       "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n",
+       "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n",
+       "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n",
+       "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                               int64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                527 |\n",
+       "|                                                888 |\n",
+       "|                                               1232 |\n",
+       "|                                               2440 |\n",
+       "|                                               3622 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.gq_hist_alt.bin_edges                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                       |\n",
+       "+----------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]          |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                               int64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.dp_hist_alt.bin_edges                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                       |\n",
+       "+----------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                               int64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.ab_hist_alt.bin_edges                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.ab_hist_alt.bin_freq |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                       |\n",
+       "+----------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                               int64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.gq_hist_all.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.gq_hist_all.bin_freq                       |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]   |\n",
+       "| [10121,5441,5610,3838,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,... |\n",
+       "| [6578,3782,4202,3336,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5... |\n",
+       "| [1429,1382,1901,2026,25677,6108,5433,3528,1753,1702,1130,485,518,282,119,... |\n",
+       "| [995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.dp_hist_all.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.dp_hist_all.bin_freq                       |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,14... |\n",
+       "| [7,37,269,903,1948,4127,5519,7075,7322,6972,6095,5249,4374,3624,2893,2106... |\n",
+       "| [2,24,149,466,1201,2614,4035,5568,6331,6725,6267,5855,5101,4268,3465,2554... |\n",
+       "| [1,9,42,122,294,787,1537,2717,3751,4972,5404,5762,5875,5161,4526,3415,252... |\n",
+       "| [2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,351... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                    744 |\n",
+       "|                                                   1130 |\n",
+       "|                                                   1487 |\n",
+       "|                                                   2622 |\n",
+       "|                                                   3743 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.gq_hist_alt.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]              |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.dp_hist_alt.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.ab_hist_alt.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.age_hists.age_hist_het.bin_edges                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                       |\n",
+       "+----------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
+       "| [0,1,0,0,1,0,0,0,0,0]                              |\n",
+       "| [0,0,1,0,0,0,0,0,0,0]                              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
+       "| [0,0,1,0,1,0,0,0,0,0]                              |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| genomes.histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                               int64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.age_hists.age_hist_het.n_larger |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.age_hists.age_hist_hom.bin_edges                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                       |\n",
+       "+----------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| genomes.histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                               int64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.age_hists.age_hist_hom.n_larger |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.freq                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e... |\n",
+       "| [(2,2.60e-05,76882,0),(4,3.15e-05,126902,0),(0,0.00e+00,34670,0),(1,1.80e... |\n",
+       "| [(1,1.17e-05,85634,0),(1,8.24e-06,121430,0),(0,0.00e+00,39236,0),(0,0.00e... |\n",
+       "| [(1,1.06e-05,94224,0),(5,4.64e-05,107704,0),(0,0.00e+00,44382,0),(1,1.80e... |\n",
+       "| [(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.faf                                                                    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(4.32e-06,1.62e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------+--------------------------------+\n",
+       "| joint.fafmax.faf95_max | joint.fafmax.faf95_max_gen_anc |\n",
+       "+------------------------+--------------------------------+\n",
+       "|                float64 | str                            |\n",
+       "+------------------------+--------------------------------+\n",
+       "|                     NA | NA                             |\n",
+       "|                     NA | NA                             |\n",
+       "|                     NA | NA                             |\n",
+       "|                     NA | NA                             |\n",
+       "|               6.27e-06 | \"nfe\"                          |\n",
+       "+------------------------+--------------------------------+\n",
+       "\n",
+       "+------------------------+--------------------------------+-----------------+\n",
+       "| joint.fafmax.faf99_max | joint.fafmax.faf99_max_gen_anc | joint.grpmax.AC |\n",
+       "+------------------------+--------------------------------+-----------------+\n",
+       "|                float64 | str                            |           int32 |\n",
+       "+------------------------+--------------------------------+-----------------+\n",
+       "|                     NA | NA                             |              NA |\n",
+       "|                     NA | NA                             |               1 |\n",
+       "|                     NA | NA                             |               1 |\n",
+       "|                     NA | NA                             |              NA |\n",
+       "|               2.35e-06 | \"nfe\"                          |               2 |\n",
+       "+------------------------+--------------------------------+-----------------+\n",
+       "\n",
+       "+-----------------+-----------------+-------------------------------+\n",
+       "| joint.grpmax.AF | joint.grpmax.AN | joint.grpmax.homozygote_count |\n",
+       "+-----------------+-----------------+-------------------------------+\n",
+       "|         float64 |           int32 |                         int32 |\n",
+       "+-----------------+-----------------+-------------------------------+\n",
+       "|              NA |              NA |                            NA |\n",
+       "|        4.07e-04 |            2456 |                             0 |\n",
+       "|        4.39e-05 |           22760 |                             0 |\n",
+       "|              NA |              NA |                            NA |\n",
+       "|        3.78e-05 |           52912 |                             0 |\n",
+       "+-----------------+-----------------+-------------------------------+\n",
+       "\n",
+       "+----------------------+\n",
+       "| joint.grpmax.gen_anc |\n",
+       "+----------------------+\n",
+       "| str                  |\n",
+       "+----------------------+\n",
+       "| NA                   |\n",
+       "| \"eas\"                |\n",
+       "| \"afr\"                |\n",
+       "| NA                   |\n",
+       "| \"nfe\"                |\n",
+       "+----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_all.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_all.bin_freq                             |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]                |\n",
+       "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15]          |\n",
+       "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9]           |\n",
+       "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n",
+       "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_all.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_all.bin_freq                             |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n",
+       "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n",
+       "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n",
+       "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n",
+       "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                              527 |\n",
+       "|                                              888 |\n",
+       "|                                             1232 |\n",
+       "|                                             2440 |\n",
+       "|                                             3622 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_alt.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]        |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_alt.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.ab_hist_alt.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.ab_hist_alt.bin_freq |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_all.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_all.bin_freq                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]   |\n",
+       "| [10121,5441,5610,3838,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,... |\n",
+       "| [6578,3782,4202,3336,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5... |\n",
+       "| [1429,1382,1901,2026,25677,6108,5433,3528,1753,1702,1130,485,518,282,119,... |\n",
+       "| [995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_all.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_all.bin_freq                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,14... |\n",
+       "| [7,37,269,903,1948,4127,5519,7075,7322,6972,6095,5249,4374,3624,2893,2106... |\n",
+       "| [2,24,149,466,1201,2614,4035,5568,6331,6725,6267,5855,5101,4268,3465,2554... |\n",
+       "| [1,9,42,122,294,787,1537,2717,3751,4972,5404,5762,5875,5161,4526,3415,252... |\n",
+       "| [2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,351... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                  744 |\n",
+       "|                                                 1130 |\n",
+       "|                                                 1487 |\n",
+       "|                                                 2622 |\n",
+       "|                                                 3743 |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_alt.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+------------------------------------------------------+\n",
+       "| [0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]            |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_alt.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+------------------------------------------------------+\n",
+       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.ab_hist_alt.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+------------------------------------------------------+\n",
+       "| [0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_het.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,1,0,0,1,0,0,0,0,0]                            |\n",
+       "| [0,0,1,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,1,0,1,0,0,0,0,0]                            |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_hom.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_hom.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq_comparison_stats.contingency_table_test                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------+\n",
+       "| freq_comparison_stats.cochran_mantel_haenszel_test.p_value |\n",
+       "+------------------------------------------------------------+\n",
+       "|                                                    float64 |\n",
+       "+------------------------------------------------------------+\n",
+       "|                                                         NA |\n",
+       "|                                                         NA |\n",
+       "|                                                         NA |\n",
+       "|                                                         NA |\n",
+       "|                                                         NA |\n",
+       "+------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| freq_comparison_stats.cochran_mantel_haenszel_test.chisq |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                  float64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+\n",
+       "| freq_comparison_stats.stat_union.p_value |\n",
+       "+------------------------------------------+\n",
+       "|                                  float64 |\n",
+       "+------------------------------------------+\n",
+       "|                                       NA |\n",
+       "|                                       NA |\n",
+       "|                                       NA |\n",
+       "|                                       NA |\n",
+       "|                                       NA |\n",
+       "+------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| freq_comparison_stats.stat_union.stat_test_name |\n",
+       "+-------------------------------------------------+\n",
+       "| str                                             |\n",
+       "+-------------------------------------------------+\n",
+       "| NA                                              |\n",
+       "| NA                                              |\n",
+       "| NA                                              |\n",
+       "| NA                                              |\n",
+       "| NA                                              |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------+\n",
+       "| freq_comparison_stats.stat_union.gen_ancs |\n",
+       "+-------------------------------------------+\n",
+       "| array                                |\n",
+       "+-------------------------------------------+\n",
+       "| NA                                        |\n",
+       "| NA                                        |\n",
+       "| NA                                        |\n",
+       "| NA                                        |\n",
+       "| NA                                        |\n",
+       "+-------------------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "354d7a5e-07a2-4f33-a830-970877cd4d63",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## All sites allele numbers\n",
+    "\n",
+    "As part of gnomAD v4.1, we [released](https://gnomad.broadinstitute.org/data#v4-all-sites-allele-number) allele number across all callable sites in the gnomAD exomes and genomes. For more information, see our [v4.1 blog post](https://gnomad.broadinstitute.org/news/2024-04-gnomad-v4-1/#allele-numbers-across-all-possible-sites)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81008401-eec4-4e95-9709-4781db066f7f",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Exomes all sites allele number Hail Table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7f1c013-a013-4fde-a7e6-fcb18d8d8a5c",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3b7c75d0-1eec-4b92-883e-410337b09c92",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(data_type='exomes', version='4.1', dataset=\"all_sites_an\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6868a2d1-6e62-492a-8086-822c910e8608",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f9c6d73b-7683-47fe-bf7d-2f5bef1d23d3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'strata_meta': array> \n",
+      "    'strata_sample_count': array \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'AN': array \n",
+      "    'outside_broad_capture_region': bool \n",
+      "    'outside_ukb_capture_region': bool \n",
+      "    'outside_broad_calling_region': bool \n",
+      "    'outside_ukb_calling_region': bool \n",
+      "----------------------------------------\n",
+      "Key: ['locus']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "626f20d9-43c1-4687-9b05-01d53115c168",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ee361058-54ae-4793-951b-1e0a6df6f685",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
locus
AN
outside_broad_capture_region
outside_ukb_capture_region
outside_broad_calling_region
outside_ukb_calling_region
locus<GRCh38>array<int64>boolboolboolbool
chr1:11719[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11720[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11721[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11722[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11723[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+\n",
+       "| locus         |\n",
+       "+---------------+\n",
+       "| locus |\n",
+       "+---------------+\n",
+       "| chr1:11719    |\n",
+       "| chr1:11720    |\n",
+       "| chr1:11721    |\n",
+       "| chr1:11722    |\n",
+       "| chr1:11723    |\n",
+       "+---------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| AN                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
+       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
+       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
+       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
+       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------+----------------------------+\n",
+       "| outside_broad_capture_region | outside_ukb_capture_region |\n",
+       "+------------------------------+----------------------------+\n",
+       "|                         bool |                       bool |\n",
+       "+------------------------------+----------------------------+\n",
+       "|                         True |                       True |\n",
+       "|                         True |                       True |\n",
+       "|                         True |                       True |\n",
+       "|                         True |                       True |\n",
+       "|                         True |                       True |\n",
+       "+------------------------------+----------------------------+\n",
+       "\n",
+       "+------------------------------+----------------------------+\n",
+       "| outside_broad_calling_region | outside_ukb_calling_region |\n",
+       "+------------------------------+----------------------------+\n",
+       "|                         bool |                       bool |\n",
+       "+------------------------------+----------------------------+\n",
+       "|                        False |                       True |\n",
+       "|                        False |                       True |\n",
+       "|                        False |                       True |\n",
+       "|                        False |                       True |\n",
+       "|                        False |                       True |\n",
+       "+------------------------------+----------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc4a4f23-d754-4e31-8e59-f62f9be65942",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Genomes all sites allele number Hail Table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65cb8d93-c5ef-409b-9c51-7c282a63bdc2",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0bb38926-f803-4be5-852c-782023b387bb",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(data_type='genomes', version='4.1', dataset=\"all_sites_an\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d5c2549-151c-4b99-bac3-23fd9024f114",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "64d2c64c-b533-433a-89e6-72d473bd6464",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'strata_meta': array> \n",
+      "    'strata_sample_count': array \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'AN': array \n",
+      "----------------------------------------\n",
+      "Key: ['locus']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "140f66aa-83d4-4752-abcf-c674bf208194",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e2271a6e-16f6-48ca-805f-735e17a8f711",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
locus
AN
locus<GRCh38>array<int64>
chr1:10001[16,18232,0,0,4,0,0,6,0,6,0,0,8,8,0,0,0,0,4,0,0,0,0,0,2,4,0,0,2,4,0,0,0,0]
chr1:10002[78,32090,12,0,22,2,0,26,0,14,2,0,30,48,8,4,0,0,4,18,2,0,0,0,12,14,0,0,2,12,2,0,0,0]
chr1:10003[200,38154,48,0,48,6,4,28,6,48,6,6,102,98,38,10,0,0,8,40,6,0,0,4,14,14,2,4,26,22,6,0,2,4]
chr1:10004[948,62380,202,0,248,18,12,172,10,258,18,10,400,548,112,90,0,0,66,182,14,4,2,10,70,102,4,6,118,140,10,8,4,6]
chr1:10005[1774,70720,356,6,444,30,18,288,18,552,32,30,782,992,206,150,2,4,134,310,18,12,6,12,116,172,6,12,268,284,16,16,10,20]
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+\n",
+       "| locus         |\n",
+       "+---------------+\n",
+       "| locus |\n",
+       "+---------------+\n",
+       "| chr1:10001    |\n",
+       "| chr1:10002    |\n",
+       "| chr1:10003    |\n",
+       "| chr1:10004    |\n",
+       "| chr1:10005    |\n",
+       "+---------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| AN                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [16,18232,0,0,4,0,0,6,0,6,0,0,8,8,0,0,0,0,4,0,0,0,0,0,2,4,0,0,2,4,0,0,0,0]   |\n",
+       "| [78,32090,12,0,22,2,0,26,0,14,2,0,30,48,8,4,0,0,4,18,2,0,0,0,12,14,0,0,2,... |\n",
+       "| [200,38154,48,0,48,6,4,28,6,48,6,6,102,98,38,10,0,0,8,40,6,0,0,4,14,14,2,... |\n",
+       "| [948,62380,202,0,248,18,12,172,10,258,18,10,400,548,112,90,0,0,66,182,14,... |\n",
+       "| [1774,70720,356,6,444,30,18,288,18,552,32,30,782,992,206,150,2,4,134,310,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bf9f31f-34ff-4385-a80e-985cbb0acfe8",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Coverage\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de70c319-787b-4d6c-9058-255a1137d81f",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Exomes coverage Hail Table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3278430c-4279-4d89-85e7-276184ec42b8",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "7f005af0-df6d-4a0a-a2e0-9cb4ff6782c2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(data_type='exomes', version='4.0', dataset=\"coverage\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "128e58ce-c219-472a-88be-6babc2ba5a15",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "6058bf38-4ce9-4d37-89a7-d11af0e4d9f1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    None\n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'mean': float64 \n",
+      "    'median_approx': int32 \n",
+      "    'total_DP': int64 \n",
+      "    'over_1': float64 \n",
+      "    'over_5': float64 \n",
+      "    'over_10': float64 \n",
+      "    'over_15': float64 \n",
+      "    'over_20': float64 \n",
+      "    'over_25': float64 \n",
+      "    'over_30': float64 \n",
+      "    'over_50': float64 \n",
+      "    'over_100': float64 \n",
+      "----------------------------------------\n",
+      "Key: ['locus']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5969ab0c-7cee-4061-8740-8b82366ae806",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "49fcf64b-1a91-4052-bb98-b329a0e8031b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
locus
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>float64int32int64float64float64float64float64float64float64float64float64float64
chr1:118191.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118201.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118211.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118221.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118231.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+----------+---------------+----------+----------+----------+\n",
+       "| locus         |     mean | median_approx | total_DP |   over_1 |   over_5 |\n",
+       "+---------------+----------+---------------+----------+----------+----------+\n",
+       "| locus |  float64 |         int32 |    int64 |  float64 |  float64 |\n",
+       "+---------------+----------+---------------+----------+----------+----------+\n",
+       "| chr1:11819    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
+       "| chr1:11820    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
+       "| chr1:11821    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
+       "| chr1:11822    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
+       "| chr1:11823    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
+       "+---------------+----------+---------------+----------+----------+----------+\n",
+       "\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "|  over_10 |  over_15 |  over_20 |  over_25 |  over_30 |  over_50 | over_100 |\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "|  float64 |  float64 |  float64 |  float64 |  float64 |  float64 |  float64 |\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
+       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
+       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
+       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
+       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19b2e9be-48fd-4859-af3d-a0775656d24c",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Genomes coverage Hail Table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5918f8e0-a723-439a-a11f-558d5ed13be8",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "a8d0be07-c35d-425a-b554-c86034e367fc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(data_type='genomes', version='3.0', dataset=\"coverage\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d129b898-d642-44d1-8243-66a9cca8d1b1",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "18afb20f-7429-4fe2-a6c5-73a22dcbdb76",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    None\n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'mean': float64 \n",
+      "    'median': int32 \n",
+      "    'count_array': array \n",
+      "    'over_1': float32 \n",
+      "    'over_5': float32 \n",
+      "    'over_10': float32 \n",
+      "    'over_15': float32 \n",
+      "    'over_20': float32 \n",
+      "    'over_25': float32 \n",
+      "    'over_30': float32 \n",
+      "    'over_50': float32 \n",
+      "    'over_100': float32 \n",
+      "----------------------------------------\n",
+      "Key: ['locus']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05d8e22c-93d6-4ecf-b9e7-711945268c82",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "b27cb655-3abb-4501-bcc9-3f634db64591",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
locus
mean
median
count_array
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>float64int32array<int32>float32float32float32float32float32float32float32float32float32
chr1:100011.93e+0116[0,20,65,141,226,328,386,442,415,385,292,198,362,367,337,273,289,266,241,226,191,169,168,151,227,194,179,190,177,166,143,131,150,130,125,121,136,94,93,83,67,60,68,68,59,33,39,39,39,38,20,18,21,25,10,9,16,8,7,6,2,3,2,4,1,2,2,2,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]1.25e-011.19e-019.12e-026.95e-025.15e-023.88e-022.62e-022.27e-030.00e+00
chr1:100022.10e+0118[0,10,41,108,209,339,482,570,581,575,483,390,702,689,639,556,587,534,516,435,417,366,346,320,437,415,405,359,333,308,283,266,272,248,218,231,241,184,176,162,138,119,127,137,118,63,82,87,66,66,46,33,39,43,22,25,26,19,19,11,7,6,7,5,3,5,2,4,2,6,2,3,2,0,1,1,1,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]2.20e-012.15e-011.79e-011.39e-011.02e-017.60e-025.06e-024.83e-032.79e-05
chr1:100032.44e+0123[0,6,16,62,100,162,233,289,294,346,342,310,537,612,664,649,661,711,723,658,649,590,548,486,679,656,640,571,533,491,439,398,412,404,349,383,360,298,263,242,207,182,186,194,159,118,123,116,96,96,67,59,61,64,34,33,34,31,30,15,12,11,13,10,7,7,3,7,5,10,3,3,5,0,2,2,1,0,0,2,1,1,1,4,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,4]2.62e-012.59e-012.41e-012.07e-011.59e-011.18e-017.77e-027.61e-035.58e-05
chr1:100042.43e+0123[0,4,21,70,101,178,270,396,417,515,550,486,864,1003,1098,1138,1183,1270,1242,1181,1162,1083,966,845,1149,1088,1047,922,857,804,725,645,633,658,525,610,537,451,411,369,343,285,290,260,235,184,190,174,151,152,96,83,96,91,52,52,56,43,47,30,20,19,22,16,13,9,9,10,10,14,8,7,8,0,5,3,3,2,1,2,1,1,1,5,3,3,2,1,0,2,0,0,1,0,1,0,1,1,0,0,12]4.27e-014.24e-013.99e-013.43e-012.60e-011.87e-011.21e-011.20e-021.67e-04
chr1:100052.45e+0123[0,4,22,67,98,177,257,381,423,511,580,560,1013,1158,1257,1314,1338,1472,1401,1341,1300,1266,1105,966,1288,1243,1198,1068,976,905,842,725,728,740,600,678,613,515,464,414,396,338,324,300,268,213,210,198,175,165,113,100,108,102,61,58,61,50,53,35,22,22,27,22,15,11,12,10,13,14,10,8,9,1,6,4,6,5,3,3,4,2,2,5,3,7,3,1,0,2,0,1,2,2,2,0,1,1,0,0,17]4.83e-014.80e-014.56e-013.92e-012.96e-012.14e-011.38e-011.42e-022.37e-04
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+----------+--------+\n",
+       "| locus         |     mean | median |\n",
+       "+---------------+----------+--------+\n",
+       "| locus |  float64 |  int32 |\n",
+       "+---------------+----------+--------+\n",
+       "| chr1:10001    | 1.93e+01 |     16 |\n",
+       "| chr1:10002    | 2.10e+01 |     18 |\n",
+       "| chr1:10003    | 2.44e+01 |     23 |\n",
+       "| chr1:10004    | 2.43e+01 |     23 |\n",
+       "| chr1:10005    | 2.45e+01 |     23 |\n",
+       "+---------------+----------+--------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| count_array                                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,20,65,141,226,328,386,442,415,385,292,198,362,367,337,273,289,266,241,... |\n",
+       "| [0,10,41,108,209,339,482,570,581,575,483,390,702,689,639,556,587,534,516,... |\n",
+       "| [0,6,16,62,100,162,233,289,294,346,342,310,537,612,664,649,661,711,723,65... |\n",
+       "| [0,4,21,70,101,178,270,396,417,515,550,486,864,1003,1098,1138,1183,1270,1... |\n",
+       "| [0,4,22,67,98,177,257,381,423,511,580,560,1013,1158,1257,1314,1338,1472,1... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "|   over_1 |   over_5 |  over_10 |  over_15 |  over_20 |  over_25 |  over_30 |\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "|  float32 |  float32 |  float32 |  float32 |  float32 |  float32 |  float32 |\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "| 1.25e-01 | 1.19e-01 | 9.12e-02 | 6.95e-02 | 5.15e-02 | 3.88e-02 | 2.62e-02 |\n",
+       "| 2.20e-01 | 2.15e-01 | 1.79e-01 | 1.39e-01 | 1.02e-01 | 7.60e-02 | 5.06e-02 |\n",
+       "| 2.62e-01 | 2.59e-01 | 2.41e-01 | 2.07e-01 | 1.59e-01 | 1.18e-01 | 7.77e-02 |\n",
+       "| 4.27e-01 | 4.24e-01 | 3.99e-01 | 3.43e-01 | 2.60e-01 | 1.87e-01 | 1.21e-01 |\n",
+       "| 4.83e-01 | 4.80e-01 | 4.56e-01 | 3.92e-01 | 2.96e-01 | 2.14e-01 | 1.38e-01 |\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "\n",
+       "+----------+----------+\n",
+       "|  over_50 | over_100 |\n",
+       "+----------+----------+\n",
+       "|  float32 |  float32 |\n",
+       "+----------+----------+\n",
+       "| 2.27e-03 | 0.00e+00 |\n",
+       "| 4.83e-03 | 2.79e-05 |\n",
+       "| 7.61e-03 | 5.58e-05 |\n",
+       "| 1.20e-02 | 1.67e-04 |\n",
+       "| 1.42e-02 | 2.37e-04 |\n",
+       "+----------+----------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {
+    "height": "613.99px",
+    "width": "526.312px"
+   },
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": true,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "202.438px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "toc-autonumbering": false,
+  "toc-showcode": false,
+  "toc-showmarkdowntxt": true,
+  "toc-showtags": false,
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
new file mode 100644
index 0000000..e6dd8fd
--- /dev/null
+++ b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
@@ -0,0 +1,6288 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4a01210b",
+   "metadata": {},
+   "source": [
+    "# Introduction to filtering the gnomAD variant data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e609a46",
+   "metadata": {
+    "toc": true
+   },
+   "source": [
+    "
Table of Contents
\n",
+    "
"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e713032",
+   "metadata": {},
+   "source": [
+    "## Import modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e69953f7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T18:04:56.165634Z",
+     "start_time": "2024-12-06T18:04:55.603516Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "    \n",
+       "    
\n",
+       "        \n",
+       "        Loading BokehJS ...\n",
+       "    
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function now() {\n",
+       "    return new Date();\n",
+       "  }\n",
+       "\n",
+       "  const force = true;\n",
+       "\n",
+       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
+       "    root._bokeh_onload_callbacks = [];\n",
+       "    root._bokeh_is_loading = undefined;\n",
+       "  }\n",
+       "\n",
+       "const JS_MIME_TYPE = 'application/javascript';\n",
+       "  const HTML_MIME_TYPE = 'text/html';\n",
+       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
+       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
+       "\n",
+       "  /**\n",
+       "   * Render data to the DOM node\n",
+       "   */\n",
+       "  function render(props, node) {\n",
+       "    const script = document.createElement(\"script\");\n",
+       "    node.appendChild(script);\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when an output is cleared or removed\n",
+       "   */\n",
+       "  function handleClearOutput(event, handle) {\n",
+       "    function drop(id) {\n",
+       "      const view = Bokeh.index.get_by_id(id)\n",
+       "      if (view != null) {\n",
+       "        view.model.document.clear()\n",
+       "        Bokeh.index.delete(view)\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    const cell = handle.cell;\n",
+       "\n",
+       "    const id = cell.output_area._bokeh_element_id;\n",
+       "    const server_id = cell.output_area._bokeh_server_id;\n",
+       "\n",
+       "    // Clean up Bokeh references\n",
+       "    if (id != null) {\n",
+       "      drop(id)\n",
+       "    }\n",
+       "\n",
+       "    if (server_id !== undefined) {\n",
+       "      // Clean up Bokeh references\n",
+       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
+       "      cell.notebook.kernel.execute(cmd_clean, {\n",
+       "        iopub: {\n",
+       "          output: function(msg) {\n",
+       "            const id = msg.content.text.trim()\n",
+       "            drop(id)\n",
+       "          }\n",
+       "        }\n",
+       "      });\n",
+       "      // Destroy server and session\n",
+       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
+       "      cell.notebook.kernel.execute(cmd_destroy);\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when a new output is added\n",
+       "   */\n",
+       "  function handleAddOutput(event, handle) {\n",
+       "    const output_area = handle.output_area;\n",
+       "    const output = handle.output;\n",
+       "\n",
+       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
+       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
+       "      return\n",
+       "    }\n",
+       "\n",
+       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
+       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
+       "      // store reference to embed id on output_area\n",
+       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "    }\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "      const bk_div = document.createElement(\"div\");\n",
+       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "      const script_attrs = bk_div.children[0].attributes;\n",
+       "      for (let i = 0; i < script_attrs.length; i++) {\n",
+       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
+       "      }\n",
+       "      // store reference to server id on output_area\n",
+       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function register_renderer(events, OutputArea) {\n",
+       "\n",
+       "    function append_mime(data, metadata, element) {\n",
+       "      // create a DOM node to render to\n",
+       "      const toinsert = this.create_output_subarea(\n",
+       "        metadata,\n",
+       "        CLASS_NAME,\n",
+       "        EXEC_MIME_TYPE\n",
+       "      );\n",
+       "      this.keyboard_manager.register_events(toinsert);\n",
+       "      // Render to node\n",
+       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+       "      render(props, toinsert[toinsert.length - 1]);\n",
+       "      element.append(toinsert);\n",
+       "      return toinsert\n",
+       "    }\n",
+       "\n",
+       "    /* Handle when an output is cleared or removed */\n",
+       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
+       "    events.on('delete.Cell', handleClearOutput);\n",
+       "\n",
+       "    /* Handle when a new output is added */\n",
+       "    events.on('output_added.OutputArea', handleAddOutput);\n",
+       "\n",
+       "    /**\n",
+       "     * Register the mime type and append_mime function with output_area\n",
+       "     */\n",
+       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+       "      /* Is output safe? */\n",
+       "      safe: true,\n",
+       "      /* Index of renderer in `output_area.display_order` */\n",
+       "      index: 0\n",
+       "    });\n",
+       "  }\n",
+       "\n",
+       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
+       "  if (root.Jupyter !== undefined) {\n",
+       "    const events = require('base/js/events');\n",
+       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+       "\n",
+       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+       "      register_renderer(events, OutputArea);\n",
+       "    }\n",
+       "  }\n",
+       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
+       "    root._bokeh_timeout = Date.now() + 5000;\n",
+       "    root._bokeh_failed_load = false;\n",
+       "  }\n",
+       "\n",
+       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
+       "     \"
\\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
+       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"
    \\n\"+\n",
+       "     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n",
+       "     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"\\n\"+\n",
+       "     \"from bokeh.resources import INLINE\\n\"+\n",
+       "     \"output_notebook(resources=INLINE)\\n\"+\n",
+       "     \"\\n\"+\n",
+       "     \"
\"}};\n",
+       "\n",
+       "  function display_loaded() {\n",
+       "    const el = document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\");\n",
+       "    if (el != null) {\n",
+       "      el.textContent = \"BokehJS is loading...\";\n",
+       "    }\n",
+       "    if (root.Bokeh !== undefined) {\n",
+       "      if (el != null) {\n",
+       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
+       "      }\n",
+       "    } else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(display_loaded, 100)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function run_callbacks() {\n",
+       "    try {\n",
+       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+       "        if (callback != null)\n",
+       "          callback();\n",
+       "      });\n",
+       "    } finally {\n",
+       "      delete root._bokeh_onload_callbacks\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
+       "  }\n",
+       "\n",
+       "  function load_libs(css_urls, js_urls, callback) {\n",
+       "    if (css_urls == null) css_urls = [];\n",
+       "    if (js_urls == null) js_urls = [];\n",
+       "\n",
+       "    root._bokeh_onload_callbacks.push(callback);\n",
+       "    if (root._bokeh_is_loading > 0) {\n",
+       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+       "      return null;\n",
+       "    }\n",
+       "    if (js_urls == null || js_urls.length === 0) {\n",
+       "      run_callbacks();\n",
+       "      return null;\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
+       "\n",
+       "    function on_load() {\n",
+       "      root._bokeh_is_loading--;\n",
+       "      if (root._bokeh_is_loading === 0) {\n",
+       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+       "        run_callbacks()\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    function on_error(url) {\n",
+       "      console.error(\"failed to load \" + url);\n",
+       "    }\n",
+       "\n",
+       "    for (let i = 0; i < css_urls.length; i++) {\n",
+       "      const url = css_urls[i];\n",
+       "      const element = document.createElement(\"link\");\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error.bind(null, url);\n",
+       "      element.rel = \"stylesheet\";\n",
+       "      element.type = \"text/css\";\n",
+       "      element.href = url;\n",
+       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+       "      document.body.appendChild(element);\n",
+       "    }\n",
+       "\n",
+       "    for (let i = 0; i < js_urls.length; i++) {\n",
+       "      const url = js_urls[i];\n",
+       "      const element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error.bind(null, url);\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "  };\n",
+       "\n",
+       "  function inject_raw_css(css) {\n",
+       "    const element = document.createElement(\"style\");\n",
+       "    element.appendChild(document.createTextNode(css));\n",
+       "    document.body.appendChild(element);\n",
+       "  }\n",
+       "\n",
+       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n",
+       "  const css_urls = [];\n",
+       "\n",
+       "  const inline_js = [    function(Bokeh) {\n",
+       "      Bokeh.set_log_level(\"info\");\n",
+       "    },\n",
+       "function(Bokeh) {\n",
+       "    }\n",
+       "  ];\n",
+       "\n",
+       "  function run_inline_js() {\n",
+       "    if (root.Bokeh !== undefined || force === true) {\n",
+       "          for (let i = 0; i < inline_js.length; i++) {\n",
+       "      inline_js[i].call(root, root.Bokeh);\n",
+       "    }\n",
+       "if (force === true) {\n",
+       "        display_loaded();\n",
+       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(run_inline_js, 100);\n",
+       "    } else if (!root._bokeh_failed_load) {\n",
+       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+       "      root._bokeh_failed_load = true;\n",
+       "    } else if (force !== true) {\n",
+       "      const cell = $(document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\")).parents('.cell').data().cell;\n",
+       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  if (root._bokeh_is_loading === 0) {\n",
+       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
+       "    run_inline_js();\n",
+       "  } else {\n",
+       "    load_libs(css_urls, js_urls, function() {\n",
+       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+       "      run_inline_js();\n",
+       "    });\n",
+       "  }\n",
+       "}(window));"
+      ],
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import hail as hl\n",
+    "\n",
+    "from gnomad_toolbox.load_data import get_gnomad_release\n",
+    "from gnomad_toolbox.filtering.variant import filter_by_intervals, filter_by_gene_symbol\n",
+    "from gnomad_toolbox.filtering.frequency import (\n",
+    "    get_ancestry_callstats, \n",
+    "    get_single_variant_ancestry_callstats,\n",
+    ")\n",
+    "from gnomad_toolbox.filtering.vep import filter_by_csqs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b3c44396-5ee1-4263-91f8-78bdb9417bab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Welcome to\n",
+      "     __  __     <>__\n",
+      "    / /_/ /__  __/ /\n",
+      "   / __  / _ `/ / /\n",
+      "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
+      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241210-2227-0.2.132-678e1f52b999.log\n",
+      "2024-12-10 22:28:45.710 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n"
+     ]
+    }
+   ],
+   "source": [
+    "hl.init(backend=\"local\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "725f9a57",
+   "metadata": {},
+   "source": [
+    "## Filter to variants in a specific gene\n",
+    "\n",
+    "Here we show two ways that you can load a variant table on the gnomAD browser:\n",
+    "  - The [region view](https://gnomad.broadinstitute.org/region/11-113409605-113475691?dataset=gnomad_r4)\n",
+    "  - The [gene page](https://gnomad.broadinstitute.org/gene/ENSG00000149295?dataset=gnomad_r4)\n",
+    "    - Only includes variants located in or within 75 base pairs of a coding exon (CDS)\n",
+    "\n",
+    "We use the *DRD2* gene as an example. "
+   ]
+  },
+  {
+   "attachments": {
+    "Screenshot%202024-11-01%20at%209.31.00%E2%80%AFPM.png": {
+     "image/png": 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"
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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "164a07b7",
+   "metadata": {},
+   "source": [
+    "### On region view (the interval of a gene)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "
   
