usage_examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ieugwaspy: Brief overview\n",
    "\n",
    "This notebook provides some examples of how to use ```ieugwaspy``` to access data from the IEU GWAS database API.\n",
    "\n",
    "First import the ```ieugwaspy``` package and ```pandas``` for data handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ieugwaspy as igd\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring available studies\n",
    "\n",
    "We get the full data on studies using the ```igd.gwasinfo()``` function, passing no parameters (if you pass a list of study IDs this function will just return the data for that subset). We then use Pandas ```DataFrame.from_dict``` function to convert this to a Pandas DataFrame, reorientating the data appropriately to one study per row. Finally, we look at the top of the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>note</th>\n",
       "      <th>access</th>\n",
       "      <th>year</th>\n",
       "      <th>mr</th>\n",
       "      <th>author</th>\n",
       "      <th>consortium</th>\n",
       "      <th>sex</th>\n",
       "      <th>priority</th>\n",
       "      <th>population</th>\n",
       "      <th>unit</th>\n",
       "      <th>sample_size</th>\n",
       "      <th>nsnp</th>\n",
       "      <th>trait</th>\n",
       "      <th>id</th>\n",
       "      <th>subcategory</th>\n",
       "      <th>category</th>\n",
       "      <th>ncase</th>\n",
       "      <th>pmid</th>\n",
       "      <th>ncontrol</th>\n",
       "      <th>sd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>eqtl-a-ENSG00000261662</th>\n",
       "      <td>NA</td>\n",
       "      <td>public</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Vosa U</td>\n",
       "      <td>NA</td>\n",
       "      <td>Males and Females</td>\n",
       "      <td>0</td>\n",
       "      <td>European</td>\n",
       "      <td>NA</td>\n",
       "      <td>5502.0</td>\n",
       "      <td>14407</td>\n",
       "      <td>ENSG00000261662</td>\n",
       "      <td>eqtl-a-ENSG00000261662</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eqtl-a-ENSG00000160460</th>\n",
       "      <td>NA</td>\n",
       "      <td>public</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Vosa U</td>\n",
       "      <td>NA</td>\n",
       "      <td>Males and Females</td>\n",
       "      <td>0</td>\n",
       "      <td>European</td>\n",
       "      <td>NA</td>\n",
       "      <td>29896.0</td>\n",
       "      <td>18645</td>\n",
       "      <td>ENSG00000160460</td>\n",
       "      <td>eqtl-a-ENSG00000160460</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eqtl-a-ENSG00000205426</th>\n",
       "      <td>NA</td>\n",
       "      <td>public</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Vosa U</td>\n",
       "      <td>NA</td>\n",
       "      <td>Males and Females</td>\n",
       "      <td>0</td>\n",
       "      <td>European</td>\n",
       "      <td>NA</td>\n",
       "      <td>31684.0</td>\n",
       "      <td>19133</td>\n",
       "      <td>ENSG00000205426</td>\n",
       "      <td>eqtl-a-ENSG00000205426</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eqtl-a-ENSG00000003436</th>\n",
       "      <td>NA</td>\n",
       "      <td>public</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Vosa U</td>\n",
       "      <td>NA</td>\n",
       "      <td>Males and Females</td>\n",
       "      <td>0</td>\n",
       "      <td>European</td>\n",
       "      <td>NA</td>\n",
       "      <td>31684.0</td>\n",
       "      <td>18041</td>\n",
       "      <td>ENSG00000003436</td>\n",
       "      <td>eqtl-a-ENSG00000003436</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prot-a-7</th>\n",
       "      <td>NA</td>\n",
       "      <td>public</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Sun BB</td>\n",
       "      <td>NA</td>\n",
       "      <td>Males and Females</td>\n",
       "      <td>0</td>\n",
       "      <td>European</td>\n",
       "      <td>NA</td>\n",
       "      <td>3301.0</td>\n",
       "      <td>10534735</td>\n",
       "      <td>Tyrosine-protein kinase ABL1</td>\n",
       "      <td>prot-a-7</td>\n",
       "      <td>Protein</td>\n",
       "      <td>Immune system</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29875488.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       note  access    year  mr  author consortium  \\\n",
       "eqtl-a-ENSG00000261662   NA  public  2018.0   1  Vosa U         NA   \n",
