diff --git a/.nojekyll b/.nojekyll
index ed3c3871..eeb30041 100644
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+++ b/.nojekyll
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diff --git a/about.html b/about.html
index d414999e..0880fbb2 100644
--- a/about.html
+++ b/about.html
@@ -8566,7 +8566,7 @@ <h2 class="anchored" data-anchor-id="nbis">NBIS</h2>
     <div class="nav-footer-right">Published with <a href="https://quarto.org/">Quarto</a> v<!--?quarto.version?--></div>
   </div>
 </footer>
-<script>var lightboxQuarto = GLightbox({"loop":true,"selector":".lightbox","descPosition":"bottom","closeEffect":"zoom","openEffect":"zoom"});</script>
+<script>var lightboxQuarto = GLightbox({"closeEffect":"zoom","descPosition":"bottom","loop":true,"openEffect":"zoom","selector":".lightbox"});</script>
 
 
 
diff --git a/exercises/data.html b/exercises/data.html
index c4bb64c2..2f6e5e86 100644
--- a/exercises/data.html
+++ b/exercises/data.html
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     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">20-Aug-2023</p>
+      <p class="date">22-Aug-2023</p>
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@@ -8993,7 +8993,7 @@ <h3 class="anchored" data-anchor-id="sec-data-simulated-ooa-outgroups">Out of Af
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 </footer>
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+<script>var lightboxQuarto = GLightbox({"openEffect":"zoom","descPosition":"bottom","selector":".lightbox","closeEffect":"zoom","loop":true});</script>
 
 
 
diff --git a/exercises/data_filtering/index.html b/exercises/data_filtering/index.html
index 61200d9c..e8b94b6e 100644
--- a/exercises/data_filtering/index.html
+++ b/exercises/data_filtering/index.html
@@ -8645,7 +8645,7 @@ <h1>Filtering of resequencing data</h1>
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</svg>" class="img-fluid figure-img" width="672"></a></p>
 <figcaption class="figure-caption">Figure 1: Coverage distributions for three hypothetical samples along with the cumulative coverage for all samples.</figcaption>
 </figure>
 </div>
@@ -8692,16 +8692,16 @@ <h2 class="anchored" data-anchor-id="input-data">Input data</h2>
 <div class="cell" data-hash="index_cache/html/e130-input-data_8998c7717a6ac884ef00ac07a340d882">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ls</span> <span class="at">-lLh</span> <span class="pp">[</span><span class="ss">CY</span><span class="pp">]*</span>.chr21.bam</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>-rw-r--r-- 1 runner docker 7.8M Aug 20 16:22 CEU-1.ooa.chr21.bam
--rw-r--r-- 1 runner docker 6.3M Aug 20 16:22 CEU-2.ooa.chr21.bam
--rw-r--r-- 1 runner docker 4.8M Aug 20 16:22 CEU-3.ooa.chr21.bam
--rw-r--r-- 1 runner docker  13M Aug 20 16:22 CEU-4.ooa.chr21.bam
--rw-r--r-- 1 runner docker 5.8M Aug 20 16:22 CHB-1.ooa.chr21.bam
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--rw-r--r-- 1 runner docker 7.6M Aug 20 16:22 YRI-3.ooa.chr21.bam</code></pre>
+<pre><code>-rw-r--r-- 1 runner docker 7.8M Aug 22 22:55 CEU-1.ooa.chr21.bam
+-rw-r--r-- 1 runner docker 6.3M Aug 22 22:55 CEU-2.ooa.chr21.bam
+-rw-r--r-- 1 runner docker 4.8M Aug 22 22:55 CEU-3.ooa.chr21.bam
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+-rw-r--r-- 1 runner docker 6.5M Aug 22 22:55 CHB-3.ooa.chr21.bam
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 </div>
 </div>
 <p>Each population consists of a collection – or set – of samples, and we will sometimes use the term <code>sample set</code> to refer to such a collection. For instance, the population (sample set) <code>CHB</code> consists of the set of samples <code>CHB-1</code>, <code>CHB-2</code> and <code>CHB-3</code>.</p>
@@ -8841,7 +8841,7 @@ <h3 class="anchored" data-anchor-id="total-coverage">Total coverage</h3>
 <div class="quarto-layout-row quarto-layout-valign-top">
 <div id="fig-plot-total-coverage-distribution-hist" class="quarto-figure quarto-figure-center anchored" style="flex-basis: 100.0%;justify-content: center;">
 <figure class="figure">
-<p><a href="fig-plot-total-coverage-distribution.png" class="lightbox" data-gallery="quarto-lightbox-gallery-4" title="Genome coverage"><img src="data:image/png;base64,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" class="img-fluid figure-img"></a></p>
+<p><a href="fig-plot-total-coverage-distribution.png" class="lightbox" title="Genome coverage" data-gallery="quarto-lightbox-gallery-4"><img src="data:image/png;base64,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" class="img-fluid figure-img"></a></p>
 <figcaption class="figure-caption">(a) Genome coverage</figcaption>
 </figure>
 </div>
@@ -8849,7 +8849,7 @@ <h3 class="anchored" data-anchor-id="total-coverage">Total coverage</h3>
 <div class="quarto-layout-row quarto-layout-valign-top">
 <div id="fig-plot-total-coverage-distribution-cumulative" class="quarto-figure quarto-figure-center anchored" style="flex-basis: 100.0%;justify-content: center;">
 <figure class="figure">
-<p><a href="fig-plot-total-coverage-distribution-cumulative.png" class="lightbox" data-gallery="quarto-lightbox-gallery-5" title="Cumulative genome coverage"><img src="data:image/png;base64,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" class="img-fluid figure-img"></a></p>
+<p><a href="fig-plot-total-coverage-distribution-cumulative.png" class="lightbox" title="Cumulative genome coverage" data-gallery="quarto-lightbox-gallery-5"><img src="data:image/png;base64,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" class="img-fluid figure-img"></a></p>
 <figcaption class="figure-caption">(b) Cumulative genome coverage</figcaption>
 </figure>
 </div>
@@ -8870,7 +8870,7 @@ <h3 class="anchored" data-anchor-id="total-coverage">Total coverage</h3>
 <figure class="figure">
 <div id="fig-plot-total-coverage-distribution-hist-zoom-in" class="quarto-figure quarto-figure-center anchored">
 <figure class="figure">
-<p><a href="fig-plot-total-coverage-distribution-hist-zoom-in.png" class="lightbox" data-gallery="quarto-lightbox-gallery-6" title="Genome coverage"><img src="data:image/png;base64,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" class="img-fluid figure-img"></a></p>
+<p><a href="fig-plot-total-coverage-distribution-hist-zoom-in.png" class="lightbox" title="Genome coverage" data-gallery="quarto-lightbox-gallery-6"><img src="data:image/png;base64,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" class="img-fluid figure-img"></a></p>
 <figcaption class="figure-caption">(a) Genome coverage</figcaption>
 </figure>
 </div>
@@ -9266,7 +9266,7 @@ <h1>Variant filtering</h1>
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diff --git a/exercises/demopy/index.html b/exercises/demopy/index.html
index 1416c33b..86927352 100644
--- a/exercises/demopy/index.html
+++ b/exercises/demopy/index.html
@@ -4859,7 +4859,7 @@ <h1 class="title">Exercise example using pgip jupyter kernel (WIP)</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">20-Aug-2023</p>
+      <p class="date">22-Aug-2023</p>
     </div>
   </div>
   