  
"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9f8e1ba4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of variants in the DRD2 interval chr11:113409605-113475691 is: 8126\n"
+     ]
+    }
+   ],
+   "source": [
+    "drd2_interval = \"chr11:113409605-113475691\"\n",
+    "drd2_interval_ht = filter_by_intervals(drd2_interval)\n",
+    "print(f\"The total number of variants in the DRD2 interval {drd2_interval} is: {drd2_interval_ht.count()}\")"
+   ]
+  },
+  {
+   "attachments": {
+    "Screenshot%202024-11-01%20at%209.28.50%E2%80%AFPM.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "ac393319",
+   "metadata": {},
+   "source": [
+    "### On gene page\n",
+    "\n",
+    "To get the number of variants shown on the gene page, if you click 'all' and check 'exomes', 'SNVs', 'Indels', and 'Filtered variants' as this: \n",
+    "\n",
+    "![Screenshot%202024-11-01%20at%209.28.50%E2%80%AFPM.png](attachment:Screenshot%202024-11-01%20at%209.28.50%E2%80%AFPM.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e6bf7236",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of variants in DRD2 is:  1764\n"
+     ]
+    }
+   ],
+   "source": [
+    "drd2_ht = filter_by_gene_symbol('drd2')\n",
+    "print(\"The total number of variants in DRD2 is: \", drd2_ht.count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bff63bb",
+   "metadata": {},
+   "source": [
+    "## Filter to variants by Ensembl Variant Effect Predictor (VEP)\n",
+    "\n",
+    "The examples below show the VEP based filtering using the Table filtered to DRD2."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "756a996c-bad2-4d24-a304-6eb5e3efca65",
+   "metadata": {},
+   "source": [
+    "### Filter to `lof`, `missense`, and `synonymous` variants passing filters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "700582e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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,"1"),("ENSP_mappings","5aer"),("ENSP_mappings","5aer"),("ENSP_mappings","6cm4"),("ENSP_mappings","6luq"),("ENSP_mappings","6vms"),("ENSP_mappings","7dfp"),("ENSP_mappings","7jvr"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000542968.5:c.1323C>A","ENSP00000442172.1:p.Leu441=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,441,441,"ENSP00000442172","Ensembl",-1,"ENST00000542968",1,["P14416-1"],"T"),(1,"L","A1","protein_coding",NA,NA,1353,1353,1320,1320,"ctC/ctA",["synonymous_variant"],NA,[("Gene3D","1"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000544518.5:c.1320C>A","ENSP00000441068.1:p.Leu440=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,440,440,"ENSP00000441068","Ensembl",-1,"ENST00000544518",1,NA,"T"),(1,NA,NA,"lncRNA",1,NA,NA,NA,NA,NA,NA,["intron_variant","non_coding_transcript_variant"],NA,NA,NA,NA,"ENSG00000256757",NA,NA,NA,NA,"ENST00000546284.1:n.245-811G>T",NA,NA,"MODIFIER","2/3",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000546284",3,NA,"T"),(1,"L",NA,"protein_coding",1,NA,1677,1677,1323,1323,"ctC/ctA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.1323C>A","NP_000786.1:p.Leu441=",NA,"LOW",NA,NA,NA,NA,NA,"ENST00000362072.8",NA,NA,441,441,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"T"),(1,"L",NA,"protein_coding",NA,NA,1590,1590,1236,1236,"ctC/ctA",["synonymous_variant"],NA,NA,"7/7",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.1236C>A","NP_057658.2:p.Leu412=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,412,412,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"T"),(1,"L",NA,"protein_coding",NA,NA,1405,1405,1323,1323,"ctC/ctA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.1323C>A","XP_016872785.1:p.Leu441=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,441,441,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"T")]"SNV"5.00e+00"AS_MQ"FalseFalseFalseFalseFalseTrueFalseFalse"multi-snv"2False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,2955,2914,2974,2923,2564,2680]07685[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,2955,2914,2974,2923,2564,2680]07685[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,1,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]002.96e+001.86e-01NA0.00e+003.00e-02-2.55e-01NANA
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+------------+\n",
+       "| locus           | alleles    |\n",
+       "+-----------------+------------+\n",
+       "| locus   | array |\n",
+       "+-----------------+------------+\n",
+       "| chr11:113410731 | [\"C\",\"A\"]  |\n",
+       "| chr11:113410731 | [\"C\",\"T\"]  |\n",
+       "| chr11:113410735 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410736 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410736 | [\"G\",\"T\"]  |\n",
+       "+-----------------+------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(2,1.37e-06,1461866,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(2,4.... |\n",
+       "| [(1,6.84e-07,1461866,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(75,5.13e-05,1461876,0),(75,5.13e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
+       "| [(15,1.03e-05,1461882,0),(15,1.03e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
+       "| [(1,6.84e-07,1461882,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|                2 |         4.47e-05 |            44724 |\n",
+       "|                1 |         8.99e-07 |          1112010 |\n",
+       "|                8 |         7.19e-06 |          1112004 |\n",
+       "|               15 |         1.35e-05 |          1112010 |\n",
+       "|                1 |         8.99e-07 |          1112010 |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                              0 | \"amr\"                 |                 2 |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                              0 | \"nfe\"                 |                 2 |\n",
+       "|                              0 | \"nfe\"                 |                 1 |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|          4.57e-05 |             43740 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|          5.71e-06 |            350102 |                               0 |\n",
+       "|          2.86e-06 |            350106 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| \"amr\"                  |\n",
+       "| NA                     |\n",
+       "| \"nfe\"                  |\n",
+       "| \"nfe\"                  |\n",
+       "| NA                     |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(7.41e-06,2.77e-06),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(4.19e-05,3.82e-05),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(6.16e-06,4.89e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                7.41e-06 | \"amr\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                3.09e-06 | \"nfe\"                           |\n",
+       "|                8.10e-06 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                2.77e-06 | \"amr\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                2.24e-06 | \"nfe\"                           |\n",
+       "|                6.42e-06 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                 7.58e-06 | \"amr\"                            |\n",
+       "|                       NA | NA                               |\n",
+       "|                 9.50e-07 | \"nfe\"                            |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                 2.84e-06 | \"amr\"                            |       1 |\n",
+       "|                       NA | NA                               |       2 |\n",
+       "|                 3.60e-07 | \"nfe\"                            |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       2 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "| was_split | rsid             | filters  |  info.FS |  info.MQ |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      bool | set         | set |  float64 |  float64 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      True | {\"rs140938110\"}  | {}       | 5.56e+00 | 6.00e+01 |\n",
+       "|      True | NA               | {}       | 5.56e+00 | 6.00e+01 |\n",
+       "|      True | {\"rs201801648\"}  | {}       | 0.00e+00 | 6.00e+01 |\n",
+       "|      True | {\"rs1159504221\"} | {}       | 5.49e-01 | 6.00e+01 |\n",
+       "|      True | NA               | {}       | 5.49e-01 | 6.00e+01 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|        float64 |           int64 |  float64 |             float64 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
+       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
+       "|       0.00e+00 |          127501 | 1.23e+01 |            3.70e-02 |\n",
+       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
+       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "| info.SB               | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "| array          |  float64 |      int32 |    float64 |    float64 |\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "| [134,83,109,52]       | 9.81e-01 |        378 |   1.31e+01 |   6.00e+01 |\n",
+       "| [134,83,109,52]       | 9.81e-01 |        378 |   0.00e+00 |   6.00e+01 |\n",
+       "| [3240,2211,2993,1962] | 7.34e-01 |      10404 |   0.00e+00 |   6.00e+01 |\n",
+       "| [386,195,316,166]     | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
+       "| [386,195,316,166]     | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|           float64 |         float64 |              int64 |    float64 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|          0.00e+00 |        2.94e-01 |               1895 |   1.06e+01 |\n",
+       "|          0.00e+00 |        9.04e-01 |               1967 |   9.88e+00 |\n",
+       "|          0.00e+00 |        1.00e+00 |             127480 |   1.23e+01 |\n",
+       "|          0.00e+00 |        9.11e-01 |              11899 |   1.17e+01 |\n",
+       "|          0.00e+00 |        3.28e-02 |                408 |   8.16e+00 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE      | info.AS_SOR | info.AS_VarDP |\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "|                float64 | array          |     float64 |         int32 |\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "|               2.52e-01 | [134,83,56,19]        |    1.42e+00 |           179 |\n",
+       "|              -5.01e-01 | [134,83,53,33]        |    6.81e-01 |           199 |\n",
+       "|               1.60e-02 | [3240,2211,2991,1961] |    7.34e-01 |         10383 |\n",
+       "|               9.70e-02 | [386,195,304,161]     |    6.47e-01 |          1013 |\n",
+       "|              -9.23e-01 | [386,195,12,5]        |    7.90e-01 |            50 |\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|           bool |                       bool |                   bool |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|          False |                         NA |                     NA |\n",
+       "|           True |                      False |                  False |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|           True |                      False |                  False |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|      bool |       bool |             bool |          bool |        float64 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|     False |      False |            False |         False |       5.27e+00 |\n",
+       "|     False |      False |            False |         False |       5.40e+00 |\n",
+       "|     False |      False |            False |         False |       8.85e+00 |\n",
+       "|     False |      False |            False |         False |       7.50e+00 |\n",
+       "|     False |      False |            False |         False |       5.00e+00 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "\n",
+       "+-----------------------+\n",
+       "| info.inbreeding_coeff |\n",
+       "+-----------------------+\n",
+       "|               float64 |\n",
+       "+-----------------------+\n",
+       "|             -1.37e-06 |\n",
+       "|             -6.84e-07 |\n",
+       "|             -5.13e-05 |\n",
+       "|             -1.03e-05 |\n",
+       "|             -6.84e-07 |\n",
+       "+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.ZonDIhdwyghm7SuWkX... |\n",
+       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.Hp20SlFHUdM7jlWmVV... |\n",
+       "| [\"ga4gh:VA.SBSoboxxiC0V-mhjjHg2geRlf3I0Ib2d\",\"ga4gh:VA.GzUD_86k_EDbtk-R58... |\n",
+       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.OXPDvbuulFZ9CSk4Xt... |\n",
+       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.4qhkDG1qrfqiD2TeOA... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| array          | array          | array          |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"A\"]           |\n",
+       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"T\"]           |\n",
+       "| [113410734,113410734] | [113410735,113410735] | [\"G\",\"A\"]           |\n",
+       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"A\"]           |\n",
+       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"T\"]           |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| str               |     int32 | str    | str                            |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| \"C/A\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tA\t.\t.\tGT\" |\n",
+       "| \"C/T\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tT\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410735 | \".\"    | \"chr11\t113410735\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"G/T\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tT\t.\t.\tGT\" |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"missense_variant\"          |\n",
+       "| \"missense_variant\"          |\n",
+       "| \"missense_variant\"          |\n",
+       "| \"synonymous_variant\"        |\n",
+       "| \"synonymous_variant\"        |\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq              |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [0,0,0,0,12,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
+       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
+       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,0,65,332,11308,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
+       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                       7715 |\n",
+       "|                                       7715 |\n",
+       "|                                       7717 |\n",
+       "|                                       7685 |\n",
+       "|                                       7685 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "|                                         32 |\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq   |\n",
+       "+----------------------------------------------+\n",
+       "| array                                 |\n",
+       "+----------------------------------------------+\n",
+       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]    |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]    |\n",
+       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0] |\n",
+       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]    |\n",
+       "+----------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq          |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [6,0,1,0,14,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
+       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
+       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,2,67,335,11310,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
+       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                           7715 |\n",
+       "|                                           7715 |\n",
+       "|                                           7717 |\n",
+       "|                                           7685 |\n",
+       "|                                           7685 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75]     |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15]     |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "|                                             32 |\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]     |\n",
+       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
+       "| [1,0,0,2,3,3,8,4,0,0]                      |\n",
+       "| [0,0,1,2,4,2,4,1,0,0]                      |\n",
+       "| [0,0,0,0,0,1,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           1 |\n",
+       "|                                           0 |\n",
+       "|                                           3 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        2.77e+01 |\n",
+       "|                                          0 |                        2.75e+01 |\n",
+       "|                                          0 |                        2.28e+01 |\n",
+       "|                                          0 |                        3.44e+00 |\n",
+       "|                                          0 |                        2.96e+00 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            4.10e+00 |                       6.02e-01 |\n",
+       "|                            4.07e+00 |                       7.19e-01 |\n",
+       "|                            2.69e+00 |                       2.67e-01 |\n",
+       "|                            2.28e-01 |                             NA |\n",
+       "|                            1.86e-01 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                 0.00e+00 |                    8.78e+00 |\n",
+       "|                                 2.00e-02 |                    8.78e+00 |\n",
+       "|                                 1.00e-02 |                    8.67e+00 |\n",
+       "|                                 1.00e-02 |                   -2.55e-01 |\n",
+       "|                                 3.00e-02 |                   -2.55e-01 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                      0.00e+00 |                          9.89e-01 |\n",
+       "|                      0.00e+00 |                          9.89e-01 |\n",
+       "|                      1.30e-01 |                          1.18e-01 |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of lof, missense, and synonymous variants passing filters in DRD2 is:  664\n"
+     ]
+    }
+   ],
+   "source": [
+    "var_ht = filter_by_csqs(['lof','missense','synonymous'], ht=drd2_interval_ht)\n",
+    "var_ht.show(5)\n",
+    "print(\"The total number of lof, missense, and synonymous variants passing filters in DRD2 is: \", var_ht.count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35bc2c92-d90e-4a26-a04b-5bd7dc4e7d23",
+   "metadata": {},
+   "source": [
+    "### Filter to `lof` variants passing filters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9887fdb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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","HC","NAGNAG_SITE",NA,"INTRON_SIZE:1491",NA,NA,NA,NA,NA,"ENSP00000441068","Ensembl",-1,"ENST00000544518",1,NA,"C"),(1,NA,NA,"lncRNA",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],768,NA,NA,NA,"ENSG00000256757",NA,NA,NA,NA,NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000546284",3,NA,"C"),(1,NA,NA,"protein_coding",1,NA,NA,NA,NA,NA,NA,["splice_acceptor_variant"],NA,NA,NA,NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.811-2A>G",NA,NA,"HIGH","6/7","HC","NAGNAG_SITE",NA,"INTRON_SIZE:1491","ENST00000362072.8",NA,NA,NA,NA,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"C"),(1,NA,NA,"protein_coding",NA,NA,NA,NA,NA,NA,NA,["splice_acceptor_variant"],NA,NA,NA,NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.724-2A>G",NA,NA,"HIGH","5/6","HC","NAGNAG_SITE",NA,"INTRON_SIZE:2537",NA,NA,NA,NA,NA,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"C"),(1,NA,NA,"protein_coding",NA,NA,NA,NA,NA,NA,NA,["splice_acceptor_variant"],NA,NA,NA,NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.811-2A>G",NA,NA,"HIGH","6/7","HC","NAGNAG_SITE",NA,"INTRON_SIZE:1491",NA,NA,NA,NA,NA,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"C")]"SNV"5.84e+00"AS_MQ"FalseFalseFalseFalseFalseTrueTrueFalse"snv"1False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,929,0,3800,0,308036,0,642,0,415672,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2699,3244,4728,36120,209411,224276,48878,7369,3147,3425,5116,7827,11506,15293,17615,18578,18759,20940]070149[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][1313,0,2,0,1481,0,3800,0,308036,0,642,0,415672,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][618,1142,2737,3303,4730,36128,209411,224276,48878,7369,3147,3425,5116,7827,11506,15293,17615,18578,18759,20940]070149[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]003.50e+015.83e+00NA9.80e-01-7.90e-016.35e+00NANA
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+-------------+\n",
+       "| locus           | alleles     |\n",
+       "+-----------------+-------------+\n",
+       "| locus   | array  |\n",
+       "+-----------------+-------------+\n",
+       "| chr11:113412554 | [\"A\",\"G\"]   |\n",
+       "| chr11:113412612 | [\"CT\",\"C\"]  |\n",
+       "| chr11:113412614 | [\"ACG\",\"A\"] |\n",
+       "| chr11:113412865 | [\"G\",\"A\"]   |\n",
+       "| chr11:113412885 | [\"T\",\"C\"]   |\n",
+       "+-----------------+-------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(1,6.85e-07,1459578,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(2,1.37e-06,1461892,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(2,1.37e-06,1461894,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(1,6.85e-07,1459642,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33422,0),(0,0.... |\n",
+       "| [(1,6.86e-07,1458160,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33408,0),(0,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|                1 |         8.99e-07 |          1111998 |\n",
+       "|                2 |         1.80e-06 |          1112010 |\n",
+       "|                2 |         1.80e-06 |          1112012 |\n",
+       "|                1 |         8.99e-07 |          1111812 |\n",
+       "|                1 |         9.00e-07 |          1111636 |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                              0 | \"nfe\"                 |                 2 |\n",
+       "|                              0 | \"nfe\"                 |                 2 |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|                NA |                NA |                              NA |\n",
+       "|          5.71e-06 |            350106 |                               0 |\n",
+       "|          5.71e-06 |            350108 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| NA                     |\n",
+       "| \"nfe\"                  |\n",
+       "| \"nfe\"                  |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                3.00e-07 | \"nfe\"                           |\n",
+       "|                3.00e-07 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                1.10e-07 | \"nfe\"                           |\n",
+       "|                1.10e-07 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                       NA | NA                               |\n",
+       "|                 9.50e-07 | \"nfe\"                            |\n",
+       "|                 9.50e-07 | \"nfe\"                            |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                       NA | NA                               |       2 |\n",
+       "|                 3.60e-07 | \"nfe\"                            |       1 |\n",
+       "|                 3.60e-07 | \"nfe\"                            |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+----------+----------+----------+----------+----------------+\n",
+       "| was_split | rsid     | filters  |  info.FS |  info.MQ | info.MQRankSum |\n",
+       "+-----------+----------+----------+----------+----------+----------------+\n",
+       "|      bool | set | set |  float64 |  float64 |        float64 |\n",
+       "+-----------+----------+----------+----------+----------+----------------+\n",
+       "|      True | NA       | {}       | 2.83e+00 | 6.00e+01 |       0.00e+00 |\n",
+       "|     False | NA       | {}       | 5.48e-01 | 6.00e+01 |       0.00e+00 |\n",
+       "|      True | NA       | {}       | 2.32e+00 | 6.00e+01 |       0.00e+00 |\n",
+       "|      True | NA       | {}       | 1.02e+00 | 6.00e+01 |       0.00e+00 |\n",