       "eqtl-a-ENSG00000160460   NA  public  2018.0   1  Vosa U         NA   \n",
       "eqtl-a-ENSG00000205426   NA  public  2018.0   1  Vosa U         NA   \n",
       "eqtl-a-ENSG00000003436   NA  public  2018.0   1  Vosa U         NA   \n",
       "prot-a-7                 NA  public  2018.0   1  Sun BB         NA   \n",
       "\n",
       "                                      sex  priority population unit  \\\n",
       "eqtl-a-ENSG00000261662  Males and Females         0   European   NA   \n",
       "eqtl-a-ENSG00000160460  Males and Females         0   European   NA   \n",
       "eqtl-a-ENSG00000205426  Males and Females         0   European   NA   \n",
       "eqtl-a-ENSG00000003436  Males and Females         0   European   NA   \n",
       "prot-a-7                Males and Females         0   European   NA   \n",
       "\n",
       "                        sample_size      nsnp                         trait  \\\n",
       "eqtl-a-ENSG00000261662       5502.0     14407               ENSG00000261662   \n",
       "eqtl-a-ENSG00000160460      29896.0     18645               ENSG00000160460   \n",
       "eqtl-a-ENSG00000205426      31684.0     19133               ENSG00000205426   \n",
       "eqtl-a-ENSG00000003436      31684.0     18041               ENSG00000003436   \n",
       "prot-a-7                     3301.0  10534735  Tyrosine-protein kinase ABL1   \n",
       "\n",
       "                                            id subcategory       category  \\\n",
       "eqtl-a-ENSG00000261662  eqtl-a-ENSG00000261662          NA             NA   \n",
       "eqtl-a-ENSG00000160460  eqtl-a-ENSG00000160460          NA             NA   \n",
       "eqtl-a-ENSG00000205426  eqtl-a-ENSG00000205426          NA             NA   \n",
       "eqtl-a-ENSG00000003436  eqtl-a-ENSG00000003436          NA             NA   \n",
       "prot-a-7                              prot-a-7     Protein  Immune system   \n",
       "\n",
       "                        ncase        pmid  ncontrol  sd  \n",
       "eqtl-a-ENSG00000261662    NaN         NaN       NaN NaN  \n",
       "eqtl-a-ENSG00000160460    NaN         NaN       NaN NaN  \n",
       "eqtl-a-ENSG00000205426    NaN         NaN       NaN NaN  \n",
       "eqtl-a-ENSG00000003436    NaN         NaN       NaN NaN  \n",
       "prot-a-7                  NaN  29875488.0       NaN NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = igd.gwasinfo()\n",
    "df = pd.DataFrame.from_dict(data, orient='index')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can query the DataFrame for studies with Body mass data using standard Pandas syntax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>note</th>\n",
       "      <th>access</th>\n",
       "      <th>year</th>\n",
       "      <th>mr</th>\n",
       "      <th>author</th>\n",
       "      <th>consortium</th>\n",
       "      <th>sex</th>\n",
       "      <th>priority</th>\n",
       "      <th>population</th>\n",
       "      <th>unit</th>\n",
       "      <th>sample_size</th>\n",
       "      <th>nsnp</th>\n",
       "      <th>trait</th>\n",
       "      <th>id</th>\n",
       "      <th>subcategory</th>\n",
       "      <th>category</th>\n",
       "      <th>ncase</th>\n",
       "      <th>pmid</th>\n",
       "      <th>ncontrol</th>\n",
       "      <th>sd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ebi-a-GCST006368</th>\n",
       "      <td>NA</td>\n",
       "      <td>public</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Hoffmann TJ</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>0</td>\n",
       "      <td>European</td>\n",
       "      <td>NA</td>\n",
       "      <td>315347.0</td>\n",
       "      <td>27854527</td>\n",
       "      <td>Body mass index</td>\n",
       "      <td>ebi-a-GCST006368</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30108127.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ukb-a-267</th>\n",
       "      <td>NA</td>\n",
       "      <td>public</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Neale</td>\n",
       "      <td>Neale Lab</td>\n",
       "      <td>Males and Females</td>\n",
       "      <td>1</td>\n",
       "      <td>European</td>\n",
       "      <td>SD</td>\n",
       "      <td>331315.0</td>\n",
       "      <td>10894596</td>\n",
       "      <td>Whole body water mass</td>\n",
       "      <td>ukb-a-267</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ukb-b-19953</th>\n",