@@ -4888,7 +4888,7 @@ <h2 class="anchored" data-anchor-id="python-code">Python code</h2>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> os</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>os.listdir()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div id="test" class="cell-output cell-output-display" data-execution_count="1">
-<pre><code>[&#39;.jupyter_cache&#39;, &#39;index.qmd&#39;, &#39;index.ipynb&#39;]</code></pre>
+<pre><code>[&#39;index.qmd&#39;, &#39;index.ipynb&#39;, &#39;.jupyter_cache&#39;]</code></pre>
 </div>
 </div>
 <div class="cell" data-execution_count="2">
diff --git a/exercises/index.html b/exercises/index.html
index e2bfa3b2..73c35324 100644
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" 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 <div id="ref-charlesworth_EffectsOfSelection_2021" class="csl-entry" role="listitem">
 Charlesworth, B., &amp; Jensen, J. D. (2021). Effects of selection at linked sites on patterns of genetic variability. <em>Annual Review of Ecology, Evolution, and Systematics</em>, <em>52</em>(1), 177–197. <a href="https://doi.org/10.1146/annurev-ecolsys-010621-044528">https://doi.org/10.1146/annurev-ecolsys-010621-044528</a>
 </div>
+<div id="ref-cooplab_PopulationGeneticsCourse_2011" class="csl-entry" role="listitem">
+cooplab. (2011). Population genetics course resources: <span>Hardy-Weinberg Eq</span>. In <em>gcbias</em>. <a href="https://gcbias.org/2011/10/13/population-genetics-course-resources-hardy-weinberg-eq/">https://gcbias.org/2011/10/13/population-genetics-course-resources-hardy-weinberg-eq/</a>
+</div>
 <div id="ref-corbett-detig_NaturalSelectionConstrains_2015" class="csl-entry" role="listitem">
 Corbett-Detig, R. B., Hartl, D. L., &amp; Sackton, T. B. (2015). Natural <span>Selection Constrains Neutral Diversity</span> across <span>A Wide Range</span> of <span>Species</span>. <em>PLOS Biology</em>, <em>13</em>(4), e1002112. <a href="https://doi.org/10.1371/journal.pbio.1002112">https://doi.org/10.1371/journal.pbio.1002112</a>
 </div>
@@ -5025,6 +5028,9 @@ <h1>References</h1>
 <div id="ref-_VertebrateGenomesProject_" class="csl-entry" role="listitem">
 <em>Vertebrate <span>Genomes Project</span></em>. (n.d.). https://vertebrategenomesproject.org. Retrieved November 3, 2022, from <a href="https://vertebrategenomesproject.org">https://vertebrategenomesproject.org</a>
 </div>
+<div id="ref-waples_WhatNeAnyway_2022" class="csl-entry" role="listitem">
+Waples, R. S. (2022). What <span>Is Ne</span>, <span>Anyway</span>? <em>Journal of Heredity</em>, <em>113</em>(4), 371–379. <a href="https://doi.org/10.1093/jhered/esac023">https://doi.org/10.1093/jhered/esac023</a>
+</div>
 <div id="ref-webstermatthew_GeneticVariationMountain_2022" class="csl-entry" role="listitem">
 Webster, Matthew, &amp; Yuanzhen Liu. (2022). <em>Genetic <span>Variation</span> in <span>Mountain Bumblebees</span></em>.
 </div>
diff --git a/search.json b/search.json
index 32364733..79272eb1 100644
--- a/search.json
+++ b/search.json
@@ -1,39 +1,4 @@
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-    "href": "slides/simulation/index.html#forward-simulation-1",
-    "title": "Simulation",
-    "section": "Forward simulation",
-    "text": "Forward simulation\nBegin with forward simulation as it makes more intuitive sense.\nOutline principles of forward simulation from Wright-Fisher model “algorithm”."
-  },
-  {
-    "objectID": "slides/simulation/index.html#slim",
-    "href": "slides/simulation/index.html#slim",
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-    "text": "SLiM\nMention advances in forward simulations"
-  },
-  {
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-    "href": "slides/simulation/index.html#forward-simulation-in-slim",
-    "title": "Simulation",
-    "section": "Forward simulation in SLiM",
-    "text": "Forward simulation in SLiM\nOutline steps in SLiM - relate to WF model"
-  },
-  {
-    "objectID": "slides/simulation/index.html#example-in-slim",
-    "href": "slides/simulation/index.html#example-in-slim",
-    "title": "Simulation",
-    "section": "Example in SLiM",
-    "text": "Example in SLiM"
-  },
-  {
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-    "href": "slides/simulation/index.html#exercise-in-slim",
-    "title": "Simulation",
-    "section": "Exercise in SLiM",
-    "text": "Exercise in SLiM\nOptional: do a simple neutral model"
-  },
   {
     "objectID": "slides/simulation/index.html#wright-fisher-model-again",
     "href": "slides/simulation/index.html#wright-fisher-model-again",
@@ -132,6 +97,13 @@
     "section": "Simulating genealogies (Hahn, 2019, p. 115)",
     "text": "Simulating genealogies (Hahn, 2019, p. 115)\n\n\n\nStart with \\(i=n\\) chromosomes\nChoose time to next coalescent event from an exponential distribution with parameter \\(\\lambda=i(i-1)/2\\)\\(^1\\)\nChoose two chromosomes at random to coalesce\nMerge the two lineages and set \\(i \\rightarrow i - 1\\)\nIf \\(i&gt;1\\), go to step 2; if not, stop.\n\n\n\\(^1\\): The exponential can be parametrized in two different ways, so that the parameter to the function is either \\(\\lambda\\) or \\(\\beta=1/\\lambda\\).\n\n\n\n\n\n\nFigure 3: A simulated genealogy. The \\(T_i\\) represent the waiting time when the state has \\(i\\) chromosomes."
   },
+  {
+    "objectID": "slides/simulation/index.html#some-properties-of-the-tree",
+    "href": "slides/simulation/index.html#some-properties-of-the-tree",
+    "title": "Simulation",
+    "section": "Some properties of the tree",
+    "text": "Some properties of the tree\n\n\n\n\n\n\nFigure 4: A coalescent tree with numbered nodes. Nodes 1-5 correspond to the samples and are leaves. The internal nodes 6-8 (and the unlabelled root) are ancestral chromosomes (unsampled). The \\(T_i\\) represent the waiting time when the state has \\(i\\) chromosomes, whereas \\(\\tau_i\\) correspond to the branch length from node \\(i\\) to its parent."
+  },
   {
     "objectID": "slides/simulation/index.html#variation-in-topology-and-height",
     "href": "slides/simulation/index.html#variation-in-topology-and-height",
@@ -144,7 +116,7 @@
     "href": "slides/simulation/index.html#adding-mutations",
     "title": "Simulation",
     "section": "Adding mutations",
-    "text": "Adding mutations\n\n\n\n\nFigure 4: Adding mutations on a coalescent genealogy."
+    "text": "Adding mutations\n\n\n\n\nFigure 5: Adding mutations on a coalescent genealogy."
   },
   {
     "objectID": "slides/simulation/index.html#the-coalescent-diminishing-returns",
@@ -167,6 +139,41 @@
     "section": "Evolutionary processes and genealogies",
     "text": "Evolutionary processes and genealogies\nNon-neutral processes change topologies in ways that we detect when applying tests (Ferretti et al., 2017)\ngenealogies change due to non Show some qualitative examples of how trees change for\n\nbottleneck"
   },
+  {
+    "objectID": "slides/simulation/index.html#exercise-on-the-coalescent",
+    "href": "slides/simulation/index.html#exercise-on-the-coalescent",
+    "title": "Simulation",
+    "section": "Exercise on the coalescent",
+    "text": "Exercise on the coalescent"
+  },
+  {
+    "objectID": "slides/simulation/index.html#chromosome-are-mosaics-of-non-recombining-units",
+    "href": "slides/simulation/index.html#chromosome-are-mosaics-of-non-recombining-units",
+    "title": "Simulation",
+    "section": "Chromosome are mosaics of non-recombining units",
+    "text": "Chromosome are mosaics of non-recombining units"
+  },
+  {
+    "objectID": "slides/simulation/index.html#the-ancestral-recombination-graph",
+    "href": "slides/simulation/index.html#the-ancestral-recombination-graph",
+    "title": "Simulation",
+    "section": "The ancestral recombination graph",
+    "text": "The ancestral recombination graph"
+  },
+  {
+    "objectID": "slides/simulation/index.html#the-sequential-markovian-coalescent",
+    "href": "slides/simulation/index.html#the-sequential-markovian-coalescent",
+    "title": "Simulation",
+    "section": "The Sequential Markovian Coalescent",
+    "text": "The Sequential Markovian Coalescent"
+  },
+  {
+    "objectID": "slides/simulation/index.html#tree-sequences",
+    "href": "slides/simulation/index.html#tree-sequences",
+    "title": "Simulation",
+    "section": "Tree sequences",
+    "text": "Tree sequences"
+  },
   {
     "objectID": "slides/simulation/index.html#msprime",
     "href": "slides/simulation/index.html#msprime",
@@ -181,6 +188,41 @@
     "section": "Exercises with msprime",
     "text": "Exercises with msprime\nUse CLI to generate genealogies and mutations."
   },
+  {
+    "objectID": "slides/simulation/index.html#forward-simulation-1",
+    "href": "slides/simulation/index.html#forward-simulation-1",
+    "title": "Simulation",
+    "section": "Forward simulation",
+    "text": "Forward simulation\nBegin with forward simulation as it makes more intuitive sense.\nOutline principles of forward simulation from Wright-Fisher model “algorithm”."
+  },
+  {
+    "objectID": "slides/simulation/index.html#slim",
+    "href": "slides/simulation/index.html#slim",
+    "title": "Simulation",
+    "section": "SLiM",
+    "text": "SLiM\nMention advances in forward simulations"
+  },
+  {
+    "objectID": "slides/simulation/index.html#forward-simulation-in-slim",
+    "href": "slides/simulation/index.html#forward-simulation-in-slim",
+    "title": "Simulation",
+    "section": "Forward simulation in SLiM",
+    "text": "Forward simulation in SLiM\nOutline steps in SLiM - relate to WF model"
+  },
+  {
+    "objectID": "slides/simulation/index.html#example-in-slim",
+    "href": "slides/simulation/index.html#example-in-slim",
+    "title": "Simulation",
+    "section": "Example in SLiM",
+    "text": "Example in SLiM"
+  },
+  {
+    "objectID": "slides/simulation/index.html#exercise-in-slim",
+    "href": "slides/simulation/index.html#exercise-in-slim",
+    "title": "Simulation",
+    "section": "Exercise in SLiM",
+    "text": "Exercise in SLiM\nOptional: do a simple neutral model"
+  },
   {
     "objectID": "slides/simulation/index.html#recapitation",
     "href": "slides/simulation/index.html#recapitation",
@@ -230,12 +272,19 @@
     "section": "Example: Population genetics of the coral Acropora millepora",
     "text": "Example: Population genetics of the coral Acropora millepora\nMotivation: corals are facing hard times and to prevent future losses of coral cover a better understanding of genetics is warranted.\n\n\n\n\n\nFigure 3: Population structure\n\n\n\n\n\n\n\nFigure 4: Selection scan\n\n\n\n\n\n\n\n\nFuller et al. (2020)"
   },
+  {
+    "objectID": "slides/pgip/index.html#population-genetics",
+    "href": "slides/pgip/index.html#population-genetics",
+    "title": "Population genomics in practice",
+    "section": "Population genetics",
+    "text": "Population genetics\n\nbriefly outline what popultation genetics studies,\n\n\nPopulation genetics focuses on the genetic basis of evolution. It is mainly a theoretical subject, owing to the slow changes of genetic variation. As such, it tries to explain the shape and structure of genetic variation from theoretical predictions and models."
+  },
   {
     "objectID": "slides/pgip/index.html#from-population-genetics-to-population-genomics",
     "href": "slides/pgip/index.html#from-population-genetics-to-population-genomics",
     "title": "Population genomics in practice",
     "section": "From population genetics to population genomics",
-    "text": "From population genetics to population genomics\n\n\n\nThe variable sites at the Drosophila melanogaster ADH locus (Kreitman, 1983)\nPopulation genetics focuses on the genetic basis of evolution. It is mainly a theoretical subject, owing to the slow changes of genetic variation. As such, it tries to explain the shape and structure of genetic variation from theoretical predictions and models.\n\nfrom locus-based studies (e.g., alcohol dehydrogenase in Drosophila (Kreitman, 1983)) to genome-wide (e.g., Drosophila population genomics (Begun et al., 2007)\nnote: studied loci have often not been randomly chosen, which is another argument for whole-genome studies\nenabler: sequencing technology\n\n(Fuller et al., 2020) paper has population genetics in title -&gt; population genetics is a key ingredient.\nRefer to Hahn’s points about learning something about global patterns:\n\nselection acts locally, demography globally\nthe structure of genetic variation and how it depends on\n\nrecombination landscapes and linked selection\ndemographic changes\nidentification of neutral loci\n\n\nSo, not simply about applying 10000 selection tests for multiple loci\nPopulation genetics in title of previous slide =&gt; population genomics is large-scale population genetics? At the very least, shows the foundational role population genetics plays.\nAll of the points above point to the importance of statistics which implies mathematics / computational skills important"
+    "text": "From population genetics to population genomics\n\n\n\nThe variable sites at the Drosophila melanogaster ADH locus (Kreitman, 1983)\n\nfrom locus-based studies (e.g., alcohol dehydrogenase in Drosophila (Kreitman, 1983)) to genome-wide (e.g., Drosophila population genomics (Begun et al., 2007)\nnote: studied loci have often not been randomly chosen, which is another argument for whole-genome studies\nenabler: sequencing technology\n\n(Fuller et al., 2020) paper has population genetics in title -&gt; population genetics is a key ingredient.\nRefer to Hahn’s points about learning something about global patterns:\n\nselection acts locally, demography globally\nthe structure of genetic variation and how it depends on\n\nrecombination landscapes and linked selection\ndemographic changes\nidentification of neutral loci\n\n\nSo, not simply about applying 10000 selection tests for multiple loci\nPopulation genetics in title of previous slide =&gt; population genomics is large-scale population genetics? At the very least, shows the foundational role population genetics plays.\nAll of the points above point to the importance of statistics which implies mathematics / computational skills important"
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     "section": "Statistical inference in population genomics",
     "text": "Statistical inference in population genomics\nThe data deluge requires advanced statistical methods and models to do inference. Today data production outpaces theoretical advances. Therefore, take care not to attach too much faith to a test that explains data well.\n\nA population genomics study should aim at generating a baseline model that takes into account the processes that shape genetic variation (Johri et al., 2022):\n\nmutation\nrecombination\ngene conversion\npurifying selection acting on functional regions and its effects on linked variants (background selection)\ngenetic drift with demographic history and geographic structure"
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-    "section": "Machine learning and AI for population genomics",
-    "text": "Machine learning and AI for population genomics"
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-    "title": "Population genomics in practice",
-    "section": "Ancestral recombination graph methods",
-    "text": "Ancestral recombination graph methods\n\ndeep population sequencing\nargweaver\ntskit\nRelate\nARG-Needle"
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-    "href": "slides/pgip/index.html#ancient-dna",
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-    "text": "Ancient DNA\n\nRelate"
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-    "section": "Coalescent and Wright-Fisher simulations",
-    "text": "Coalescent and Wright-Fisher simulations\nUse to determine significance in genome-wide scans"
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     "section": "Bibliography",
-    "text": "Bibliography\n\n\nBegun, D. J., Holloway, A. K., Stevens, K., Hillier, L. W., Poh, Y.-P., Hahn, M. W., Nista, P. M., Jones, C. D., Kern, A. D., Dewey, C. N., Pachter, L., Myers, E., & Langley, C. H. (2007). Population Genomics: Whole-Genome Analysis of Polymorphism and Divergence in Drosophila simulans. PLOS Biology, 5(11), e310. https://doi.org/10.1371/journal.pbio.0050310\n\n\nCasillas, S., & Barbadilla, A. (2017). Molecular Population Genetics. Genetics, 205(3), 1003–1035. https://doi.org/10.1534/genetics.116.196493\n\n\nFuller, Z. L., Mocellin, V. J. L., Morris, L. A., Cantin, N., Shepherd, J., Sarre, L., Peng, J., Liao, Y., Pickrell, J., Andolfatto, P., Matz, M., Bay, L. K., & Przeworski, M. (2020). Population genetics of the coral Acropora millepora: Toward genomic prediction of bleaching. Science, 369(6501), eaba4674. https://doi.org/10.1126/science.aba4674\n\n\nHahn, M. (2019). Molecular Population Genetics (First). Oxford University Press.\n\n\nHartl, D. L., & Clark, A. G. (1997). Principles of population genetics. Sinauer Associates.\n\n\nJohri, P., Aquadro, C. F., Beaumont, M., Charlesworth, B., Excoffier, L., Eyre-Walker, A., Keightley, P. D., Lynch, M., McVean, G., Payseur, B. A., Pfeifer, S. P., Stephan, W., & Jensen, J. D. (2022). Recommendations for improving statistical inference in population genomics. PLOS Biology, 20(5), e3001669. https://doi.org/10.1371/journal.pbio.3001669\n\n\nKreitman, M. (1983). Nucleotide polymorphism at the alcohol dehydrogenase locus of Drosophila melanogaster. Nature, 304(5925), 412. https://doi.org/10.1038/304412a0\n\n\nLi, H., & Durbin, R. (2011). Inference of human population history from individual whole-genome sequences. Nature, 475(7357), 493–496. https://doi.org/10.1038/nature10231\n\n\nWetterstrand, KA. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP). www.genome.gov/sequencingcostsdata\n\n\n\n\nA brief history of population genetics\n\nMendel’s peas\nGalton, Pearson and Bateson: Of blending and particulate inheritance (the latter championed by Bateson)\nMorgan’s flies\nFisher and natural selection\nThe modern synthesis\nWright and drift\nKimura and the diffusion approximation - Follow (Casillas & Barbadilla, 2017) history?\nThe allozyme era (FIXME: picture of allozyme gel)\n\nDevelopment of genetic diversity measures\n\nH (gene diversity, heterozygosity): average proportion heterozygous loci\nP (gene polymorphism): average proportion polymorphic loci\n\n\nThe nucleotide diversity era\nThe population genomics era"
+    "text": "Bibliography\n\n\nBegun, D. J., Holloway, A. K., Stevens, K., Hillier, L. W., Poh, Y.-P., Hahn, M. W., Nista, P. M., Jones, C. D., Kern, A. D., Dewey, C. N., Pachter, L., Myers, E., & Langley, C. H. (2007). Population Genomics: Whole-Genome Analysis of Polymorphism and Divergence in Drosophila simulans. PLOS Biology, 5(11), e310. https://doi.org/10.1371/journal.pbio.0050310\n\n\nFuller, Z. L., Mocellin, V. J. L., Morris, L. A., Cantin, N., Shepherd, J., Sarre, L., Peng, J., Liao, Y., Pickrell, J., Andolfatto, P., Matz, M., Bay, L. K., & Przeworski, M. (2020). Population genetics of the coral Acropora millepora: Toward genomic prediction of bleaching. Science, 369(6501), eaba4674. https://doi.org/10.1126/science.aba4674\n\n\nHahn, M. (2019). Molecular Population Genetics (First). Oxford University Press.\n\n\nHartl, D. L., & Clark, A. G. (1997). Principles of population genetics. Sinauer Associates.\n\n\nJohri, P., Aquadro, C. F., Beaumont, M., Charlesworth, B., Excoffier, L., Eyre-Walker, A., Keightley, P. D., Lynch, M., McVean, G., Payseur, B. A., Pfeifer, S. P., Stephan, W., & Jensen, J. D. (2022). Recommendations for improving statistical inference in population genomics. PLOS Biology, 20(5), e3001669. https://doi.org/10.1371/journal.pbio.3001669\n\n\nKreitman, M. (1983). Nucleotide polymorphism at the alcohol dehydrogenase locus of Drosophila melanogaster. Nature, 304(5925), 412. https://doi.org/10.1038/304412a0\n\n\nLi, H., & Durbin, R. (2011). Inference of human population history from individual whole-genome sequences. Nature, 475(7357), 493–496. https://doi.org/10.1038/nature10231\n\n\nWetterstrand, KA. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP). www.genome.gov/sequencingcostsdata"
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     "text": "Describing DNA variation - nucleotide diversity\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n\n\n\n\nT\nT\nA\nC\nA\nA\nT\nC\nC\nG\nA\nT\nC\nG\nT\n\n\nT\nT\nA\nC\nG\nA\nT\nG\nC\nG\nC\nT\nC\nG\nT\n\n\nT\nC\nA\nC\nA\nA\nT\nG\nC\nG\nA\nT\nG\nG\nA\n\n\nT\nT\nA\nC\nG\nA\nT\nG\nC\nG\nC\nT\nC\nG\nT\n\n\n\n*\n\n\n*\n\n\n*\n\n\n*\n\n*\n\n*\n\n\n\n\n\n\n\n\nNucleotide diversity \\(\\pi\\)\nThe nucleotide diversity is the sum of site heterozygosities:\n\\[\n\\pi = \\sum_{j=1}^S h_j\n\\]\nwhere \\(S\\) is the number of segregating sites\n\n\n\n\nCalculate the nucleotide diversity\n\n\nObservation: \\(h_i\\) either 1/2 or 2/3 (for sites with \\(p_{major}=p_{minor}\\)).\n\n\n\n\\[\n\\pi = \\frac{1}{2} + \\frac{2}{3} + \\frac{1}{2} + \\frac{2}{3} + \\frac{1}{2} + \\frac{1}{2} = 3\\frac{1}{3}\n\\]\n\n\n\nOften we provide \\(\\pi\\) per site:\n\\[\n\\pi = 3.33/15 = 0.222\n\\]"
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     "title": "Population genetics",
-    "section": "Equilibrium state of alleles in absence of evolutionary forces",
-    "text": "Equilibrium state of alleles in absence of evolutionary forces\nThe Hardy-Weinberg equilibrium\nFor a locus, let \\(A_1\\) and \\(A_2\\) be two different alleles and let \\(p\\) be the frequency of the \\(A_1\\) allele and \\(q=1-p\\) the frequency of the \\(A_2\\) allele. In the absence of mutation, drift, migration, and other evolutionary processes, the equilibrium state is given by the Hardy-Weinberg equilibrium (HWE).\n\n\n\nGenotype:\n\\(A_1A_1\\)\n\\(A_1A_2\\)\n\\(A_2A_2\\)\n\n\nFrequency:\n\\(p^2\\)\n\\(2pq\\)\n\\(q^2\\)\n\n\n\nImportantly, under the HWE assumption, we can calculate allele frequencies from genotype frequencies and vice versa:\n\\[\np = p^2 + pq\\\\\nq = q^2 + pq\n\\]\n\nSegway: apart from describing the variation we want to model how allele frequencies change in time. As (Gillespie, 2004) points out, measuring allele frequencies is easy, measuring their change, however, is not"
+    "section": "In absence of evolutionary forces alleles are in equilibrium",
+    "text": "In absence of evolutionary forces alleles are in equilibrium\nThe Hardy-Weinberg equilibrium\nFor a locus, let \\(A\\) and \\(a\\) be two different alleles and let \\(p\\) be the frequency of the \\(A\\) allele and \\(q=1-p\\) the frequency of the \\(a\\) allele. In the absence of mutation, drift, migration, and other evolutionary processes, the equilibrium state is given by the Hardy-Weinberg equilibrium (HWE).\n\n\n\nGenotype:\n\\(AA\\)\n\\(Aa\\)\n\\(aa\\)\n\n\nFrequency:\n\\(p^2\\)\n\\(2pq\\)\n\\(q^2\\)\n\n\n\nImportantly, under the HWE assumption, we can calculate allele frequencies from genotype frequencies and vice versa:\n\\[\np = p^2 + pq\\\\\nq = q^2 + pq\n\\]\n\nSegue: apart from describing the variation we want to model how allele frequencies change in time. As (Gillespie, 2004, preface p. xi) points out, “While genotype frequencies are easily measured, their change is not”\n\n\nSegue: describing variation fine, but where does it come from and how does it change?\nIf assumptions of HWE hold, we have no change of variation. However, we want to look at change of variation and disentangle the forces that imposes change"
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+    "title": "Population genetics",
+    "section": "Natural populations do mate randomly?",
+    "text": "Natural populations do mate randomly?\n\nFigure 1: Hardy-Weinberg proportions in 10,000 SNPs on chromosome 22 from three populations based on 1000 genomes data. Illustration inspired by cooplab (2011)\n\nSee https://stackoverflow.com/questions/26587940/ggplot2-different-legend-symbols-for-points-and-lines for legend customization."
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+    "title": "Population genetics",
+    "section": "The obsession of population genetics",
+    "text": "The obsession of population genetics\nPopulation genetics is about (Gillespie, 2004)\n\ndescribing the genetic structure of populations\nconstructing theories on the forces that influence genetic variation\n\n\nQuestions to ponder:\n\nwhy does variation look the way it does?\nhow is variation maintained?\nhow does variation change over time (\\(\\Delta p\\))?\nwhat forces shape the genetic structure of populations?\n\n\n\nFrom HWE: we want to look a the creation, maintenance and loss of variation and what forces affect it\nGoal: describe theory behind evolving populations. This has been an obsession for a long time, even before DNA was known -&gt; back to basics"
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     "section": "Wright-Fisher model",
-    "text": "Wright-Fisher model\n\n\nModel of evolution that describes sampling of alleles in a population under the following assumptions (Hein et al., 2005):\n\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\n\n\n\n\n\n\nSee Hein et al. (2004) for more assumptions."
+    "text": "Wright-Fisher model\n\n\nModel of populations that describes genealogical relationships of genes (chromosomes) in a population under the following assumptions (Hein et al., 2005):\n\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\n\n\n\n\n\n\nSee Hein et al. (2004) for more assumptions. The second assumption means we can use 2N chromosome interchangeably for haploid (n=2N) and diploid (n=N) populations."
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     "section": "Wright-Fisher model",
-    "text": "Wright-Fisher model\n\n\nModel of evolution that describes sampling of alleles in a population under the following assumptions (Hein et al., 2005):\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\nAlgorithm\n\nSetup starting population at time zero"
+    "text": "Wright-Fisher model\n\n\nModel of populations that describes genealogical relationships of genes (chromosomes) in a population under the following assumptions (Hein et al., 2005):\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\nAlgorithm\n\nSetup starting population at time zero"
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     "section": "Wright-Fisher model",
-    "text": "Wright-Fisher model\n\n\nModel of evolution that describes sampling of alleles in a population under the following assumptions (Hein et al., 2005):\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\nAlgorithm\n\nSetup starting population at time zero\nAdd offspring (same size) at time one"
+    "text": "Wright-Fisher model\n\n\nModel of populations that describes genealogical relationships of genes (chromosomes) in a population under the following assumptions (Hein et al., 2005):\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\nAlgorithm\n\nSetup starting population at time zero\nAdd offspring (same size) at time one"
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-    "text": "Wright-Fisher model\n\n\nModel of evolution that describes sampling of alleles in a population under the following assumptions (Hein et al., 2005):\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\nAlgorithm\n\nSetup starting population at time zero\nAdd offspring (same size) at time one\nSelect parents to offspring at random"
+    "text": "Wright-Fisher model\n\n\nModel of populations that describes genealogical relationships of genes (chromosomes) in a population under the following assumptions (Hein et al., 2005):\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\nAlgorithm\n\nSetup starting population at time zero\nAdd offspring (same size) at time one\nSelect parents to offspring at random"
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-    "text": "Wright-Fisher model\n\n\nModel of evolution that describes sampling of alleles in a population under the following assumptions (Hein et al., 2005):\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\nAlgorithm\n\nSetup starting population at time zero\nAdd offspring (same size) at time one\nSelect parents to offspring at random"
+    "text": "Wright-Fisher model\n\n\nModel of populations that describes genealogical relationships of genes (chromosomes) in a population under the following assumptions (Hein et al., 2005):\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\nAlgorithm\n\nSetup starting population at time zero\nAdd offspring (same size) at time one\nSelect parents to offspring at random"
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     "section": "Wright-Fisher model",
-    "text": "Wright-Fisher model\n\n\nModel of evolution that describes sampling of alleles in a population under the following assumptions (Hein et al., 2005):\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\nAlgorithm\n\nSetup starting population at time zero\nAdd offspring (same size) at time one\nSelect parents to offspring at random"
+    "text": "Wright-Fisher model\n\n\nModel of populations that describes genealogical relationships of genes (chromosomes) in a population under the following assumptions (Hein et al., 2005):\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\nAlgorithm\n\nSetup starting population at time zero\nAdd offspring (same size) at time one\nSelect parents to offspring at random"
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     "section": "Wright-Fisher model",
-    "text": "Wright-Fisher model\n\n\nModel of evolution that describes sampling of alleles in a population under the following assumptions (Hein et al., 2005):\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\nAlgorithm\n\nSetup starting population at time zero\nAdd offspring (same size) at time one\nSelect parents to offspring at random"
+    "text": "Wright-Fisher model\n\n\nModel of populations that describes genealogical relationships of genes (chromosomes) in a population under the following assumptions (Hein et al., 2005):\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\nAlgorithm\n\nSetup starting population at time zero\nAdd offspring (same size) at time one\nSelect parents to offspring at random"
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     "section": "Wright-Fisher model",
-    "text": "Wright-Fisher model\n\n\nModel of evolution that describes sampling of alleles in a population under the following assumptions (Hein et al., 2005):\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\nAlgorithm\n\nSetup starting population at time zero\nAdd offspring (same size) at time one\nSelect parents to offspring at random"
+    "text": "Wright-Fisher model\n\n\nModel of populations that describes genealogical relationships of genes (chromosomes) in a population under the following assumptions (Hein et al., 2005):\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\nAlgorithm\n\nSetup starting population at time zero\nAdd offspring (same size) at time one\nSelect parents to offspring at random"
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     "title": "Population genetics",
     "section": "Wright-Fisher model",
-    "text": "Wright-Fisher model\n\n\nModel of evolution that describes sampling of alleles in a population under the following assumptions (Hein et al., 2005):\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\nAlgorithm\n\nSetup starting population at time zero\nAdd offspring (same size) at time one\nSelect parents to offspring at random"
+    "text": "Wright-Fisher model\n\n\nModel of populations that describes genealogical relationships of genes (chromosomes) in a population under the following assumptions (Hein et al., 2005):\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\nAlgorithm\n\nSetup starting population at time zero\nAdd offspring (same size) at time one\nSelect parents to offspring at random"
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     "title": "Population genetics",
     "section": "Wright-Fisher model",
-    "text": "Wright-Fisher model\n\n\nModel of evolution that describes sampling of alleles in a population under the following assumptions (Hein et al., 2005):\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\nAlgorithm\n\nSetup starting population at time zero\nAdd offspring (same size) at time one\nSelect parents to offspring at random"
+    "text": "Wright-Fisher model\n\n\nModel of populations that describes genealogical relationships of genes (chromosomes) in a population under the following assumptions (Hein et al., 2005):\n\ndiscrete and non-overlapping generations\nhaploid individuals or two subpopulations (males and females)\nconstant population size\nall individuals are equally fit\npopulation has no geographical or social structure\nno recombination\n\nAlgorithm\n\nSetup starting population at time zero\nAdd offspring (same size) at time one\nSelect parents to offspring at random"
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     "section": "Wright-Fisher model",
-    "text": "Wright-Fisher model\n\nFigure 1: Wright-Fisher model\nSee https://mikeyharper.uk/animated-plots-with-r/"
+    "text": "Wright-Fisher model\n\nFigure 2: Wright-Fisher model\nSee https://mikeyharper.uk/animated-plots-with-r/"
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   {
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-    "text": "Wright-Fisher model\n\n\n\n\n\n\nFigure 2: WF model indicating time direction from past (top) to present (bottom).\n\n\n\n\n\n\n\n\n\n\n\nFigure 3: WF model tracing the genealogies of three extant genes"
+    "text": "Wright-Fisher model\n\n\n\n\n\n\nFigure 3: WF model indicating time direction from past (top) to present (bottom).\n\n\n\n\n\n\n\n\n\n\n\nFigure 4: WF model tracing the genealogies of three extant genes"
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+    "title": "Population genetics",
+    "section": "The Wright-Fisher sampling model",
+    "text": "The Wright-Fisher sampling model\nLet’s formalise the sampling process of the Wright-Fisher model1. We assume\n\na single locus in a haploid population of size \\(2N\\) (or diploid of size \\(N\\) when random mationg)\nno mutation and selection\ndiscrete generations\n\nEach generation we sample \\(2N\\) new chromosomes from the previous generation. The probability of choosing a chromosome \\(v\\) is \\(1/2N\\) (coin flip with probability of succes \\(1/2N\\)). Since the trials are independent, and we perform \\(2N\\) trials, the number of offspring of a given chromosome \\(v\\) is binomially distributed \\(\\mathrm{Bin}(m, p)\\), with parameters \\(m=2N\\) and probability of success \\(p=\\frac{1}{2N}\\):\n\\[\nP(v=k) = {2N\\choose k}\\left( \\frac{1}{2N} \\right)^k\n\\left(1 - \\frac{1}{2N} \\right)^{(2N - k)}\n\\]\nFor a more extensive treatment, see Hermisson (2017) or Hein et al. (2005)"
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+    "href": "slides/foundations/index.html#properties-of-wright-fisher-sampling",
+    "title": "Population genetics",
+    "section": "Properties of Wright-Fisher sampling",
+    "text": "Properties of Wright-Fisher sampling\n\\[\nE(v) = mp = 2N\\frac{1}{2N} = 1\n\\]\n\nPoisson approximation: when \\(2N\\) large\n\\[\nP(v=k) \\approx \\frac{1}{k!}e^{-1}\n\\]\nSince \\(P(v=0) = e^{-1} \\approx 0.37\\), a fraction 0.37 of genes lack descendants\n\n\nProbability that parents are identical: pick a parent \\(v\\), then a second parent \\(u\\). Then \\(P(v=u)=\\frac{1}{2N}\\), and \\(P(v\\neq u)=1-\\frac{1}{2N}\\).\n\n\nTime for two chromosomes \\(u\\) and \\(v\\) to find common ancestor distributed as \\((1 - \\frac{1}{2N})^{j-1}\\frac{1}{2N}\\) (\\(j-1\\) failures followed by success). This is the geometric distribution \\(Ge(p)\\), with parameter \\(p=\\frac{1}{2N}\\), and expected value \\(\\frac{1}{p}={2N}\\). That is, the expected time for two chromosomes to find a common ancester (i.e., coalesce) is \\(2N\\) generations.\n\n\nSince a large fraction of genes lack descendants, very quickly (compared to 2N) a population will descend from a small proportion of genes.\nDerivation of average time to having same parent (i.e., coalescence) requires knowledge of geometric distribution. The probability that the genes find common parent \\(j\\) generations ago is\n\\[\n(1-\\frac{1}{2N})^{(j-1)}\\frac{1}{2N}\n\\]\ndue to independence between generations. This is the geometric distribution \\(Ge(p)\\) with \\(p=1/2N\\), which has expected value \\(1/p\\), i.e., 2N in this case."
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     "title": "Population genetics",
     "section": "Genetic drift with alleles",
-    "text": "Genetic drift with alleles\n\n\n\n\n\n\n\n\n\n\n\n\n\nWright-Fisher model showing the evolution of population of 10 genes over 16 generations. Allele variants are shown in white and black. Starting frequency black variant is 0.3."
+    "text": "Genetic drift with alleles\n\n\n\n\n\n\n\n\n\n\n\n\n\nWright-Fisher model showing the evolution of population of 10 genes over 16 generations. Allele variants are shown in white and black. Starting frequency black variant is 0.3.\n\n\nEnter allelic variants; we are looking at two variants\nImportant: point out that the fate of any allele under drift is to be lost or fixed, which ultimately means variation is lost with drift"
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     "title": "Population genetics",
-    "section": "The Wright-Fisher sampling model",
-    "text": "The Wright-Fisher sampling model\nLet’s formalise the sampling process of the Wright-Fisher model1\nWe assume\n\na single locus in a haploid population of size N\nno mutation and selection\ndiscrete generations\n\nEach generation we sample N new individuals from the previous generation, which is equivalent to multinomial sampling with replacement. Then, the number of offspring of a given parent i is binomially distributed, with probability of success \\(p=\\frac{1}{N}\\):\n\\[\n\\mathrm{Pr}(x) = {N\\choose x}\\left( \\frac{1}{N} \\right)^x\n\\left(1 - \\frac{1}{N} \\right)^{(N - x)}\n\\]\nUnder the assumption of random mating, a diploid population of size \\(N\\) can be described by a haploid population of size \\(2N\\)\nFor a more extensive treatment, see (Hermisson, 2017)"
+    "section": "Sampling model again",
+    "text": "Sampling model again\nWe assume two gene variants \\(A\\), \\(a\\), each with \\(i\\) and \\(j=2N-i\\) copies in generation \\(t\\). Let \\(p_t=i/2N\\) be the frequency of \\(A\\) in generation \\(t\\), and \\(q_t=1-p_t\\) the frequency of \\(a\\).\nThe sampling model for next generation \\(t+1\\) is then\n\\[\np_{t+1} = P(v_A=k) = {2N\\choose k}\\left( \\frac{i}{2N} \\right)^k\n\\left(1 - \\frac{i}{2N} \\right)^{(2N - k)} = \\mathsf{Bin}(2N, \\frac{i}{2N})\n\\]\n\nNB: we here slightly change notation, with \\(p_{t+1} = P(X=k)\\) p"
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     "section": "Genetic drift",
-    "text": "Genetic drift\n\n\nTo capture dynamics, follow allele frequency trajectory as function of time.\n\n\n##' Wright Fisher model - follow allele frequency distribution\n##'\n##' @param p0 Starting frequency\n##' @param n Population size\n##' @param generations Number of generations to simulate\n##'\nwright_fisher &lt;- function(p0, n, generations) {\n    x &lt;- vector(mode = \"numeric\", length = generations)\n    x[1] &lt;- p0\n    for (i in seq(2, length(x))) {\n        x[i] &lt;- rbinom(1, size = n, prob = x[i - 1])/n\n    }\n    x\n}\n\n\n\nNote dependency of “jumpyness” (variance) on population size N\n\n\n\n\n\n\nFigure 4: - Genetic drift for different population sizes, - starting frequency \\(p_0\\)=0.5\n\n\n\n\n\n\n\n\nClassic graph of allele: start at say 0.2 and drift with different Ne. Point out that variation becomes smaller with large Ne, but that eventually, an allele is fixed or lost (formula).\nMention neutral here as this is how alleles behave under drift: like gas particle diffusing up and down\nSee alternative way of visualizing variation faceting on initial frequency (showing P(fixation)) and population size (showing less variance))\nhttps://stephens999.github.io/fiveMinuteStats/wright_fisher_model.html"
+    "text": "Genetic drift\n\n\nTo capture dynamics, follow allele frequency trajectory as function of time.\n\n\n##' Wright Fisher model - follow allele frequency distribution\n##'\n##' @param p0 Starting frequency\n##' @param n Population size\n##' @param generations Number of generations to simulate\n##'\nwright_fisher &lt;- function(p0, n, generations) {\n    x &lt;- vector(mode = \"numeric\", length = generations)\n    x[1] &lt;- p0\n    for (i in seq(2, length(x))) {\n        x[i] &lt;- rbinom(1, size = n, prob = x[i - 1])/n\n    }\n    x\n}\n\n\n\nNote dependency of variance on population size 2N\n\n\n\n\n\n\nFigure 5: Genetic drift for different population sizes, starting frequency \\(p_0\\)=0.5\n\n\n\n\n\n\n\n\nMention neutral here as this is how alleles behave under drift: like gas particle diffusing up and down. Allele is eventually either fixed or lost."
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     "section": "Genetic drift",
-    "text": "Genetic drift\n\n\n\n\n\n\n\nFigure 5: Genetic drift for different combinations of starting frequency and population size for n=50 repetitions per parameter combination. Note how variation and time to fixation depends on population size and starting frequency.\n\n\n\n\n\n\n\nProbability of fixation \\(\\propto p_0\\)\n\n\n\nSimplifying assumptions -&gt; rate of drift \\(propto \\frac{1}{N}\\) (Leffler et al., 2012)\nIn reality population fluctuates and therefore Ne is substituted for N. Larger Ne -&gt; smaller fluctuation in allele frequency (as evidenced in plots) -&gt; maintain larger genetic diversity"
+    "text": "Genetic drift\n\n\n\n\n\n\n\nFigure 6: Genetic drift for different combinations of starting frequency and population size for n=50 repetitions per parameter combination. Note how variation and time to fixation depends on population size and starting frequency.\n\n\n\n\n\n\n\n\n\nfate of allele: fixation or loss \\(\\rightarrow\\) eventually loss of variation\nprobability of fixation \\(\\pi(p)=p\\), where \\(p\\) is the current frequency\nrate of drift (loss of variation) \\(\\propto \\frac{1}{2N}\\)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMathematical treatment of drift can become complicated: easier to study dynamics of heterozygosity\n\n\nSimplifying assumptions -&gt; rate of drift \\(propto \\frac{1}{N}\\) (Leffler et al., 2012)\nIn reality population fluctuates and therefore Ne is substituted for N. Larger Ne -&gt; smaller fluctuation in allele frequency (as evidenced in plots) -&gt; maintain larger genetic diversity"
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     "title": "Population genetics",
     "section": "Heterozygosity dynamics",
-    "text": "Heterozygosity dynamics\n\n\n\n\n\n\nFigure 6: Illustration of identity by descent and state.\n\n\n\n\n\n\nLet \\(\\mathcal{H}_t\\) be the probability that two alleles are identical by state. The time course evolution of \\(\\mathcal{H}_t\\) in a randomly mating population consisting of \\(N\\) diploid hermaphroditic individuals is\n\\[\n\\mathcal{H}_t = \\mathcal{H}_0 \\left( 1 - \\frac{1}{2N} \\right)^t\n\\]\nImportant consequence: heterozygosity in WF population lost at rate \\(1/2N\\).\n\n\n\nMotivation: mathematical description of genetic drift complicated for populations with more than one individual. Easier to study the evolution of heterozygosity."
+    "text": "Heterozygosity dynamics\n\n\n\n\n\n\nFigure 7: Illustration of identity by descent (IBD) and state (IBS). Alleles in generation \\(n\\) are IBD but not IBS.\n\n\n\n\n\n\nLet \\(\\mathcal{H}_t\\) be the probability that two alleles are different by state. The time course evolution of \\(\\mathcal{H}_t\\) in a randomly mating population consisting of \\(N\\) diploid hermaphroditic individuals is\n\\[\n\\mathcal{H}_t = \\mathcal{H}_0 \\left( 1 - \\frac{1}{2N} \\right)^t\n\\]\nImportant consequence: heterozygosity in WF population lost at rate \\(1/2N\\).\n\n\n\nAlleles in generation \\(n\\) are IDB but not IBS\nMotivation: mathematical description of genetic drift complicated for populations with more than one individual. Easier to study the evolution of heterozygosity."
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     "title": "Population genetics",
     "section": "Heterozygosity dynamics",
-    "text": "Heterozygosity dynamics\n\n\n\n\n\n\nFigure 7: Plot of \\(\\mathcal{H}_t\\).\n\n\n\n\n\n\n\n\n\n\nFigure 8: Heterozygosity in black-footed ferret (Wisely et al., 2002)"
+    "text": "Heterozygosity dynamics\n\n\n\n\n\n\nFigure 8: Plot of \\(\\mathcal{H}_t\\) illustrating how loss depends on population size\n\n\n\n\n\n\n\n\n\n\n\nFigure 9: Heterozygosity in black-footed ferret (Wisely et al., 2002)"
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     "title": "Population genetics",
     "section": "Effective population size",
-    "text": "Effective population size"
+    "text": "Effective population size\nAssumptions underlying Wright-Fisher model seldom fulfilled for natural populations. In particular\n\nnon-random mating (populations structured)\nfluctuations of population census size\n\nTherefore, magnitude of drift experienced by a population different from that predicted by population size\nTechnically correct definition (but see Waples (2022)):\n\n\\(N_e\\) is the size of an ideal population that would experience the same rate of genetic drift as the population in question.\n\n\nideal population: discrete generations with random mating, no evolutionary forces besides drift, no selective advantages"
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     "section": "Mutation",
-    "text": "Mutation\n\n\nGenetic drift “moves” frequencies to the point that variation is lost via allele fixation or loss. New variation is introduced through mutation. We typically assume mutations are described by a Poisson process with rate \\(\\mu\\) (per generation).\nThe mutation rate is denoted \\(\\mu\\), and the population scaled mutation rate is \\(2N_e\\mu\\) for haploid populations, \\(4N_e\\mu\\) for diploid, where \\(N_e\\) is the effective population size.\nThe mutation - drift balance is when the diversity lost due to drift equals the diversity gained due to mutation.\n\n\n\n\n\nFigure 9: Variation is introduced by mutations (black) at rate \\(\\mu=1e^{-4}\\) and is occasionally lost through genetic drift."
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-    "title": "Population genetics",
-    "section": "Tracing the evolution of mutations",
-    "text": "Tracing the evolution of mutations\n\n\n\n\n\n\nFigure 10: Different mutations suffer different fates. Most mutations are lost in a couple of generations. Mutant alleles are colored black and their genealogies are highlighted with thicker edges."
+    "text": "Mutation\n\n\nGenetic drift “moves” frequencies to the point that variation is lost via allele fixation or loss. New variation is introduced through mutation. We typically assume mutations are described by a Poisson process with rate \\(\\mu\\) (per generation).\n\nThe mutation rate is denoted \\(\\mu\\), and the population scaled mutation rate is \\(2N_e\\mu\\) for haploid populations, \\(4N_e\\mu\\) for diploid, where \\(N_e\\) is the effective population size.\n\n\nThe mutation - drift balance is when the diversity lost due to drift equals the diversity gained due to mutation.\n\n\n\n\n\n\n\nFigure 10: Variation is introduced by mutations (black) at rate \\(\\mu=1e^{-4}\\) and is occasionally lost through genetic drift.\n\n\n\n\n\n\n\n\n\nSegue: so if drift removes variation, what introduces it? Mutation!"
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-    "text": "Selection\n\\[\n\\begin{matrix}\n\\mathrm{Genotype} & A_1A_1 & A_1A_2 & A_2A_2 \\\\\n\\mathrm{Fitnesses} & w_{11} & w_{12} & w_{22}\\\\\n\\mathrm{Observations} & 1.5 & 1.1 & 1.0 \\\\\n\\mathrm{Relative\\ fitness} & 1 & 1-hs & 1-s\\\\\n\\mathrm{Normalized} & 1 & 11 / 15 & 10 / 15\n\\end{matrix}\n\\]\nRelative fitness.\nDefine s, positive and negative selection"
+    "text": "Models of mutation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nModel B: mainly for repeats"
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     "title": "Population genetics",
-    "section": "Direct selection",
-    "text": "Direct selection\nd_N/d_S"
+    "section": "Tracing the evolution of mutations",
+    "text": "Tracing the evolution of mutations\n\n\n\n\n\n\nFigure 11: Different mutations suffer different fates. Most mutations are lost in a couple of generations. Mutant alleles are colored black and their genealogies are highlighted with thicker edges.\n\n\n\n\n\n\n\nObservation: most mutations are in fact lost\n\n\nRecall: fixation probability \\(\\pi(p)=p\\)\n\n\n\n::::\n\nDifference to previous figure: here we highlight the genealogies"
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     "title": "Population genetics",
-    "section": "Selective sweeps",
-    "text": "Selective sweeps\nShow WF-model with sweep"
+    "section": "Mutation drift balance",
+    "text": "Mutation drift balance\nDrift removes variation. Mutation reintroduces it. At equilibrium the change in variation by definition is 0. In terms of \\(\\mathcal{H}_t\\) (the probability that two alleles are not identical by state), \\(\\Delta\\mathcal{H}=0\\).\nOne can show1 the classical formula that the equilibrium heterozysity value is\n\\[\n\\hat{\\mathcal{H}} = \\frac{4N_e\\mu}{1 + 4N_e\\mu}\n\\]\n\n\\(\\mu\\) is often assumed known, and heterozygosity is easily calculated from data, which provides a way of estimating \\(N_e\\).\n\nsee (Gillespie, 2004, p. 29 – 31) for a concise treatement"
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     "title": "Population genetics",
-    "section": "Two-locus dynamics",
-    "text": "Two-locus dynamics\nGraph focal locus and flanking loci (Charlesworth figure)\n\nLinkage disequilibrium and recombination (each probably need separate treatment)\nContrast linked selection with direct selection\nGenetic draft / background selection"
+    "section": "Molecular evolution and rate of substitution",
+    "text": "Molecular evolution and rate of substitution\nMutation enters populations and may be fixed by drift. Therefore, with time there will be fixed differences, or substitions (typically in the evolution of species) between populations, or species. In molecular evolution, the substition rate, \\(\\rho\\), is the most interesting quantity.\n\nThe total number of new mutations in every generation is \\(2N\\mu\\) (total number of gametes times mutation rate)\n\n\nNew mutations fix at a rate \\(1/2N\\)\n\n\nTherefore, the average rate of substitution, \\(\\rho\\), is \\(2N\\mu\\times1/2N\\), or\n\\[\n\\rho=\\mu\n\\]\nwhich is independent of population size!\n\n\nOn \\(2N\\mu\\): the larger the population, the larger the number of mutations that can fix\nStress importance of results and how it defies logic - it is independent of population size\nIf \\(4N\\mu&gt;&gt;0\\) no new mutations can fix - no explicit mention of DNA sequence -&gt; infinite sites model"
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     "title": "Population genetics",
-    "section": "Soft sweep",
-    "text": "Soft sweep"
+    "section": "Selection",
+    "text": "Selection\n\nMuch confusion exists in the literature regarding how various types of selection are defined, in particular because some of the terminology is used slightly differently within different scientific communities (Nielsen, 2005)\n\n\\[\n\\begin{matrix}\n\\mathrm{Genotype} & aa & aA & AA \\\\\n\\mathrm{Frequency\\ in\\ newborns} & p^2 & 2pq & q^2\\\\\n\\mathrm{Viability} & w_{aa} & w_{aA} & w_{AA}\\\\\n\\mathrm{Frequency\\ after\\ selection} & p^2w_{aa} / \\bar{w} &\n    2pqw_{Aa} / \\bar{w} & q^2w_{AA} / \\bar{w} \\\\\n\\mathrm{Relative\\ fitness} & 1 & 1-hs & 1-s\\\\\n\\end{matrix}\n\\]\nwhere \\(\\bar{w} = p^2w_{aa} + 2pqw_{Aa} + q^2w_{AA}\\) is the mean fitness\n\n\n\n\n\\(h=0\\)\n\\(a\\) dominant, \\(A\\) recessive\n\n\n\\(h=1\\)\n\\(A\\) dominant, \\(a\\) recessive\n\n\n\\(0&lt;h&lt;1\\)\nincomplete dominance\n\n\n\\(h&lt;0\\)\noverdominance (heterozygote advantage)\n\n\n\\(h&gt;1\\)\nunderdominance"
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     "title": "Population genetics",
-    "section": "Neutral theory",
-    "text": "Neutral theory\nMove right after drift?\n\nHahn: \\(\\mu = \\nu*f_0\\)\nwhy population size matters\nFig 4.2, Eq 4.2 from Lynch\n\n\nMutation - drift balance. Simplifying assumptions -&gt; rate of drift \\(propto \\frac{1}{N}\\) (Leffler et al., 2012)\nIn reality population fluctuates and therefore Ne is substituted for N. Larger Ne -&gt; smaller fluctuation in allele frequency (as evidenced in plots) -&gt; maintain larger genetic diversity\nNeutral diversity patterns depend not only on drift but also on rate at which variation changes at linked sites (Leffler et al., 2012)"
+    "section": "Mean fitness of a population",
+    "text": "Mean fitness of a population\n\nFigure 12: Mean fitness of the population as a function of \\(p\\) for \\(s=0.1\\) and \\(h=-0.5\\), \\(0.5\\), and \\(1.5\\)"
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     "title": "Population genetics",
-    "section": "Nearly neutral theory",
-    "text": "Nearly neutral theory\n\nDistribution of fitness effects?"
+    "section": "Selection and drift",
+    "text": "Selection and drift\n\n\n\n\n\n\nFigure 13: The fixation probability relative to the neutral probability of fixation under the assumption \\(s&lt;0.1\\).\n\n\n\n\n\n\n\n\n\n\nEven if a new mutation has, say, \\(s=0.01\\), it still needs to overcome effects of drift.\nIn figure, we assume \\(s&lt;0.1\\), which is almost always the case."
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     "title": "Population genetics",
-    "section": "Effect on genetic diversity",
-    "text": "Effect on genetic diversity\n\nMutation - drift balance. Mode of selection important:\n\nselection for adaptation at new mutations decreases diversity more than if at standing variation\nbalancing selection may increase variation\n\nRecombination\n\nlow recombination -&gt; lower diversity (less reshuffling of variation)\n\n\n\n\nPopulation genetics foundations\n\n\n\nCasillas, S., & Barbadilla, A. (2017). Molecular Population Genetics. Genetics, 205(3), 1003–1035. https://doi.org/10.1534/genetics.116.196493\n\n\nGillespie, J. H. (2004). Population Genetics: A Concise Guide (2nd edition). Johns Hopkins University Press.\n\n\nHahn, M. (2019). Molecular Population Genetics (First). Oxford University Press.\n\n\nHein, J., Schierup, M. H., & Wiuf, C. (2005). Gene genealogies, variation and evolution: A primer in coalescent theory. Oxford University Press. https://books.google.se/books?id=CCmLNAEACAAJ\n\n\nHein, J., Schierup, M., & Wiuf, C. (2004). Gene genealogies, variation and evolution. A primer in coalescent theory. In Systematic Biology - SYST BIOL (Vol. 54).\n\n\nHermisson, J. (2017). Mathematical population genetics. https://www.mabs.at/teaching/files/Lecture%20Notes%202017.pdf\n\n\nLeffler, E. M., Bullaughey, K., Matute, D. R., Meyer, W. K., Ségurel, L., Venkat, A., Andolfatto, P., & Przeworski, M. (2012). Revisiting an Old Riddle: What Determines Genetic Diversity Levels within Species? PLOS Biology, 10(9), e1001388. https://doi.org/10.1371/journal.pbio.1001388\n\n\nNei, M., & Kumar, S. (2000). Molecular Evolution and Phylogenetics. Oxford University Press.\n\n\nWisely, S. M., Buskirk, S. W., Fleming, M. A., McDonald, D. B., & Ostrander, E. A. (2002). Genetic Diversity and Fitness in Black-Footed Ferrets Before and During a Bottleneck. Journal of Heredity, 93(4), 231–237. https://doi.org/10.1093/jhered/93.4.231"
+    "section": "Bibliography",
+    "text": "Bibliography\n\n\nCasillas, S., & Barbadilla, A. (2017). Molecular Population Genetics. Genetics, 205(3), 1003–1035. https://doi.org/10.1534/genetics.116.196493\n\n\ncooplab. (2011). Population genetics course resources: Hardy-Weinberg Eq. In gcbias. https://gcbias.org/2011/10/13/population-genetics-course-resources-hardy-weinberg-eq/\n\n\nGillespie, J. H. (2004). Population Genetics: A Concise Guide (2nd edition). Johns Hopkins University Press.\n\n\nHahn, M. (2019). Molecular Population Genetics (First). Oxford University Press.\n\n\nHein, J., Schierup, M. H., & Wiuf, C. (2005). Gene genealogies, variation and evolution: A primer in coalescent theory. Oxford University Press. https://books.google.se/books?id=CCmLNAEACAAJ\n\n\nHein, J., Schierup, M., & Wiuf, C. (2004). Gene genealogies, variation and evolution. A primer in coalescent theory. In Systematic Biology - SYST BIOL (Vol. 54).\n\n\nHermisson, J. (2017). Mathematical population genetics. https://www.mabs.at/teaching/files/Lecture%20Notes%202017.pdf\n\n\nLeffler, E. M., Bullaughey, K., Matute, D. R., Meyer, W. K., Ségurel, L., Venkat, A., Andolfatto, P., & Przeworski, M. (2012). Revisiting an Old Riddle: What Determines Genetic Diversity Levels within Species? PLOS Biology, 10(9), e1001388. https://doi.org/10.1371/journal.pbio.1001388\n\n\nNei, M., & Kumar, S. (2000). Molecular Evolution and Phylogenetics. Oxford University Press.\n\n\nNielsen, R. (2005). Molecular Signatures of Natural Selection. Annual Review of Genetics, 39(1), 197–218. https://doi.org/10.1146/annurev.genet.39.073003.112420\n\n\nWaples, R. S. (2022). What Is Ne, Anyway? Journal of Heredity, 113(4), 371–379. https://doi.org/10.1093/jhered/esac023\n\n\nWisely, S. M., Buskirk, S. W., Fleming, M. A., McDonald, D. B., & Ostrander, E. A. (2002). Genetic Diversity and Fitness in Black-Footed Ferrets Before and During a Bottleneck. Journal of Heredity, 93(4), 231–237. https://doi.org/10.1093/jhered/93.4.231\n\n\n\n\nPopulation genetics foundations"
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-    "text": "References\n\n\nA genome atlas of european biodiversity. (n.d.). https://www.erga-biodiversity.eu. Retrieved November 3, 2022, from https://www.erga-biodiversity.eu\n\n\nAllendorf, F. W., Hohenlohe, P. A., & Luikart, G. (2010). Genomics and the future of conservation genetics. Nature Reviews Genetics, 11(10), 697–709. https://doi.org/10.1038/nrg2844\n\n\nBaird, N. A., Etter, P. D., Atwood, T. S., Currey, M. C., Shiver, A. L., Lewis, Z. A., Selker, E. U., Cresko, W. A., & Johnson, E. A. (2008). Rapid SNP Discovery and Genetic Mapping Using Sequenced RAD Markers. PLOS ONE, 3(10), e3376. https://doi.org/10.1371/journal.pone.0003376\n\n\nBegun, D. J., Holloway, A. K., Stevens, K., Hillier, L. W., Poh, Y.-P., Hahn, M. W., Nista, P. M., Jones, C. D., Kern, A. D., Dewey, C. N., Pachter, L., Myers, E., & Langley, C. H. (2007). Population Genomics: Whole-Genome Analysis of Polymorphism and Divergence in Drosophila simulans. PLOS Biology, 5(11), e310. https://doi.org/10.1371/journal.pbio.0050310\n\n\nBergeron, L. A., Besenbacher, S., Zheng, J., Li, P., Bertelsen, M. F., Quintard, B., Hoffman, J. I., Li, Z., St. Leger, J., Shao, C., Stiller, J., Gilbert, M. T. P., Schierup, M. H., & Zhang, G. (2023). Evolution of the germline mutation rate across vertebrates. Nature, 615(7951), 285–291. https://doi.org/10.1038/s41586-023-05752-y\n\n\nBerner, D. (2019). Allele Frequency Difference AFD to FST for Quantifying Genetic Population Differentiation. Genes, 10(4), 308. https://doi.org/10.3390/genes10040308\n\n\nBhatia, G., Patterson, N., Sankararaman, S., & Price, A. L. (2013). Estimating and interpreting FST: The impact of rare variants. Genome Research, 23(9), 1514–1521. https://doi.org/10.1101/gr.154831.113\n\n\nCasillas, S., & Barbadilla, A. (2017). Molecular Population Genetics. Genetics, 205(3), 1003–1035. https://doi.org/10.1534/genetics.116.196493\n\n\nChan, A. H., Jenkins, P. A., & Song, Y. S. (2012). Genome-Wide Fine-Scale Recombination Rate Variation in Drosophila melanogaster. PLOS Genetics, 8(12), e1003090. https://doi.org/10.1371/journal.pgen.1003090\n\n\nChang, C. C. (2020). Data Management and Summary Statistics with PLINK. In J. Y. Dutheil (Ed.), Statistical Population Genomics (pp. 49–65). Springer US. https://doi.org/10.1007/978-1-0716-0199-0_3\n\n\nCharlesworth, B., & Jensen, J. D. (2021). Effects of selection at linked sites on patterns of genetic variability. Annual Review of Ecology, Evolution, and Systematics, 52(1), 177–197. https://doi.org/10.1146/annurev-ecolsys-010621-044528\n\n\nCorbett-Detig, R. B., Hartl, D. L., & Sackton, T. B. (2015). Natural Selection Constrains Neutral Diversity across A Wide Range of Species. PLOS Biology, 13(4), e1002112. https://doi.org/10.1371/journal.pbio.1002112\n\n\nDanecek, P., Bonfield, J. K., Liddle, J., Marshall, J., Ohan, V., Pollard, M. O., Whitwham, A., Keane, T., McCarthy, S. A., Davies, R. M., & Li, H. (2021). Twelve years of SAMtools and BCFtools. GigaScience, 10(2), giab008. https://doi.org/10.1093/gigascience/giab008\n\n\nDavey, J. W., & Blaxter, M. L. (2010). RADSeq: Next-generation population genetics. Briefings in Functional Genomics, 9(5-6), 416–423. https://doi.org/10.1093/bfgp/elq031\n\n\nDavydov, E. V., Goode, D. L., Sirota, M., Cooper, G. M., Sidow, A., & Batzoglou, S. (2010). Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP++. PLOS Computational Biology, 6(12), e1001025. https://doi.org/10.1371/journal.pcbi.1001025\n\n\nDes Roches, S., Pendleton, L. H., Shapiro, B., & Palkovacs, E. P. (2021). Conserving intraspecific variation for nature’s contributions to people. Nature Ecology & Evolution, 5(5), 574–582. https://doi.org/10.1038/s41559-021-01403-5\n\n\nEllegren, H., & Galtier, N. (2016). Determinants of genetic diversity. Nature Reviews Genetics, 17(7), 422–433. https://doi.org/10.1038/nrg.2016.58\n\n\nExcoffier, L. (2004). Analysis of Population Subdivision. In Handbook of Statistical Genetics. John Wiley & Sons, Ltd. https://doi.org/10.1002/0470022620.bbc25\n\n\nFerretti, L., Ledda, A., Wiehe, T., Achaz, G., & Ramos-Onsins, S. E. (2017). Decomposing the Site Frequency Spectrum: The Impact of Tree Topology on Neutrality Tests. Genetics, 207(1), 229–240. https://doi.org/10.1534/genetics.116.188763\n\n\nFitzpatrick, M. C., & Keller, S. R. (2015). Ecological genomics meets community-level modelling of biodiversity: Mapping the genomic landscape of current and future environmental adaptation. Ecology Letters, 18(1), 1–16. https://doi.org/10.1111/ele.12376\n\n\nFoll, M., & Gaggiotti, O. (2008). A Genome-Scan Method to Identify Selected Loci Appropriate for Both Dominant and Codominant Markers: A Bayesian Perspective. 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Exploring Population Structure with Admixture Models and Principal Component Analysis. In J. Y. Dutheil (Ed.), Statistical Population Genomics (pp. 67–86). Springer US. https://doi.org/10.1007/978-1-0716-0199-0_4\n\n\nLou, R. N., Jacobs, A., Wilder, A. P., & Therkildsen, N. O. (2021). A beginner’s guide to low-coverage whole genome sequencing for population genomics. Molecular Ecology, 30(23), 5966–5993. https://doi.org/10.1111/mec.16077\n\n\nLuu, K., Bazin, E., & Blum, M. G. B. (2017). Pcadapt: An R package to perform genome scans for selection based on principal component analysis. Molecular Ecology Resources, 17(1), 67–77. https://doi.org/10.1111/1755-0998.12592\n\n\nMaier, R., Flegontov, P., Flegontova, O., Changmai, P., & Reich, D. (2022). On the limits of fitting complex models of population history to genetic data (p. 2022.05.08.491072). bioRxiv. https://doi.org/10.1101/2022.05.08.491072\n\n\nMaier, Robert. (2022). Paper notes. https://uqrmaie1.github.io/admixtools/articles/paper.html\n\n\nMary-Huard, T., & Balding, D. (2023). Fast and accurate joint inference of coancestry parameters for populations and/or individuals. PLOS Genetics, 19(1), e1010054. https://doi.org/10.1371/journal.pgen.1010054\n\n\nMcLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., Flicek, P., & Cunningham, F. (2016). The Ensembl Variant Effect Predictor. Genome Biology, 17(1), 122. https://doi.org/10.1186/s13059-016-0974-4\n\n\nMcLaren, W., Pritchard, B., Rios, D., Chen, Y., Flicek, P., & Cunningham, F. (2010). Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics, 26(16), 2069–2070. https://doi.org/10.1093/bioinformatics/btq330\n\n\nNei, M., & Kumar, S. (2000). Molecular Evolution and Phylogenetics. Oxford University Press.\n\n\nNielsen, R. (2005). Molecular Signatures of Natural Selection. Annual Review of Genetics, 39(1), 197–218. https://doi.org/10.1146/annurev.genet.39.073003.112420\n\n\nPatterson, N., Price, A. L., & Reich, D. (2006). Population Structure and Eigenanalysis. PLOS Genetics, 2(12), e190. https://doi.org/10.1371/journal.pgen.0020190\n\n\nPedersen, B. S., & Quinlan, A. R. (2018). Mosdepth: Quick coverage calculation for genomes and exomes. Bioinformatics, 34(5), 867–868. https://doi.org/10.1093/bioinformatics/btx699\n\n\nPeter, B. M. (2016). Admixture, Population Structure, and F-Statistics. Genetics, 202(4), 1485–1501. https://doi.org/10.1534/genetics.115.183913\n\n\nQuinlan, A. R., & Hall, I. M. (2010). BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6), 841–842. https://doi.org/10.1093/bioinformatics/btq033\n\n\nRomiguier, J., Gayral, P., Ballenghien, M., Bernard, A., Cahais, V., Chenuil, A., Chiari, Y., Dernat, R., Duret, L., Faivre, N., Loire, E., Lourenco, J. M., Nabholz, B., Roux, C., Tsagkogeorga, G., Weber, A. a.-T., Weinert, L. A., Belkhir, K., Bierne, N., … Galtier, N. (2014). Comparative population genomics in animals uncovers the determinants of genetic diversity. Nature, 515(7526), 261–263. https://doi.org/10.1038/nature13685\n\n\nSchlötterer, C., Tobler, R., Kofler, R., & Nolte, V. (2014). Sequencing pools of individuals - mining genome-wide polymorphism data without big funding. Nature Reviews. Genetics, 15(11), 749–763. https://doi.org/10.1038/nrg3803\n\n\nShen, W., Le, S., Li, Y., & Hu, F. (2016). SeqKit: A Cross-Platform and Ultrafast Toolkit for FASTA/Q File Manipulation. PLOS ONE, 11(10), e0163962. https://doi.org/10.1371/journal.pone.0163962\n\n\nTalla, V., Soler, L., Kawakami, T., Dincă, V., Vila, R., Friberg, M., Wiklund, C., & Backström, N. (2019). Dissecting the Effects of Selection and Mutation on Genetic Diversity in Three Wood White (Leptidea) Butterfly Species. Genome Biology and Evolution, 11(10), 2875–2886. https://doi.org/10.1093/gbe/evz212\n\n\nVertebrate Genomes Project. (n.d.). https://vertebrategenomesproject.org. Retrieved November 3, 2022, from https://vertebrategenomesproject.org\n\n\nWaples, R. S. (2022). What Is Ne, Anyway? Journal of Heredity, 113(4), 371–379. https://doi.org/10.1093/jhered/esac023\n\n\nWebster, Matthew, & Yuanzhen Liu. (2022). Genetic Variation in Mountain Bumblebees.\n\n\nWetterstrand, KA. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP). www.genome.gov/sequencingcostsdata\n\n\nWilli, Y., Kristensen, T. N., Sgrò, C. M., Weeks, A. R., Ørsted, M., & Hoffmann, A. A. (2022). Conservation genetics as a management tool: The five best-supported paradigms to assist the management of threatened species. Proceedings of the National Academy of Sciences, 119(1), e2105076119. https://doi.org/10.1073/pnas.2105076119\n\n\nWisely, S. M., Buskirk, S. W., Fleming, M. A., McDonald, D. B., & Ostrander, E. A. (2002). Genetic Diversity and Fitness in Black-Footed Ferrets Before and During a Bottleneck. Journal of Heredity, 93(4), 231–237. https://doi.org/10.1093/jhered/93.4.231"
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     "title": "Filtering of resequencing data",
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-    "text": "FIXME: text\nRegardless of how a raw variant call set has been produced, the calls will be of varying quality for a number of reasons. For high-coverage sequencing, the two most common are incompleteness of the reference sequence and misaligmnents in repetitive regions (Li, 2014). Low-coverage sequencing comes with its own biases, such as\n\n\n\n\n\n\nFigure 1: Coverage distributions for three hypothetical samples along with the cumulative coverage for all samples.\n\n\n\n\n\n\n\n\n\n\n\nFIXME\n\n\n\nDiscuss different types of relevant filters, e.g.\n\nfiltering on read depth, both high to eliminate collapsed repeats, and low for mappability issues. This is particularly important for incomplete assemblies\nalso briefly mention filtering on polymorphic sites\n\n\n\n\n\n\ncreate per sample, per population, and total depth of coverage profiles\ngenerate mask files for downstream processing\n\n\n\n\n\nmosdepth (Pedersen & Quinlan, 2018)\nsamtools (Danecek et al., 2021)\ncsvtk\nbedtools (Quinlan & Hall, 2010)\nseqkit (Shen et al., 2016)\n\n\n\n\nWe will be working with the Out of Africa dataset, consisting of 10 samples from the CEU, CHB, and YRI populations. The input consists of 10 bam files:\n\nls -lLh [CY]*.chr21.bam\n\n-rw-r--r-- 1 runner docker 7.8M Aug 20 16:22 CEU-1.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 6.3M Aug 20 16:22 CEU-2.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 4.8M Aug 20 16:22 CEU-3.ooa.chr21.bam\n-rw-r--r-- 1 runner docker  13M Aug 20 16:22 CEU-4.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 5.8M Aug 20 16:22 CHB-1.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 5.2M Aug 20 16:22 CHB-2.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 6.5M Aug 20 16:22 CHB-3.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 6.1M Aug 20 16:22 YRI-1.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 6.0M Aug 20 16:22 YRI-2.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 7.6M Aug 20 16:22 YRI-3.ooa.chr21.bam\n\n\nEach population consists of a collection – or set – of samples, and we will sometimes use the term sample set to refer to such a collection. For instance, the population (sample set) CHB consists of the set of samples CHB-1, CHB-2 and CHB-3.\n\n\n\nMapped reads provide information about how well a given genomic region has been represented during sequencing, and this information is usually summarized as the sequencing coverage. For any locus, this is equivalent to the number of reads mapping to that locus.\nSequencing coverage is typically not uniformly distributed over the reference. Reasons may vary but include uneven mapping coverage due to repeat regions, low coverage due to mis-assemblies, or coverage biases generated in the sequencing process. Importantly, both variable and monomorphic sites must be treated identically in the filtering process to eliminate biases between the two kinds of sites.\nIn this part, we will use mosdepth and bedtools to quickly generate depth of coverage profiles of mapped data. mosdepth is an ultra-fast command line tool for calculating coverage from a bam file. By default, it generates a summary of the global distribution, and per-base coverage in bed.gz format. We will be using the per-base coverage for filtering.\nAlternatively, mosdepth can also output results in a highly compressed format d4, which has been developed to handle the ever increasing size of resequencing projects. Files in d4 format can be processed with the d4-tools tool (Hou et al., 2021). For instance, d4tools view will display the coverage in bed format. We mention this in passing as it may be relevant when working with large genomes or sample sizes, but given the size of our sample data, we will be using bedtools from now on.\n\n\nWe start by calculating per-sample coverages with mosdepth. For downstream purposes, we need to save the size of the chromosomes we’re looking at, and for many applications, a fasta index file is sufficient.\n\nsamtools faidx ooa.chr21.ref.fa\n\nThe syntax to generate coverage information for a bam file is mosdepth &lt;prefix&gt; &lt;input file&gt;. Here, we add the -Q option to exclude reads with a mapping quality less than 20:\n\nmosdepth -Q 20 CEU-1 CEU-1.ooa.chr21.bam\n\nThe per-base coverage output file will be named CEU-1.per-base.bed.gz and can be viewed with bgzip:\n\nbgzip -c -d CEU-1.per-base.bed.gz | head -n 5\n\nchr21   0   7   0\nchr21   7   22  1\nchr21   22  45  2\nchr21   45  47  3\nchr21   47  54  4\n\n\nTo get an idea of what the coverage looks like over the chromsome, we can use bedtools and cvstk in a one-liner to generate a simple coverage plot (Figure 2)1\n\nbedtools intersect -a &lt;(bedtools makewindows -g ooa.chr21.ref.fa.fai -w 5000) \\\n      -b CEU-1.per-base.bed.gz -wa -wb | \\\n  bedtools groupby -i - -g 1,2,3 -c 7 -o mean | \\\n  csvtk plot -t line -x 2 -y 4 --point-size 0.01 --xlab Position \\\n      --ylab Coverage --width 9.0 --height 3.5 &gt; fig-plot-coverage.png\n\n\n\n\n\n\n\n\nFigure 2: Coverage for sample CEU-1 in 5kb windows. Experiment changing the window size (-w) parameter to change smoothing.\n\n\nApparently there are some high-coverage regions that could be associated with, e.g., collapsed repeat regions in the assembly. Let’s compile coverage results for all samples, using bash string manipulation to generate file prefix2\n\nfor f in [CY]*.chr21.bam; do\n  prefix=${f%.ooa.chr21.bam}\n  mosdepth -Q 20 $prefix $f\n  echo -e -n \"$prefix\\t\"\n  cat $prefix.mosdepth.summary.txt | grep total\ndone &gt; ALL.mosdepth.summary.txt\ncat ALL.mosdepth.summary.txt\n\nCEU-1   total   1000000 17917183    17.92   0   68\nCEU-2   total   1000000 14292139    14.29   0   67\nCEU-3   total   1000000 10428655    10.43   0   52\nCEU-4   total   1000000 30572047    30.57   0   115\nCHB-1   total   1000000 12830597    12.83   0   62\nCHB-2   total   1000000 11392259    11.39   0   52\nCHB-3   total   1000000 14835251    14.84   0   60\nYRI-1   total   1000000 13699799    13.70   0   63\nYRI-2   total   1000000 13437937    13.44   0   59\nYRI-3   total   1000000 17218621    17.22   0   69\n\n\nWe can calculate the total coverage with csvtk as follows:\n\ncsvtk summary -H -t ALL.mosdepth.summary.txt -f 5:sum\n\nto get the total coverage 156.63, which gives a hint at where the diploid coverage peak should be.\n\n\n\nWe can combine coverage intervals with bedtools unionbedg. We collect the bed file names and generate matching sample names to pass as arguments to option -names. Also, we include positions with no coverage (-empty) which requires the use of a genome file (option -g). The bed output is piped to bgzip which compresses the output, before finally indexing with tabix:\n\n\nBEDGZ=$(for f in *.per-base.bed.gz; do echo \"$f \"; done)\nSAMPLES=$(for f in $BEDGZ; do echo -e -n \"${f%.per-base.bed.gz} \"; done)\nbedtools unionbedg -header -names $SAMPLES -g ooa.chr21.ref.fa.fai -empty -i $BEDGZ | bgzip &gt; ALL.bg.gz\ntabix -f -p bed -S 1 ALL.bg.gz\n\n\nWe also need to combine coverages per populations CEU, CHB, and YRI. Using the previous command as a template, try to generate per population coverage files.\n\n\n\nCode\nfor pop in CEU CHB YRI; do\n  BEDGZ=$(for f in ${pop}*.per-base.bed.gz; do echo \"$f \"; done)\n  SAMPLES=$(for f in $BEDGZ; do echo -e -n \"${f%.per-base.bed.gz} \"; done)\n  bedtools unionbedg -header -names $SAMPLES -g ooa.chr21.ref.fa.fai -empty -i $BEDGZ | bgzip &gt; $pop.bed.gz\n  tabix -f -p bed -S 1 $pop.bed.gz\ndone\n\n\n\n\n\n\nSince we eventually want to filter on total coverage, we sum per sample coverages for each sample set with awk:\n\n\nbgzip -c -d ALL.bg.gz | awk -v FS=\"\\t\" -v OFS=\"\\t\" 'NR &gt; 1 {sum=0; for (i=4; i&lt;=NF; i++) sum+=$i; print $1, $2, $3, sum}' | bgzip &gt; ALL.sum.bed.gz\ntabix -f -p bed ALL.sum.bed.gz\n\n\nFor illustration, we plot the total coverage:\n\n\nbedtools intersect -a &lt;(bedtools makewindows -g ooa.chr21.ref.fa.fai -w 5000) \\\n    -b ALL.sum.bed.gz -wa -wb | \\\n  bedtools groupby -i - -g 1,2,3 -c 7 -o mean | \\\n  csvtk plot -t line -x 2 -y 4 --point-size 0.01 --xlab Position \\\n    --ylab Coverage --width 9.0 --height 3.5 &gt; fig-plot-total-coverage.png\n\n\n\n\n\n\n\n\n\nFigure 3: Total coverage in 5kb windows.\n\n\nIn order to define thresholds for subsequent filtering, we plot the proportion of the genome coverage versus depth of coverage (similar to k-mer plots in sequence assembly projects). In\n\n\n\n\n\n\n\n(a) Genome coverage\n\n\n\n\n\n\n\n(b) Cumulative genome coverage\n\n\n\nFigure 4: Genome coverage vs depth of coverage.\n\n\n\nIn Figure 4 a, a diploid peak is evident just below coverage X=150; we zoom in on that region to get a better view:\n\ncat ALL.sum.bed.csv | \\\n  csvtk plot line -x 1 -y 2 --point-size 0.01 \\\n    --xlab \"Depth of coverage (X)\" --ylab \"Genome coverage (kbp)\" \\\n    --width 9.0 --height 3.5 --x-min 90 --x-max 160\n\n\n\n\n\n\n\n(a) Genome coverage\n\n\nFigure 5: Genome coverage vs depth of coverage.\n\n\n\n(Lou et al., 2021) point out that appropriate thresholds depend on the data set, but as a general rule recommends a minimum depth threshold at &lt;0.8x average coverage, and a maximum depth threshold at mean coverage plus one or two standard deviations. Given the unrealistically homogenous coverage of our simulated data, we set thresholds based on Figure 5 to e.g., 100-160.\nWe then use these thresholds to generate a bed file containing regions that are accessible, i.e., have sufficient coverage for downstream analyses. We also calculate the number of bases that pass the filtering criteria.\n\n\nzcat ALL.sum.bed.gz | awk '{if (($4 &gt;= 100) && ($4 &lt;= 160)) print $0}' &gt; ALL.sum.depth.bed.gz\nbedtools genomecov -i ALL.sum.depth.bed.gz -g ooa.chr21.ref.fa.fai  | grep genome\n\ngenome  0   112885  1000000 0.112885\ngenome  1   887115  1000000 0.887115\n\n\n\nConsequently, 88.7% of the genome is accessible by depth.\n\n\n\n\n\n\nExercise\n\n\n\nGenerate coverage sums for sample sets CEU, CHB, and YRI, and from these determine coverage thresholds and apply the thresholds to generate bed files with accessible regions.\n\n\n\nCode\nfor pop in CEU CHB YRI; do\n  bgzip -c -d $pop.bed.gz | \\\n    awk -v FS=\"\\t\" -v OFS=\"\\t\" 'NR &gt; 1 {sum=0; for (i=4; i&lt;=NF; i++) sum+=$i; print $1, $2, $3, sum}' | \\\n    bgzip &gt; $pop.sum.bed.gz\n  tabix -f -p bed $pop.sum.bed.gz\n  zcat $pop.sum.bed.gz | \\\n    awk -v h=0 'BEGIN {OFS=\",\"; cumsum=0} {a[$4] += ($3-$2)/1000; if ($4 &gt; h) {h=$4}} END{for (i=0; i&lt;=h; i++) {if (a[i]) {cumsum+=a[i]} else {a[i]=0;} print i, a[i], cumsum}}' \\\n    &gt; $pop.sum.bed.csv\ndone\n\n\n\n\n\nCode\nfor pop in CEU CHB YRI; do\ncat ${pop}.sum.bed.csv | \\\n  csvtk plot line -x 1 -y 2 --point-size 0.01 \\\n    --xlab \"Depth of coverage (X)\" --ylab \"Genome coverage (kbp)\" \\\n    --width 9.0 --height 3.5 --x-min 10 --x-max 100 &gt; \\\n    fig-plot-total-coverage-distribution-hist-zoom-in-$pop.png\ndone\n\n\n\n\nNow we have combined total per sample coverage for ALL samples, and for sample sets CEU, CHB, and YRI. The upcoming task will be to generate sequence masks from the total coverage and minimum number of individuals with coverage greater than zero.\n\n\n\nIn addition to filtering on coverage, we will also filter on the minimum number of individuals with calls. This is to account for cases where coverages that pass the coverage filter originate from just a few samples. Here, we will remove sites where less than 50% of individuals have a call.\n\n\nbgzip -c -d ALL.bg.gz | \\\n  awk -v FS=\"\\t\" 'BEGIN {OFS=\"\\t\"} NR &gt; 1 {count=0; for (i=4; i&lt;=NF; i++) {if ($i&gt;0) count+=1}; if (count&gt;=((NF-3)*0.5)) {print $1, $2, $3}}' | \\\n  bgzip &gt; ALL.ind.bed.gz\ntabix -f -p bed ALL.ind.bed.gz\n\n\n\n\nGenerate coverage sums for sample sets CEU, CHB, and YRI.\n\n\n\nCode\nfor pop in CEU CHB YRI; do\n  bgzip -c -d $pop.bed.gz | awk -v FS=\"\\t\" 'BEGIN {OFS=\"\\t\"} NR &gt; 1 {count=0; for (i=4; i&lt;=NF; i++) {if ($i&gt;0) count+=1}; if (count&gt;=((NF-3)*0.5)) {print $1, $2, $3}}' | bgzip &gt; $pop.ind.bed.gz\n  tabix -f -p bed $pop.ind.bed.gz\ndone\n\n\n\n\n\n\n\n\nFinally, for each sample set, we will use bedtools intersect to generate combined bed files for total sum coverages and the filter on number of individuals. bedtools intersect makes it easy to combine multiple bed files, so other filters could be added.\nFIXME: make coverage filter above and intersect with ROI files\n\n# bedtools intersect -a ALL.sum.bed.gz -b ALL.ind.bed.gz \\\n# -g ooa.chr21.ref.fa.fai | bgzip &gt; ALL.mask.bed.gz\n#tabix -f -p bed ALL.mask.bed.gz\n\nNow we can use the command bedtools makefasta to make a sequence mask file in fasta format. The file will consist of characters 0 and 1, where the latter are regions that will be masked out in subsequent analyses. First, we make a mask file that consists solely of 1’s:\n\n\nawk 'BEGIN {OFS=\"\\t\"} {print $1, 0, $2}' ooa.chr21.ref.fa.fai &gt; ooa.chr21.ref.fa.bed\nbedtools maskfasta -fi ooa.chr21.ref.fa -mc 1 -fo ooa.chr21.mask.ref.fa -bed ooa.chr21.ref.fa.bed"
+    "text": "FIXME: text\nRegardless of how a raw variant call set has been produced, the calls will be of varying quality for a number of reasons. For high-coverage sequencing, the two most common are incompleteness of the reference sequence and misaligmnents in repetitive regions (Li, 2014). Low-coverage sequencing comes with its own biases, such as\n\n\n\n\n\n\nFigure 1: Coverage distributions for three hypothetical samples along with the cumulative coverage for all samples.\n\n\n\n\n\n\n\n\n\n\n\nFIXME\n\n\n\nDiscuss different types of relevant filters, e.g.\n\nfiltering on read depth, both high to eliminate collapsed repeats, and low for mappability issues. This is particularly important for incomplete assemblies\nalso briefly mention filtering on polymorphic sites\n\n\n\n\n\n\ncreate per sample, per population, and total depth of coverage profiles\ngenerate mask files for downstream processing\n\n\n\n\n\nmosdepth (Pedersen & Quinlan, 2018)\nsamtools (Danecek et al., 2021)\ncsvtk\nbedtools (Quinlan & Hall, 2010)\nseqkit (Shen et al., 2016)\n\n\n\n\nWe will be working with the Out of Africa dataset, consisting of 10 samples from the CEU, CHB, and YRI populations. The input consists of 10 bam files:\n\nls -lLh [CY]*.chr21.bam\n\n-rw-r--r-- 1 runner docker 7.8M Aug 22 22:55 CEU-1.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 6.3M Aug 22 22:55 CEU-2.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 4.8M Aug 22 22:55 CEU-3.ooa.chr21.bam\n-rw-r--r-- 1 runner docker  13M Aug 22 22:55 CEU-4.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 5.8M Aug 22 22:55 CHB-1.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 5.2M Aug 22 22:55 CHB-2.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 6.5M Aug 22 22:55 CHB-3.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 6.1M Aug 22 22:55 YRI-1.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 6.0M Aug 22 22:55 YRI-2.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 7.6M Aug 22 22:55 YRI-3.ooa.chr21.bam\n\n\nEach population consists of a collection – or set – of samples, and we will sometimes use the term sample set to refer to such a collection. For instance, the population (sample set) CHB consists of the set of samples CHB-1, CHB-2 and CHB-3.\n\n\n\nMapped reads provide information about how well a given genomic region has been represented during sequencing, and this information is usually summarized as the sequencing coverage. For any locus, this is equivalent to the number of reads mapping to that locus.\nSequencing coverage is typically not uniformly distributed over the reference. Reasons may vary but include uneven mapping coverage due to repeat regions, low coverage due to mis-assemblies, or coverage biases generated in the sequencing process. Importantly, both variable and monomorphic sites must be treated identically in the filtering process to eliminate biases between the two kinds of sites.\nIn this part, we will use mosdepth and bedtools to quickly generate depth of coverage profiles of mapped data. mosdepth is an ultra-fast command line tool for calculating coverage from a bam file. By default, it generates a summary of the global distribution, and per-base coverage in bed.gz format. We will be using the per-base coverage for filtering.\nAlternatively, mosdepth can also output results in a highly compressed format d4, which has been developed to handle the ever increasing size of resequencing projects. Files in d4 format can be processed with the d4-tools tool (Hou et al., 2021). For instance, d4tools view will display the coverage in bed format. We mention this in passing as it may be relevant when working with large genomes or sample sizes, but given the size of our sample data, we will be using bedtools from now on.\n\n\nWe start by calculating per-sample coverages with mosdepth. For downstream purposes, we need to save the size of the chromosomes we’re looking at, and for many applications, a fasta index file is sufficient.\n\nsamtools faidx ooa.chr21.ref.fa\n\nThe syntax to generate coverage information for a bam file is mosdepth &lt;prefix&gt; &lt;input file&gt;. Here, we add the -Q option to exclude reads with a mapping quality less than 20:\n\nmosdepth -Q 20 CEU-1 CEU-1.ooa.chr21.bam\n\nThe per-base coverage output file will be named CEU-1.per-base.bed.gz and can be viewed with bgzip:\n\nbgzip -c -d CEU-1.per-base.bed.gz | head -n 5\n\nchr21   0   7   0\nchr21   7   22  1\nchr21   22  45  2\nchr21   45  47  3\nchr21   47  54  4\n\n\nTo get an idea of what the coverage looks like over the chromsome, we can use bedtools and cvstk in a one-liner to generate a simple coverage plot (Figure 2)1\n\nbedtools intersect -a &lt;(bedtools makewindows -g ooa.chr21.ref.fa.fai -w 5000) \\\n      -b CEU-1.per-base.bed.gz -wa -wb | \\\n  bedtools groupby -i - -g 1,2,3 -c 7 -o mean | \\\n  csvtk plot -t line -x 2 -y 4 --point-size 0.01 --xlab Position \\\n      --ylab Coverage --width 9.0 --height 3.5 &gt; fig-plot-coverage.png\n\n\n\n\n\n\n\n\nFigure 2: Coverage for sample CEU-1 in 5kb windows. Experiment changing the window size (-w) parameter to change smoothing.\n\n\nApparently there are some high-coverage regions that could be associated with, e.g., collapsed repeat regions in the assembly. Let’s compile coverage results for all samples, using bash string manipulation to generate file prefix2\n\nfor f in [CY]*.chr21.bam; do\n  prefix=${f%.ooa.chr21.bam}\n  mosdepth -Q 20 $prefix $f\n  echo -e -n \"$prefix\\t\"\n  cat $prefix.mosdepth.summary.txt | grep total\ndone &gt; ALL.mosdepth.summary.txt\ncat ALL.mosdepth.summary.txt\n\nCEU-1   total   1000000 17917183    17.92   0   68\nCEU-2   total   1000000 14292139    14.29   0   67\nCEU-3   total   1000000 10428655    10.43   0   52\nCEU-4   total   1000000 30572047    30.57   0   115\nCHB-1   total   1000000 12830597    12.83   0   62\nCHB-2   total   1000000 11392259    11.39   0   52\nCHB-3   total   1000000 14835251    14.84   0   60\nYRI-1   total   1000000 13699799    13.70   0   63\nYRI-2   total   1000000 13437937    13.44   0   59\nYRI-3   total   1000000 17218621    17.22   0   69\n\n\nWe can calculate the total coverage with csvtk as follows:\n\ncsvtk summary -H -t ALL.mosdepth.summary.txt -f 5:sum\n\nto get the total coverage 156.63, which gives a hint at where the diploid coverage peak should be.\n\n\n\nWe can combine coverage intervals with bedtools unionbedg. We collect the bed file names and generate matching sample names to pass as arguments to option -names. Also, we include positions with no coverage (-empty) which requires the use of a genome file (option -g). The bed output is piped to bgzip which compresses the output, before finally indexing with tabix:\n\n\nBEDGZ=$(for f in *.per-base.bed.gz; do echo \"$f \"; done)\nSAMPLES=$(for f in $BEDGZ; do echo -e -n \"${f%.per-base.bed.gz} \"; done)\nbedtools unionbedg -header -names $SAMPLES -g ooa.chr21.ref.fa.fai -empty -i $BEDGZ | bgzip &gt; ALL.bg.gz\ntabix -f -p bed -S 1 ALL.bg.gz\n\n\nWe also need to combine coverages per populations CEU, CHB, and YRI. Using the previous command as a template, try to generate per population coverage files.\n\n\n\nCode\nfor pop in CEU CHB YRI; do\n  BEDGZ=$(for f in ${pop}*.per-base.bed.gz; do echo \"$f \"; done)\n  SAMPLES=$(for f in $BEDGZ; do echo -e -n \"${f%.per-base.bed.gz} \"; done)\n  bedtools unionbedg -header -names $SAMPLES -g ooa.chr21.ref.fa.fai -empty -i $BEDGZ | bgzip &gt; $pop.bed.gz\n  tabix -f -p bed -S 1 $pop.bed.gz\ndone\n\n\n\n\n\n\nSince we eventually want to filter on total coverage, we sum per sample coverages for each sample set with awk:\n\n\nbgzip -c -d ALL.bg.gz | awk -v FS=\"\\t\" -v OFS=\"\\t\" 'NR &gt; 1 {sum=0; for (i=4; i&lt;=NF; i++) sum+=$i; print $1, $2, $3, sum}' | bgzip &gt; ALL.sum.bed.gz\ntabix -f -p bed ALL.sum.bed.gz\n\n\nFor illustration, we plot the total coverage:\n\n\nbedtools intersect -a &lt;(bedtools makewindows -g ooa.chr21.ref.fa.fai -w 5000) \\\n    -b ALL.sum.bed.gz -wa -wb | \\\n  bedtools groupby -i - -g 1,2,3 -c 7 -o mean | \\\n  csvtk plot -t line -x 2 -y 4 --point-size 0.01 --xlab Position \\\n    --ylab Coverage --width 9.0 --height 3.5 &gt; fig-plot-total-coverage.png\n\n\n\n\n\n\n\n\n\nFigure 3: Total coverage in 5kb windows.\n\n\nIn order to define thresholds for subsequent filtering, we plot the proportion of the genome coverage versus depth of coverage (similar to k-mer plots in sequence assembly projects). In\n\n\n\n\n\n\n\n(a) Genome coverage\n\n\n\n\n\n\n\n(b) Cumulative genome coverage\n\n\n\nFigure 4: Genome coverage vs depth of coverage.\n\n\n\nIn Figure 4 a, a diploid peak is evident just below coverage X=150; we zoom in on that region to get a better view:\n\ncat ALL.sum.bed.csv | \\\n  csvtk plot line -x 1 -y 2 --point-size 0.01 \\\n    --xlab \"Depth of coverage (X)\" --ylab \"Genome coverage (kbp)\" \\\n    --width 9.0 --height 3.5 --x-min 90 --x-max 160\n\n\n\n\n\n\n\n(a) Genome coverage\n\n\nFigure 5: Genome coverage vs depth of coverage.\n\n\n\n(Lou et al., 2021) point out that appropriate thresholds depend on the data set, but as a general rule recommends a minimum depth threshold at &lt;0.8x average coverage, and a maximum depth threshold at mean coverage plus one or two standard deviations. Given the unrealistically homogenous coverage of our simulated data, we set thresholds based on Figure 5 to e.g., 100-160.\nWe then use these thresholds to generate a bed file containing regions that are accessible, i.e., have sufficient coverage for downstream analyses. We also calculate the number of bases that pass the filtering criteria.\n\n\nzcat ALL.sum.bed.gz | awk '{if (($4 &gt;= 100) && ($4 &lt;= 160)) print $0}' &gt; ALL.sum.depth.bed.gz\nbedtools genomecov -i ALL.sum.depth.bed.gz -g ooa.chr21.ref.fa.fai  | grep genome\n\ngenome  0   112885  1000000 0.112885\ngenome  1   887115  1000000 0.887115\n\n\n\nConsequently, 88.7% of the genome is accessible by depth.\n\n\n\n\n\n\nExercise\n\n\n\nGenerate coverage sums for sample sets CEU, CHB, and YRI, and from these determine coverage thresholds and apply the thresholds to generate bed files with accessible regions.\n\n\n\nCode\nfor pop in CEU CHB YRI; do\n  bgzip -c -d $pop.bed.gz | \\\n    awk -v FS=\"\\t\" -v OFS=\"\\t\" 'NR &gt; 1 {sum=0; for (i=4; i&lt;=NF; i++) sum+=$i; print $1, $2, $3, sum}' | \\\n    bgzip &gt; $pop.sum.bed.gz\n  tabix -f -p bed $pop.sum.bed.gz\n  zcat $pop.sum.bed.gz | \\\n    awk -v h=0 'BEGIN {OFS=\",\"; cumsum=0} {a[$4] += ($3-$2)/1000; if ($4 &gt; h) {h=$4}} END{for (i=0; i&lt;=h; i++) {if (a[i]) {cumsum+=a[i]} else {a[i]=0;} print i, a[i], cumsum}}' \\\n    &gt; $pop.sum.bed.csv\ndone\n\n\n\n\n\nCode\nfor pop in CEU CHB YRI; do\ncat ${pop}.sum.bed.csv | \\\n  csvtk plot line -x 1 -y 2 --point-size 0.01 \\\n    --xlab \"Depth of coverage (X)\" --ylab \"Genome coverage (kbp)\" \\\n    --width 9.0 --height 3.5 --x-min 10 --x-max 100 &gt; \\\n    fig-plot-total-coverage-distribution-hist-zoom-in-$pop.png\ndone\n\n\n\n\nNow we have combined total per sample coverage for ALL samples, and for sample sets CEU, CHB, and YRI. The upcoming task will be to generate sequence masks from the total coverage and minimum number of individuals with coverage greater than zero.\n\n\n\nIn addition to filtering on coverage, we will also filter on the minimum number of individuals with calls. This is to account for cases where coverages that pass the coverage filter originate from just a few samples. Here, we will remove sites where less than 50% of individuals have a call.\n\n\nbgzip -c -d ALL.bg.gz | \\\n  awk -v FS=\"\\t\" 'BEGIN {OFS=\"\\t\"} NR &gt; 1 {count=0; for (i=4; i&lt;=NF; i++) {if ($i&gt;0) count+=1}; if (count&gt;=((NF-3)*0.5)) {print $1, $2, $3}}' | \\\n  bgzip &gt; ALL.ind.bed.gz\ntabix -f -p bed ALL.ind.bed.gz\n\n\n\n\nGenerate coverage sums for sample sets CEU, CHB, and YRI.\n\n\n\nCode\nfor pop in CEU CHB YRI; do\n  bgzip -c -d $pop.bed.gz | awk -v FS=\"\\t\" 'BEGIN {OFS=\"\\t\"} NR &gt; 1 {count=0; for (i=4; i&lt;=NF; i++) {if ($i&gt;0) count+=1}; if (count&gt;=((NF-3)*0.5)) {print $1, $2, $3}}' | bgzip &gt; $pop.ind.bed.gz\n  tabix -f -p bed $pop.ind.bed.gz\ndone\n\n\n\n\n\n\n\n\nFinally, for each sample set, we will use bedtools intersect to generate combined bed files for total sum coverages and the filter on number of individuals. bedtools intersect makes it easy to combine multiple bed files, so other filters could be added.\nFIXME: make coverage filter above and intersect with ROI files\n\n# bedtools intersect -a ALL.sum.bed.gz -b ALL.ind.bed.gz \\\n# -g ooa.chr21.ref.fa.fai | bgzip &gt; ALL.mask.bed.gz\n#tabix -f -p bed ALL.mask.bed.gz\n\nNow we can use the command bedtools makefasta to make a sequence mask file in fasta format. The file will consist of characters 0 and 1, where the latter are regions that will be masked out in subsequent analyses. First, we make a mask file that consists solely of 1’s:\n\n\nawk 'BEGIN {OFS=\"\\t\"} {print $1, 0, $2}' ooa.chr21.ref.fa.fai &gt; ooa.chr21.ref.fa.bed\nbedtools maskfasta -fi ooa.chr21.ref.fa -mc 1 -fo ooa.chr21.mask.ref.fa -bed ooa.chr21.ref.fa.bed"
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-    "text": "We will be working with the Out of Africa dataset, consisting of 10 samples from the CEU, CHB, and YRI populations. The input consists of 10 bam files:\n\nls -lLh [CY]*.chr21.bam\n\n-rw-r--r-- 1 runner docker 7.8M Aug 20 16:22 CEU-1.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 6.3M Aug 20 16:22 CEU-2.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 4.8M Aug 20 16:22 CEU-3.ooa.chr21.bam\n-rw-r--r-- 1 runner docker  13M Aug 20 16:22 CEU-4.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 5.8M Aug 20 16:22 CHB-1.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 5.2M Aug 20 16:22 CHB-2.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 6.5M Aug 20 16:22 CHB-3.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 6.1M Aug 20 16:22 YRI-1.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 6.0M Aug 20 16:22 YRI-2.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 7.6M Aug 20 16:22 YRI-3.ooa.chr21.bam\n\n\nEach population consists of a collection – or set – of samples, and we will sometimes use the term sample set to refer to such a collection. For instance, the population (sample set) CHB consists of the set of samples CHB-1, CHB-2 and CHB-3."
+    "text": "We will be working with the Out of Africa dataset, consisting of 10 samples from the CEU, CHB, and YRI populations. The input consists of 10 bam files:\n\nls -lLh [CY]*.chr21.bam\n\n-rw-r--r-- 1 runner docker 7.8M Aug 22 22:55 CEU-1.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 6.3M Aug 22 22:55 CEU-2.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 4.8M Aug 22 22:55 CEU-3.ooa.chr21.bam\n-rw-r--r-- 1 runner docker  13M Aug 22 22:55 CEU-4.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 5.8M Aug 22 22:55 CHB-1.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 5.2M Aug 22 22:55 CHB-2.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 6.5M Aug 22 22:55 CHB-3.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 6.1M Aug 22 22:55 YRI-1.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 6.0M Aug 22 22:55 YRI-2.ooa.chr21.bam\n-rw-r--r-- 1 runner docker 7.6M Aug 22 22:55 YRI-3.ooa.chr21.bam\n\n\nEach population consists of a collection – or set – of samples, and we will sometimes use the term sample set to refer to such a collection. For instance, the population (sample set) CHB consists of the set of samples CHB-1, CHB-2 and CHB-3."
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-    "text": "Python code\n\nimport os\nos.listdir()\n\n['.jupyter_cache', 'index.qmd', 'index.ipynb']\n\n\n\nimport pgip\ndir(pgip)\n\n['__all__',\n '__builtins__',\n '__cached__',\n '__doc__',\n '__file__',\n '__loader__',\n '__name__',\n '__package__',\n '__path__',\n '__spec__',\n '__version__',\n '_version']"
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-    "text": "Code formatting\nInline code\n\nCode can be defined inline where `this` looks like this.\nR code can be executed inline `r Sys.Date()` producing 2 023-08-20.\n\nLigatures\n== != && ++ |&gt; &lt;&gt; &lt;- &lt;= &lt;~ /= |=&gt; -&gt;&gt;\n== != && ++ |&gt; &lt;&gt; &lt;- &lt;= &lt;~ /= |=&gt; -&gt;&gt;\nCode font used is JetBrains Mono. For full set of ligatures, see here."
+    "text": "Code formatting\nInline code\n\nCode can be defined inline where `this` looks like this.\nR code can be executed inline `r Sys.Date()` producing 2 023-08-22.\n\nLigatures\n== != && ++ |&gt; &lt;&gt; &lt;- &lt;= &lt;~ /= |=&gt; -&gt;&gt;\n== != && ++ |&gt; &lt;&gt; &lt;- &lt;= &lt;~ /= |=&gt; -&gt;&gt;\nCode font used is JetBrains Mono. For full set of ligatures, see here."
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-    "text": "Code formatting\n\n\nCode chunks\nCode can also be defined inside chunks.\n```\nThis is code\n```\nThis is code\nR code is executed inside code blocks like this\n```{r}\nSys.Date()\n```\nTest\nwhich shows the code and output.\n\nSys.Date()\n\n[1] \"2023-08-20\"\n\n\n\nThe code and results can be hidden by\n```{r}\n#| echo: false\n#| results: hide\ndata(iris)\n```\n\ndata(iris)\nhead(iris[1:3, 1:2])\n\n  Sepal.Length Sepal.Width\n1          5.1         3.5\n2          4.9         3.0\n3          4.7         3.2\n\n\nThese are called chunk options. For more options, see here and complete list here."
+    "text": "Code formatting\n\n\nCode chunks\nCode can also be defined inside chunks.\n```\nThis is code\n```\nThis is code\nR code is executed inside code blocks like this\n```{r}\nSys.Date()\n```\nTest\nwhich shows the code and output.\n\nSys.Date()\n\n[1] \"2023-08-22\"\n\n\n\nThe code and results can be hidden by\n```{r}\n#| echo: false\n#| results: hide\ndata(iris)\n```\n\ndata(iris)\nhead(iris[1:3, 1:2])\n\n  Sepal.Length Sepal.Width\n1          5.1         3.5\n2          4.9         3.0\n3          4.7         3.2\n\n\nThese are called chunk options. For more options, see here and complete list here."
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-    "text": "General tips\n\nReplace assets/cover.jpg to change cover slide picture\nReplace assets/end.jpg to change end slide picture\nAdd custom css under YAML css: \"styles.css\"\nIf content overflows the slide in vertical direction, add class .scrollable\n\n\n\n\n                                      sysname \n                                      \"Linux\" \n                                      release \n                          \"5.15.0-1042-azure\" \n                                      version \n\"#49-Ubuntu SMP Tue Jul 11 17:28:46 UTC 2023\" \n                                     nodename \n                               \"fv-az564-192\" \n                                      machine \n                                     \"x86_64\" \n                                        login \n                                    \"unknown\" \n                                         user \n                                     \"runner\" \n                               effective_user \n                                     \"runner\" \n\n\n\n\nExport HTML to PDF using PDF export mode by pressing e\nFor complete Quarto revealjs documentation, click here"
+    "text": "General tips\n\nReplace assets/cover.jpg to change cover slide picture\nReplace assets/end.jpg to change end slide picture\nAdd custom css under YAML css: \"styles.css\"\nIf content overflows the slide in vertical direction, add class .scrollable\n\n\n\n\n                                      sysname \n                                      \"Linux\" \n                                      release \n                          \"5.15.0-1041-azure\" \n                                      version \n\"#48-Ubuntu SMP Tue Jun 20 20:34:08 UTC 2023\" \n                                     nodename \n                               \"fv-az614-922\" \n                                      machine \n                                     \"x86_64\" \n                                        login \n                                    \"unknown\" \n                                         user \n                                     \"runner\" \n                               effective_user \n                                     \"runner\" \n\n\n\n\nExport HTML to PDF using PDF export mode by pressing e\nFor complete Quarto revealjs documentation, click here"
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-    "text": "Python code (WIP)\n\nimport os\nos.listdir()\n\n['index_files', 'index.qmd', 'index.rmarkdown', 'index_cache', 'assets']"
+    "text": "Python code (WIP)\n\nimport os\nos.listdir()\n\n['index_files', 'assets', 'index.qmd', 'index.rmarkdown', 'index_cache']"
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index 7d79e84d..cf88227f 100644
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@@ -351,7 +351,7 @@ <h1 class="title">Population admixture</h1>
   <h3 class="subtitle">Admixture</h3>
   <h4 class="author">Per Unneberg</h4>
   <h4 class="institute">NBIS</h4>
-  <h4 class="date">20-Aug-2023</h4>
+  <h4 class="date">22-Aug-2023</h4>
 </section><section id="TOC">
 <nav role="doc-toc"> 
 <h2 id="toc-title">Table of contents</h2>
diff --git a/slides/applications_selection/index.html b/slides/applications_selection/index.html
index c63d1295..e57ec328 100644
--- a/slides/applications_selection/index.html
+++ b/slides/applications_selection/index.html
@@ -351,7 +351,7 @@ <h1 class="title">Genome scans to detect selection</h1>
   <h3 class="subtitle">Genome scans</h3>
   <h4 class="author">Per Unneberg</h4>
   <h4 class="institute">NBIS</h4>
-  <h4 class="date">20-Aug-2023</h4>
+  <h4 class="date">22-Aug-2023</h4>
 </section><section id="TOC">
 <nav role="doc-toc"> 
 <h2 id="toc-title">Table of contents</h2>
diff --git a/slides/datageneration/index.html b/slides/datageneration/index.html
index 79fb1d14..7410abad 100644
--- a/slides/datageneration/index.html
+++ b/slides/datageneration/index.html
@@ -351,7 +351,7 @@ <h1 class="title">Data generation and processing</h1>
   <h3 class="subtitle">Sequencing protocols, best practice variant calling and filtering</h3>
   <h4 class="author">Per Unneberg</h4>
   <h4 class="institute">NBIS</h4>
-  <h4 class="date">20-Aug-2023</h4>
+  <h4 class="date">22-Aug-2023</h4>
 </section><section id="TOC">
 <nav role="doc-toc"> 
 <h2 id="toc-title">Table of contents</h2>
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index f57e881d..9a3466ef 100644
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index 677e331f..8958d933 100644
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index c95c76ce..31effa7c 100644
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diff --git a/slides/demo/index.html b/slides/demo/index.html
index b91dc921..fd181e62 100644
--- a/slides/demo/index.html
+++ b/slides/demo/index.html
@@ -484,7 +484,7 @@ <h1 class="title">Demo</h1>
   <h3 class="subtitle">Demo slides</h3>
   <h4 class="author">Per Unneberg</h4>
   <h4 class="institute">NBIS</h4>
-  <h4 class="date">20-Aug-2023</h4>
+  <h4 class="date">22-Aug-2023</h4>
 </section>
 