+       "|     False | NA       | {}       | 2.45e+00 | 6.00e+01 |       0.00e+00 |\n",
+       "+-----------+----------+----------+----------+----------+----------------+\n",
+       "\n",
+       "+-----------------+----------+---------------------+---------------+----------+\n",
+       "| info.QUALapprox |  info.QD | info.ReadPosRankSum | info.SB       | info.SOR |\n",
+       "+-----------------+----------+---------------------+---------------+----------+\n",
+       "|           int64 |  float64 |             float64 | array  |  float64 |\n",
+       "+-----------------+----------+---------------------+---------------+----------+\n",
+       "|            1498 | 1.05e+01 |           -5.05e-01 | [63,17,52,10] | 1.04e+00 |\n",
+       "|            3458 | 1.79e+01 |            4.32e-01 | [65,38,58,32] | 7.50e-01 |\n",
+       "|            5336 | 1.56e+01 |            4.62e-01 | [99,81,94,68] | 8.22e-01 |\n",
+       "|             553 | 7.79e+00 |            2.96e-01 | [14,32,9,16]  | 4.68e-01 |\n",
+       "|             890 | 1.11e+01 |            1.80e-01 | [7,37,8,28]   | 3.79e-01 |\n",
+       "+-----------------+----------+---------------------+---------------+----------+\n",
+       "\n",
+       "+------------+------------+------------+-------------------+-----------------+\n",
+       "| info.VarDP | info.AS_FS | info.AS_MQ | info.AS_MQRankSum | info.AS_pab_max |\n",
+       "+------------+------------+------------+-------------------+-----------------+\n",
+       "|      int32 |    float64 |    float64 |           float64 |         float64 |\n",
+       "+------------+------------+------------+-------------------+-----------------+\n",
+       "|        142 |   1.10e+00 |   6.00e+01 |          0.00e+00 |        1.00e+00 |\n",
+       "|        193 |   5.48e-01 |   6.00e+01 |          0.00e+00 |        5.90e-01 |\n",
+       "|        342 |   8.20e+00 |   6.00e+01 |          0.00e+00 |        6.65e-01 |\n",
+       "|         71 |   1.04e+00 |   6.00e+01 |          0.00e+00 |        3.92e-01 |\n",
+       "|         80 |   2.45e+00 |   6.00e+01 |          0.00e+00 |        4.53e-01 |\n",
+       "+------------+------------+------------+-------------------+-----------------+\n",
+       "\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "| info.AS_QUALapprox | info.AS_QD | info.AS_ReadPosRankSum | info.AS_SB_TABLE |\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "|              int64 |    float64 |                float64 | array     |\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "|                661 |   1.35e+01 |              -1.83e+00 | [63,17,21,4]     |\n",
+       "|               3458 |   1.79e+01 |               4.32e-01 | [65,38,58,32]    |\n",
+       "|               3455 |   1.80e+01 |               4.62e-01 | [99,81,58,32]    |\n",
+       "|                531 |   8.56e+00 |               2.96e-01 | [14,32,8,15]     |\n",
+       "|                890 |   1.11e+01 |               1.80e-01 | [7,37,8,28]      |\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "| info.AS_SOR | info.AS_VarDP | info.singleton | info.transmitted_singleton |\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "|     float64 |         int32 |           bool |                       bool |\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "|    9.29e-01 |            49 |           True |                      False |\n",
+       "|    7.50e-01 |           193 |          False |                         NA |\n",
+       "|    1.15e+00 |           192 |          False |                         NA |\n",
+       "|    5.03e-01 |            62 |           True |                      False |\n",
+       "|    3.79e-01 |            80 |          False |                      False |\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "| info.sibling_singleton | info.omni | info.mills | info.monoallelic |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "|                   bool |      bool |       bool |             bool |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                     NA |     False |      False |            False |\n",
+       "|                     NA |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "\n",
+       "+---------------+----------------+-----------------------+\n",
+       "| info.only_het | info.AS_VQSLOD | info.inbreeding_coeff |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "|          bool |        float64 |               float64 |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "|         False |       5.30e+00 |             -6.84e-07 |\n",
+       "|         False |       4.25e+00 |             -1.37e-06 |\n",
+       "|         False |       3.61e+00 |             -1.37e-06 |\n",
+       "|         False |       5.39e+00 |             -6.84e-07 |\n",
+       "|         False |       5.84e+00 |             -1.37e-06 |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.mqU9WPvGr3hduyybbeN-AKJ1zN-kclsI\",\"ga4gh:VA.uvlicmv1vZnrBBqm0U... |\n",
+       "| [\"ga4gh:VA.NztLKtEJA7H0h_Tz31YRmTUR4hT2PNxO\",\"ga4gh:VA.5xtydtW8O1beQhV-1c... |\n",
+       "| [\"ga4gh:VA.Lx5RcGLe0vnqmstRd134JgqaJUNZIzCF\",\"ga4gh:VA.N3JXRaksYTV6CSLvZ_... |\n",
+       "| [\"ga4gh:VA.uAq5BRYMw_lO7PZcOF5TpbGC5hA8JCCE\",\"ga4gh:VA.rN4HBsKxAnhIfQxMXe... |\n",
+       "| [\"ga4gh:VA.Hj6Aa_k4TOMxqWIs6lE_cgxiRrPFWp8C\",\"ga4gh:VA.NDTpZX0YUzM19pYVv6... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| array          | array          | array          |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| [113412553,113412553] | [113412554,113412554] | [\"A\",\"G\"]           |\n",
+       "| [113412611,113412612] | [113412613,113412613] | [\"CT\",\"\"]           |\n",
+       "| [113412613,113412614] | [113412616,113412616] | [\"ACG\",\"\"]          |\n",
+       "| [113412864,113412864] | [113412865,113412865] | [\"G\",\"A\"]           |\n",
+       "| [113412884,113412884] | [113412885,113412885] | [\"T\",\"C\"]           |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "\n",
+       "+-------------------+-----------+--------+----------------------------------+\n",
+       "| vep.allele_string |   vep.end | vep.id | vep.input                        |\n",
+       "+-------------------+-----------+--------+----------------------------------+\n",
+       "| str               |     int32 | str    | str                              |\n",
+       "+-------------------+-----------+--------+----------------------------------+\n",
+       "| \"A/G\"             | 113412554 | \".\"    | \"chr11\t113412554\t.\tA\tG\t.\t.\tGT\"   |\n",
+       "| \"T/-\"             | 113412613 | \".\"    | \"chr11\t113412612\t.\tCT\tC\t.\t.\tGT\"  |\n",
+       "| \"CG/-\"            | 113412616 | \".\"    | \"chr11\t113412614\t.\tACG\tA\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113412865 | \".\"    | \"chr11\t113412865\t.\tG\tA\t.\t.\tGT\"   |\n",
+       "| \"T/C\"             | 113412885 | \".\"    | \"chr11\t113412885\t.\tT\tC\t.\t.\tGT\"   |\n",
+       "+-------------------+-----------+--------+----------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"splice_donor_variant\"      |\n",
+       "| \"frameshift_variant\"        |\n",
+       "| \"frameshift_variant\"        |\n",
+       "| \"stop_gained\"               |\n",
+       "| \"splice_acceptor_variant\"   |\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq                 |\n",
+       "+------------------------------------------------------------+\n",
+       "| array                                               |\n",
+       "+------------------------------------------------------------+\n",
+       "| [0,0,0,0,134,0,1469,0,311660,0,82,0,416443,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,1,0,2,0,314386,0,0,0,416555,0,0,0,0,0,0,2]        |\n",
+       "| [0,0,0,0,1,0,1,0,314388,0,0,0,416555,0,0,0,0,0,0,2]        |\n",
+       "| [0,0,0,0,547,0,1910,0,310815,0,33,0,416515,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,929,0,3800,0,308036,0,642,0,415672,0,0,0,0,0,0,1] |\n",
+       "+------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,1555,817,16216,229270,272936,45138,9370,3566,3616,4059,4521,5017,549... |\n",
+       "| [0,0,1,279,16685,230939,273577,45298,9412,3597,3653,4094,4550,5033,5504,5... |\n",
+       "| [0,0,0,278,16685,230938,273578,45300,9412,3597,3653,4094,4550,5033,5504,5... |\n",
+       "| [0,0,1141,1461,3821,36585,211757,225880,49280,7441,3163,3441,5118,7834,11... |\n",
+       "| [0,0,2699,3244,4728,36120,209411,224276,48878,7369,3147,3425,5116,7827,11... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                     102137 |\n",
+       "|                                     102187 |\n",
+       "|                                     102188 |\n",
+       "|                                      70182 |\n",
+       "|                                      70149 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          1 |\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq                 |\n",
+       "+----------------------------------------------------------------+\n",
+       "| array                                                   |\n",
+       "+----------------------------------------------------------------+\n",
+       "| [441,0,0,0,850,1,1469,0,311660,0,82,0,416443,0,0,0,0,0,0,1]    |\n",
+       "| [1,0,0,0,1,0,2,0,314386,0,0,0,416555,0,0,0,0,0,0,2]            |\n",
+       "| [0,0,0,0,1,0,1,0,314388,0,0,0,416555,0,0,0,0,0,0,2]            |\n",
+       "| [890,0,0,0,783,0,1910,0,310815,0,33,0,416515,0,0,0,0,0,0,1]    |\n",
+       "| [1313,0,2,0,1481,0,3800,0,308036,0,642,0,415672,0,0,0,0,0,0,1] |\n",
+       "+----------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [244,858,1593,833,16218,229270,272936,45138,9370,3566,3616,4059,4521,5017... |\n",
+       "| [0,0,1,279,16685,230939,273577,45298,9412,3597,3653,4094,4550,5034,5504,5... |\n",
+       "| [0,0,0,278,16685,230938,273578,45300,9412,3597,3653,4094,4550,5033,5504,5... |\n",
+       "| [431,622,1175,1493,3823,36589,211757,225880,49281,7441,3163,3441,5118,783... |\n",
+       "| [618,1142,2737,3303,4730,36128,209411,224276,48878,7369,3147,3425,5116,78... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                         102137 |\n",
+       "|                                         102187 |\n",
+       "|                                         102188 |\n",
+       "|                                          70182 |\n",
+       "|                                          70149 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              1 |\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,1,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           1 |\n",
+       "|                                           1 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        3.40e+01 |\n",
+       "|                                          0 |                        3.20e+01 |\n",
+       "|                                          0 |                        3.30e+01 |\n",
+       "|                                          0 |                        4.50e+01 |\n",
+       "|                                          0 |                        3.50e+01 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            5.66e+00 |                             NA |\n",
+       "|                            4.56e+00 |                             NA |\n",
+       "|                            4.99e+00 |                             NA |\n",
+       "|                            8.76e+00 |                             NA |\n",
+       "|                            5.83e+00 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                             9.60e-01 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             9.80e-01 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                -8.30e-01 |                    6.33e+00 |\n",
+       "|                                -4.00e-02 |                    8.89e+00 |\n",
+       "|                                -1.20e-01 |                    3.00e-01 |\n",
+       "|                                -1.10e-01 |                    8.79e+00 |\n",
+       "|                                -7.90e-01 |                    6.35e+00 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of lof variants passing filters in DRD2 is:  17\n"
+     ]
+    }
+   ],
+   "source": [
+    "var_ht = filter_by_csqs(['lof'], ht=drd2_interval_ht)\n",
+    "var_ht.show(5)\n",
+    "print(\"The total number of lof variants passing filters in DRD2 is: \", var_ht.count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "871c539b-a1c2-41a3-a33d-37649b603de2",
+   "metadata": {},
+   "source": [
+    "### Filter to `missense` variants passing filters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "86596aaf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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A,[("Gene3D","1"),("ENSP_mappings","5aer"),("ENSP_mappings","5aer"),("ENSP_mappings","6cm4"),("ENSP_mappings","6luq"),("ENSP_mappings","6vms"),("ENSP_mappings","7dfp"),("ENSP_mappings","7jvr"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000542968.5:c.1319T>C","ENSP00000442172.1:p.Ile440Thr",NA,"MODERATE",NA,NA,NA,NA,NA,NA,NA,NA,440,440,"ENSP00000442172","Ensembl",-1,"ENST00000542968",1,["P14416-1"],"G"),(1,"I/T","A1","protein_coding",NA,NA,1349,1349,1316,1316,"aTc/aCc",["missense_variant"],NA,[("Gene3D","1"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000544518.5:c.1316T>C","ENSP00000441068.1:p.Ile439Thr",NA,"MODERATE",NA,NA,NA,NA,NA,NA,NA,NA,439,439,"ENSP00000441068","Ensembl",-1,"ENST00000544518",1,NA,"G"),(1,NA,NA,"lncRNA",1,NA,NA,NA,NA,NA,NA,["intron_variant","non_coding_transcript_variant"],NA,NA,NA,NA,"ENSG00000256757",NA,NA,NA,NA,"ENST00000546284.1:n.245-807A>G",NA,NA,"MODIFIER","2/3",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000546284",3,NA,"G"),(1,"I/T",NA,"protein_coding",1,NA,1673,1673,1319,1319,"aTc/aCc",["missense_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.1319T>C","NP_000786.1:p.Ile440Thr",NA,"MODERATE",NA,NA,NA,NA,NA,"ENST00000362072.8",NA,NA,440,440,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"G"),(1,"I/T",NA,"protein_coding",NA,NA,1586,1586,1232,1232,"aTc/aCc",["missense_variant"],NA,NA,"7/7",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.1232T>C","NP_057658.2:p.Ile411Thr",NA,"MODERATE",NA,NA,NA,NA,NA,NA,NA,NA,411,411,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"G"),(1,"I/T",NA,"protein_coding",NA,NA,1401,1401,1319,1319,"aTc/aCc",["missense_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.1319T>C","XP_016872785.1:p.Ile440Thr",NA,"MODERATE",NA,NA,NA,NA,NA,NA,NA,NA,440,440,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"G")]"SNV"4.72e+00"AS_MQ"FalseFalseFalseFalseFalseTrueFalseFalse"snv"1False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,12,0,49,0,314328,0,18,0,416534,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,49,301,11269,107358,345061,189138,36333,5605,2378,2755,3073,2932,2954,2914,2973,2922,2564,2680]07683[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][4,0,0,0,13,0,49,0,314328,0,18,0,416534,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,50,304,11269,107358,345061,189138,36333,5605,2378,2755,3073,2932,2954,2914,2973,2922,2564,2680]07683[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]002.72e+014.02e+006.41e-010.00e+003.00e-026.25e+000.00e+005.08e-01
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+------------+\n",
+       "| locus           | alleles    |\n",
+       "+-----------------+------------+\n",
+       "| locus   | array |\n",
+       "+-----------------+------------+\n",
+       "| chr11:113410731 | [\"C\",\"A\"]  |\n",
+       "| chr11:113410731 | [\"C\",\"T\"]  |\n",
+       "| chr11:113410735 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410738 | [\"G\",\"T\"]  |\n",
+       "| chr11:113410740 | [\"A\",\"G\"]  |\n",
+       "+-----------------+------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(2,1.37e-06,1461866,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(2,4.... |\n",
+       "| [(1,6.84e-07,1461866,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(75,5.13e-05,1461876,0),(75,5.13e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
+       "| [(2,1.37e-06,1461886,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(1,6.84e-07,1461884,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(1,2.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|                2 |         4.47e-05 |            44724 |\n",
+       "|                1 |         8.99e-07 |          1112010 |\n",
+       "|                8 |         7.19e-06 |          1112004 |\n",
+       "|                2 |         5.04e-05 |            39700 |\n",
+       "|                1 |         2.24e-05 |            44724 |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                              0 | \"amr\"                 |                 2 |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                              0 | \"nfe\"                 |                 2 |\n",
+       "|                              0 | \"eas\"                 |                 1 |\n",
+       "|                              0 | \"amr\"                 |                 1 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|          4.57e-05 |             43740 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|          5.71e-06 |            350102 |                               0 |\n",
+       "|          2.77e-05 |             36070 |                               0 |\n",
+       "|          2.29e-05 |             43740 |                               0 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| \"amr\"                  |\n",
+       "| NA                     |\n",
+       "| \"nfe\"                  |\n",
+       "| \"eas\"                  |\n",
+       "| \"amr\"                  |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(7.41e-06,2.77e-06),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(4.19e-05,3.82e-05),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(8.35e-06,3.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                7.41e-06 | \"amr\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                3.09e-06 | \"nfe\"                           |\n",
+       "|                8.35e-06 | \"eas\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                2.77e-06 | \"amr\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                2.24e-06 | \"nfe\"                           |\n",
+       "|                3.12e-06 | \"eas\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                 7.58e-06 | \"amr\"                            |\n",
+       "|                       NA | NA                               |\n",
+       "|                 9.50e-07 | \"nfe\"                            |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                 2.84e-06 | \"amr\"                            |       1 |\n",
+       "|                       NA | NA                               |       2 |\n",
+       "|                 3.60e-07 | \"nfe\"                            |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "| was_split | rsid             | filters  |  info.FS |  info.MQ |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      bool | set         | set |  float64 |  float64 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      True | {\"rs140938110\"}  | {}       | 5.56e+00 | 6.00e+01 |\n",
+       "|      True | NA               | {}       | 5.56e+00 | 6.00e+01 |\n",
+       "|      True | {\"rs201801648\"}  | {}       | 0.00e+00 | 6.00e+01 |\n",
+       "|     False | {\"rs1223741945\"} | {}       | 1.38e+01 | 6.00e+01 |\n",
+       "|     False | NA               | {}       | 3.13e+00 | 6.00e+01 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|        float64 |           int64 |  float64 |             float64 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
+       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
+       "|       0.00e+00 |          127501 | 1.23e+01 |            3.70e-02 |\n",
+       "|       0.00e+00 |            3253 | 1.30e+01 |           -1.33e+00 |\n",
+       "|       0.00e+00 |             402 | 1.01e+01 |            7.13e-01 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "| info.SB               | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "| array          |  float64 |      int32 |    float64 |    float64 |\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "| [134,83,109,52]       | 9.81e-01 |        378 |   1.31e+01 |   6.00e+01 |\n",
+       "| [134,83,109,52]       | 9.81e-01 |        378 |   0.00e+00 |   6.00e+01 |\n",
+       "| [3240,2211,2993,1962] | 7.34e-01 |      10404 |   0.00e+00 |   6.00e+01 |\n",
+       "| [63,66,76,46]         | 1.28e+00 |        251 |   1.38e+01 |   6.00e+01 |\n",
+       "| [17,8,12,3]           | 1.29e+00 |         40 |   3.13e+00 |   6.00e+01 |\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|           float64 |         float64 |              int64 |    float64 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|          0.00e+00 |        2.94e-01 |               1895 |   1.06e+01 |\n",
+       "|          0.00e+00 |        9.04e-01 |               1967 |   9.88e+00 |\n",
+       "|          0.00e+00 |        1.00e+00 |             127480 |   1.23e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |               3253 |   1.30e+01 |\n",
+       "|          0.00e+00 |        1.54e-01 |                402 |   1.01e+01 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE      | info.AS_SOR | info.AS_VarDP |\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "|                float64 | array          |     float64 |         int32 |\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "|               2.52e-01 | [134,83,56,19]        |    1.42e+00 |           179 |\n",
+       "|              -5.01e-01 | [134,83,53,33]        |    6.81e-01 |           199 |\n",
+       "|               1.60e-02 | [3240,2211,2991,1961] |    7.34e-01 |         10383 |\n",
+       "|              -1.33e+00 | [63,66,76,46]         |    1.28e+00 |           251 |\n",
+       "|               7.13e-01 | [17,8,12,3]           |    1.29e+00 |            40 |\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|           bool |                       bool |                   bool |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|          False |                         NA |                     NA |\n",
+       "|           True |                      False |                  False |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|           True |                      False |                  False |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|      bool |       bool |             bool |          bool |        float64 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|     False |      False |            False |         False |       5.27e+00 |\n",
+       "|     False |      False |            False |         False |       5.40e+00 |\n",
+       "|     False |      False |            False |         False |       8.85e+00 |\n",
+       "|     False |      False |            False |         False |       3.73e+00 |\n",
+       "|     False |      False |            False |         False |       4.72e+00 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "\n",
+       "+-----------------------+\n",
+       "| info.inbreeding_coeff |\n",
+       "+-----------------------+\n",
+       "|               float64 |\n",
+       "+-----------------------+\n",
+       "|             -1.37e-06 |\n",
+       "|             -6.84e-07 |\n",
+       "|             -5.13e-05 |\n",
+       "|             -1.37e-06 |\n",
+       "|             -6.84e-07 |\n",
+       "+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.ZonDIhdwyghm7SuWkX... |\n",
+       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.Hp20SlFHUdM7jlWmVV... |\n",
+       "| [\"ga4gh:VA.SBSoboxxiC0V-mhjjHg2geRlf3I0Ib2d\",\"ga4gh:VA.GzUD_86k_EDbtk-R58... |\n",
+       "| [\"ga4gh:VA.cROnHWMnTSTuzMfVIhSezHexCPbFdF3z\",\"ga4gh:VA.WAkJ8ZSeVrsh6ThoH7... |\n",
+       "| [\"ga4gh:VA.gXv9EmAG-Lbw5gSNFxiBHWB_WT8twCnG\",\"ga4gh:VA.S-YUvVERFiE1cgkxu2... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| array          | array          | array          |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"A\"]           |\n",
+       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"T\"]           |\n",
+       "| [113410734,113410734] | [113410735,113410735] | [\"G\",\"A\"]           |\n",
+       "| [113410737,113410737] | [113410738,113410738] | [\"G\",\"T\"]           |\n",