       "      <td>21001: Output from GWAS pipeline using Phesant...</td>\n",
       "      <td>public</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ben Elsworth</td>\n",
       "      <td>MRC-IEU</td>\n",
       "      <td>Males and Females</td>\n",
       "      <td>1</td>\n",
       "      <td>European</td>\n",
       "      <td>SD</td>\n",
       "      <td>461460.0</td>\n",
       "      <td>9851867</td>\n",
       "      <td>Body mass index (BMI)</td>\n",
       "      <td>ukb-b-19953</td>\n",
       "      <td>NA</td>\n",
       "      <td>Continuous</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ukb-a-265</th>\n",
       "      <td>NA</td>\n",
       "      <td>public</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Neale</td>\n",
       "      <td>Neale Lab</td>\n",
       "      <td>Males and Females</td>\n",
       "      <td>1</td>\n",
       "      <td>European</td>\n",
       "      <td>SD</td>\n",
       "      <td>330762.0</td>\n",
       "      <td>10894596</td>\n",
       "      <td>Whole body fat mass</td>\n",
       "      <td>ukb-a-265</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ukb-a-266</th>\n",
       "      <td>NA</td>\n",
       "      <td>public</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Neale</td>\n",
       "      <td>Neale Lab</td>\n",
       "      <td>Males and Females</td>\n",
       "      <td>1</td>\n",
       "      <td>European</td>\n",
       "      <td>SD</td>\n",
       "      <td>331291.0</td>\n",
       "      <td>10894596</td>\n",
       "      <td>Whole body fat-free mass</td>\n",
       "      <td>ukb-a-266</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "                                                               note  access  \\\n",
       "ebi-a-GCST006368                                                 NA  public   \n",
       "ukb-a-267                                                        NA  public   \n",
       "ukb-b-19953       21001: Output from GWAS pipeline using Phesant...  public   \n",
       "ukb-a-265                                                        NA  public   \n",
       "ukb-a-266                                                        NA  public   \n",
       "\n",
       "                    year  mr        author consortium                sex  \\\n",
       "ebi-a-GCST006368  2018.0   1   Hoffmann TJ         NA                 NA   \n",
       "ukb-a-267         2017.0   1         Neale  Neale Lab  Males and Females   \n",
       "ukb-b-19953       2018.0   1  Ben Elsworth    MRC-IEU  Males and Females   \n",
       "ukb-a-265         2017.0   1         Neale  Neale Lab  Males and Females   \n",
       "ukb-a-266         2017.0   1         Neale  Neale Lab  Males and Females   \n",
       "\n",
       "                  priority population unit  sample_size      nsnp  \\\n",
       "ebi-a-GCST006368         0   European   NA     315347.0  27854527   \n",
       "ukb-a-267                1   European   SD     331315.0  10894596   \n",
       "ukb-b-19953              1   European   SD     461460.0   9851867   \n",
       "ukb-a-265                1   European   SD     330762.0  10894596   \n",
       "ukb-a-266                1   European   SD     331291.0  10894596   \n",
       "\n",
       "                                     trait                id subcategory  \\\n",
       "ebi-a-GCST006368           Body mass index  ebi-a-GCST006368          NA   \n",
       "ukb-a-267            Whole body water mass         ukb-a-267          NA   \n",
       "ukb-b-19953          Body mass index (BMI)       ukb-b-19953          NA   \n",
       "ukb-a-265              Whole body fat mass         ukb-a-265          NA   \n",
       "ukb-a-266         Whole body fat-free mass         ukb-a-266          NA   \n",
       "\n",
       "                    category  ncase        pmid  ncontrol  sd  \n",
       "ebi-a-GCST006368          NA    NaN  30108127.0       NaN NaN  \n",
       "ukb-a-267                 NA    NaN         NaN       NaN NaN  \n",
       "ukb-b-19953       Continuous    NaN         NaN       NaN NaN  \n",
       "ukb-a-265                 NA    NaN         NaN       NaN NaN  \n",
       "ukb-a-266                 NA    NaN         NaN       NaN NaN  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmistudies = df[df['trait'].str.contains(\"body\", case=False) & df['trait'].str.contains(\"mass\", case=False)]\n",