 <section id="introduction" class="slide level2">
@@ -950,7 +950,7 @@ <h2>Code formatting</h2>
 <p><strong>Inline code</strong></p>
 <ul>
 <li>Code can be defined inline where <code>`this`</code> looks like <code>this</code>.</li>
-<li>R code can be executed inline <code>`r Sys.Date()`</code> producing 2 023-08-20.</li>
+<li>R code can be executed inline <code>`r Sys.Date()`</code> producing 2 023-08-22.</li>
 </ul>
 <p><strong>Ligatures</strong></p>
 <p>== != &amp;&amp; ++ |&gt; &lt;&gt; &lt;- &lt;= &lt;~ /= |=&gt; -&gt;&gt;</p>
@@ -976,7 +976,7 @@ <h2>Code formatting</h2>
 <div class="cell" data-hash="index_cache/revealjs/unnamed-chunk-8_bb8ef50a13fe0ece6d1af6d8f94f8a5d">
 <div class="sourceCode cell-code" id="cb22"><pre class="sourceCode numberSource r number-lines code-with-copy"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1"></a><span class="fu">Sys.Date</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>[1] "2023-08-20"</code></pre>
+<pre><code>[1] "2023-08-22"</code></pre>
 </div>
 </div>
 </div><div class="column" style="width:50%;">
@@ -1290,8 +1290,8 @@ <h2>Tables • <code>DT</code></h2>
 <span id="cb38-2"><a href="#cb38-2"></a>DT<span class="sc">::</span><span class="fu">datatable</span>(iris[<span class="dv">1</span><span class="sc">:</span><span class="dv">20</span>, ], <span class="at">options =</span> <span class="fu">list</span>(<span class="at">pageLength =</span> <span class="dv">7</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output-display">
 