+       "| [113410739,113410739] | [113410740,113410740] | [\"A\",\"G\"]           |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| str               |     int32 | str    | str                            |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| \"C/A\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tA\t.\t.\tGT\" |\n",
+       "| \"C/T\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tT\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410735 | \".\"    | \"chr11\t113410735\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"G/T\"             | 113410738 | \".\"    | \"chr11\t113410738\t.\tG\tT\t.\t.\tGT\" |\n",
+       "| \"A/G\"             | 113410740 | \".\"    | \"chr11\t113410740\t.\tA\tG\t.\t.\tGT\" |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"missense_variant\"          |\n",
+       "| \"missense_variant\"          |\n",
+       "| \"missense_variant\"          |\n",
+       "| \"missense_variant\"          |\n",
+       "| \"missense_variant\"          |\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq              |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [0,0,0,0,12,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
+       "| [0,0,0,0,12,0,66,0,314321,0,31,0,416511,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,12,0,49,0,314328,0,18,0,416534,0,0,0,0,0,0,1]  |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,0,65,332,11308,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
+       "| [0,0,57,319,11282,107355,345042,189122,36330,5605,2379,2756,3073,2932,295... |\n",
+       "| [0,0,49,301,11269,107358,345061,189138,36333,5605,2378,2755,3073,2932,295... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                       7715 |\n",
+       "|                                       7715 |\n",
+       "|                                       7717 |\n",
+       "|                                       7684 |\n",
+       "|                                       7683 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "|                                         32 |\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq   |\n",
+       "+----------------------------------------------+\n",
+       "| array                                 |\n",
+       "+----------------------------------------------+\n",
+       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]    |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]    |\n",
+       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0] |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0]    |\n",
+       "| [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]    |\n",
+       "+----------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq          |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [6,0,1,0,14,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
+       "| [3,0,0,0,13,0,66,0,314321,0,31,0,416511,0,0,0,0,0,0,2]  |\n",
+       "| [4,0,0,0,13,0,49,0,314328,0,18,0,416534,0,0,0,0,0,0,1]  |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,2,67,335,11310,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
+       "| [0,1,58,321,11282,107355,345042,189122,36330,5605,2379,2756,3073,2932,295... |\n",
+       "| [0,1,50,304,11269,107358,345061,189138,36333,5605,2378,2755,3073,2932,295... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                           7715 |\n",
+       "|                                           7715 |\n",
+       "|                                           7717 |\n",
+       "|                                           7684 |\n",
+       "|                                           7683 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75]     |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "|                                             32 |\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
+       "| [1,0,0,2,3,3,8,4,0,0]                      |\n",
+       "| [0,0,0,1,0,0,0,1,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           1 |\n",
+       "|                                           0 |\n",
+       "|                                           3 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        2.77e+01 |\n",
+       "|                                          0 |                        2.75e+01 |\n",
+       "|                                          0 |                        2.28e+01 |\n",
+       "|                                          0 |                        2.53e+01 |\n",
+       "|                                          0 |                        2.72e+01 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            4.10e+00 |                       6.02e-01 |\n",
+       "|                            4.07e+00 |                       7.19e-01 |\n",
+       "|                            2.69e+00 |                       2.67e-01 |\n",
+       "|                            3.67e+00 |                       2.65e-01 |\n",
+       "|                            4.02e+00 |                       6.41e-01 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                 0.00e+00 |                    8.78e+00 |\n",
+       "|                                 2.00e-02 |                    8.78e+00 |\n",
+       "|                                 1.00e-02 |                    8.67e+00 |\n",
+       "|                                 1.00e-02 |                    4.85e+00 |\n",
+       "|                                 3.00e-02 |                    6.25e+00 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                      0.00e+00 |                          9.89e-01 |\n",
+       "|                      0.00e+00 |                          9.89e-01 |\n",
+       "|                      1.30e-01 |                          1.18e-01 |\n",
+       "|                      3.00e-02 |                          9.36e-01 |\n",
+       "|                      0.00e+00 |                          5.08e-01 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of missense variants passing filters in DRD2 is:  409\n"
+     ]
+    }
+   ],
+   "source": [
+    "var_ht = filter_by_csqs(['missense'], ht=drd2_interval_ht)\n",
+    "var_ht.show(5)\n",
+    "print(\"The total number of missense variants passing filters in DRD2 is: \", var_ht.count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b980b4f-13bd-4bb4-bdbc-5584a6a59ca5",
+   "metadata": {},
+   "source": [
+    "### Filter to `synonymous` variants passing filters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b7e4368b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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],3110,NA,NA,NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",-1,"ENST00000540600",1,NA,"T"),(1,"K","P2","protein_coding",NA,"CCDS8361.1",1626,1626,1305,1305,"aaG/aaA",["synonymous_variant"],NA,[("Gene3D","1"),("ENSP_mappings","5aer"),("ENSP_mappings","5aer"),("ENSP_mappings","6cm4"),("ENSP_mappings","6luq"),("ENSP_mappings","6vms"),("ENSP_mappings","7dfp"),("ENSP_mappings","7jvr"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321"),("CDD","cd15309")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000542968.5:c.1305G>A","ENSP00000442172.1:p.Lys435=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,435,435,"ENSP00000442172","Ensembl",-1,"ENST00000542968",1,["P14416-1"],"T"),(1,"K","A1","protein_coding",NA,NA,1335,1335,1302,1302,"aaG/aaA",["synonymous_variant"],NA,[("Gene3D","1"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321"),("CDD","cd15309")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000544518.5:c.1302G>A","ENSP00000441068.1:p.Lys434=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,434,434,"ENSP00000441068","Ensembl",-1,"ENST00000544518",1,NA,"T"),(1,NA,NA,"lncRNA",1,NA,NA,NA,NA,NA,NA,["intron_variant","non_coding_transcript_variant"],NA,NA,NA,NA,"ENSG00000256757",NA,NA,NA,NA,"ENST00000546284.1:n.245-793C>T",NA,NA,"MODIFIER","2/3",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000546284",3,NA,"T"),(1,"K",NA,"protein_coding",1,NA,1659,1659,1305,1305,"aaG/aaA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.1305G>A","NP_000786.1:p.Lys435=",NA,"LOW",NA,NA,NA,NA,NA,"ENST00000362072.8",NA,NA,435,435,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"T"),(1,"K",NA,"protein_coding",NA,NA,1572,1572,1218,1218,"aaG/aaA",["synonymous_variant"],NA,NA,"7/7",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.1218G>A","NP_057658.2:p.Lys406=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,406,406,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"T"),(1,"K",NA,"protein_coding",NA,NA,1387,1387,1305,1305,"aaG/aaA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.1305G>A","XP_016872785.1:p.Lys435=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,435,435,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"T")]"SNV"8.87e+00"AS_MQ"FalseFalseFalseFalseFalseTrueFalseFalse"snv"1False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,3,0,24,0,314081,0,0,0,416264,0,0,0,0,0,0,574]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,266,11219,107342,344994,189134,36335,5603,2386,2754,3071,2938,2971,2945,3008,2939,2587,2701]07737[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,574]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,1,1,4,13,22,21,26,25,30,25,35,40,41,25,31,27]0207[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,3,1,13,43,101,196,147,52,11,5,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][1,0,0,0,3,0,24,0,314081,0,0,0,416264,0,0,0,0,0,0,574]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,267,11219,107342,344994,189134,36335,5603,2386,2754,3071,2938,2971,2945,3008,2939,2587,2701]07737[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,574]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,1,1,4,13,22,21,26,25,30,25,35,40,41,25,31,27]0207[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,3,1,13,43,101,196,147,52,11,5,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][3,2,30,42,65,52,79,55,7,3]153[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,1,0,0,0,1,0]009.48e+008.09e-01NA0.00e+001.00e-025.82e+00NANA
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+------------+\n",
+       "| locus           | alleles    |\n",
+       "+-----------------+------------+\n",
+       "| locus   | array |\n",
+       "+-----------------+------------+\n",
+       "| chr11:113410736 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410736 | [\"G\",\"T\"]  |\n",
+       "| chr11:113410739 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410751 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410754 | [\"C\",\"T\"]  |\n",
+       "+-----------------+------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(15,1.03e-05,1461882,0),(15,1.03e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
+       "| [(1,6.84e-07,1461882,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(1,6.84e-07,1461882,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33478,0),(0,0.... |\n",
+       "| [(1,6.84e-07,1461894,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(576,3.94e-04,1461892,2),(576,3.94e-04,1461894,2),(12,3.58e-04,33480,1),... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|               15 |         1.35e-05 |          1112010 |\n",
+       "|                1 |         8.99e-07 |          1112010 |\n",
+       "|               NA |               NA |               NA |\n",
+       "|                1 |         8.99e-07 |          1112012 |\n",
+       "|                8 |         1.39e-03 |             5768 |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                              0 | \"nfe\"                 |                 1 |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                              0 | \"nfe\"                 |                 1 |\n",
+       "|                              0 | \"mid\"                 |                 4 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|          2.86e-06 |            350106 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|          2.86e-06 |            350108 |                               0 |\n",
+       "|          9.64e-04 |              4148 |                               0 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| \"nfe\"                  |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "| \"nfe\"                  |\n",
+       "| \"mid\"                  |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(6.16e-06,4.89e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(3.67e-04,3.57e-04),(2.06e-04,1.62e-04),(7.33e-04,6.53e-04),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                8.10e-06 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                7.33e-04 | \"amr\"                           |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                6.42e-06 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                6.53e-04 | \"amr\"                           |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                 6.90e-04 | \"amr\"                            |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       2 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                 6.12e-04 | \"amr\"                            |       1 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "| was_split | rsid             | filters  |  info.FS |  info.MQ |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      bool | set         | set |  float64 |  float64 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      True | {\"rs1159504221\"} | {}       | 5.49e-01 | 6.00e+01 |\n",
+       "|      True | NA               | {}       | 5.49e-01 | 6.00e+01 |\n",
+       "|     False | NA               | {}       | 4.26e+00 | 6.00e+01 |\n",
+       "|     False | NA               | {}       | 8.26e+00 | 6.00e+01 |\n",
+       "|     False | {\"rs77930100\"}   | {}       | 1.45e-15 | 6.00e+01 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|        float64 |           int64 |  float64 |             float64 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
+       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
+       "|       0.00e+00 |             574 | 1.10e+01 |            5.32e-01 |\n",
+       "|       0.00e+00 |            1098 | 2.03e+01 |           -7.69e-01 |\n",
+       "|       0.00e+00 |          947678 | 1.23e+01 |           -7.00e-03 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "| info.SB                   | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "| array              |  float64 |      int32 |    float64 |    float64 |\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "| [386,195,316,166]         | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
+       "| [386,195,316,166]         | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
+       "| [23,8,13,8]               | 2.93e-01 |         52 |   4.26e+00 |   6.00e+01 |\n",
+       "| [5,11,21,18]              | 4.64e-01 |         54 |   8.26e+00 |   6.00e+01 |\n",
+       "| [20804,19237,19605,17401] | 7.35e-01 |      77027 |   1.45e-15 |   6.00e+01 |\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|           float64 |         float64 |              int64 |    float64 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|          0.00e+00 |        9.11e-01 |              11899 |   1.17e+01 |\n",
+       "|          0.00e+00 |        3.28e-02 |                408 |   8.16e+00 |\n",
+       "|          0.00e+00 |        2.12e-01 |                574 |   1.10e+01 |\n",
+       "|          0.00e+00 |        3.84e-03 |               1098 |   2.03e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |             947678 |   1.23e+01 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE          | info.AS_SOR |\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "|                float64 | array              |     float64 |\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "|               9.70e-02 | [386,195,304,161]         |    6.47e-01 |\n",
+       "|              -9.23e-01 | [386,195,12,5]            |    7.90e-01 |\n",
+       "|               5.32e-01 | [23,8,13,8]               |    2.93e-01 |\n",
+       "|              -7.69e-01 | [5,11,21,18]              |    4.64e-01 |\n",
+       "|               7.00e-03 | [20804,19237,19605,17401] |    7.35e-01 |\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "\n",
+       "+---------------+----------------+----------------------------+\n",
+       "| info.AS_VarDP | info.singleton | info.transmitted_singleton |\n",
+       "+---------------+----------------+----------------------------+\n",
+       "|         int32 |           bool |                       bool |\n",
+       "+---------------+----------------+----------------------------+\n",
+       "|          1013 |          False |                         NA |\n",
+       "|            50 |           True |                      False |\n",
+       "|            52 |           True |                      False |\n",
+       "|            54 |           True |                      False |\n",
+       "|         77027 |          False |                         NA |\n",
+       "+---------------+----------------+----------------------------+\n",
+       "\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "| info.sibling_singleton | info.omni | info.mills | info.monoallelic |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "|                   bool |      bool |       bool |             bool |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "|                     NA |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                     NA |     False |      False |            False |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "\n",
+       "+---------------+----------------+-----------------------+\n",
+       "| info.only_het | info.AS_VQSLOD | info.inbreeding_coeff |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "|          bool |        float64 |               float64 |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "|         False |       7.50e+00 |             -1.03e-05 |\n",
+       "|         False |       5.00e+00 |             -6.84e-07 |\n",
+       "|         False |       5.73e+00 |             -6.84e-07 |\n",
+       "|         False |       2.35e+00 |             -6.84e-07 |\n",
+       "|         False |       8.87e+00 |              6.55e-03 |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.OXPDvbuulFZ9CSk4Xt... |\n",
+       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.4qhkDG1qrfqiD2TeOA... |\n",
+       "| [\"ga4gh:VA.uv4czuJ2nBK7lcR9wkK2XxHxMSui2-ME\",\"ga4gh:VA.bA0gzyCzudiyDon8QV... |\n",
+       "| [\"ga4gh:VA.euCgAx3O0dYWO86rUZqEKUoRwnhnhr-k\",\"ga4gh:VA.XQ_C7TKjlSKQw_ABwx... |\n",
+       "| [\"ga4gh:VA.8tDWBt70tPv5g5-MDqwnSl1-pElrIl1E\",\"ga4gh:VA.f3fUPUzazutlUq0Gul... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| array          | array          | array          |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"A\"]           |\n",
+       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"T\"]           |\n",
+       "| [113410738,113410738] | [113410739,113410739] | [\"G\",\"A\"]           |\n",
+       "| [113410750,113410750] | [113410751,113410751] | [\"G\",\"A\"]           |\n",
+       "| [113410753,113410753] | [113410754,113410754] | [\"C\",\"T\"]           |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| str               |     int32 | str    | str                            |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| \"G/A\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"G/T\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tT\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410739 | \".\"    | \"chr11\t113410739\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410751 | \".\"    | \"chr11\t113410751\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"C/T\"             | 113410754 | \".\"    | \"chr11\t113410754\t.\tC\tT\t.\t.\tGT\" |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"synonymous_variant\"        |\n",
+       "| \"synonymous_variant\"        |\n",
+       "| \"synonymous_variant\"        |\n",
+       "| \"synonymous_variant\"        |\n",
+       "| \"synonymous_variant\"        |\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq             |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n",
+       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n",
+       "| [0,0,0,0,9,0,68,0,314320,0,24,0,416519,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,1,0,28,0,314363,0,2,0,416552,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,3,0,24,0,314081,0,0,0,416264,0,0,0,0,0,0,574] |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "| [0,0,55,309,11275,107353,345050,189133,36332,5605,2379,2755,3073,2932,295... |\n",
+       "| [0,0,18,272,11233,107401,345096,189157,36337,5604,2379,2755,3073,2932,295... |\n",
+       "| [0,0,16,266,11219,107342,344994,189134,36335,5603,2386,2754,3071,2938,297... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                       7685 |\n",
+       "|                                       7685 |\n",
+       "|                                       7683 |\n",
+       "|                                       7683 |\n",
+       "|                                       7737 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq  |\n",
+       "+---------------------------------------------+\n",
+       "| array                                |\n",
+       "+---------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,574] |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq             |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,1,1,4,13,22,21,26,25,30,25,35,40,41,25,31,27] |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                        207 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq          |\n",
+       "+-----------------------------------------------------+\n",
+       "| array                                        |\n",
+       "+-----------------------------------------------------+\n",
+       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]           |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]           |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]           |\n",
+       "| [0,0,0,0,3,1,13,43,101,196,147,52,11,5,0,0,0,0,0,0] |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq         |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n",
+       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n",
+       "| [5,0,0,0,10,0,68,0,314320,0,24,0,416519,0,0,0,0,0,0,1] |\n",
+       "| [0,0,0,0,1,0,28,0,314363,0,2,0,416552,0,0,0,0,0,0,1]   |\n",
+       "| [1,0,0,0,3,0,24,0,314081,0,0,0,416264,0,0,0,0,0,0,574] |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "| [0,1,56,312,11275,107353,345051,189133,36332,5605,2379,2755,3073,2932,295... |\n",
+       "| [0,0,18,272,11233,107401,345096,189157,36337,5604,2379,2755,3073,2932,295... |\n",
+       "| [0,0,16,267,11219,107342,344994,189134,36335,5603,2386,2754,3071,2938,297... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                           7685 |\n",
+       "|                                           7685 |\n",
+       "|                                           7683 |\n",
+       "|                                           7683 |\n",
+       "|                                           7737 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15]     |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,574]    |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq         |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,1,1,4,13,22,21,26,25,30,25,35,40,41,25,31,27] |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                            207 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq      |\n",
+       "+-----------------------------------------------------+\n",
+       "| array                                        |\n",
+       "+-----------------------------------------------------+\n",
+       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]           |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]           |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]           |\n",
+       "| [0,0,0,0,3,1,13,43,101,196,147,52,11,5,0,0,0,0,0,0] |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,1,2,4,2,4,1,0,0]                      |\n",
+       "| [0,0,0,0,0,1,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [3,2,30,42,65,52,79,55,7,3]                |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                          15 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          3 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,1,0,0,0,1,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        3.44e+00 |\n",