    "bmistudies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract association data\n",
    "\n",
    "### Get tophits for a single study\n",
    "\n",
    "Here we use one of the studies returned by the query for \"Body mass\" above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <th>443</th>\n",
       "      <td>1.599560e-291</td>\n",
       "      <td>0.002014</td>\n",
       "      <td>461460</td>\n",
       "      <td>0.073497</td>\n",
       "      <td>53806453</td>\n",
       "      <td>16</td>\n",
       "      <td>ukb-b-19953</td>\n",
       "      <td>rs56094641</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0.404564</td>\n",
       "      <td>Body mass index (BMI)</td>\n",
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       "      <th>271</th>\n",
       "      <td>2.301440e-118</td>\n",
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       "      <td>C</td>\n",
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       "      <th>284</th>\n",
       "      <td>4.499870e-100</td>\n",
       "      <td>0.002612</td>\n",
       "      <td>461460</td>\n",
       "      <td>0.055468</td>\n",
       "      <td>628504</td>\n",
       "      <td>2</td>\n",
       "      <td>ukb-b-19953</td>\n",
       "      <td>rs6744646</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0.828299</td>\n",
       "      <td>Body mass index (BMI)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1.999860e-91</td>\n",
       "      <td>0.002443</td>\n",
       "      <td>461460</td>\n",
       "      <td>0.049529</td>\n",
       "      <td>177889025</td>\n",
       "      <td>1</td>\n",
       "      <td>ukb-b-19953</td>\n",
       "      <td>rs539515</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>0.204942</td>\n",
       "      <td>Body mass index (BMI)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>4.600450e-87</td>\n",
       "      <td>0.004609</td>\n",
       "      <td>461460</td>\n",
       "      <td>-0.091156</td>\n",
       "      <td>422144</td>\n",
       "      <td>2</td>\n",
       "      <td>ukb-b-19953</td>\n",
       "      <td>rs62107261</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>0.048327</td>\n",
       "      <td>Body mass index (BMI)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 p        se       n      beta   position chr           id  \\\n",
       "443  1.599560e-291  0.002014  461460  0.073497   53806453  16  ukb-b-19953   \n",
       "271  2.301440e-118  0.002342  461460  0.054172   57829135  18  ukb-b-19953   \n",
       "284  4.499870e-100  0.002612  461460  0.055468     628504   2  ukb-b-19953   \n",
       "100   1.999860e-91  0.002443  461460  0.049529  177889025   1  ukb-b-19953   \n",
       "183   4.600450e-87  0.004609  461460 -0.091156     422144   2  ukb-b-19953   \n",
       "\n",
       "           rsid ea nea       eaf                  trait  \n",
       "443  rs56094641  G   A  0.404564  Body mass index (BMI)  \n",
       "271   rs6567160  C   T  0.232714  Body mass index (BMI)  \n",
       "284   rs6744646  G   A  0.828299  Body mass index (BMI)  \n",
       "100    rs539515  C   A  0.204942  Body mass index (BMI)  \n",
       "183  rs62107261  C   T  0.048327  Body mass index (BMI)  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tophits = pd.DataFrame.from_dict(igd.tophits([\"ukb-b-19953\"])).sort_values(by='p', ascending=True)\n",
    "tophits.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract association data for a combination of studies and SNPs\n",
    "\n",
    "This allows us to extract a specific set of SNPs from a specific set of studies, both passed as Python lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>se</th>\n",
       "      <th>position</th>\n",
       "      <th>p</th>\n",
       "      <th>chr</th>\n",
       "      <th>beta</th>\n",
       "      <th>n</th>\n",
       "      <th>id</th>\n",
       "      <th>rsid</th>\n",
       "      <th>ea</th>\n",
       "      <th>nea</th>\n",
       "      <th>eaf</th>\n",
       "      <th>target_snp</th>\n",
       "      <th>proxy_snp</th>\n",
       "      <th>proxy</th>\n",
       "      <th>target_a1</th>\n",
       "      <th>target_a2</th>\n",
       "      <th>proxy_a1</th>\n",
       "      <th>proxy_a2</th>\n",
       "      <th>trait</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.002447</td>\n",
       "      <td>53806453</td>\n",
       "      <td>1.258930e-191</td>\n",
       "      <td>16</td>\n",
       "      <td>0.072289</td>\n",
       "      <td>336107</td>\n",