-<div class="datatables html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-4e80eb8d8daa1f156ef6" style="width:100%;height:auto;"></div>
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+<div class="datatables html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-32e7f48a9f21a6babfe6" style="width:100%;height:auto;"></div>
+<script type="application/json" data-for="htmlwidget-32e7f48a9f21a6babfe6">{"x":{"filter":"none","vertical":false,"data":[["1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20"],[5.1,4.9,4.7,4.6,5,5.4,4.6,5,4.4,4.9,5.4,4.8,4.8,4.3,5.8,5.7,5.4,5.1,5.7,5.1],[3.5,3,3.2,3.1,3.6,3.9,3.4,3.4,2.9,3.1,3.7,3.4,3,3,4,4.4,3.9,3.5,3.8,3.8],[1.4,1.4,1.3,1.5,1.4,1.7,1.4,1.5,1.4,1.5,1.5,1.6,1.4,1.1,1.2,1.5,1.3,1.4,1.7,1.5],[0.2,0.2,0.2,0.2,0.2,0.4,0.3,0.2,0.2,0.1,0.2,0.2,0.1,0.1,0.2,0.4,0.4,0.3,0.3,0.3],["setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa","setosa"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>Sepal.Length<\/th>\n      <th>Sepal.Width<\/th>\n      <th>Petal.Length<\/th>\n      <th>Petal.Width<\/th>\n      <th>Species<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"pageLength":7,"columnDefs":[{"className":"dt-right","targets":[1,2,3,4]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[7,10,25,50,100]}},"evals":[],"jsHooks":[]}</script>
 </div>
 </div>
 </section>
@@ -1336,8 +1336,8 @@ <h2>Interactive plots • <code>highcharter</code></h2>
 <span id="cb41-10"><a href="#cb41-10"></a>htmltools<span class="sc">::</span><span class="fu">tagList</span>(<span class="fu">list</span>(h))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output-display">
 