+       "|                                          0 |                        2.96e+00 |\n",
+       "|                                          0 |                        1.03e+01 |\n",
+       "|                                          0 |                        1.20e+01 |\n",
+       "|                                          0 |                        9.48e+00 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            2.28e-01 |                             NA |\n",
+       "|                            1.86e-01 |                             NA |\n",
+       "|                            8.89e-01 |                             NA |\n",
+       "|                            1.03e+00 |                             NA |\n",
+       "|                            8.09e-01 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                 1.00e-02 |                   -2.55e-01 |\n",
+       "|                                 3.00e-02 |                   -2.55e-01 |\n",
+       "|                                 0.00e+00 |                    3.26e+00 |\n",
+       "|                                 0.00e+00 |                    8.67e+00 |\n",
+       "|                                 1.00e-02 |                    5.82e+00 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of synonymous variants passing filters in DRD2 is:  238\n"
+     ]
+    }
+   ],
+   "source": [
+    "var_ht = filter_by_csqs(['synonymous'], ht=drd2_interval_ht)\n",
+    "var_ht.show(5)\n",
+    "print(\"The total number of synonymous variants passing filters in DRD2 is: \", var_ht.count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbcd2fa0-5108-4d94-a681-493882c295bf",
+   "metadata": {},
+   "source": [
+    "### Filter to 'Other' variants passing filters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4141ccb3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
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showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+------------+\n",
+       "| locus           | alleles    |\n",
+       "+-----------------+------------+\n",
+       "| locus   | array |\n",
+       "+-----------------+------------+\n",
+       "| chr11:113409636 | [\"G\",\"C\"]  |\n",
+       "| chr11:113409693 | [\"G\",\"A\"]  |\n",
+       "| chr11:113409717 | [\"C\",\"T\"]  |\n",
+       "| chr11:113409758 | [\"C\",\"T\"]  |\n",
+       "| chr11:113410002 | [\"C\",\"A\"]  |\n",
+       "+-----------------+------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(1,1.87e-03,534,0),(5,7.95e-06,628788,2),(0,0.00e+00,8,0),(0,NA,0,0),(0,... |\n",
+       "| [(1,1.20e-03,834,0),(16,2.54e-05,628790,3),(0,0.00e+00,6,0),(0,0.00e+00,2... |\n",
+       "| [(1,1.01e-03,990,0),(5,7.95e-06,628794,2),(0,0.00e+00,10,0),(0,0.00e+00,4... |\n",
+       "| [(1,1.23e-03,810,0),(4,6.36e-06,628786,1),(0,0.00e+00,12,0),(0,0.00e+00,4... |\n",
+       "| [(128,1.34e-01,952,15),(18463,2.94e-02,628994,6837),(0,0.00e+00,6,0),(17,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|               NA |               NA |               NA |\n",
+       "|                1 |         2.87e-03 |              348 |\n",
+       "|                1 |         2.17e-03 |              460 |\n",
+       "|               NA |               NA |               NA |\n",
+       "|                2 |         5.00e-01 |                4 |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                              0 | \"nfe\"                 |                 1 |\n",
+       "|                              0 | \"nfe\"                 |                 1 |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                              0 | \"eas\"                 |                 2 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|                NA |                NA |                              NA |\n",
+       "|          2.87e-03 |               348 |                               0 |\n",
+       "|          2.17e-03 |               460 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|          5.00e-01 |                 4 |                               0 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| NA                     |\n",
+       "| \"nfe\"                  |\n",
+       "| \"nfe\"                  |\n",
+       "| NA                     |\n",
+       "| \"eas\"                  |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(1.16e-01,1.08e-01),(0.00e+00,0.00e+00),(1.23e-01,1.01e-01),(8.88e-02,3.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                1.23e-01 | \"amr\"                           |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                1.02e-01 | \"nfe\"                           |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                 1.23e-01 | \"amr\"                            |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       2 |\n",
+       "|                       NA | NA                               |       2 |\n",
+       "|                 1.02e-01 | \"nfe\"                            |       1 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+-----------------+----------+----------+----------+\n",
+       "| was_split | rsid            | filters  |  info.FS |  info.MQ |\n",
+       "+-----------+-----------------+----------+----------+----------+\n",
+       "|      bool | set        | set |  float64 |  float64 |\n",
+       "+-----------+-----------------+----------+----------+----------+\n",
+       "|      True | {\"rs200733424\"} | {}       | 0.00e+00 | 6.00e+01 |\n",
+       "|      True | NA              | {}       | 5.32e+00 | 6.00e+01 |\n",
+       "|      True | NA              | {}       | 4.82e-16 | 6.00e+01 |\n",
+       "|      True | {\"rs200557458\"} | {}       | 0.00e+00 | 6.00e+01 |\n",
+       "|      True | {\"rs6278\"}      | {}       | 2.80e+00 | 6.00e+01 |\n",
+       "+-----------+-----------------+----------+----------+----------+\n",
+       "\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|        float64 |           int64 |  float64 |             float64 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|       0.00e+00 |             582 | 1.94e+01 |           -2.11e+00 |\n",
+       "|       0.00e+00 |             898 | 9.76e+00 |            0.00e+00 |\n",
+       "|       0.00e+00 |             209 | 9.09e+00 |            8.42e-01 |\n",
+       "|       0.00e+00 |             947 | 1.26e+01 |           -6.74e-01 |\n",
+       "|       0.00e+00 |         1998239 | 1.99e+01 |            0.00e+00 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "| info.SB                   | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "| array              |  float64 |      int32 |    float64 |    float64 |\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "| [8,2,15,5]                | 5.82e-01 |         30 |   0.00e+00 |   6.00e+01 |\n",
+       "| [31,19,21,21]             | 3.30e-01 |         92 |   4.52e+00 |   6.00e+01 |\n",
+       "| [6,7,4,6]                 | 9.17e-01 |         23 |   0.00e+00 |   6.00e+01 |\n",
+       "| [16,19,18,22]             | 7.22e-01 |         75 |   0.00e+00 |   6.00e+01 |\n",
+       "| [19752,12842,45058,22649] | 9.83e-01 |     100292 |   2.80e+00 |   6.00e+01 |\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|           float64 |         float64 |              int64 |    float64 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|          0.00e+00 |        5.41e-01 |                582 |   1.94e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |                553 |   8.01e+00 |\n",
+       "|          0.00e+00 |        2.67e-01 |                178 |   1.05e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |                703 |   1.12e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |            1998092 |   1.99e+01 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE          | info.AS_SOR |\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "|                float64 | array              |     float64 |\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "|              -2.11e+00 | [8,2,15,5]                |    5.82e-01 |\n",
+       "|               0.00e+00 | [31,19,15,15]             |    3.30e-01 |\n",
+       "|               7.20e-02 | [6,7,3,5]                 |    1.00e+00 |\n",
+       "|              -1.13e+00 | [16,19,14,17]             |    7.13e-01 |\n",
+       "|               0.00e+00 | [19752,12842,45055,22646] |    9.83e-01 |\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "\n",
+       "+---------------+----------------+----------------------------+\n",
+       "| info.AS_VarDP | info.singleton | info.transmitted_singleton |\n",
+       "+---------------+----------------+----------------------------+\n",
+       "|         int32 |           bool |                       bool |\n",
+       "+---------------+----------------+----------------------------+\n",
+       "|            30 |          False |                      False |\n",
+       "|            69 |          False |                      False |\n",
+       "|            17 |          False |                      False |\n",
+       "|            63 |          False |                      False |\n",
+       "|        100286 |          False |                         NA |\n",
+       "+---------------+----------------+----------------------------+\n",
+       "\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "| info.sibling_singleton | info.omni | info.mills | info.monoallelic |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "|                   bool |      bool |       bool |             bool |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                     NA |      True |      False |            False |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "\n",
+       "+---------------+----------------+-----------------------+\n",
+       "| info.only_het | info.AS_VQSLOD | info.inbreeding_coeff |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "|          bool |        float64 |               float64 |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "|         False |       4.51e+00 |              8.00e-01 |\n",
+       "|         False |       5.16e+00 |              3.75e-01 |\n",
+       "|         False |       6.19e+00 |              8.00e-01 |\n",
+       "|         False |       6.36e+00 |              5.00e-01 |\n",
+       "|         False |       5.57e+00 |              7.33e-01 |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.BlCz08hG7ZEQzvW20V8Kp4xJMx-pMKes\",\"ga4gh:VA.U4eD7PXtXRCClN6FQt... |\n",
+       "| [\"ga4gh:VA.aXxME51cngsJBnH_3WX01B0Ms-SLay9X\",\"ga4gh:VA.ynryFZCH9dBU67nwPr... |\n",
+       "| [\"ga4gh:VA.Q6p6okAv6qHSPSV8MOvy8Hhl0kZ6tQ4U\",\"ga4gh:VA.WIfYWxgEc5ICUUByiD... |\n",
+       "| [\"ga4gh:VA.k_gesWx8TbLs23MHIkc3NaVscBWVDrSE\",\"ga4gh:VA.NJz3hsVBrqbTn17g5T... |\n",
+       "| [\"ga4gh:VA.HjAJ4gj43VVFJQmV-h5WuPZZPBmNwSFZ\",\"ga4gh:VA.sT14AsCSWlf2AqH0wh... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| array          | array          | array          |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| [113409635,113409635] | [113409636,113409636] | [\"G\",\"C\"]           |\n",
+       "| [113409692,113409692] | [113409693,113409693] | [\"G\",\"A\"]           |\n",
+       "| [113409716,113409716] | [113409717,113409717] | [\"C\",\"T\"]           |\n",
+       "| [113409757,113409757] | [113409758,113409758] | [\"C\",\"T\"]           |\n",
+       "| [113410001,113410001] | [113410002,113410002] | [\"C\",\"A\"]           |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| str               |     int32 | str    | str                            |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| \"G/C\"             | 113409636 | \".\"    | \"chr11\t113409636\t.\tG\tC\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113409693 | \".\"    | \"chr11\t113409693\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"C/T\"             | 113409717 | \".\"    | \"chr11\t113409717\t.\tC\tT\t.\t.\tGT\" |\n",
+       "| \"C/T\"             | 113409758 | \".\"    | \"chr11\t113409758\t.\tC\tT\t.\t.\tGT\" |\n",
+       "| \"C/A\"             | 113410002 | \".\"    | \"chr11\t113410002\t.\tC\tA\t.\t.\tGT\" |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"3_prime_UTR_variant\"       |\n",
+       "| \"3_prime_UTR_variant\"       |\n",
+       "| \"3_prime_UTR_variant\"       |\n",
+       "| \"3_prime_UTR_variant\"       |\n",
+       "| \"3_prime_UTR_variant\"       |\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq      |\n",
+       "+-------------------------------------------------+\n",
+       "| array                                    |\n",
+       "+-------------------------------------------------+\n",
+       "| [0,0,0,0,28,0,76,0,162,0,0,0,0,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,25,0,186,0,206,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,95,0,197,0,202,0,0,0,0,0,0,0,1,0,0,0]  |\n",
+       "| [0,0,0,0,100,0,111,0,193,0,0,0,0,0,1,0,0,0,0,0] |\n",
+       "| [0,0,0,0,120,1,223,5,34,1,5,0,2,5,7,1,3,2,1,66] |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq       |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,101,28,30,82,26,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,211,29,38,105,34,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "| [0,0,286,30,40,105,34,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "| [0,0,200,35,36,102,32,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "| [0,0,438,30,5,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0]     |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq  |\n",
+       "+---------------------------------------------+\n",
+       "| array                                |\n",
+       "+---------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]   |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,1,1,11,5,2,1,5,0,2,5,7,1,3,2,1,66] |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq  |\n",
+       "+---------------------------------------------+\n",
+       "| array                                |\n",
+       "+---------------------------------------------+\n",
+       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,108,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq  |\n",
+       "+---------------------------------------------+\n",
+       "| array                                |\n",
+       "+---------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,3,8,0,4,8,6,30,15,8,9,3,1,3,0,0,0] |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [313709,2,2,0,442,0,76,0,162,0,0,0,0,0,0,0,0,0,0,1]                          |\n",
+       "| [313080,4,2,0,906,6,189,2,206,0,0,0,0,0,0,0,0,0,0,0]                         |\n",
+       "| [313071,2,3,0,920,0,198,0,202,0,0,0,0,0,0,0,1,0,0,0]                         |\n",
+       "| [313441,3,2,1,640,0,111,1,193,0,0,0,0,0,1,0,0,0,0,0]                         |\n",
+       "| [301848,5644,543,415,1889,1584,436,212,123,210,485,568,76,48,44,53,135,57... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq            |\n",
+       "+-----------------------------------------------------------+\n",
+       "| array                                              |\n",
+       "+-----------------------------------------------------------+\n",
+       "| [240827,73298,103,28,30,82,26,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [240299,73676,214,29,38,105,34,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "| [240226,73668,294,30,40,105,34,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "| [240307,73671,209,36,36,102,32,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "| [238198,75776,482,33,5,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0]     |\n",
+       "+-----------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]                                    |\n",
+       "| [0,3,0,0,1,5,3,1,0,0,0,0,0,0,0,0,0,0,0,0]                                    |\n",
+       "| [0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]                                    |\n",
+       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]                                    |\n",
+       "| [398,5642,541,415,716,1584,224,212,91,210,485,568,76,48,44,53,135,57,19,108] |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq    |\n",
+       "+---------------------------------------------------+\n",
+       "| array                                      |\n",
+       "+---------------------------------------------------+\n",
+       "| [2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]         |\n",
+       "| [8,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]         |\n",
+       "| [2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]         |\n",
+       "| [1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]         |\n",
+       "| [9330,2176,115,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq                   |\n",
+       "+------------------------------------------------------------------+\n",
+       "| array                                                     |\n",
+       "+------------------------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]                        |\n",
+       "| [0,0,0,0,1,1,2,0,1,0,1,0,0,4,0,0,0,0,0,0]                        |\n",
+       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]                        |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]                        |\n",
+       "| [0,0,4,4,27,107,217,41,416,6,1084,414,43,1634,53,495,208,34,2,0] |\n",
+       "+------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,1,1,5,4,3,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,1,2,2,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        8.46e+00 |\n",
+       "|                                          0 |                        9.08e+00 |\n",
+       "|                                          0 |                        1.51e+01 |\n",
+       "|                                          0 |                        9.13e+00 |\n",
+       "|                                          0 |                        2.02e+00 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            7.01e-01 |                             NA |\n",
+       "|                            7.68e-01 |                             NA |\n",
+       "|                            1.40e+00 |                             NA |\n",
+       "|                            7.73e-01 |                             NA |\n",
+       "|                            9.23e-02 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             1.00e-02 |\n",
+       "|                             0.00e+00 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                 2.00e-02 |                    1.94e+00 |\n",
+       "|                                 0.00e+00 |                    5.70e-01 |\n",
+       "|                                 0.00e+00 |                    1.05e+00 |\n",
+       "|                                 3.00e-02 |                    2.58e+00 |\n",
+       "|                                 0.00e+00 |                    1.61e+00 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of other variants passing filters in DRD2 is:  2075\n"
+     ]
+    }
+   ],
+   "source": [
+    "var_ht = filter_by_csqs(['other'], ht=drd2_interval_ht)\n",
+    "var_ht.show(5)\n",
+    "print(\"The total number of other variants passing filters in DRD2 is: \", var_ht.count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b104a39b",
+   "metadata": {},
+   "source": [
+    "## Get frequency information for specific genetic ancestry groups\n",
+    "\n",
+    "The examples below show frequency filtering using the Table filtered to DRD2 `synonymous` variants."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a25ddd1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drd2_synonymous_ht = filter_by_csqs(['synonymous'], ht=drd2_interval_ht)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69814beb",
+   "metadata": {},
+   "source": [
+    "### Single genetic ancestry group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4f78166f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
afr
locus
alleles
filters
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>set<str>int32float64int32int64
chr11:113410736["G","A"]{}00.00e+00334800
chr11:113410736["G","T"]{}00.00e+00334800
chr11:113410739["G","A"]{}00.00e+00334780
chr11:113410751["G","A"]{}00.00e+00334800
chr11:113410754["C","T"]{}123.58e-04334801
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "| locus           | alleles    | filters  | afr.AC |   afr.AF | afr.AN |\n",
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "| locus   | array | set |  int32 |  float64 |  int32 |\n",
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "| chr11:113410736 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410736 | [\"G\",\"T\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410739 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33478 |\n",
+       "| chr11:113410751 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410754 | [\"C\",\"T\"]  | {}       |     12 | 3.58e-04 |  33480 |\n",
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "\n",
+       "+----------------------+\n",
+       "| afr.homozygote_count |\n",
+       "+----------------------+\n",
+       "|                int64 |\n",
+       "+----------------------+\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    1 |\n",
+       "+----------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "var_ht = get_ancestry_callstats(gen_ancs='afr', ht=drd2_synonymous_ht)\n",
+    "var_ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e741e138",
+   "metadata": {},
+   "source": [
+    "### Multiple genetic ancestry groups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e3a07848",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
afr
amr
eas
mid
nfe
sas
locus
alleles
filters
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>set<str>int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64
chr11:113410736["G","A"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+0057620151.35e-051112010000.00e+00862540
chr11:113410736["G","T"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005762018.99e-071112010000.00e+00862540
chr11:113410739["G","A"]{}00.00e+0033478000.00e+0044724000.00e+0039700000.00e+005764000.00e+001112010000.00e+00862560
chr11:113410751["G","A"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005768018.99e-071112012000.00e+00862580
chr11:113410754["C","T"]{}123.58e-04334801439.61e-0444724000.00e+0039700081.39e-03576803282.95e-041112012000.00e+00862580
chr11:113410757["G","A"]{}00.00e+0033480000.00e+0044724012.52e-0539700000.00e+005766000.00e+001112012000.00e+00862580
chr11:113410757["G","T"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005766000.00e+001112012033.48e-05862580
chr11:113410763["C","T"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005768000.00e+001112010011.16e-05862580
chr11:113410769["G","A"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005768021.80e-061112012000.00e+00862580
chr11:113410775["G","A"]{}00.00e+0033480000.00e+0044724012.52e-0539700000.00e+005768018.99e-071112010000.00e+00862580
showing top 10 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "| locus           | alleles    | filters  | afr.AC |   afr.AF | afr.AN |\n",
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "| locus   | array | set |  int32 |  float64 |  int32 |\n",
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "| chr11:113410736 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410736 | [\"G\",\"T\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410739 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33478 |\n",
+       "| chr11:113410751 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410754 | [\"C\",\"T\"]  | {}       |     12 | 3.58e-04 |  33480 |\n",
+       "| chr11:113410757 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410757 | [\"G\",\"T\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410763 | [\"C\",\"T\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410769 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410775 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "| afr.homozygote_count | amr.AC |   amr.AF | amr.AN | amr.homozygote_count |\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "|                int64 |  int32 |  float64 |  int32 |                int64 |\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    1 |     43 | 9.61e-04 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "\n",
+       "+--------+----------+--------+----------------------+--------+----------+\n",
+       "| eas.AC |   eas.AF | eas.AN | eas.homozygote_count | mid.AC |   mid.AF |\n",