       "      <td>ukb-a-248</td>\n",
       "      <td>rs56094641</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0.403318</td>\n",
       "      <td>rs56094641</td>\n",
       "      <td>rs56094641</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Body mass index (BMI)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.002834</td>\n",
       "      <td>57829135</td>\n",
       "      <td>2.541560e-75</td>\n",
       "      <td>18</td>\n",
       "      <td>0.052066</td>\n",
       "      <td>336107</td>\n",
       "      <td>ukb-a-248</td>\n",
       "      <td>rs6567160</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>0.234055</td>\n",
       "      <td>rs6567160</td>\n",
       "      <td>rs6567160</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Body mass index (BMI)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.004100</td>\n",
       "      <td>53821862</td>\n",
       "      <td>5.667610e-74</td>\n",
       "      <td>16</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>171366</td>\n",
       "      <td>ieu-a-974</td>\n",
       "      <td>rs56094641</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0.4667</td>\n",
       "      <td>rs56094641</td>\n",
       "      <td>rs7201850</td>\n",
       "      <td>True</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>Body mass index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.003187</td>\n",
       "      <td>628504</td>\n",
       "      <td>5.767660e-58</td>\n",
       "      <td>2</td>\n",
       "      <td>0.051158</td>\n",
       "      <td>336107</td>\n",
       "      <td>ukb-a-248</td>\n",
       "      <td>rs6744646</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0.829019</td>\n",
       "      <td>rs6744646</td>\n",
       "      <td>rs6744646</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Body mass index (BMI)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.004600</td>\n",
       "      <td>57829135</td>\n",
       "      <td>5.082760e-34</td>\n",
       "      <td>18</td>\n",
       "      <td>0.056300</td>\n",
       "      <td>171875</td>\n",
       "      <td>ieu-a-974</td>\n",
       "      <td>rs6567160</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>0.2833</td>\n",
       "      <td>rs6567160</td>\n",
       "      <td>rs6567160</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Body mass index</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         se  position              p chr      beta       n         id  \\\n",
       "6  0.002447  53806453  1.258930e-191  16  0.072289  336107  ukb-a-248   \n",
       "7  0.002834  57829135   2.541560e-75  18  0.052066  336107  ukb-a-248   \n",
       "0  0.004100  53821862   5.667610e-74  16  0.074000  171366  ieu-a-974   \n",
       "8  0.003187    628504   5.767660e-58   2  0.051158  336107  ukb-a-248   \n",
       "1  0.004600  57829135   5.082760e-34  18  0.056300  171875  ieu-a-974   \n",
       "\n",
       "         rsid ea nea       eaf  target_snp   proxy_snp  proxy target_a1  \\\n",
       "6  rs56094641  G   A  0.403318  rs56094641  rs56094641  False      None   \n",
       "7   rs6567160  C   T  0.234055   rs6567160   rs6567160  False      None   \n",
       "0  rs56094641  G   A    0.4667  rs56094641   rs7201850   True         G   \n",
       "8   rs6744646  G   A  0.829019   rs6744646   rs6744646  False      None   \n",
       "1   rs6567160  C   T    0.2833   rs6567160   rs6567160  False      None   \n",
       "\n",
       "  target_a2 proxy_a1 proxy_a2                  trait  \n",
       "6      None     None     None  Body mass index (BMI)  \n",
       "7      None     None     None  Body mass index (BMI)  \n",
       "0         A        T        C        Body mass index  \n",
       "8      None     None     None  Body mass index (BMI)  \n",
       "1      None     None     None        Body mass index  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assocs = pd.DataFrame.from_dict(igd.associations([\"rs56094641\",\"rs6567160\",\"rs6744646\"],\n",
    "                                                 [\"ieu-a-974\",\"ieu-a-785\",\"ukb-a-248\"], \n",
    "                                                 proxies=1, \n",
    "                                                 r2=0.8, \n",
    "                                                 align_alleles=1, \n",
    "                                                 palindromes=1, \n",
    "                                                 maf_threshold=0.3)\n",