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@@ -1351,8 +1351,8 @@ <h2>Interactive plots • <code>plotly</code></h2>
 <span id="cb42-4"><a href="#cb42-4"></a>    <span class="fu">add_markers</span>()</span>
 <span id="cb42-5"><a href="#cb42-5"></a>p</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -1366,8 +1366,8 @@ <h2>Interactive plots • <code>ggplotly</code></h2>
 <span id="cb43-4"><a href="#cb43-4"></a></span>
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@@ -1385,8 +1385,8 @@ <h2>Interactive plots • <code>ggiraph</code></h2>
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id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e35' cx='61.72' cy='99.21' r='1.87pt' fill='#F8766D' fill-opacity='1' stroke='#F8766D' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 1.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;4.9&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;setosa'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e36' cx='65.91' cy='104.15' r='1.87pt' fill='#F8766D' fill-opacity='1' stroke='#F8766D' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 1.2&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;setosa'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e37' cx='86.85' cy='102.5' r='1.87pt' fill='#F8766D' fill-opacity='1' stroke='#F8766D' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 1.3&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;setosa'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e38' cx='61.72' cy='100.86' r='1.87pt' fill='#F8766D' fill-opacity='1' stroke='#F8766D' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 1.4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;4.9&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;setosa'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e39' cx='40.78' cy='102.5' r='1.87pt' fill='#F8766D' fill-opacity='1' stroke='#F8766D' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal 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stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 1.4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;4.8&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;setosa'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e47' cx='70.1' cy='97.56' r='1.87pt' fill='#F8766D' fill-opacity='1' stroke='#F8766D' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 1.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.1&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;setosa'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e48' cx='49.16' cy='100.86' r='1.87pt' fill='#F8766D' fill-opacity='1' stroke='#F8766D' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal 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&amp;lt;/b&amp;gt;5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;setosa'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e51' cx='149.67' cy='46.54' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e52' cx='124.54' cy='49.84' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle 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stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e56' cx='95.23' cy='49.84' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e57' cx='120.36' cy='46.54' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.3&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e58' cx='61.72' cy='69.59' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 3.3&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;4.9&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e59' cx='132.92' cy='48.19' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e60' cx='74.29' cy='59.71' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 3.9&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.2&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e61' cx='65.91' cy='66.29' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 3.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle 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stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.1&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e65' cx='91.04' cy='64.65' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 3.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e66' cx='137.11' cy='51.48' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e67' cx='91.04' cy='49.84' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e68' cx='99.41' cy='56.42' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.1&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.8&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e69' cx='116.17' cy='49.84' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.2&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e70' cx='91.04' cy='59.71' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 3.9&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e71' cx='103.6' cy='44.9' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.8&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.9&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e72' cx='111.98' cy='58.06' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.1&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e73' cx='120.36' cy='43.25' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.9&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.3&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e74' cx='111.98' cy='46.54' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.1&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e75' cx='124.54' cy='53.13' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.3&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e76' cx='132.92' cy='51.48' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e77' cx='141.3' cy='44.9' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.8&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.8&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e78' cx='137.11' cy='41.61' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e79' cx='107.79' cy='49.84' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e80' cx='95.23' cy='66.29' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 3.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e81' cx='86.85' cy='61.36' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 3.8&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e82' cx='86.85' cy='63' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 3.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e83' cx='99.41' cy='59.71' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 3.9&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.8&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e84' cx='107.79' cy='39.96' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 5.1&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e85' cx='82.66' cy='49.84' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e86' cx='107.79' cy='49.84' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e87' cx='137.11' cy='46.54' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e88' cx='120.36' cy='51.48' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.3&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e89' cx='91.04' cy='56.42' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.1&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e90' cx='86.85' cy='58.06' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e91' cx='86.85' cy='51.48' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e92' cx='111.98' cy='48.19' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.1&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e93' cx='99.41' cy='58.06' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.8&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e94' cx='65.91' cy='69.59' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 3.3&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_29a9edc6_824a_4501_a6d1_1a6981b03291_e95' cx='91.04' cy='54.77' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.2&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: 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stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 1.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.2&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;setosa'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e29' cx='74.29' cy='100.86' r='1.87pt' fill='#F8766D' fill-opacity='1' stroke='#F8766D' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 1.4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.2&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;setosa'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e30' cx='53.35' cy='97.56' r='1.87pt' fill='#F8766D' fill-opacity='1' stroke='#F8766D' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal 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stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 1.3&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;setosa'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e38' cx='61.72' cy='100.86' r='1.87pt' fill='#F8766D' fill-opacity='1' stroke='#F8766D' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 1.4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;4.9&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;setosa'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e39' cx='40.78' cy='102.5' r='1.87pt' fill='#F8766D' fill-opacity='1' stroke='#F8766D' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal 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&amp;lt;/b&amp;gt;5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;setosa'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e51' cx='149.67' cy='46.54' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e52' cx='124.54' cy='49.84' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e53' cx='145.48' cy='43.25' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.9&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.9&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e54' cx='86.85' cy='58.06' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e55' cx='128.73' cy='48.19' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e56' cx='95.23' cy='49.84' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e57' cx='120.36' cy='46.54' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.3&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e58' cx='61.72' cy='69.59' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 3.3&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;4.9&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e59' cx='132.92' cy='48.19' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e60' cx='74.29' cy='59.71' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 3.9&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.2&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e61' cx='65.91' cy='66.29' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 3.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e62' cx='103.6' cy='54.77' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.2&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.9&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e63' cx='107.79' cy='58.06' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e64' cx='111.98' cy='46.54' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.1&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e65' cx='91.04' cy='64.65' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 3.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e66' cx='137.11' cy='51.48' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e67' cx='91.04' cy='49.84' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e68' cx='99.41' cy='56.42' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.1&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.8&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e69' cx='116.17' cy='49.84' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.2&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e70' cx='91.04' cy='59.71' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 3.9&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle 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stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.9&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.3&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e74' cx='111.98' cy='46.54' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.1&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e75' cx='124.54' cy='53.13' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.3&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e76' cx='132.92' cy='51.48' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e77' cx='141.3' cy='44.9' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.8&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.8&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e78' cx='137.11' cy='41.61' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e79' cx='107.79' cy='49.84' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle 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stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 3.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e83' cx='99.41' cy='59.71' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 3.9&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.8&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e84' cx='107.79' cy='39.96' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 5.1&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e85' cx='82.66' cy='49.84' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e86' cx='107.79' cy='49.84' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: 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id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e89' cx='91.04' cy='56.42' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.1&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e90' cx='86.85' cy='58.06' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e91' cx='86.85' cy='51.48' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e92' cx='111.98' cy='48.19' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.1&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e93' cx='99.41' cy='58.06' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.8&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e94' cx='65.91' cy='69.59' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 3.3&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e95' cx='91.04' cy='54.77' r='1.87pt' fill='#00BA38' fill-opacity='1' stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.2&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: 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stroke='#00BA38' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 4.1&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;5.7&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;versicolor'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e101' cx='120.36' cy='25.15' r='1.87pt' fill='#619CFF' fill-opacity='1' stroke='#619CFF' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' title='&amp;lt;b&amp;gt;Petal Length:&amp;lt;/b&amp;gt; 6&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Sepal Length: &amp;lt;/b&amp;gt;6.3&amp;lt;br/&amp;gt;&amp;lt;b&amp;gt;Species: &amp;lt;/b&amp;gt;virginica'/>\n   <circle id='svg_b690f5d2_c1a9_4e35_ad1f_e1310ece68fd_e102' cx='99.41' cy='39.96' r='1.87pt' fill='#619CFF' fill-opacity='1' stroke='#619CFF' stroke-opacity='1' stroke-width='0.71' stroke-linejoin='round' stroke-linecap='round' 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 <!-- markdownlint-enable MD013 -->
@@ -1400,8 +1400,8 @@ <h2>Interactive time series • <code>dygraphs</code></h2>
 <span id="cb45-3"><a href="#cb45-3"></a><span class="fu">dygraph</span>(lungDeaths, <span class="at">main =</span> <span class="st">"Deaths from Lung Disease (UK)"</span>) <span class="sc">%&gt;%</span></span>
 <span id="cb45-4"><a href="#cb45-4"></a>    <span class="fu">dyOptions</span>(<span class="at">colors =</span> <span class="fu">c</span>(<span class="st">"#66C2A5"</span>, <span class="st">"#FC8D62"</span>, <span class="st">"#8DA0CB"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -1486,11 +1486,11 @@ <h2>General tips</h2>
 <pre><code>                                      sysname 
                                       "Linux" 
                                       release 
-                          "5.15.0-1042-azure" 
+                          "5.15.0-1041-azure" 
                                       version 
-"#49-Ubuntu SMP Tue Jul 11 17:28:46 UTC 2023" 
+"#48-Ubuntu SMP Tue Jun 20 20:34:08 UTC 2023" 
                                      nodename 
-                               "fv-az564-192" 
+                               "fv-az614-922" 
                                       machine 
                                      "x86_64" 
                                         login 
@@ -1517,7 +1517,7 @@ <h2>Python code (WIP)</h2>
 <div class="sourceCode cell-code" id="cb52"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb52-1"><a href="#cb52-1"></a><span class="im">import</span> os</span>
 <span id="cb52-2"><a href="#cb52-2"></a>os.listdir()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>['index_files', 'index.qmd', 'index.rmarkdown', 'index_cache', 'assets']</code></pre>
+<pre><code>['index_files', 'assets', 'index.qmd', 'index.rmarkdown', 'index_cache']</code></pre>
 </div>
 </div>
 </section>
diff --git a/slides/demography/index.html b/slides/demography/index.html
index 9e02464b..77e6ee06 100644
--- a/slides/demography/index.html
+++ b/slides/demography/index.html
@@ -332,7 +332,7 @@ <h1 class="title">Demography</h1>
   <h3 class="subtitle">Demographic inference</h3>
   <h4 class="author">Per Unneberg</h4>
   <h4 class="institute">NBIS</h4>
-  <h4 class="date">20-Aug-2023</h4>
+  <h4 class="date">22-Aug-2023</h4>
 </section><section id="TOC">
 <nav role="doc-toc"> 
 <h2 id="toc-title">Table of contents</h2>
diff --git a/slides/direct_selection/index.html b/slides/direct_selection/index.html
index 15785a9b..6671fd02 100644
--- a/slides/direct_selection/index.html
+++ b/slides/direct_selection/index.html
@@ -331,7 +331,7 @@
   <h1 class="title">Direct selection</h1>
   <h4 class="author">Per Unneberg</h4>
   <h4 class="institute">NBIS</h4>
-  <h4 class="date">20-Aug-2023</h4>
+  <h4 class="date">22-Aug-2023</h4>
 </section><section id="TOC">
 <nav role="doc-toc"> 
 <h2 id="toc-title">Table of contents</h2>
diff --git a/slides/foundations/index.html b/slides/foundations/index.html
index 81161dce..18c8783a 100644
--- a/slides/foundations/index.html
+++ b/slides/foundations/index.html
@@ -416,7 +416,7 @@ <h1 class="title">Population genetics</h1>
   <h3 class="subtitle">Foundations</h3>
   <h4 class="author">Per Unneberg</h4>
   <h4 class="institute">NBIS</h4>
-  <h4 class="date">20-Aug-2023</h4>
+  <h4 class="date">22-Aug-2023</h4>
 </section><section id="TOC">
 <nav role="doc-toc"> 
 <h2 id="toc-title">Table of contents</h2>
@@ -433,14 +433,19 @@ <h2 id="toc-title">Table of contents</h2>
 <section id="dna-variation" class="title-slide slide level1 center">
 <h1>DNA variation</h1>
 <aside class="notes">
-<p>From <span class="citation" data-cites="nei_MolecularEvolutionPhylogenetics_2000">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Nei &amp; Kumar, 2000, p. 231</a>)</span>:</p>
+<p>Goal of section: look at the data that forms the foundation for population genomic analyses</p>
+<p>From <span class="citation" data-cites="nei_MolecularEvolutionPhylogenetics_2000">(<a href="#/bibliography" role="doc-biblioref" onclick="">Nei &amp; Kumar, 2000, p. 231</a>)</span>:</p>
 <blockquote>
 <p>The main subject of population genetics is to study the generation and maintenance of genetic polymorphism and to understand the mechanisms of evolution at the population level</p>
 </blockquote>
-<p><span class="citation" data-cites="casillas_MolecularPopulationGenetics_2017">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Casillas &amp; Barbadilla, 2017, p. 1026</a>)</span>:</p>
+<p><span class="citation" data-cites="casillas_MolecularPopulationGenetics_2017">(<a href="#/bibliography" role="doc-biblioref" onclick="">Casillas &amp; Barbadilla, 2017, p. 1026</a>)</span>:</p>
 <blockquote>
 <p>Big data samples of complete genome sequences of many individuals from natural populations of many species have transformed population genetics inferences on samples of loci to population genomics: the analysis of genome-wide patterns of DNA variation within and between species.</p>
 </blockquote>
+<p><span class="citation" data-cites="gillespie_PopulationGeneticsConcise_2004">(<a href="#/bibliography" role="doc-biblioref" onclick="">Gillespie, 2004, p. 1</a>)</span></p>
+<blockquote>
+<p>Population geneticists spend most of their time doing one of two things: describing the genetic structure of populations or theorizing on the evolutionary forces acting on populations</p>
+</blockquote>
 <style type="text/css">
         span.MJX_Assistive_MathML {
           position:absolute!important;
@@ -457,7 +462,7 @@ <h1>DNA variation</h1>
 <h2>DNA variation</h2>
 <div class="dna small">
 <table style="width:75%;">
-<caption>Sequence aligmnent of four DNA sequences <span class="citation" data-cites="hahn_MolecularPopulationGenetics_2019">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Hahn, 2019</a>, Fig 1.1)</span>. </caption>
+<caption>Sequence aligmnent of four DNA sequences <span class="citation" data-cites="hahn_MolecularPopulationGenetics_2019">(<a href="#/bibliography" role="doc-biblioref" onclick="">Hahn, 2019</a>, Fig 1.1)</span>. </caption>
 <colgroup>
 <col style="width: 5%">
 <col style="width: 5%">
@@ -566,7 +571,7 @@ <h2>DNA variation</h2>
 </tbody>
 </table>
 </div>
-<p>The main data for molecular population genetics are DNA sequences. The alignment above shows a <em>sample</em> of four DNA <em>sequences</em>. Each sequence has 15 nucleotides (<em>sites</em>) “from the same <em>locus</em> (location) on a chromosome” <span class="citation" data-cites="hahn_MolecularPopulationGenetics_2019">(p.2 <a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Hahn, 2019</a>)</span></p>
+<p>The main data for molecular population genetics are DNA sequences. The alignment above shows a <em>sample</em> of four DNA <em>sequences</em>. Each sequence has 15 nucleotides (<em>sites</em>) “from the same <em>locus</em> (location) on a chromosome” <span class="citation" data-cites="hahn_MolecularPopulationGenetics_2019">(p.2 <a href="#/bibliography" role="doc-biblioref" onclick="">Hahn, 2019</a>)</span></p>
 <div class="columns">
 <div class="column" style="width:60%;">
 <div class="fragment">
@@ -1454,17 +1459,17 @@ <h3 id="calculate-the-nucleotide-diversity">Calculate the nucleotide diversity</
 </div>
 </div>
 </section>
-<section id="equilibrium-state-of-alleles-in-absence-of-evolutionary-forces" class="slide level2">
-<h2>Equilibrium state of alleles in absence of evolutionary forces</h2>
+<section id="in-absence-of-evolutionary-forces-alleles-are-in-equilibrium" class="slide level2">
+<h2>In absence of evolutionary forces alleles are in equilibrium</h2>
 <h3 id="the-hardy-weinberg-equilibrium">The Hardy-Weinberg equilibrium</h3>
-<p>For a locus, let <span class="math inline">\(A_1\)</span> and <span class="math inline">\(A_2\)</span> be two different alleles and let <span class="math inline">\(p\)</span> be the frequency of the <span class="math inline">\(A_1\)</span> allele and <span class="math inline">\(q=1-p\)</span> the frequency of the <span class="math inline">\(A_2\)</span> allele. In the <strong>absence</strong> of <strong>mutation</strong>, <strong>drift</strong>, <strong>migration</strong>, and other evolutionary processes, the equilibrium state is given by the <strong>Hardy-Weinberg equilibrium</strong> (<strong>HWE</strong>).</p>
+<p>For a locus, let <span class="math inline">\(A\)</span> and <span class="math inline">\(a\)</span> be two different alleles and let <span class="math inline">\(p\)</span> be the frequency of the <span class="math inline">\(A\)</span> allele and <span class="math inline">\(q=1-p\)</span> the frequency of the <span class="math inline">\(a\)</span> allele. In the <strong>absence</strong> of <strong>mutation</strong>, <strong>drift</strong>, <strong>migration</strong>, and other evolutionary processes, the equilibrium state is given by the <strong>Hardy-Weinberg equilibrium</strong> (<strong>HWE</strong>).</p>
 <table>
 <tbody>
 <tr class="odd">
 <td>Genotype:</td>
-<td><span class="math inline">\(A_1A_1\)</span></td>
-<td><span class="math inline">\(A_1A_2\)</span></td>
-<td><span class="math inline">\(A_2A_2\)</span></td>
+<td><span class="math inline">\(AA\)</span></td>
+<td><span class="math inline">\(Aa\)</span></td>
+<td><span class="math inline">\(aa\)</span></td>
 </tr>
 <tr class="even">
 <td>Frequency:</td>
@@ -1480,7 +1485,71 @@ <h3 id="the-hardy-weinberg-equilibrium">The Hardy-Weinberg equilibrium</h3>
 q = q^2 + pq
 \]</span></p>
 <aside class="notes">
-<p>Segway: apart from describing the variation we want to model how allele frequencies <em>change</em> in time. As <span class="citation" data-cites="gillespie_PopulationGeneticsConcise_2004">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Gillespie, 2004</a>)</span> points out, measuring allele frequencies is easy, measuring their change, however, is not</p>
+<p>Segue: apart from describing the variation we want to model how allele frequencies <em>change</em> in time. As <span class="citation" data-cites="gillespie_PopulationGeneticsConcise_2004">(<a href="#/bibliography" role="doc-biblioref" onclick="">Gillespie, 2004</a>, preface p.&nbsp;xi)</span> points out, “While genotype frequencies are easily measured, their change is not”</p>
+<style type="text/css">
+        span.MJX_Assistive_MathML {
+          position:absolute!important;
+          clip: rect(1px, 1px, 1px, 1px);
+          padding: 1px 0 0 0!important;
+          border: 0!important;
+          height: 1px!important;
+          width: 1px!important;
+          overflow: hidden!important;
+          display:block!important;
+      }</style></aside>
+<aside class="notes">
+<p>Segue: describing variation fine, but where does it come from and how does it change?</p>
+<p>If assumptions of HWE hold, we have no change of variation. However, we <strong>want</strong> to look at <strong>change</strong> of variation and disentangle the forces that imposes change</p>
+<style type="text/css">
+        span.MJX_Assistive_MathML {
+          position:absolute!important;
+          clip: rect(1px, 1px, 1px, 1px);
+          padding: 1px 0 0 0!important;
+          border: 0!important;
+          height: 1px!important;
+          width: 1px!important;
+          overflow: hidden!important;
+          display:block!important;
+      }</style></aside>
+</section>
+<section id="natural-populations-do-mate-randomly" class="slide level2">
+<h2>Natural populations do mate randomly?</h2>
+
+<img data-src="index_files/figure-revealjs/fig-hwe-1000genomes-1.png" width="1152" class="r-stretch quarto-figure-left"><p class="caption">Figure&nbsp;1: Hardy-Weinberg proportions in 10,000 SNPs on chromosome 22 from three populations based on 1000 genomes data. Illustration inspired by <span class="citation" data-cites="cooplab_PopulationGeneticsCourse_2011">cooplab (<a href="#/bibliography" role="doc-biblioref" onclick="">2011</a>)</span></p><aside class="notes">
+<!-- markdownlint-disable MD013 -->
+<p>See <a href="https://stackoverflow.com/questions/26587940/ggplot2-different-legend-symbols-for-points-and-lines" class="uri">https://stackoverflow.com/questions/26587940/ggplot2-different-legend-symbols-for-points-and-lines</a> for legend customization.</p>
+<!-- markdownlint-enable MD013 -->
+<style type="text/css">
+        span.MJX_Assistive_MathML {
+          position:absolute!important;
+          clip: rect(1px, 1px, 1px, 1px);
+          padding: 1px 0 0 0!important;
+          border: 0!important;
+          height: 1px!important;
+          width: 1px!important;
+          overflow: hidden!important;
+          display:block!important;
+      }</style></aside>
+</section>
+<section id="the-obsession-of-population-genetics" class="slide level2">
+<h2>The obsession of population genetics</h2>
+<p>Population genetics is about <span class="citation" data-cites="gillespie_PopulationGeneticsConcise_2004">(<a href="#/bibliography" role="doc-biblioref" onclick="">Gillespie, 2004</a>)</span></p>
+<ol type="1">
+<li>describing the genetic structure of populations</li>
+<li>constructing theories on the forces that influence genetic variation</li>
+</ol>
+<div class="fragment">
+<p>Questions to ponder:</p>
+<ul>
+<li>why does variation look the way it does?</li>
+<li>how is variation maintained?</li>
+<li>how does variation change over time (<span class="math inline">\(\Delta p\)</span>)?</li>
+<li>what forces shape the genetic structure of <em>populations</em>?</li>
+</ul>
+</div>
+<aside class="notes">
+<p>From HWE: we want to look a the creation, maintenance and loss of variation and what forces affect it</p>
+<p>Goal: describe theory behind evolving populations. This has been an obsession for a long time, even before DNA was known -&gt; back to basics</p>
 <style type="text/css">
         span.MJX_Assistive_MathML {
           position:absolute!important;
@@ -1496,13 +1565,26 @@ <h3 id="the-hardy-weinberg-equilibrium">The Hardy-Weinberg equilibrium</h3>
 <section>
 <section id="models-of-populations" class="title-slide slide level1 center">
 <h1>Models of populations</h1>
-
+<aside class="notes">
+<p>Segue: take a step back to the days before the nature of genetic inheritance was known. First focus on the population.</p>
+<p>Goal of lecture is also to understand genetic relationships in the context of genealogies -&gt; coalescent.</p>
+<style type="text/css">
+        span.MJX_Assistive_MathML {
+          position:absolute!important;
+          clip: rect(1px, 1px, 1px, 1px);
+          padding: 1px 0 0 0!important;
+          border: 0!important;
+          height: 1px!important;
+          width: 1px!important;
+          overflow: hidden!important;
+          display:block!important;
+      }</style></aside>
 </section>
 <section id="wright-fisher-model" class="slide level2 smaller">
 <h2>Wright-Fisher model</h2>
 <div class="columns">
 <div class="column" style="width:50%;">
-<p>Model of evolution that describes sampling of alleles in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
+<p>Model of populations that describes <strong>genealogical</strong> relationships of genes (chromosomes) in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/bibliography" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
 <div>
 <ul>
 <li class="fragment">discrete and non-overlapping generations</li>
@@ -1518,7 +1600,7 @@ <h2>Wright-Fisher model</h2>
 </div>
 </div>
 <aside class="notes">
-<p>See <span class="citation" data-cites="hein_GeneGenealogies_2004">Hein et al. (<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">2004</a>)</span> for more assumptions.</p>
+<p>See <span class="citation" data-cites="hein_GeneGenealogies_2004">Hein et al. (<a href="#/bibliography" role="doc-biblioref" onclick="">2004</a>)</span> for more assumptions. The second assumption means we can use 2N chromosome interchangeably for haploid (n=2N) and diploid (n=N) populations.</p>
 <style type="text/css">
         span.MJX_Assistive_MathML {
           position:absolute!important;
@@ -1535,7 +1617,7 @@ <h2>Wright-Fisher model</h2>
 <h2>Wright-Fisher model</h2>
 <div class="columns">
 <div class="column" style="width:50%;">
-<p>Model of evolution that describes sampling of alleles in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
+<p>Model of populations that describes <strong>genealogical</strong> relationships of genes (chromosomes) in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/bibliography" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
 <ul>
 <li>discrete and non-overlapping generations</li>
 <li>haploid individuals or two subpopulations (males and females)</li>
@@ -1561,7 +1643,7 @@ <h3 id="algorithm">Algorithm</h3>
 <h2>Wright-Fisher model</h2>
 <div class="columns">
 <div class="column" style="width:50%;">
-<p>Model of evolution that describes sampling of alleles in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
+<p>Model of populations that describes <strong>genealogical</strong> relationships of genes (chromosomes) in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/bibliography" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
 <ul>
 <li>discrete and non-overlapping generations</li>
 <li>haploid individuals or two subpopulations (males and females)</li>
@@ -1588,7 +1670,7 @@ <h3 id="algorithm-1">Algorithm</h3>
 <h2>Wright-Fisher model</h2>
 <div class="columns">
 <div class="column" style="width:50%;">
-<p>Model of evolution that describes sampling of alleles in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
+<p>Model of populations that describes <strong>genealogical</strong> relationships of genes (chromosomes) in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/bibliography" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
 <ul>
 <li>discrete and non-overlapping generations</li>
 <li>haploid individuals or two subpopulations (males and females)</li>
@@ -1616,7 +1698,7 @@ <h3 id="algorithm-2">Algorithm</h3>
 <h2>Wright-Fisher model</h2>
 <div class="columns">
 <div class="column" style="width:50%;">
-<p>Model of evolution that describes sampling of alleles in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
+<p>Model of populations that describes <strong>genealogical</strong> relationships of genes (chromosomes) in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/bibliography" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
 <ul>
 <li>discrete and non-overlapping generations</li>
 <li>haploid individuals or two subpopulations (males and females)</li>
@@ -1644,7 +1726,7 @@ <h3 id="algorithm-3">Algorithm</h3>
 <h2>Wright-Fisher model</h2>
 <div class="columns">
 <div class="column" style="width:50%;">
-<p>Model of evolution that describes sampling of alleles in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
+<p>Model of populations that describes <strong>genealogical</strong> relationships of genes (chromosomes) in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/bibliography" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
 <ul>
 <li>discrete and non-overlapping generations</li>
 <li>haploid individuals or two subpopulations (males and females)</li>
@@ -1672,7 +1754,7 @@ <h3 id="algorithm-4">Algorithm</h3>
 <h2>Wright-Fisher model</h2>
 <div class="columns">
 <div class="column" style="width:50%;">
-<p>Model of evolution that describes sampling of alleles in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
+<p>Model of populations that describes <strong>genealogical</strong> relationships of genes (chromosomes) in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/bibliography" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
 <ul>
 <li>discrete and non-overlapping generations</li>
 <li>haploid individuals or two subpopulations (males and females)</li>
@@ -1700,7 +1782,7 @@ <h3 id="algorithm-5">Algorithm</h3>
 <h2>Wright-Fisher model</h2>
 <div class="columns">
 <div class="column" style="width:50%;">
-<p>Model of evolution that describes sampling of alleles in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
+<p>Model of populations that describes <strong>genealogical</strong> relationships of genes (chromosomes) in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/bibliography" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
 <ul>
 <li>discrete and non-overlapping generations</li>
 <li>haploid individuals or two subpopulations (males and females)</li>
@@ -1728,7 +1810,7 @@ <h3 id="algorithm-6">Algorithm</h3>
 <h2>Wright-Fisher model</h2>
 <div class="columns">
 <div class="column" style="width:50%;">
-<p>Model of evolution that describes sampling of alleles in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
+<p>Model of populations that describes <strong>genealogical</strong> relationships of genes (chromosomes) in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/bibliography" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
 <ul>
 <li>discrete and non-overlapping generations</li>
 <li>haploid individuals or two subpopulations (males and females)</li>
@@ -1756,7 +1838,7 @@ <h3 id="algorithm-7">Algorithm</h3>
 <h2>Wright-Fisher model</h2>
 <div class="columns">
 <div class="column" style="width:50%;">
-<p>Model of evolution that describes sampling of alleles in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
+<p>Model of populations that describes <strong>genealogical</strong> relationships of genes (chromosomes) in a population under the following assumptions <span class="citation" data-cites="hein2005gene">(<a href="#/bibliography" role="doc-biblioref" onclick="">Hein et al., 2005</a>)</span>:</p>
 <ul>
 <li>discrete and non-overlapping generations</li>
 <li>haploid individuals or two subpopulations (males and females)</li>
@@ -1783,7 +1865,7 @@ <h3 id="algorithm-8">Algorithm</h3>
 <section id="wright-fisher-model-10" class="slide level2 smaller">
 <h2>Wright-Fisher model</h2>
 