+       "+--------+----------+--------+----------------------+--------+----------+\n",
+       "|  int32 |  float64 |  int32 |                int64 |  int32 |  float64 |\n",
+       "+--------+----------+--------+----------------------+--------+----------+\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      8 | 1.39e-03 |\n",
+       "|      1 | 2.52e-05 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      1 | 2.52e-05 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "+--------+----------+--------+----------------------+--------+----------+\n",
+       "\n",
+       "+--------+----------------------+--------+----------+---------+\n",
+       "| mid.AN | mid.homozygote_count | nfe.AC |   nfe.AF |  nfe.AN |\n",
+       "+--------+----------------------+--------+----------+---------+\n",
+       "|  int32 |                int64 |  int32 |  float64 |   int32 |\n",
+       "+--------+----------------------+--------+----------+---------+\n",
+       "|   5762 |                    0 |     15 | 1.35e-05 | 1112010 |\n",
+       "|   5762 |                    0 |      1 | 8.99e-07 | 1112010 |\n",
+       "|   5764 |                    0 |      0 | 0.00e+00 | 1112010 |\n",
+       "|   5768 |                    0 |      1 | 8.99e-07 | 1112012 |\n",
+       "|   5768 |                    0 |    328 | 2.95e-04 | 1112012 |\n",
+       "|   5766 |                    0 |      0 | 0.00e+00 | 1112012 |\n",
+       "|   5766 |                    0 |      0 | 0.00e+00 | 1112012 |\n",
+       "|   5768 |                    0 |      0 | 0.00e+00 | 1112010 |\n",
+       "|   5768 |                    0 |      2 | 1.80e-06 | 1112012 |\n",
+       "|   5768 |                    0 |      1 | 8.99e-07 | 1112010 |\n",
+       "+--------+----------------------+--------+----------+---------+\n",
+       "\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "| nfe.homozygote_count | sas.AC |   sas.AF | sas.AN | sas.homozygote_count |\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "|                int64 |  int32 |  float64 |  int32 |                int64 |\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "|                    0 |      0 | 0.00e+00 |  86254 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86254 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86256 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
+       "|                    0 |      3 | 3.48e-05 |  86258 |                    0 |\n",
+       "|                    0 |      1 | 1.16e-05 |  86258 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "showing top 10 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "var_ht = get_ancestry_callstats(gen_ancs=['afr', 'amr', 'eas', 'mid', 'nfe', 'sas'], ht=drd2_synonymous_ht)\n",
+    "var_ht.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe2e98b8",
+   "metadata": {},
+   "source": [
+    "## Get frequency information for a specific genetic ancestry group at a specific variant"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a253b3db-0a50-4c71-ae3d-dd7d5e0bfde0",
+   "metadata": {},
+   "source": [
+    "### Example when the variant exists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4845be1d-d4c0-4b83-9e92-bd72379b8a99",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "
afr
locus
alleles
filters
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>set<str>int32float64int32int64
chr22:15528692["C","G"]{}6351.90e-02333806
"
+      ],
+      "text/plain": [
+       "+----------------+------------+----------+--------+----------+--------+\n",
+       "| locus          | alleles    | filters  | afr.AC |   afr.AF | afr.AN |\n",
+       "+----------------+------------+----------+--------+----------+--------+\n",
+       "| locus  | array | set |  int32 |  float64 |  int32 |\n",
+       "+----------------+------------+----------+--------+----------+--------+\n",
+       "| chr22:15528692 | [\"C\",\"G\"]  | {}       |    635 | 1.90e-02 |  33380 |\n",
+       "+----------------+------------+----------+--------+----------+--------+\n",
+       "\n",
+       "+----------------------+\n",
+       "| afr.homozygote_count |\n",
+       "+----------------------+\n",
+       "|                int64 |\n",
+       "+----------------------+\n",
+       "|                    6 |\n",
+       "+----------------------+"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "var_ht = get_single_variant_ancestry_callstats(gen_ancs='AFR', contig='chr22', position=15528692, ref='C', alt='G')\n",
+    "var_ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c60cef2a-ae30-459b-a96a-82e754710bb2",
+   "metadata": {},
+   "source": [
+    "### Example when the variant *doesn't* exist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "bee28829",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "
afr
locus
alleles
filters
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>set<str>int32float64int32int64
"
+      ],
+      "text/plain": [
+       "+---------------+------------+----------+--------+---------+--------+\n",
+       "| locus         | alleles    | filters  | afr.AC |  afr.AF | afr.AN |\n",
+       "+---------------+------------+----------+--------+---------+--------+\n",
+       "| locus | array | set |  int32 | float64 |  int32 |\n",
+       "+---------------+------------+----------+--------+---------+--------+\n",
+       "+---------------+------------+----------+--------+---------+--------+\n",
+       "\n",
+       "+----------------------+\n",
+       "| afr.homozygote_count |\n",
+       "+----------------------+\n",
+       "|                int64 |\n",
+       "+----------------------+\n",
+       "+----------------------+"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "var_ht = get_single_variant_ancestry_callstats(gen_ancs='AFR', contig='chr22', position=15528692, ref='C', alt='A')\n",
+    "var_ht.show(5)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {
+    "height": "613.99px",
+    "width": "526.312px"
+   },
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": true,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "241.438px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/gnomad_toolbox/notebooks/needs_a_name.ipynb b/gnomad_toolbox/notebooks/needs_a_name.ipynb
new file mode 100644
index 0000000..c4b688a
--- /dev/null
+++ b/gnomad_toolbox/notebooks/needs_a_name.ipynb
@@ -0,0 +1,602 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8e609a46",
+   "metadata": {
+    "toc": true
+   },
+   "source": [
+    "
Table of Contents
\n",
+    "
"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e713032",
+   "metadata": {},
+   "source": [
+    "## Import modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e69953f7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T18:04:56.165634Z",
+     "start_time": "2024-12-06T18:04:55.603516Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "    \n",
+       "    
\n",
+       "        \n",
+       "        Loading BokehJS ...\n",
+       "    
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function now() {\n",
+       "    return new Date();\n",
+       "  }\n",
+       "\n",
+       "  const force = true;\n",
+       "\n",
+       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
+       "    root._bokeh_onload_callbacks = [];\n",
+       "    root._bokeh_is_loading = undefined;\n",
+       "  }\n",
+       "\n",
+       "const JS_MIME_TYPE = 'application/javascript';\n",
+       "  const HTML_MIME_TYPE = 'text/html';\n",
+       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
+       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
+       "\n",
+       "  /**\n",
+       "   * Render data to the DOM node\n",
+       "   */\n",
+       "  function render(props, node) {\n",
+       "    const script = document.createElement(\"script\");\n",
+       "    node.appendChild(script);\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when an output is cleared or removed\n",
+       "   */\n",
+       "  function handleClearOutput(event, handle) {\n",
+       "    function drop(id) {\n",
+       "      const view = Bokeh.index.get_by_id(id)\n",
+       "      if (view != null) {\n",
+       "        view.model.document.clear()\n",
+       "        Bokeh.index.delete(view)\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    const cell = handle.cell;\n",
+       "\n",
+       "    const id = cell.output_area._bokeh_element_id;\n",
+       "    const server_id = cell.output_area._bokeh_server_id;\n",
+       "\n",
+       "    // Clean up Bokeh references\n",
+       "    if (id != null) {\n",
+       "      drop(id)\n",
+       "    }\n",
+       "\n",
+       "    if (server_id !== undefined) {\n",
+       "      // Clean up Bokeh references\n",
+       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
+       "      cell.notebook.kernel.execute(cmd_clean, {\n",
+       "        iopub: {\n",
+       "          output: function(msg) {\n",
+       "            const id = msg.content.text.trim()\n",
+       "            drop(id)\n",
+       "          }\n",
+       "        }\n",
+       "      });\n",
+       "      // Destroy server and session\n",
+       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
+       "      cell.notebook.kernel.execute(cmd_destroy);\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when a new output is added\n",
+       "   */\n",
+       "  function handleAddOutput(event, handle) {\n",
+       "    const output_area = handle.output_area;\n",
+       "    const output = handle.output;\n",
+       "\n",
+       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
+       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
+       "      return\n",
+       "    }\n",
+       "\n",
+       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
+       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
+       "      // store reference to embed id on output_area\n",
+       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "    }\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "      const bk_div = document.createElement(\"div\");\n",
+       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "      const script_attrs = bk_div.children[0].attributes;\n",
+       "      for (let i = 0; i < script_attrs.length; i++) {\n",
+       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
+       "      }\n",
+       "      // store reference to server id on output_area\n",
+       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function register_renderer(events, OutputArea) {\n",
+       "\n",
+       "    function append_mime(data, metadata, element) {\n",
+       "      // create a DOM node to render to\n",
+       "      const toinsert = this.create_output_subarea(\n",
+       "        metadata,\n",
+       "        CLASS_NAME,\n",
+       "        EXEC_MIME_TYPE\n",
+       "      );\n",
+       "      this.keyboard_manager.register_events(toinsert);\n",
+       "      // Render to node\n",
+       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+       "      render(props, toinsert[toinsert.length - 1]);\n",
+       "      element.append(toinsert);\n",
+       "      return toinsert\n",
+       "    }\n",
+       "\n",
+       "    /* Handle when an output is cleared or removed */\n",
+       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
+       "    events.on('delete.Cell', handleClearOutput);\n",
+       "\n",
+       "    /* Handle when a new output is added */\n",
+       "    events.on('output_added.OutputArea', handleAddOutput);\n",
+       "\n",
+       "    /**\n",
+       "     * Register the mime type and append_mime function with output_area\n",
+       "     */\n",
+       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+       "      /* Is output safe? */\n",
+       "      safe: true,\n",
+       "      /* Index of renderer in `output_area.display_order` */\n",
+       "      index: 0\n",
+       "    });\n",
+       "  }\n",
+       "\n",
+       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
+       "  if (root.Jupyter !== undefined) {\n",
+       "    const events = require('base/js/events');\n",
+       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+       "\n",
+       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+       "      register_renderer(events, OutputArea);\n",
+       "    }\n",
+       "  }\n",
+       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
+       "    root._bokeh_timeout = Date.now() + 5000;\n",
+       "    root._bokeh_failed_load = false;\n",
+       "  }\n",
+       "\n",
+       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
+       "     \"
\\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
+       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"
    \\n\"+\n",
+       "     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n",
+       "     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"\\n\"+\n",
+       "     \"from bokeh.resources import INLINE\\n\"+\n",
+       "     \"output_notebook(resources=INLINE)\\n\"+\n",
+       "     \"\\n\"+\n",
+       "     \"
\"}};\n",
+       "\n",
+       "  function display_loaded() {\n",
+       "    const el = document.getElementById(\"eb2ddde4-530e-4ce3-9772-cc3703c8cf6d\");\n",
+       "    if (el != null) {\n",
+       "      el.textContent = \"BokehJS is loading...\";\n",
+       "    }\n",
+       "    if (root.Bokeh !== undefined) {\n",
+       "      if (el != null) {\n",
+       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
+       "      }\n",
+       "    } else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(display_loaded, 100)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function run_callbacks() {\n",
+       "    try {\n",
+       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+       "        if (callback != null)\n",
+       "          callback();\n",
+       "      });\n",
+       "    } finally {\n",
+       "      delete root._bokeh_onload_callbacks\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
+       "  }\n",
+       "\n",
+       "  function load_libs(css_urls, js_urls, callback) {\n",
+       "    if (css_urls == null) css_urls = [];\n",
+       "    if (js_urls == null) js_urls = [];\n",
+       "\n",
+       "    root._bokeh_onload_callbacks.push(callback);\n",
+       "    if (root._bokeh_is_loading > 0) {\n",
+       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+       "      return null;\n",
+       "    }\n",
+       "    if (js_urls == null || js_urls.length === 0) {\n",
+       "      run_callbacks();\n",
+       "      return null;\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
+       "\n",
+       "    function on_load() {\n",
+       "      root._bokeh_is_loading--;\n",
+       "      if (root._bokeh_is_loading === 0) {\n",
+       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+       "        run_callbacks()\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    function on_error(url) {\n",
+       "      console.error(\"failed to load \" + url);\n",
+       "    }\n",
+       "\n",
+       "    for (let i = 0; i < css_urls.length; i++) {\n",
+       "      const url = css_urls[i];\n",
+       "      const element = document.createElement(\"link\");\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error.bind(null, url);\n",
+       "      element.rel = \"stylesheet\";\n",
+       "      element.type = \"text/css\";\n",
+       "      element.href = url;\n",
+       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+       "      document.body.appendChild(element);\n",
+       "    }\n",
+       "\n",
+       "    for (let i = 0; i < js_urls.length; i++) {\n",
+       "      const url = js_urls[i];\n",
+       "      const element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error.bind(null, url);\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "  };\n",
+       "\n",
+       "  function inject_raw_css(css) {\n",
+       "    const element = document.createElement(\"style\");\n",
+       "    element.appendChild(document.createTextNode(css));\n",
+       "    document.body.appendChild(element);\n",
+       "  }\n",
+       "\n",
+       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n",
+       "  const css_urls = [];\n",
+       "\n",
+       "  const inline_js = [    function(Bokeh) {\n",
+       "      Bokeh.set_log_level(\"info\");\n",
+       "    },\n",
+       "function(Bokeh) {\n",
+       "    }\n",
+       "  ];\n",
+       "\n",
+       "  function run_inline_js() {\n",
+       "    if (root.Bokeh !== undefined || force === true) {\n",
+       "          for (let i = 0; i < inline_js.length; i++) {\n",
+       "      inline_js[i].call(root, root.Bokeh);\n",
+       "    }\n",
+       "if (force === true) {\n",
+       "        display_loaded();\n",
+       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(run_inline_js, 100);\n",
+       "    } else if (!root._bokeh_failed_load) {\n",
+       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+       "      root._bokeh_failed_load = true;\n",
+       "    } else if (force !== true) {\n",
+       "      const cell = $(document.getElementById(\"eb2ddde4-530e-4ce3-9772-cc3703c8cf6d\")).parents('.cell').data().cell;\n",
+       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  if (root._bokeh_is_loading === 0) {\n",
+       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
+       "    run_inline_js();\n",
+       "  } else {\n",
+       "    load_libs(css_urls, js_urls, function() {\n",
+       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+       "      run_inline_js();\n",
+       "    });\n",
+       "  }\n",
+       "}(window));"
+      ],
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"eb2ddde4-530e-4ce3-9772-cc3703c8cf6d\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"eb2ddde4-530e-4ce3-9772-cc3703c8cf6d\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import hail as hl\n",
+    "\n",
+    "from gnomad_toolbox.analysis.general import get_variant_count_by_freq_bin\n",
+    "from gnomad_toolbox.filtering.variant import filter_by_intervals\n",
+    "from gnomad_toolbox.filtering.vep import filter_by_csqs\n",
+    "from gnomad_toolbox.load_data import get_gnomad_release"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b3c44396-5ee1-4263-91f8-78bdb9417bab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Welcome to\n",
+      "     __  __     <>__\n",
+      "    / /_/ /__  __/ /\n",
+      "   / __  / _ `/ / /\n",
+      "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
+      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241210-2229-0.2.132-678e1f52b999.log\n"
+     ]
+    }
+   ],
+   "source": [
+    "hl.init(backend=\"local\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d36c12aa-d395-4f6c-891c-77caa4779e47",
+   "metadata": {},
+   "source": [
+    "## Get variant count by allele frequency bin\n",
+    "\n",
+    "The examples below show variant counts using the Table filtered to DRD2."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dec1dc9c-f145-46cb-8d33-ca43d24dd06a",
+   "metadata": {},
+   "source": [
+    "### Counts for AF bins: *0.1% - 1%* and *>1.0%*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "261a3380-8dba-41cb-b51b-033b23c963d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'0.1% - 1.0%': 49, '>1.0%': 28, 'AC0 - 0.1%': 2662}\n"
+     ]
+    }
+   ],
+   "source": [
+    "drd_interval_ht = filter_by_intervals(\"chr11:113409605-113475691\")\n",
+    "af_bin_ht = get_variant_count_by_freq_bin(ht=drd_interval_ht)\n",
+    "print(af_bin_ht)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d05fb1c6-91a9-42cf-87bf-eccb50f6e9b4",
+   "metadata": {},
+   "source": [
+    "### Counts for *singletons*, *doubletons*, and AF bins: *doubletons - 0.05%*, *0.05% - 0.1%*, *0.1% - 1%*, and *>1%*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0a07a84-584e-4b08-b356-9d38767a9c50",
+   "metadata": {},
+   "source": [
+    "#### All DRD2 variants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "aaa8dfac-f7b3-4ce2-b13d-b86a1648b7b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'0.05% - 0.1%': 11, '0.1% - 1.0%': 34, '>1.0%': 26, 'doubletons': 384, 'doubletons - 0.05%': 894, 'singletons': 1390}\n"
+     ]
+    }
+   ],
+   "source": [
+    "af_bin_ht = get_variant_count_by_freq_bin(\n",
+    "    af_cutoffs=[0.0005, 0.001, 0.01], \n",
+    "    singletons=True, \n",
+    "    doubletons=True, \n",
+    "    ht=drd_interval_ht,\n",
+    ")\n",
+    "print(af_bin_ht)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fcabb6e-12b5-4a51-aa06-da280ec8a654",
+   "metadata": {},
+   "source": [
+    "#### All DRD2 coding variants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a0fd192a-b1f3-4726-96cd-e88c0d8baf08",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'0.05% - 0.1%': 4, '0.1% - 1.0%': 8, '>1.0%': 4, 'doubletons': 111, 'doubletons - 0.05%': 291, 'singletons': 365}\n"
+     ]
+    }
+   ],
+   "source": [
+    "af_bin_ht = get_variant_count_by_freq_bin(\n",
+    "    af_cutoffs=[0.0005, 0.001, 0.01], \n",
+    "    singletons=True, \n",
+    "    doubletons=True, \n",
+    "    ht=filter_by_csqs(['coding'], ht=drd_interval_ht),\n",
+    ")\n",
+    "print(af_bin_ht)"
+   ]
+  },
+  {
+   "attachments": {
+    "Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "f9b2d921",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Filter to pLOF variants that we used to compute constraint metrics\n",
+    "pLOF variants meets the following requirements:\n",
+    "* High-confidence LOFTEE variants (without any flags),\n",
+    "* Only variants in the MANE Select transcript,\n",
+    "* PASS variants that are SNVs with MAF ≤ 0.1%, and\n",
+    "* Exome median depth ≥ 30 (**This is changing in v4 constraint?**)\n",
+    "\n",
+    "**Note: this number should match the number of observed pLOF SNVs on the gene page of gnomAD Browser.**\n",
+    "\n",
+    "![Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png](attachment:Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6ce87a77",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'coverage' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[7], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# TODO: add function for this\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m coverage_ht \u001b[38;5;241m=\u001b[39m coverage(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexomes\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mht()\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m#Filter to PASS SNVs with AF <= 0.1% and median exome depth ≥ 30.\u001b[39;00m\n\u001b[1;32m      6\u001b[0m ht \u001b[38;5;241m=\u001b[39m ht\u001b[38;5;241m.\u001b[39mfilter(\n\u001b[1;32m      7\u001b[0m     (hl\u001b[38;5;241m.\u001b[39mlen(ht\u001b[38;5;241m.\u001b[39mfilters) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m) \n\u001b[1;32m      8\u001b[0m     \u001b[38;5;241m&\u001b[39m (ht\u001b[38;5;241m.\u001b[39mallele_info\u001b[38;5;241m.\u001b[39mallele_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msnv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      9\u001b[0m     \u001b[38;5;241m&\u001b[39m (ht\u001b[38;5;241m.\u001b[39mfreq[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mAF \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.001\u001b[39m)\n\u001b[1;32m     10\u001b[0m     \u001b[38;5;241m&\u001b[39m (coverage_ht[ht\u001b[38;5;241m.\u001b[39mlocus]\u001b[38;5;241m.\u001b[39mmedian_approx \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m30\u001b[39m)\n\u001b[1;32m     11\u001b[0m )\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'coverage' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: add function for this\n",
+    "\n",
+    "coverage_ht = coverage(\"exomes\").ht()\n",
+    "\n",
+    "#Filter to PASS SNVs with AF <= 0.1% and median exome depth ≥ 30.\n",
+    "ht = ht.filter(\n",
+    "    (hl.len(ht.filters) == 0) \n",
+    "    & (ht.allele_info.allele_type == \"snv\")\n",
+    "    & (ht.freq[0].AF <= 0.001)\n",
+    "    & (coverage_ht[ht.locus].median_approx >= 30)\n",
+    ")\n",
+    "\n",
+    "\n",
+    "print(f\"Number of variants: {ht.count()}\")\n",
+    "ht.select(\n",
+    "    freq=ht.freq[0],\n",
+    "    csq=ht.vep.transcript_consequences[0].consequence_terms,\n",
+    "    coverage=coverage_ht[ht.locus],\n",
+    ").show(-1)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {
+    "height": "613.99px",
+    "width": "526.312px"
+   },
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": true,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "219.438px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 710bdcf5919f196d2a63ae671ffb98b9ca6fc952 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Fri, 13 Dec 2024 13:07:59 -0500
Subject: [PATCH 29/33] Fix unterminated string error