    "                                                ).sort_values(by='p', ascending=True)\n",
    "assocs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run a PheWAS\n",
    "\n",
    "Extract results against all traits for one SNP from the BMI example above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>position</th>\n",
       "      <th>beta</th>\n",
       "      <th>chr</th>\n",
       "      <th>p</th>\n",
       "      <th>se</th>\n",
       "      <th>id</th>\n",
       "      <th>rsid</th>\n",
       "      <th>ea</th>\n",
       "      <th>nea</th>\n",
       "      <th>eaf</th>\n",
       "      <th>trait</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>461460</td>\n",
       "      <td>53806453</td>\n",
       "      <td>0.073497</td>\n",
       "      <td>16</td>\n",
       "      <td>1.599560e-291</td>\n",
       "      <td>0.002014</td>\n",
       "      <td>ukb-b-19953</td>\n",
       "      <td>rs56094641</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0.404564</td>\n",
       "      <td>Body mass index (BMI)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>454884</td>\n",
       "      <td>53806453</td>\n",
       "      <td>0.073344</td>\n",
       "      <td>16</td>\n",
       "      <td>4.395420e-286</td>\n",
       "      <td>0.002029</td>\n",
       "      <td>ukb-b-2303</td>\n",
       "      <td>rs56094641</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0.404637</td>\n",
       "      <td>Body mass index (BMI)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>461632</td>\n",
       "      <td>53806453</td>\n",
       "      <td>0.060550</td>\n",
       "      <td>16</td>\n",
       "      <td>3.499450e-257</td>\n",
       "      <td>0.001768</td>\n",
       "      <td>ukb-b-11842</td>\n",
       "      <td>rs56094641</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0.404568</td>\n",
       "      <td>Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>454893</td>\n",
       "      <td>53806453</td>\n",
       "      <td>0.060498</td>\n",
       "      <td>16</td>\n",
       "      <td>3.999450e-253</td>\n",
       "      <td>0.001780</td>\n",
       "      <td>ukb-b-12039</td>\n",
       "      <td>rs56094641</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0.404637</td>\n",
       "      <td>Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>454718</td>\n",
       "      <td>53806453</td>\n",
       "      <td>0.047362</td>\n",
       "      <td>16</td>\n",
       "      <td>6.998420e-242</td>\n",
       "      <td>0.001426</td>\n",
       "      <td>ukb-b-4650</td>\n",
       "      <td>rs56094641</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0.404698</td>\n",
       "      <td>Comparative body size at age 10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          n  position      beta chr              p        se           id  \\\n",
       "259  461460  53806453  0.073497  16  1.599560e-291  0.002014  ukb-b-19953   \n",
       "237  454884  53806453  0.073344  16  4.395420e-286  0.002029   ukb-b-2303   \n",
       "295  461632  53806453  0.060550  16  3.499450e-257  0.001768  ukb-b-11842   \n",
       "211  454893  53806453  0.060498  16  3.999450e-253  0.001780  ukb-b-12039   \n",
       "230  454718  53806453  0.047362  16  6.998420e-242  0.001426   ukb-b-4650   \n",
       "\n",
       "           rsid ea nea       eaf                            trait  \n",
       "259  rs56094641  G   A  0.404564            Body mass index (BMI)  \n",
       "237  rs56094641  G   A  0.404637            Body mass index (BMI)  \n",
       "295  rs56094641  G   A  0.404568                           Weight  \n",
       "211  rs56094641  G   A  0.404637                           Weight  \n",
       "230  rs56094641  G   A  0.404698  Comparative body size at age 10  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phewas = pd.DataFrame.from_dict(igd.phewas([\"rs56094641\"])).sort_values(by='p', ascending=True)\n",
    "phewas.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert chr:pos variant IDs to rsid\n",
    "\n",
    "This is just a helper function to convert variants in chromosome:position format to dbSNP rsids for queries against the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['rs7777', 'rs234']\n"
     ]
    }
   ],
   "source": [
    "variants = igd.variants_to_rsid(['10:44865737','7:105561135-105563135'])\n",
    "print(variants)"
   ]
  }
 ],
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