-<img data-src="index_files/figure-revealjs/fig-wright-fisher-model-gganimation-.gif" width="14" class="r-stretch quarto-figure-left"><p class="caption">Figure&nbsp;1: Wright-Fisher model</p><aside class="notes">
+<img data-src="index_files/figure-revealjs/fig-wright-fisher-model-gganimation-.gif" width="14" class="r-stretch quarto-figure-left"><p class="caption">Figure&nbsp;2: Wright-Fisher model</p><aside class="notes">
 <p>See <a href="https://mikeyharper.uk/animated-plots-with-r/" class="uri">https://mikeyharper.uk/animated-plots-with-r/</a></p>
 <style type="text/css">
         span.MJX_Assistive_MathML {
@@ -1805,7 +1887,7 @@ <h2>Wright-Fisher model</h2>
 <div class="cell-output-display">
 <div id="fig-wright-fisher-model-gganimation-last" class="quarto-figure quarto-figure-left">
 <figure>
-<figcaption>Figure&nbsp;2: WF model indicating time direction from past (top) to present (bottom).</figcaption>
+<figcaption>Figure&nbsp;3: WF model indicating time direction from past (top) to present (bottom).</figcaption>
 <p><img data-src="index_files/figure-revealjs/fig-wright-fisher-model-gganimation-last-1.png" style="width:100.0%"></p>
 </figure>
 </div>
@@ -1817,7 +1899,7 @@ <h2>Wright-Fisher model</h2>
 <div class="cell-output-display">
 <div id="fig-wf-model-genealogy" class="quarto-figure quarto-figure-left">
 <figure>
-<figcaption>Figure&nbsp;3: WF model tracing the genealogies of three extant genes</figcaption>
+<figcaption>Figure&nbsp;4: WF model tracing the genealogies of three extant genes</figcaption>
 <p><img data-src="index_files/figure-revealjs/fig-wf-model-genealogy-1.png" width="960"></p>
 </figure>
 </div>
@@ -1826,6 +1908,62 @@ <h2>Wright-Fisher model</h2>
 </div>
 </div>
 </div>
+</section>
+<section id="the-wright-fisher-sampling-model" class="slide level2">
+<h2>The Wright-Fisher sampling model</h2>
+<p>Let’s formalise the sampling process of the Wright-Fisher model<sup>1</sup>. We assume</p>
+<ol type="1">
+<li>a single locus in a haploid population of size <span class="math inline">\(2N\)</span> (or diploid of size <span class="math inline">\(N\)</span> when random mationg)</li>
+<li>no mutation and selection</li>
+<li>discrete generations</li>
+</ol>
+<p>Each generation we sample <span class="math inline">\(2N\)</span> new chromosomes from the previous generation. The probability of choosing a chromosome <span class="math inline">\(v\)</span> is <span class="math inline">\(1/2N\)</span> (coin flip with probability of succes <span class="math inline">\(1/2N\)</span>). Since the trials are independent, and we perform <span class="math inline">\(2N\)</span> trials, the number of offspring of a given chromosome <span class="math inline">\(v\)</span> is <em>binomially distributed</em> <span class="math inline">\(\mathrm{Bin}(m, p)\)</span>, with parameters <span class="math inline">\(m=2N\)</span> and probability of success <span class="math inline">\(p=\frac{1}{2N}\)</span>:</p>
+<p><span class="math display">\[
+P(v=k) = {2N\choose k}\left( \frac{1}{2N} \right)^k
+\left(1 - \frac{1}{2N} \right)^{(2N - k)}
+\]</span></p>
+<aside><ol class="aside-footnotes"><li id="fn1"><p>For a more extensive treatment, see <span class="citation" data-cites="hermisson_MathematicalPopulationGenetics_2017">Hermisson (<a href="#/bibliography" role="doc-biblioref" onclick="">2017</a>)</span> or <span class="citation" data-cites="hein2005gene">Hein et al. (<a href="#/bibliography" role="doc-biblioref" onclick="">2005</a>)</span></p></li></ol></aside></section>
+<section id="properties-of-wright-fisher-sampling" class="slide level2">
+<h2>Properties of Wright-Fisher sampling</h2>
+<p><span class="math display">\[
+E(v) = mp = 2N\frac{1}{2N} = 1
+\]</span></p>
+<div class="fragment">
+<p>Poisson approximation: when <span class="math inline">\(2N\)</span> large</p>
+<p><span class="math display">\[
+P(v=k) \approx \frac{1}{k!}e^{-1}
+\]</span></p>
+<p>Since <span class="math inline">\(P(v=0) = e^{-1} \approx 0.37\)</span>, a fraction 0.37 of genes lack descendants</p>
+</div>
+<div class="fragment">
+<p>Probability that parents are identical: pick a parent <span class="math inline">\(v\)</span>, then a second parent <span class="math inline">\(u\)</span>. Then <span class="math inline">\(P(v=u)=\frac{1}{2N}\)</span>, and <span class="math inline">\(P(v\neq u)=1-\frac{1}{2N}\)</span>.</p>
+</div>
+<div class="fragment">
+<p>Time for two chromosomes <span class="math inline">\(u\)</span> and <span class="math inline">\(v\)</span> to find common ancestor distributed as <span class="math inline">\((1 - \frac{1}{2N})^{j-1}\frac{1}{2N}\)</span> (<span class="math inline">\(j-1\)</span> failures followed by success). This is the <a href="https://en.wikipedia.org/wiki/Geometric_distribution">geometric distribution</a> <span class="math inline">\(Ge(p)\)</span>, with parameter <span class="math inline">\(p=\frac{1}{2N}\)</span>, and expected value <span class="math inline">\(\frac{1}{p}={2N}\)</span>. That is, the <strong>expected time for two chromosomes to find a common ancester (i.e., coalesce) is <span class="math inline">\(2N\)</span> generations</strong>.</p>
+</div>
+<aside class="notes">
+<p>Since a large fraction of genes lack descendants, very quickly (compared to 2N) a population will descend from a small proportion of genes.</p>
+<p>Derivation of average time to having same parent (i.e., coalescence) requires knowledge of geometric distribution. The probability that the genes find common parent <span class="math inline">\(j\)</span> generations ago is</p>
+<p><span class="math display">\[
+(1-\frac{1}{2N})^{(j-1)}\frac{1}{2N}
+\]</span></p>
+<p>due to independence between generations. This is the geometric distribution <span class="math inline">\(Ge(p)\)</span> with <span class="math inline">\(p=1/2N\)</span>, which has expected value <span class="math inline">\(1/p\)</span>, i.e., 2N in this case.</p>
+<style type="text/css">
+        span.MJX_Assistive_MathML {
+          position:absolute!important;
+          clip: rect(1px, 1px, 1px, 1px);
+          padding: 1px 0 0 0!important;
+          border: 0!important;
+          height: 1px!important;
+          width: 1px!important;
+          overflow: hidden!important;
+          display:block!important;
+      }</style></aside>
+</section></section>
+<section>
+<section id="origin-and-change-of-variation" class="title-slide slide level1 center">
+<h1>Origin and change of variation</h1>
+
 </section>
 <section id="genetic-drift-with-alleles" class="slide level2">
 <h2>Genetic drift with alleles</h2>
@@ -1844,23 +1982,43 @@ <h2>Genetic drift with alleles</h2>
 </div>
 </div><p>Wright-Fisher model showing the evolution of population of 10 genes over 16 generations. Allele variants are shown in white and black. Starting frequency black variant is 0.3.</p>
 </div>
+<aside class="notes">
+<p>Enter allelic variants; we are looking at two variants</p>
+<p>Important: point out that the fate of any allele under drift is to be <strong>lost</strong> or <strong>fixed</strong>, which ultimately means variation is <strong>lost</strong> with drift</p>
+<style type="text/css">
+        span.MJX_Assistive_MathML {
+          position:absolute!important;
+          clip: rect(1px, 1px, 1px, 1px);
+          padding: 1px 0 0 0!important;
+          border: 0!important;
+          height: 1px!important;
+          width: 1px!important;
+          overflow: hidden!important;
+          display:block!important;
+      }</style></aside>
 </section>
-<section id="the-wright-fisher-sampling-model" class="slide level2 smaller">
-<h2>The Wright-Fisher sampling model</h2>
-<p>Let’s formalise the sampling process of the Wright-Fisher model<sup>1</sup></p>
-<p>We assume</p>
-<ol type="1">
-<li>a single locus in a haploid population of size N</li>
-<li>no mutation and selection</li>
-<li>discrete generations</li>
-</ol>
-<p>Each generation we sample N new individuals from the previous generation, which is equivalent to <em>multinomial sampling with replacement</em>. Then, the number of offspring of a given parent <em>i</em> is <em>binomially distributed</em>, with probability of success <span class="math inline">\(p=\frac{1}{N}\)</span>:</p>
+<section id="sampling-model-again" class="slide level2">
+<h2>Sampling model again</h2>
+<p>We assume two gene variants <span class="math inline">\(A\)</span>, <span class="math inline">\(a\)</span>, each with <span class="math inline">\(i\)</span> and <span class="math inline">\(j=2N-i\)</span> copies in generation <span class="math inline">\(t\)</span>. Let <span class="math inline">\(p_t=i/2N\)</span> be the <em>frequency</em> of <span class="math inline">\(A\)</span> in generation <span class="math inline">\(t\)</span>, and <span class="math inline">\(q_t=1-p_t\)</span> the frequency of <span class="math inline">\(a\)</span>.</p>
+<p>The sampling model for next generation <span class="math inline">\(t+1\)</span> is then</p>
 <p><span class="math display">\[
-\mathrm{Pr}(x) = {N\choose x}\left( \frac{1}{N} \right)^x
-\left(1 - \frac{1}{N} \right)^{(N - x)}
+p_{t+1} = P(v_A=k) = {2N\choose k}\left( \frac{i}{2N} \right)^k
+\left(1 - \frac{i}{2N} \right)^{(2N - k)} = \mathsf{Bin}(2N, \frac{i}{2N})
 \]</span></p>
-<p>Under the assumption of random mating, a diploid population of size <span class="math inline">\(N\)</span> can be described by a haploid population of size <span class="math inline">\(2N\)</span></p>
-<aside><ol class="aside-footnotes"><li id="fn1"><p>For a more extensive treatment, see <span class="citation" data-cites="hermisson_MathematicalPopulationGenetics_2017">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Hermisson, 2017</a>)</span></p></li></ol></aside></section>
+<aside class="notes">
+<p>NB: we here slightly change notation, with <span class="math inline">\(p_{t+1} = P(X=k)\)</span> p</p>
+<style type="text/css">
+        span.MJX_Assistive_MathML {
+          position:absolute!important;
+          clip: rect(1px, 1px, 1px, 1px);
+          padding: 1px 0 0 0!important;
+          border: 0!important;
+          height: 1px!important;
+          width: 1px!important;
+          overflow: hidden!important;
+          display:block!important;
+      }</style></aside>
+</section>
 <section id="genetic-drift" class="slide level2">
 <h2>Genetic drift</h2>
 <div class="columns">
@@ -1885,14 +2043,14 @@ <h2>Genetic drift</h2>
 </div>
 </div>
 <div class="fragment" data-fragment-index="2">
-<p>Note dependency of “jumpyness” (variance) on population size N</p>
+<p>Note dependency of variance on population size 2N</p>
 </div>
 </div><div class="column fragment" data-fragment-index="1" style="width:50%;">
-<div class="cell" data-animation.hook="gifski" data-hash="index_cache/revealjs/fig-genetic-drift-N-10_88bd326b6e3161b3cb263d91ceeb55e2">
+<div class="cell" data-animation.hook="gifski" data-hash="index_cache/revealjs/fig-genetic-drift-N-10_36cbc2effa8fc4f0685bf2b276b86725">
 <div class="cell-output-display">
 <div id="fig-genetic-drift-N-10" class="quarto-figure quarto-figure-left">
 <figure>
-<figcaption>Figure&nbsp;4: - Genetic drift for different population sizes, - starting frequency <span class="math inline">\(p_0\)</span>=0.5</figcaption>
+<figcaption>Figure&nbsp;5: Genetic drift for different population sizes, starting frequency <span class="math inline">\(p_0\)</span>=0.5</figcaption>
 <p><img data-src="index_files/figure-revealjs/fig-genetic-drift-N-10-1.gif" style="width:60.0%"></p>
 </figure>
 </div>
@@ -1901,10 +2059,7 @@ <h2>Genetic drift</h2>
 </div>
 </div>
 <aside class="notes">
-<p>Classic graph of allele: start at say 0.2 and drift with different Ne. Point out that variation becomes smaller with large Ne, but that eventually, an allele is fixed or lost (formula).</p>
-<p>Mention <em>neutral</em> here as this is how alleles behave under drift: like gas particle diffusing up and down</p>
-<p>See alternative way of visualizing variation faceting on initial frequency (showing P(fixation)) and population size (showing less variance))</p>
-<p><a href="https://stephens999.github.io/fiveMinuteStats/wright_fisher_model.html" class="uri">https://stephens999.github.io/fiveMinuteStats/wright_fisher_model.html</a></p>
+<p>Mention <em>neutral</em> here as this is how alleles behave under drift: like gas particle diffusing up and down. Allele is eventually either fixed or lost.</p>
 <style type="text/css">
         span.MJX_Assistive_MathML {
           position:absolute!important;
@@ -1920,25 +2075,47 @@ <h2>Genetic drift</h2>
 <section id="genetic-drift-1" class="slide level2">
 <h2>Genetic drift</h2>
 <div class="columns">
-<div class="column" style="width:65%;">
+<div class="column" style="width:50%;">
 <!-- markdownlint-disable MD013 -->
-<div class="cell" data-hash="index_cache/revealjs/fig-genetic-drift-many-repetitions_9b73fbe1c68309b51a8af811458f14e4">
+<div class="cell" data-hash="index_cache/revealjs/fig-genetic-drift-many-repetitions_b7628e8341a0c3ea323dfed7c7738e34">
 <div class="cell-output-display">
 <div id="fig-genetic-drift-many-repetitions" class="quarto-figure quarto-figure-left">
 <figure>
-<figcaption>Figure&nbsp;5: Genetic drift for different combinations of starting frequency and population size for n=50 repetitions per parameter combination. Note how variation and time to fixation depends on population size and starting frequency.</figcaption>
-<p><img data-src="index_files/figure-revealjs/fig-genetic-drift-many-repetitions-1.png" width="1152"></p>
+<figcaption>Figure&nbsp;6: Genetic drift for different combinations of starting frequency and population size for n=50 repetitions per parameter combination. Note how variation and time to fixation depends on population size and starting frequency.</figcaption>
+<p><img data-src="index_files/figure-revealjs/fig-genetic-drift-many-repetitions-1.png" width="560" height="380"></p>
 </figure>
 </div>
 </div>
 </div>
 <!-- markdownlint-enable MD013 -->
-</div><div class="column" style="width:35%;">
-<p>Probability of fixation <span class="math inline">\(\propto p_0\)</span></p>
+</div><div class="column" style="width:50%;">
+<div>
+<ul>
+<li class="fragment">fate of allele: fixation or loss <span class="math inline">\(\rightarrow\)</span> eventually loss of variation</li>
+<li class="fragment"><strong>probability of fixation <span class="math inline">\(\pi(p)=p\)</span></strong>, where <span class="math inline">\(p\)</span> is the <em>current</em> frequency</li>
+<li class="fragment">rate of drift (loss of variation) <span class="math inline">\(\propto \frac{1}{2N}\)</span></li>
+</ul>
 </div>
+<div class="fragment">
+<div class="center">
+<div class="cell" data-layout-align="center" data-hash="index_cache/revealjs/gas-particle-drift_072c24f366a9bf20c002c0bc72825271">
+<div class="cell-output-display">
+<div class="quarto-figure quarto-figure-center">
+<figure>
+<p><img data-src="index_files/figure-revealjs/gas-particle-drift-1.svg" width="960" height="200"></p>
+</figure>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="fragment">
+<p>Mathematical treatment of drift can become complicated: easier to study dynamics of <em>heterozygosity</em></p>
 </div>
 <aside class="notes">
-<p>Simplifying assumptions -&gt; rate of drift <span class="math inline">\(propto \frac{1}{N}\)</span> <span class="citation" data-cites="leffler_RevisitingOldRiddle_2012">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Leffler et al., 2012</a>)</span></p>
+<p>Simplifying assumptions -&gt; rate of drift <span class="math inline">\(propto \frac{1}{N}\)</span> <span class="citation" data-cites="leffler_RevisitingOldRiddle_2012">(<a href="#/bibliography" role="doc-biblioref" onclick="">Leffler et al., 2012</a>)</span></p>
 <p>In reality population fluctuates and therefore Ne is substituted for N. Larger Ne -&gt; smaller fluctuation in allele frequency (as evidenced in plots) -&gt; maintain larger genetic diversity</p>
 <style type="text/css">
         span.MJX_Assistive_MathML {
@@ -1956,18 +2133,18 @@ <h2>Genetic drift</h2>
 <h2>Heterozygosity dynamics</h2>
 <div class="columns">
 <div class="column" style="width:50%;">
-<div class="cell" data-hash="index_cache/revealjs/fig-identity-by-state-and-descent_f998d4454f8e711f556970ff1392f30b">
+<div class="cell" data-hash="index_cache/revealjs/fig-identity-by-state-and-descent_5d999686489d8e0a89f520740726bf1e">
 <div class="cell-output-display">
 <div id="fig-identity-by-state-and-descent" class="quarto-figure quarto-figure-left">
 <figure>
-<figcaption>Figure&nbsp;6: Illustration of identity by descent and state.</figcaption>
+<figcaption>Figure&nbsp;7: Illustration of identity by descent (IBD) and state (IBS). Alleles in generation <span class="math inline">\(n\)</span> are IBD but not IBS.</figcaption>
 <p><img data-src="index_files/figure-revealjs/fig-identity-by-state-and-descent-1.svg" style="width:70.0%"></p>
 </figure>
 </div>
 </div>
 </div>
 </div><div class="column" style="width:50%;">
-<p>Let <span class="math inline">\(\mathcal{H}_t\)</span> be the probability that two alleles are <em>identical by state</em>. The time course evolution of <span class="math inline">\(\mathcal{H}_t\)</span> in a randomly mating population consisting of <span class="math inline">\(N\)</span> diploid hermaphroditic individuals is</p>
+<p>Let <span class="math inline">\(\mathcal{H}_t\)</span> be the probability that two alleles are <strong>different by state</strong>. The time course evolution of <span class="math inline">\(\mathcal{H}_t\)</span> in a randomly mating population consisting of <span class="math inline">\(N\)</span> diploid hermaphroditic individuals is</p>
 <p><span class="math display">\[
 \mathcal{H}_t = \mathcal{H}_0 \left( 1 - \frac{1}{2N} \right)^t
 \]</span></p>
@@ -1975,6 +2152,7 @@ <h2>Heterozygosity dynamics</h2>
 </div>
 </div>
 <aside class="notes">
+<p>Alleles in generation <span class="math inline">\(n\)</span> are IDB but not IBS</p>
 <p>Motivation: mathematical description of genetic drift complicated for populations with more than one individual. Easier to study the evolution of heterozygosity.</p>
 <style type="text/css">
         span.MJX_Assistive_MathML {
@@ -1992,22 +2170,23 @@ <h2>Heterozygosity dynamics</h2>
 <h2>Heterozygosity dynamics</h2>
 <div class="columns">
 <div class="column" style="width:50%;">
-<div class="cell" data-hash="index_cache/revealjs/fig-gillespie-2.4_f7bd6699fc84473f60af1e607f3175b4">
+<div class="cell" data-hash="index_cache/revealjs/fig-gillespie-2.4_d14f95b244b4e152d6b65bb6d6a14968">
 <div class="cell-output-display">
 <div id="fig-gillespie-2.4" class="quarto-figure quarto-figure-left">
 <figure>
-<figcaption>Figure&nbsp;7: Plot of <span class="math inline">\(\mathcal{H}_t\)</span>.</figcaption>
+<figcaption>Figure&nbsp;8: Plot of <span class="math inline">\(\mathcal{H}_t\)</span> illustrating how loss depends on population size</figcaption>
 <p><img data-src="index_files/figure-revealjs/fig-gillespie-2.4-1.png" style="width:70.0%"></p>
 </figure>
 </div>
 </div>
 </div>
 </div><div class="column" style="width:50%;">
+<div class="fragment">
 <div class="cell" data-hash="index_cache/revealjs/fig-black-footed-ferret_f8419bf1dde49e5984ee63244f5ff196">
 <div class="cell-output-display">
 <div id="fig-black-footed-ferret" class="quarto-figure quarto-figure-left">
 <figure>
-<figcaption>Figure&nbsp;8: Heterozygosity in black-footed ferret <span class="citation" data-cites="wisely_GeneticDiversityFitness_2002">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Wisely et al., 2002</a>)</span></figcaption>
+<figcaption>Figure&nbsp;9: Heterozygosity in black-footed ferret <span class="citation" data-cites="wisely_GeneticDiversityFitness_2002">(<a href="#/bibliography" role="doc-biblioref" onclick="">Wisely et al., 2002</a>)</span></figcaption>
 <p><img data-src="index_files/figure-revealjs/fig-black-footed-ferret-1.png" style="width:70.0%"></p>
 </figure>
 </div>
@@ -2015,54 +2194,73 @@ <h2>Heterozygosity dynamics</h2>
 </div>
 </div>
 </div>
+</div>
 </section>
 <section id="effective-population-size" class="slide level2">
 <h2>Effective population size</h2>
-</section></section>
-<section>
-<section id="origin-and-change-of-variation" class="title-slide slide level1 center">
-<h1>Origin and change of variation</h1>
-
+<p>Assumptions underlying Wright-Fisher model seldom fulfilled for natural populations. In particular</p>
+<ul>
+<li>non-random mating (populations structured)</li>
+<li>fluctuations of population census size</li>
+</ul>
+<p>Therefore, magnitude of drift experienced by a population different from that predicted by population size</p>
+<p>Technically correct definition (but see <span class="citation" data-cites="waples_WhatNeAnyway_2022">Waples (<a href="#/bibliography" role="doc-biblioref" onclick="">2022</a>)</span>):</p>
+<blockquote>
+<p><span class="math inline">\(N_e\)</span> is the size of an ideal population that would experience the same rate of genetic drift as the population in question.</p>
+</blockquote>
+<aside class="notes">
+<p>ideal population: discrete generations with random mating, no evolutionary forces besides drift, no selective advantages</p>
+<style type="text/css">
+        span.MJX_Assistive_MathML {
+          position:absolute!important;
+          clip: rect(1px, 1px, 1px, 1px);
+          padding: 1px 0 0 0!important;
+          border: 0!important;
+          height: 1px!important;
+          width: 1px!important;
+          overflow: hidden!important;
+          display:block!important;
+      }</style></aside>
 </section>
 <section id="mutation" class="slide level2">
 <h2>Mutation</h2>
 <div class="columns">
 <div class="column" style="width:60%;">
-<p>Genetic drift “moves” frequencies to the point that variation is lost via allele fixation or loss. New variation is introduced through <em>mutation</em>. We typically assume mutations are described by a Poisson process with rate <span class="math inline">\(\mu\)</span> (per generation).</p>
-<p>The <em>mutation rate</em> is denoted <span class="math inline">\(\mu\)</span>, and the <em>population scaled</em> mutation rate is <span class="math inline">\(2N_e\mu\)</span> for haploid populations, <span class="math inline">\(4N_e\mu\)</span> for diploid, where <span class="math inline">\(N_e\)</span> is the <em>effective population size</em>.</p>
-<p>The <em>mutation - drift balance</em> is when the diversity lost due to drift equals the diversity gained due to mutation.</p>
+<p>Genetic drift “moves” frequencies to the point that variation is lost via allele fixation or loss. New variation is introduced through <strong>mutation</strong>. We typically assume mutations are described by a Poisson process with rate <span class="math inline">\(\mu\)</span> (per generation).</p>
+<div class="fragment">
+<p>The <strong>mutation rate</strong> is denoted <span class="math inline">\(\mu\)</span>, and the <strong>population scaled</strong> mutation rate is <span class="math inline">\(2N_e\mu\)</span> for haploid populations, <span class="math inline">\(4N_e\mu\)</span> for diploid, where <span class="math inline">\(N_e\)</span> is the <strong>effective population size</strong>.</p>
+</div>
+<div class="fragment">
+<p>The <strong>mutation - drift balance</strong> is when the diversity lost due to drift equals the diversity gained due to mutation.</p>
+</div>
 </div><div class="column" style="width:40%;">
+<div class="fragment">
 <div class="cell" data-hash="index_cache/revealjs/fig-wf-mutation_991753748542c1791bf543655453779e">
 <div class="cell-output-display">
 <div id="fig-wf-mutation" class="quarto-figure quarto-figure-left">
 <figure>
-<figcaption>Figure&nbsp;9: Variation is introduced by mutations (black) at rate <span class="math inline">\(\mu=1e^{-4}\)</span> and is occasionally lost through genetic drift.</figcaption>
+<figcaption>Figure&nbsp;10: Variation is introduced by mutations (black) at rate <span class="math inline">\(\mu=1e^{-4}\)</span> and is occasionally lost through genetic drift.</figcaption>
 <p><img data-src="index_files/figure-revealjs/fig-wf-mutation-1.png" width="400" height="500"></p>
 </figure>
 </div>
 </div>
 </div>
 </div>
-</div>
-</section>
-<section id="tracing-the-evolution-of-mutations" class="slide level2">
-<h2>Tracing the evolution of mutations</h2>
-<div class="columns">
-<div class="column" style="width:50%;">
-<div class="cell" data-hash="index_cache/revealjs/fig-tracing-evolution-of-mutations_f377734fdc8762e6e74527e73ef38252">
-<div class="cell-output-display">
-<div id="fig-tracing-evolution-of-mutations" class="quarto-figure quarto-figure-left">
-<figure>
-<figcaption>Figure&nbsp;10: Different mutations suffer different fates. Most mutations are lost in a couple of generations. Mutant alleles are colored black and their genealogies are highlighted with thicker edges.</figcaption>
-<p><img data-src="index_files/figure-revealjs/fig-tracing-evolution-of-mutations-1.png" width="600" height="600"></p>
-</figure>
-</div>
-</div>
-</div>
-</div><div class="column" style="width:50%;">
-
 </div>
 </div>
+<aside class="notes">
+<p>Segue: so if drift removes variation, what introduces it? Mutation!</p>
+<style type="text/css">
+        span.MJX_Assistive_MathML {
+          position:absolute!important;
+          clip: rect(1px, 1px, 1px, 1px);
+          padding: 1px 0 0 0!important;
+          border: 0!important;
+          height: 1px!important;
+          width: 1px!important;
+          overflow: hidden!important;
+          display:block!important;
+      }</style></aside>
 </section>
 <section id="models-of-mutation" class="slide level2">
 <h2>Models of mutation</h2>
@@ -2080,61 +2278,106 @@ <h2>Models of mutation</h2>
 </div>
 </div>
 <!-- markdownlint-enable MD013 -->
+<aside class="notes">
+<p>Model B: mainly for repeats</p>
+<style type="text/css">
+        span.MJX_Assistive_MathML {
+          position:absolute!important;
+          clip: rect(1px, 1px, 1px, 1px);
+          padding: 1px 0 0 0!important;
+          border: 0!important;
+          height: 1px!important;
+          width: 1px!important;
+          overflow: hidden!important;
+          display:block!important;
+      }</style></aside>
+</section>
+<section id="tracing-the-evolution-of-mutations" class="slide level2">
+<h2>Tracing the evolution of mutations</h2>
+<div class="columns">
+<div class="column" style="width:50%;">
+<div class="cell" data-hash="index_cache/revealjs/fig-tracing-evolution-of-mutations_ce09383e788b131e1853399217cbdc94">
+<div class="cell-output-display">
+<div id="fig-tracing-evolution-of-mutations" class="quarto-figure quarto-figure-left">
+<figure>
+<figcaption>Figure&nbsp;11: Different mutations suffer different fates. Most mutations are lost in a couple of generations. Mutant alleles are colored black and their genealogies are highlighted with thicker edges.</figcaption>
+<p><img data-src="index_files/figure-revealjs/fig-tracing-evolution-of-mutations-1.png" width="500" height="500"></p>
+</figure>
+</div>
+</div>
+</div>
+</div><div class="column" style="width:50%;">
+<div class="fragment">
+<p>Observation: most mutations are in fact lost</p>
+</div>
+<div class="fragment">
+<p>Recall: fixation probability <span class="math inline">\(\pi(p)=p\)</span></p>
+</div>
+</div>
+</div>
+<p>::::</p>
+<aside class="notes">
+<p>Difference to previous figure: here we highlight the genealogies</p>
+<style type="text/css">
+        span.MJX_Assistive_MathML {
+          position:absolute!important;
+          clip: rect(1px, 1px, 1px, 1px);
+          padding: 1px 0 0 0!important;
+          border: 0!important;
+          height: 1px!important;
+          width: 1px!important;
+          overflow: hidden!important;
+          display:block!important;
+      }</style></aside>
 </section>
 <section id="mutation-drift-balance" class="slide level2">
 <h2>Mutation drift balance</h2>
+<p>Drift removes variation. Mutation reintroduces it. At equilibrium the change in variation by definition is 0. In terms of <span class="math inline">\(\mathcal{H}_t\)</span> (the probability that two alleles are <em>not</em> identical by state), <span class="math inline">\(\Delta\mathcal{H}=0\)</span>.</p>
+<p>One can show<sup>1</sup> the classical formula that the equilibrium <em>heterozysity</em> value is</p>
+<p><span class="math display">\[
+\hat{\mathcal{H}} = \frac{4N_e\mu}{1 + 4N_e\mu}
+\]</span></p>
+<div class="fragment">
+<p><span class="math inline">\(\mu\)</span> is often assumed known, and heterozygosity is easily calculated from data, which provides a way of estimating <span class="math inline">\(N_e\)</span>.</p>
+</div>
+<aside><ol class="aside-footnotes"><li id="fn2"><p>see <span class="citation" data-cites="gillespie_PopulationGeneticsConcise_2004">(<a href="#/bibliography" role="doc-biblioref" onclick="">Gillespie, 2004, p. 29</a> – 31)</span> for a concise treatement</p></li></ol></aside></section>
+<section id="molecular-evolution-and-rate-of-substitution" class="slide level2">
+<h2>Molecular evolution and rate of substitution</h2>
+<p>Mutation enters populations and may be fixed by drift. Therefore, with time there will be fixed differences, or <strong>substitions</strong> (typically in the evolution of species) between populations, or species. In molecular evolution, the <strong>substition rate</strong>, <strong><span class="math inline">\(\rho\)</span></strong>, is the most interesting quantity.</p>
+<div class="fragment">
+<p>The total number of new mutations in every generation is <span class="math inline">\(2N\mu\)</span> (total number of gametes times mutation rate)</p>
+</div>
+<div class="fragment">
+<p>New mutations fix at a rate <span class="math inline">\(1/2N\)</span></p>
+</div>
+<div class="fragment">
+<p>Therefore, the <strong>average rate of substitution</strong>, <strong><span class="math inline">\(\rho\)</span></strong>, is <span class="math inline">\(2N\mu\times1/2N\)</span>, or</p>
+<p><span class="math display">\[
+\rho=\mu
+\]</span></p>
+<p>which is independent of population size!</p>
+</div>
+<aside class="notes">
+<p>On <span class="math inline">\(2N\mu\)</span>: the larger the population, the larger the number of mutations that can fix</p>
+<p>Stress importance of results and how it defies logic - it is independent of population size</p>
+<p>If <span class="math inline">\(4N\mu&gt;&gt;0\)</span> no new mutations can fix - no explicit mention of DNA sequence -&gt; infinite sites model</p>
+<style type="text/css">
+        span.MJX_Assistive_MathML {
+          position:absolute!important;
+          clip: rect(1px, 1px, 1px, 1px);
+          padding: 1px 0 0 0!important;
+          border: 0!important;
+          height: 1px!important;
+          width: 1px!important;
+          overflow: hidden!important;
+          display:block!important;
+      }</style></aside>
 </section></section>
 <section>
 <section id="selection" class="title-slide slide level1 center">
 <h1>Selection</h1>
-
-</section>
-<section id="selection-1" class="slide level2">
-<h2>Selection</h2>
-<p><span class="math display">\[
-\begin{matrix}
-\mathrm{Genotype} &amp; A_1A_1 &amp; A_1A_2 &amp; A_2A_2 \\
-\mathrm{Fitnesses} &amp; w_{11} &amp; w_{12} &amp; w_{22}\\
-\mathrm{Observations} &amp; 1.5 &amp; 1.1 &amp; 1.0 \\
-\mathrm{Relative\ fitness} &amp; 1 &amp; 1-hs &amp; 1-s\\
-\mathrm{Normalized} &amp; 1 &amp; 11 / 15 &amp; 10 / 15
-\end{matrix}
-\]</span></p>
-<p>Relative fitness.</p>
-<p>Define s, positive and negative selection</p>
-</section>
-<section id="direct-selection" class="slide level2">
-<h2>Direct selection</h2>
-<p>d_N/d_S</p>
-</section>
-<section id="selective-sweeps" class="slide level2">
-<h2>Selective sweeps</h2>
-<p>Show WF-model with sweep</p>
-</section>
-<section id="two-locus-dynamics" class="slide level2">
-<h2>Two-locus dynamics</h2>
-<p>Graph focal locus and flanking loci (Charlesworth figure)</p>
-<ul>
-<li>Linkage disequilibrium and recombination (each probably need separate treatment)</li>
-<li>Contrast linked selection with direct selection</li>
-<li>Genetic draft / background selection</li>
-</ul>
-</section>
-<section id="soft-sweep" class="slide level2">
-<h2>Soft sweep</h2>
-</section>
-<section id="neutral-theory" class="slide level2">
-<h2>Neutral theory</h2>
-<p>Move right after drift?</p>
-<ul>
-<li>Hahn: <span class="math inline">\(\mu = \nu*f_0\)</span></li>
-<li>why population size matters</li>
-<li>Fig 4.2, Eq 4.2 from Lynch</li>
-</ul>
 <aside class="notes">
-<p>Mutation - drift balance. Simplifying assumptions -&gt; rate of drift <span class="math inline">\(propto \frac{1}{N}\)</span> <span class="citation" data-cites="leffler_RevisitingOldRiddle_2012">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Leffler et al., 2012</a>)</span></p>
-<p>In reality population fluctuates and therefore Ne is substituted for N. Larger Ne -&gt; smaller fluctuation in allele frequency (as evidenced in plots) -&gt; maintain larger genetic diversity</p>
-<p>Neutral diversity patterns depend not only on drift but also on rate at which variation changes at <em>linked sites</em> <span class="citation" data-cites="leffler_RevisitingOldRiddle_2012">(<a href="#/effect-on-genetic-diversity" role="doc-biblioref" onclick="">Leffler et al., 2012</a>)</span></p>
+<p>For a list of methods see <a href="https://methodspopgen.com/methods-to-detect-selection/" class="uri">https://methodspopgen.com/methods-to-detect-selection/</a></p>
 <style type="text/css">
         span.MJX_Assistive_MathML {
           position:absolute!important;
@@ -2147,24 +2390,73 @@ <h2>Neutral theory</h2>
           display:block!important;
       }</style></aside>
 </section>
-<section id="nearly-neutral-theory" class="slide level2">
-<h2>Nearly neutral theory</h2>
-<ul>
-<li>Distribution of fitness effects?</li>
-</ul>
+<section id="selection-1" class="slide level2 smaller">
+<h2>Selection</h2>
+<blockquote>
+<p>Much confusion exists in the literature regarding how various types of selection are defined, in particular because some of the terminology is used slightly differently within different scientific communities <span class="citation" data-cites="nielsen_MolecularSignaturesNatural_2005">(<a href="#/bibliography" role="doc-biblioref" onclick="">Nielsen, 2005</a>)</span></p>
+</blockquote>
+<p><span class="math display">\[
+\begin{matrix}
+\mathrm{Genotype} &amp; aa &amp; aA &amp; AA \\
+\mathrm{Frequency\ in\ newborns} &amp; p^2 &amp; 2pq &amp; q^2\\
+\mathrm{Viability} &amp; w_{aa} &amp; w_{aA} &amp; w_{AA}\\
+\mathrm{Frequency\ after\ selection} &amp; p^2w_{aa} / \bar{w} &amp;
+    2pqw_{Aa} / \bar{w} &amp; q^2w_{AA} / \bar{w} \\
+\mathrm{Relative\ fitness} &amp; 1 &amp; 1-hs &amp; 1-s\\
+\end{matrix}
+\]</span></p>
+<p>where <span class="math inline">\(\bar{w} = p^2w_{aa} + 2pqw_{Aa} + q^2w_{AA}\)</span> is the <strong>mean fitness</strong></p>
+<p><br></p>
+<table>
+<tbody>
+<tr class="odd">
+<td><span class="math inline">\(h=0\)</span></td>
+<td><span class="math inline">\(a\)</span> dominant, <span class="math inline">\(A\)</span> recessive</td>
+</tr>
+<tr class="even">
+<td><span class="math inline">\(h=1\)</span></td>
+<td><span class="math inline">\(A\)</span> dominant, <span class="math inline">\(a\)</span> recessive</td>
+</tr>
+<tr class="odd">
+<td><span class="math inline">\(0&lt;h&lt;1\)</span></td>
+<td>incomplete dominance</td>
+</tr>
+<tr class="even">
+<td><span class="math inline">\(h&lt;0\)</span></td>
+<td>overdominance (heterozygote advantage)</td>
+</tr>
+<tr class="odd">
+<td><span class="math inline">\(h&gt;1\)</span></td>
+<td>underdominance</td>
+</tr>
+</tbody>
+</table>
 </section>
-<section id="effect-on-genetic-diversity" class="slide level2 smaller scrollable">
-<h2>Effect on genetic diversity</h2>
+<section id="mean-fitness-of-a-population" class="slide level2">
+<h2>Mean fitness of a population</h2>
+
+<img data-src="index_files/figure-revealjs/fig-dominance-1.png" width="960" class="r-stretch quarto-figure-left"><p class="caption">Figure&nbsp;12: Mean fitness of the population as a function of <span class="math inline">\(p\)</span> for <span class="math inline">\(s=0.1\)</span> and <span class="math inline">\(h=-0.5\)</span>, <span class="math inline">\(0.5\)</span>, and <span class="math inline">\(1.5\)</span></p></section>
+<section id="selection-and-drift" class="slide level2">
+<h2>Selection and drift</h2>
+<div class="columns">
+<div class="column" style="width:80%;">
+<div class="cell" data-hash="index_cache/revealjs/fig-selection-and-drift_f17a6253fe47d76f6abe9f4718c8bc6e">
+<div class="cell-output-display">
+<div id="fig-selection-and-drift" class="quarto-figure quarto-figure-left">
+<figure>
+<figcaption>Figure&nbsp;13: The fixation probability relative to the neutral probability of fixation under the assumption <span class="math inline">\(s&lt;0.1\)</span>.</figcaption>
+<p><img data-src="index_files/figure-revealjs/fig-selection-and-drift-1.png" width="1152"></p>
+</figure>
+</div>
+</div>
+</div>
+</div><div class="column" style="width:20%;">
+
+</div>
+</div>
 <aside class="notes">
-<p>Mutation - drift balance. Mode of selection important:</p>
-<ul>
-<li>selection for adaptation at new mutations decreases diversity more than if at standing variation</li>
-<li>balancing selection may <em>increase</em> variation</li>
-</ul>
-<p>Recombination</p>
-<ul>
-<li>low recombination -&gt; lower diversity (less reshuffling of variation)</li>
-</ul>
+<p>Even if a new mutation has, say, <span class="math inline">\(s=0.01\)</span>, it still needs to overcome effects of drift.</p>
+<p>In figure, we assume <span class="math inline">\(s&lt;0.1\)</span>, which is almost always the case.</p>
 <style type="text/css">
         span.MJX_Assistive_MathML {
           position:absolute!important;
@@ -2176,14 +2468,16 @@ <h2>Effect on genetic diversity</h2>
           overflow: hidden!important;
           display:block!important;
       }</style></aside>
-
-<div class="footer footer-default">
-<p>Population genetics foundations</p>
-</div>
+</section>
+<section id="bibliography" class="slide level2 unnumbered unlisted smaller scrollable">
+<h2>Bibliography</h2>
 <div id="refs" class="references csl-bib-body hanging-indent" data-line-spacing="2" role="list">
 <div id="ref-casillas_MolecularPopulationGenetics_2017" class="csl-entry" role="listitem">
 Casillas, S., &amp; Barbadilla, A. (2017). Molecular <span>Population Genetics</span>. <em>Genetics</em>, <em>205</em>(3), 1003–1035. <a href="https://doi.org/10.1534/genetics.116.196493">https://doi.org/10.1534/genetics.116.196493</a>
 </div>
+<div id="ref-cooplab_PopulationGeneticsCourse_2011" class="csl-entry" role="listitem">
+cooplab. (2011). Population genetics course resources: <span>Hardy-Weinberg Eq</span>. In <em>gcbias</em>. <a href="https://gcbias.org/2011/10/13/population-genetics-course-resources-hardy-weinberg-eq/">https://gcbias.org/2011/10/13/population-genetics-course-resources-hardy-weinberg-eq/</a>
+</div>
 <div id="ref-gillespie_PopulationGeneticsConcise_2004" class="csl-entry" role="listitem">
 Gillespie, J. H. (2004). <em>Population <span>Genetics</span>: <span>A Concise Guide</span></em> (2nd edition). <span>Johns Hopkins University Press</span>.
 </div>
@@ -2205,10 +2499,20 @@ <h2>Effect on genetic diversity</h2>
 <div id="ref-nei_MolecularEvolutionPhylogenetics_2000" class="csl-entry" role="listitem">
 Nei, M., &amp; Kumar, S. (2000). <em>Molecular <span>Evolution</span> and <span>Phylogenetics</span></em>. <span>Oxford University Press</span>.
 </div>
+<div id="ref-nielsen_MolecularSignaturesNatural_2005" class="csl-entry" role="listitem">
+Nielsen, R. (2005). Molecular <span>Signatures</span> of <span>Natural Selection</span>. <em>Annual Review of Genetics</em>, <em>39</em>(1), 197–218. <a href="https://doi.org/10.1146/annurev.genet.39.073003.112420">https://doi.org/10.1146/annurev.genet.39.073003.112420</a>
+</div>
+<div id="ref-waples_WhatNeAnyway_2022" class="csl-entry" role="listitem">
+Waples, R. S. (2022). What <span>Is Ne</span>, <span>Anyway</span>? <em>Journal of Heredity</em>, <em>113</em>(4), 371–379. <a href="https://doi.org/10.1093/jhered/esac023">https://doi.org/10.1093/jhered/esac023</a>
+</div>
 <div id="ref-wisely_GeneticDiversityFitness_2002" class="csl-entry" role="listitem">
 Wisely, S. M., Buskirk, S. W., Fleming, M. A., McDonald, D. B., &amp; Ostrander, E. A. (2002). Genetic <span>Diversity</span> and <span>Fitness</span> in <span>Black-Footed Ferrets Before</span> and <span>During</span> a <span>Bottleneck</span>. <em>Journal of Heredity</em>, <em>93</em>(4), 231–237. <a href="https://doi.org/10.1093/jhered/93.4.231">https://doi.org/10.1093/jhered/93.4.231</a>
 </div>
 </div>
+
+<div class="footer footer-default">
+<p>Population genetics foundations</p>
+</div>
 </section></section>
 