---
 gnomad_toolbox/load_data.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index d591bed..f0b9d95 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -165,8 +165,7 @@ def _get_gnomad_release(
         )
     else:
         raise ValueError(
-            f"Version {version} is not available for {
-                data_type} in the {dataset} dataset. "
+            f"Version {version} is not available for {data_type} in the {dataset} dataset. "
             f"Available versions: GRCh38 - {data_type_releases['GRCh38']}, "
             f"GRCh37 - {data_type_releases['GRCh37']}."
         )

From 4bfc305cdbddf5f4492a8550028e15e3bd20e107 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 18 Dec 2024 09:01:14 -0700
Subject: [PATCH 30/33] Apply suggestions from code review

Co-authored-by: Qin He <44242118+KoalaQin@users.noreply.github.com>
---
 gnomad_toolbox/analysis/general.py |  6 ++++--
 gnomad_toolbox/load_data.py        | 11 ++++-------
 2 files changed, 8 insertions(+), 9 deletions(-)

diff --git a/gnomad_toolbox/analysis/general.py b/gnomad_toolbox/analysis/general.py
index bcdde4a..391f9e4 100644
--- a/gnomad_toolbox/analysis/general.py
+++ b/gnomad_toolbox/analysis/general.py
@@ -36,7 +36,9 @@ def freq_bin_expr(
 