     </div>
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diff --git a/slides/genetic_diversity/index.html b/slides/genetic_diversity/index.html
index 0798872b..6b84202b 100644
--- a/slides/genetic_diversity/index.html
+++ b/slides/genetic_diversity/index.html
@@ -351,7 +351,7 @@ <h1 class="title">Genetic diversity</h1>
   <h3 class="subtitle">Genetic diversity</h3>
   <h4 class="author">Per Unneberg</h4>
   <h4 class="institute">NBIS</h4>
-  <h4 class="date">20-Aug-2023</h4>
+  <h4 class="date">22-Aug-2023</h4>
 </section><section id="TOC">
 <nav role="doc-toc"> 
 <h2 id="toc-title">Table of contents</h2>
diff --git a/slides/index.html b/slides/index.html
index 2fa8a3ac..3dd23085 100644
--- a/slides/index.html
+++ b/slides/index.html
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@@ -4948,7 +4948,7 @@ <h5 class="no-anchor card-title listing-title">
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diff --git a/slides/introduction/index.html b/slides/introduction/index.html
index 92efe399..0170f6f9 100644
--- a/slides/introduction/index.html
+++ b/slides/introduction/index.html
@@ -351,7 +351,7 @@ <h1 class="title">Population genomics in practice</h1>
   <h3 class="subtitle">Welcome and information</h3>
   <h4 class="author">Per Unneberg</h4>
   <h4 class="institute">NBIS</h4>
-  <h4 class="date">20-Aug-2023</h4>
+  <h4 class="date">22-Aug-2023</h4>
 </section><section id="TOC">
 <nav role="doc-toc"> 
 <h2 id="toc-title">Table of contents</h2>
diff --git a/slides/ld_and_recombination/index.html b/slides/ld_and_recombination/index.html
index 5bab6f05..42c32e7c 100644
--- a/slides/ld_and_recombination/index.html
+++ b/slides/ld_and_recombination/index.html
@@ -332,7 +332,7 @@ <h1 class="title">Linkage disequilibrium and recombination</h1>
   <h3 class="subtitle">LD and recombination</h3>
   <h4 class="author">Per Unneberg</h4>
   <h4 class="institute">NBIS</h4>
-  <h4 class="date">20-Aug-2023</h4>
+  <h4 class="date">22-Aug-2023</h4>
 </section><section id="TOC">
 <nav role="doc-toc"> 
 <h2 id="toc-title">Table of contents</h2>
diff --git a/slides/linked_selection/index.html b/slides/linked_selection/index.html
index 54045749..5085863f 100644
--- a/slides/linked_selection/index.html
+++ b/slides/linked_selection/index.html
@@ -415,7 +415,7 @@
   <h1 class="title">Linked selection</h1>
   <h4 class="author">Per Unneberg</h4>
   <h4 class="institute">NBIS</h4>
-  <h4 class="date">20-Aug-2023</h4>
+  <h4 class="date">22-Aug-2023</h4>
 </section><section id="TOC">
 <nav role="doc-toc"> 
 <h2 id="toc-title">Table of contents</h2>
diff --git a/slides/pgip/index.html b/slides/pgip/index.html
index 57656590..bbf3d24f 100644
--- a/slides/pgip/index.html
+++ b/slides/pgip/index.html
@@ -351,7 +351,7 @@ <h1 class="title">Population genomics in practice</h1>
   <h3 class="subtitle">What is population genomics?</h3>
   <h4 class="author">Per Unneberg</h4>
   <h4 class="institute">NBIS</h4>
-  <h4 class="date">20-Aug-2023</h4>
+  <h4 class="date">22-Aug-2023</h4>
 </section>
 