     :param freq_expr: Array of structs containing frequency information.
     :param index: Which index of freq_expr to use for annotation. Default is 0.
-    :param ac_cutoffs:
+    :param ac_cutoffs: List of AC cutoffs to use for binning. 
+    :param af_cutoffs: List of AF cutoffs to use for binning.
+    :param upper_af: Upper AF cutoff to use for binning.
     :return: StringExpression containing bin name based on input AC or AF.
     """
     if isinstance(freq_expr, hl.expr.ArrayExpression):
@@ -46,7 +48,7 @@ def freq_bin_expr(
         ac_cutoffs = [(c, f"AC{c}") for c in ac_cutoffs]
 
     if af_cutoffs and isinstance(af_cutoffs[0], float):
-        af_cutoffs = [(c, f"{c*100}%") for c in af_cutoffs]
+        af_cutoffs = [(f, f"{f*100}%") for f in af_cutoffs]
 
     if isinstance(upper_af, float):
         upper_af = (upper_af, f"{upper_af*100}%")
diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index f0b9d95..bc5a57d 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -83,8 +83,7 @@ def set_default_data(
         # Validate data type.
         if data_type and data_type not in DATA_TYPES:
             raise ValueError(
-                f"Data type {data_type} is invalid. Choose from 'exomes', 'genomes', "
-                f"or 'joint'."
+                f"Data type {data_type} is invalid. Choose from {DATA_TYPES}"
             )
 
         # Get all possible versions.
@@ -101,8 +100,7 @@ def set_default_data(
         # Check version availability.
         if version not in possible_versions:
             raise ValueError(
-                f"Version {version} is not available"
-                f"{'' if data_type else f' for {data_type}'}. "
+                f"Version {version} for {data_type} is not available."
             )
 
         self.data_type = data_type
@@ -142,7 +140,7 @@ def _get_gnomad_release(
 
     # Validate dataset.
     if releases is None:
-        raise ValueError(f"{dataset} is invalid. Choose from {RELEASES.keys()}")
+        raise ValueError(f"{dataset} is invalid. Choose from {list(RELEASES.keys())}")
 
     # Get all releases for the given dataset and data_type.
     data_type_releases = releases.get(data_type)
@@ -150,8 +148,7 @@ def _get_gnomad_release(
     # Validate data type.
     if data_type_releases is None:
         raise ValueError(
-            f"Data type {data_type} is invalid. Choose from 'exomes', 'genomes', or "
-            "'joint'."
+            f"Invalid data_type '{data_type}' for dataset '{dataset}'."
         )
 
     # Check version availability for GRCh38 and GRCh37.

From ecb664ba88f1902948eff17d685731bbe0fa0953 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 18 Dec 2024 09:08:23 -0700
Subject: [PATCH 31/33] format

---
 gnomad_toolbox/analysis/general.py | 2 +-
 gnomad_toolbox/load_data.py        | 8 ++------
 2 files changed, 3 insertions(+), 7 deletions(-)

diff --git a/gnomad_toolbox/analysis/general.py b/gnomad_toolbox/analysis/general.py
index 391f9e4..704173f 100644
--- a/gnomad_toolbox/analysis/general.py
+++ b/gnomad_toolbox/analysis/general.py
@@ -36,7 +36,7 @@ def freq_bin_expr(
 
     :param freq_expr: Array of structs containing frequency information.
     :param index: Which index of freq_expr to use for annotation. Default is 0.
-    :param ac_cutoffs: List of AC cutoffs to use for binning. 
+    :param ac_cutoffs: List of AC cutoffs to use for binning.
     :param af_cutoffs: List of AF cutoffs to use for binning.
     :param upper_af: Upper AF cutoff to use for binning.
     :return: StringExpression containing bin name based on input AC or AF.
diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index bc5a57d..de29e85 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -99,9 +99,7 @@ def set_default_data(
 
         # Check version availability.
         if version not in possible_versions:
-            raise ValueError(
-                f"Version {version} for {data_type} is not available."
-            )
+            raise ValueError(f"Version {version} for {data_type} is not available.")
 
         self.data_type = data_type
         self.version = version
@@ -147,9 +145,7 @@ def _get_gnomad_release(
 
     # Validate data type.
     if data_type_releases is None:
-        raise ValueError(
-            f"Invalid data_type '{data_type}' for dataset '{dataset}'."
-        )
+        raise ValueError(f"Invalid data_type '{data_type}' for dataset '{dataset}'.")
 
     # Check version availability for GRCh38 and GRCh37.
     if data_type_releases["GRCh38"] and version in data_type_releases["GRCh38"]:

From 06de9807d30e3afafa155b573c5342e1cac72e63 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Wed, 18 Dec 2024 11:22:31 -0500
Subject: [PATCH 32/33] Put back setup.py

---
 gnomad_toolbox/setup.py | 40 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 40 insertions(+)
 create mode 100644 gnomad_toolbox/setup.py

diff --git a/gnomad_toolbox/setup.py b/gnomad_toolbox/setup.py
new file mode 100644
index 0000000..8bc0d5d
--- /dev/null
+++ b/gnomad_toolbox/setup.py
@@ -0,0 +1,40 @@
+"""Setup script."""
+
+import setuptools
+
+with open("README.md", "r") as readme_file:
+    long_description = readme_file.read()
+
+install_requires = []
+with open("requirements.txt", "r") as requirements_file:
+    for req in (line.strip() for line in requirements_file):
+        if req != "hail":
+            install_requires.append(req)
+
+
+setuptools.setup(
+    name="gnomad_toolbox",
+    version="0.0.1",
+    author="The Genome Aggregation Database",
+    author_email="gnomad@broadinstitute.org",
+    description="Toolbox to help users process gnomAD release files",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/broadinstitute/gnomad-toolbox",
+    packages=setuptools.find_namespace_packages(include=["gnomad_toolbox.*"]),
+    project_urls={
+        "Documentation": "https://broadinstitute.github.io/gnomad-toolbox/",
+        "Source Code": "https://github.com/broadinstitute/gnomad-toolbox",
+        "Issues": "https://github.com/broadinstitute/gnomad-toolbox/issues",
+        "Change Log": "https://github.com/broadinstitute/gnomad-toolbox/releases",
+    },
+    classifiers=[
+        "Topic :: Scientific/Engineering :: Bio-Informatics",
+        "Intended Audience :: Science/Research",
+        "License :: OSI Approved :: BSD 3-Clause License",
+        "Programming Language :: Python :: 3",
+        "Development Status :: 4 - Beta",
+    ],
+    python_requires=">=3.9",
+    install_requires=install_requires,
+)

From c37ad6cc016b2841384c95eedf05d48cc9e4b360 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 18 Dec 2024 11:52:30 -0700
Subject: [PATCH 33/33] move setup to the correct location

---
 gnomad_toolbox/setup.py => setup.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename gnomad_toolbox/setup.py => setup.py (100%)

diff --git a/gnomad_toolbox/setup.py b/setup.py
similarity index 100%
rename from gnomad_toolbox/setup.py
rename to setup.py