 <section id="example-population-genetics-of-the-coral-acropora-millepora" class="slide level2">
@@ -440,13 +440,31 @@ <h2>Example: Population genetics of the coral <em>Acropora millepora</em></h2>
 <p><span class="citation" data-cites="fuller_PopulationGeneticsCoral_2020">Fuller et al. (<a href="#/bibliography" role="doc-biblioref" onclick="">2020</a>)</span></p>
 </div>
 </section>
+<section id="population-genetics" class="slide level2">
+<h2>Population genetics</h2>
+<ul>
+<li>briefly outline what popultation genetics studies,</li>
+</ul>
+<aside class="notes">
+<p>Population genetics focuses on the genetic basis of evolution. It is mainly a theoretical subject, owing to the slow changes of genetic variation. As such, it tries to explain the shape and structure of genetic variation from theoretical predictions and models.</p>
+<style type="text/css">
+        span.MJX_Assistive_MathML {
+          position:absolute!important;
+          clip: rect(1px, 1px, 1px, 1px);
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+          width: 1px!important;
+          overflow: hidden!important;
+          display:block!important;
+      }</style></aside>
+</section>
 <section id="from-population-genetics-to-population-genomics" class="slide level2">
 <h2>From population genetics to population genomics</h2>
 <!-- markdownlint-disable MD013 -->
 
 <!-- markdownlint-enable MD013 -->
 <img data-src="assets/images/kreitman-1983-table1.png" class="r-stretch quarto-figure-left"><p class="caption">The variable sites at the <em>Drosophila melanogaster</em> ADH locus <span class="citation" data-cites="kreitman_NucleotidePolymorphismAlcohol_1983">(<a href="#/bibliography" role="doc-biblioref" onclick="">Kreitman, 1983</a>)</span></p><aside class="notes">
-<p>Population genetics focuses on the genetic basis of evolution. It is mainly a theoretical subject, owing to the slow changes of genetic variation. As such, it tries to explain the shape and structure of genetic variation from theoretical predictions and models.</p>
 <ul>
 <li>from locus-based studies (e.g., alcohol dehydrogenase in Drosophila <span class="citation" data-cites="kreitman_NucleotidePolymorphismAlcohol_1983">(<a href="#/bibliography" role="doc-biblioref" onclick="">Kreitman, 1983</a>)</span>) to genome-wide (e.g., Drosophila population genomics <span class="citation" data-cites="begun_PopulationGenomicsWholeGenome_2007">(<a href="#/bibliography" role="doc-biblioref" onclick="">Begun et al., 2007</a>)</span></li>
 <li>note: studied loci have often not been randomly chosen, which is another argument for whole-genome studies</li>
@@ -524,46 +542,12 @@ <h2>Statistical inference in population genomics</h2>
 </ol>
 </div>
 </section>
-<section>
-<section id="population-genomics-in-the-future" class="title-slide slide level1 center">
-<h1>Population genomics in the future</h1>
-
-</section>
-<section id="machine-learning-and-ai-for-population-genomics" class="slide level2">
-<h2>Machine learning and AI for population genomics</h2>
-</section>
-<section id="ancestral-recombination-graph-methods" class="slide level2">
-<h2>Ancestral recombination graph methods</h2>
-<ul>
-<li>deep population sequencing</li>
-<li>argweaver</li>
-<li>tskit</li>
-<li>Relate</li>
-<li>ARG-Needle</li>
-</ul>
-</section>
-<section id="ancient-dna" class="slide level2">
-<h2>Ancient DNA</h2>
-<ul>
-<li>Relate</li>
-</ul>
-</section>
-<section id="coalescent-and-wright-fisher-simulations" class="slide level2">
-<h2>Coalescent and Wright-Fisher simulations</h2>
-<p>Use to determine significance in genome-wide scans</p>
-</section>
-<section id="structural-variation-in-natural-populations" class="slide level2">
-<h2>Structural variation in natural populations</h2>
-</section>
 <section id="bibliography" class="slide level2 unnumbered unlisted smaller scrollable">
 <h2>Bibliography</h2>
 <div id="refs" class="references csl-bib-body hanging-indent" data-line-spacing="2" role="list">
 <div id="ref-begun_PopulationGenomicsWholeGenome_2007" class="csl-entry" role="listitem">
 Begun, D. J., Holloway, A. K., Stevens, K., Hillier, L. W., Poh, Y.-P., Hahn, M. W., Nista, P. M., Jones, C. D., Kern, A. D., Dewey, C. N., Pachter, L., Myers, E., &amp; Langley, C. H. (2007). Population <span>Genomics</span>: <span>Whole-Genome Analysis</span> of <span>Polymorphism</span> and <span>Divergence</span> in <span>Drosophila</span> simulans. <em>PLOS Biology</em>, <em>5</em>(11), e310. <a href="https://doi.org/10.1371/journal.pbio.0050310">https://doi.org/10.1371/journal.pbio.0050310</a>
 </div>
-<div id="ref-casillas_MolecularPopulationGenetics_2017" class="csl-entry" role="listitem">
-Casillas, S., &amp; Barbadilla, A. (2017). Molecular <span>Population Genetics</span>. <em>Genetics</em>, <em>205</em>(3), 1003–1035. <a href="https://doi.org/10.1534/genetics.116.196493">https://doi.org/10.1534/genetics.116.196493</a>
-</div>
 <div id="ref-fuller_PopulationGeneticsCoral_2020" class="csl-entry" role="listitem">
 Fuller, Z. L., Mocellin, V. J. L., Morris, L. A., Cantin, N., Shepherd, J., Sarre, L., Peng, J., Liao, Y., Pickrell, J., Andolfatto, P., Matz, M., Bay, L. K., &amp; Przeworski, M. (2020). Population genetics of the coral <span>Acropora</span> millepora: <span>Toward</span> genomic prediction of bleaching. <em>Science</em>, <em>369</em>(6501), eaba4674. <a href="https://doi.org/10.1126/science.aba4674">https://doi.org/10.1126/science.aba4674</a>
 </div>
@@ -586,45 +570,11 @@ <h2>Bibliography</h2>
 Wetterstrand, KA. <em><span>DNA Sequencing Costs</span>: <span>Data</span> from the <span>NHGRI Genome Sequencing Program</span> (<span>GSP</span>)</em>. <a href="https://www.genome.gov/sequencingcostsdata">www.genome.gov/sequencingcostsdata</a>
 </div>
 </div>
-<aside class="notes">
-<ul>
-<li>A brief history of population genetics
-<ul>
-<li>Mendel’s peas</li>
-<li>Galton, Pearson and Bateson: Of blending and particulate inheritance (the latter championed by Bateson)</li>
-<li>Morgan’s flies</li>
-<li>Fisher and natural selection</li>
-<li>The modern synthesis</li>
-<li>Wright and drift</li>
-<li>Kimura and the diffusion approximation - Follow <span class="citation" data-cites="casillas_MolecularPopulationGenetics_2017">(<a href="#/bibliography" role="doc-biblioref" onclick="">Casillas &amp; Barbadilla, 2017</a>)</span> history?</li>
-<li>The allozyme era (FIXME: picture of allozyme gel)
-<ul>
-<li>Development of genetic diversity measures
-<ul>
-<li><em>H</em> (<em>gene diversity</em>, <em>heterozygosity</em>): average proportion heterozygous loci</li>
-<li><em>P</em> (<em>gene polymorphism</em>): average proportion polymorphic loci</li>
-</ul></li>
-</ul></li>
-<li>The nucleotide diversity era</li>
-<li>The population genomics era</li>
-</ul></li>
-</ul>
-<style type="text/css">
-        span.MJX_Assistive_MathML {
-          position:absolute!important;
-          clip: rect(1px, 1px, 1px, 1px);
-          padding: 1px 0 0 0!important;
-          border: 0!important;
-          height: 1px!important;
-          width: 1px!important;
-          overflow: hidden!important;
-          display:block!important;
-      }</style></aside>
 
 <div class="footer footer-default">
 
 </div>
-</section></section>
+</section>
     </div>
   </div>
 
diff --git a/slides/population_structure/index.html b/slides/population_structure/index.html
index d5e95401..79307b0b 100644
--- a/slides/population_structure/index.html
+++ b/slides/population_structure/index.html
@@ -351,7 +351,7 @@ <h1 class="title">Population structure</h1>
   <h3 class="subtitle">Fixation indices, admixture and more</h3>
   <h4 class="author">Per Unneberg</h4>
   <h4 class="institute">NBIS</h4>
-  <h4 class="date">20-Aug-2023</h4>
+  <h4 class="date">22-Aug-2023</h4>
 </section><section id="TOC">
 <nav role="doc-toc"> 
 <h2 id="toc-title">Table of contents</h2>
diff --git a/slides/population_structure/index_files/figure-revealjs/fst-independent-population-model-1.svg b/slides/population_structure/index_files/figure-revealjs/fst-independent-population-model-1.svg
index 7c4140b1..23ed5907 100644
--- a/slides/population_structure/index_files/figure-revealjs/fst-independent-population-model-1.svg
+++ b/slides/population_structure/index_files/figure-revealjs/fst-independent-population-model-1.svg
@@ -2,9 +2,9 @@
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diff --git a/slides/recap_day1/index.html b/slides/recap_day1/index.html
index fa437ae6..7232443f 100644
--- a/slides/recap_day1/index.html
+++ b/slides/recap_day1/index.html
@@ -331,7 +331,7 @@
   <h1 class="title">Recap day 1</h1>
   <h4 class="author">Per Unneberg</h4>
   <h4 class="institute">NBIS</h4>
-  <h4 class="date">20-Aug-2023</h4>
+  <h4 class="date">22-Aug-2023</h4>
 </section><section id="TOC">
 <nav role="doc-toc"> 
 <h2 id="toc-title">Table of contents</h2>
diff --git a/slides/recap_day2/index.html b/slides/recap_day2/index.html
index 190f0f09..5d150887 100644
--- a/slides/recap_day2/index.html
+++ b/slides/recap_day2/index.html
@@ -331,7 +331,7 @@
   <h1 class="title">Recap day 2</h1>
   <h4 class="author">Per Unneberg</h4>
   <h4 class="institute">NBIS</h4>
-  <h4 class="date">20-Aug-2023</h4>
+  <h4 class="date">22-Aug-2023</h4>
 </section><section id="TOC">
 <nav role="doc-toc"> 
 <h2 id="toc-title">Table of contents</h2>
diff --git a/slides/recap_day3/index.html b/slides/recap_day3/index.html
index 6be7ea2d..355314e7 100644
--- a/slides/recap_day3/index.html
+++ b/slides/recap_day3/index.html
@@ -331,7 +331,7 @@
   <h1 class="title">Recap day 3</h1>
   <h4 class="author">Per Unneberg</h4>
   <h4 class="institute">NBIS</h4>
-  <h4 class="date">20-Aug-2023</h4>
+  <h4 class="date">22-Aug-2023</h4>
 </section><section id="TOC">
 <nav role="doc-toc"> 
 <h2 id="toc-title">Table of contents</h2>
diff --git a/slides/recap_day4/index.html b/slides/recap_day4/index.html
index bc9a7c63..695594bb 100644
--- a/slides/recap_day4/index.html
+++ b/slides/recap_day4/index.html
@@ -331,7 +331,7 @@
   <h1 class="title">Recap day 4</h1>
   <h4 class="author">Per Unneberg</h4>
   <h4 class="institute">NBIS</h4>
-  <h4 class="date">20-Aug-2023</h4>
+  <h4 class="date">22-Aug-2023</h4>
 </section><section id="TOC">
 <nav role="doc-toc"> 
 <h2 id="toc-title">Table of contents</h2>
diff --git a/slides/simulation/index.html b/slides/simulation/index.html
index ff37f9d8..2fd02915 100644
--- a/slides/simulation/index.html
+++ b/slides/simulation/index.html
@@ -348,46 +348,22 @@
 
 <section id="title-slide" data-background-image="../../assets/logos/nbis-scilifelab.svg" data-background-position="top left" data-background-size="500px" class="center">
   <h1 class="title">Simulation</h1>
-  <h3 class="subtitle">Primer on forward simulation and the coalescent</h3>
+  <h3 class="subtitle">Primer on the coalescent and forward simulation</h3>
   <h4 class="author">Per Unneberg</h4>
   <h4 class="institute">NBIS</h4>
-  <h4 class="date">20-Aug-2023</h4>
+  <h4 class="date">22-Aug-2023</h4>
 </section><section id="TOC">
 <nav role="doc-toc"> 
 <h2 id="toc-title">Table of contents</h2>
 <ul>
-<li><a href="#/forward-simulation" id="/toc-forward-simulation">Forward simulation</a></li>
 <li><a href="#/the-coalescent" id="/toc-the-coalescent">The coalescent</a></li>
+<li><a href="#/the-coalescent-with-recombination" id="/toc-the-coalescent-with-recombination">The coalescent with recombination</a></li>
+<li><a href="#/forward-simulation" id="/toc-forward-simulation">Forward simulation</a></li>
 <li><a href="#/combining-forward-and-backward-simulations" id="/toc-combining-forward-and-backward-simulations">Combining forward and backward simulations</a></li>
 </ul>
 </nav>
 </section>
 
-<section>
-<section id="forward-simulation" class="title-slide slide level1 center">
-<h1>Forward simulation</h1>
-
-</section>
-<section id="forward-simulation-1" class="slide level2">
-<h2>Forward simulation</h2>
-<p>Begin with forward simulation as it makes more intuitive sense.</p>
-<p>Outline principles of forward simulation from Wright-Fisher model “algorithm”.</p>
-</section>
-<section id="slim" class="slide level2">
-<h2>SLiM</h2>
-<p>Mention advances in forward simulations</p>
-</section>
-<section id="forward-simulation-in-slim" class="slide level2">
-<h2>Forward simulation in SLiM</h2>
-<p>Outline steps in SLiM - relate to WF model</p>
-</section>
-<section id="example-in-slim" class="slide level2">
-<h2>Example in SLiM</h2>
-</section>
-<section id="exercise-in-slim" class="slide level2">
-<h2>Exercise in SLiM</h2>
-<p>Optional: do a simple neutral model</p>
-</section></section>
 <section>
 <section id="the-coalescent" class="title-slide slide level1 center">
 <h1>The coalescent</h1>
@@ -685,6 +661,25 @@ <h2>Simulating genealogies <span class="citation" data-cites="hahn_MolecularPopu
 </div>
 </div>
 </div>
+</div>
+</div>
+</section>
+<section id="some-properties-of-the-tree" class="slide level2">
+<h2>Some properties of the tree</h2>
+<div class="columns">
+<div class="column" style="width:50%;">
+<div class="cell" data-hash="index_cache/revealjs/fig-genealogy-properties_3e7a3f6cc17d800de40104e0ada916fc">
+<div class="cell-output-display">
+<div id="fig-genealogy-properties" class="quarto-figure quarto-figure-left">
+<figure>
+<figcaption>Figure&nbsp;4: A coalescent tree with numbered nodes. Nodes 1-5 correspond to the samples and are leaves. The internal nodes 6-8 (and the unlabelled root) are ancestral chromosomes (unsampled). The <span class="math inline">\(T_i\)</span> represent the waiting time when the state has <span class="math inline">\(i\)</span> chromosomes, whereas <span class="math inline">\(\tau_i\)</span> correspond to the branch length from node <span class="math inline">\(i\)</span> to its parent.</figcaption>
+<p><img data-src="index_files/figure-revealjs/fig-genealogy-properties-1.svg" width="400" height="500"></p>
+</figure>
+</div>
+</div>
+</div>
+</div><div class="column" style="width:50%;">
+
 </div>
 </div>
 </section>
@@ -706,7 +701,7 @@ <h2>Adding mutations</h2>
 <div class="cell-output-display">
 <div id="fig-add-mutations" class="quarto-figure quarto-figure-left">
 <figure>
-<figcaption>Figure&nbsp;4: Adding mutations on a coalescent genealogy.</figcaption>
+<figcaption>Figure&nbsp;5: Adding mutations on a coalescent genealogy.</figcaption>
 <p><img data-src="index_files/figure-revealjs/fig-add-mutations-1.svg" width="500" height="500"></p>
 </figure>
 </div>
@@ -729,6 +724,26 @@ <h2>Evolutionary processes and genealogies</h2>
 <li>bottleneck</li>
 </ul>
 </section>
+<section id="exercise-on-the-coalescent" class="slide level2">
+<h2>Exercise on the coalescent</h2>
+</section></section>
+<section>
+<section id="the-coalescent-with-recombination" class="title-slide slide level1 center">
+<h1>The coalescent with recombination</h1>
+
+</section>
+<section id="chromosome-are-mosaics-of-non-recombining-units" class="slide level2">
+<h2>Chromosome are mosaics of non-recombining units</h2>
+</section>
+<section id="the-ancestral-recombination-graph" class="slide level2">
+<h2>The ancestral recombination graph</h2>
+</section>
+<section id="the-sequential-markovian-coalescent" class="slide level2">
+<h2>The Sequential Markovian Coalescent</h2>
+</section>
+<section id="tree-sequences" class="slide level2">
+<h2>Tree sequences</h2>
+</section>
 <section id="msprime" class="slide level2">
 <h2>msprime</h2>
 </section>
@@ -737,6 +752,31 @@ <h2>Exercises with msprime</h2>
 <p>Use CLI to generate genealogies and mutations.</p>
 </section></section>
 <section>
+<section id="forward-simulation" class="title-slide slide level1 center">
+<h1>Forward simulation</h1>
+
+</section>
+<section id="forward-simulation-1" class="slide level2">
+<h2>Forward simulation</h2>
+<p>Begin with forward simulation as it makes more intuitive sense.</p>
+<p>Outline principles of forward simulation from Wright-Fisher model “algorithm”.</p>
+</section>
+<section id="slim" class="slide level2">
+<h2>SLiM</h2>
+<p>Mention advances in forward simulations</p>
+</section>
+<section id="forward-simulation-in-slim" class="slide level2">
+<h2>Forward simulation in SLiM</h2>
+<p>Outline steps in SLiM - relate to WF model</p>
+</section>
+<section id="example-in-slim" class="slide level2">
+<h2>Example in SLiM</h2>
+</section>
+<section id="exercise-in-slim" class="slide level2">
+<h2>Exercise in SLiM</h2>
+<p>Optional: do a simple neutral model</p>
+</section></section>
+<section>
 <section id="combining-forward-and-backward-simulations" class="title-slide slide level1 center">
 <h1>Combining forward and backward simulations</h1>
 
diff --git a/slides/simulation/index_files/figure-revealjs/fig-genealogy-properties-1.svg b/slides/simulation/index_files/figure-revealjs/fig-genealogy-properties-1.svg
new file mode 100644
index 00000000..2321b853
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+++ b/slides/simulation/index_files/figure-revealjs/fig-genealogy-properties-1.svg
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