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recla-analysis.html
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recla-analysis.html
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<title>Recla Analysis</title>
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<h1 class="title toc-ignore">Recla Analysis</h1>
<h4 class="author">Frederick Boehm</h4>
<h4 class="date">2020-06-29</h4>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a>is_inst <-<span class="st"> </span><span class="cf">function</span>(pkg) {</span>
<span id="cb1-2"><a href="#cb1-2"></a> <span class="kw">nzchar</span>(<span class="kw">system.file</span>(<span class="dt">package =</span> pkg))</span>
<span id="cb1-3"><a href="#cb1-3"></a>}</span>
<span id="cb1-4"><a href="#cb1-4"></a>qtl2_indic <-<span class="st"> </span><span class="kw">is_inst</span>(<span class="st">"qtl2"</span>)</span>
<span id="cb1-5"><a href="#cb1-5"></a>knitr<span class="op">::</span>opts_chunk<span class="op">$</span><span class="kw">set</span>(<span class="dt">eval =</span> qtl2_indic)</span></code></pre></div>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a><span class="kw">library</span>(dplyr)</span>
<span id="cb2-2"><a href="#cb2-2"></a><span class="kw">library</span>(ggplot2)</span>
<span id="cb2-3"><a href="#cb2-3"></a><span class="kw">library</span>(qtl2pleio)</span>
<span id="cb2-4"><a href="#cb2-4"></a><span class="kw">library</span>(qtl2)</span></code></pre></div>
<div id="load-recla-data-from-qtl2data-github-repository" class="section level2">
<h2>Load Recla data from <code>qtl2data</code> github repository</h2>
<p>We illustrate functions from <code>qtl2pleio</code> by analyzing data from 261 Diversity Outbred mice <span class="citation">(Recla et al. 2014, @logan2013high)</span>.</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a>file <-<span class="st"> </span><span class="kw">paste0</span>(<span class="st">"https://raw.githubusercontent.com/rqtl/"</span>,</span>
<span id="cb3-2"><a href="#cb3-2"></a> <span class="st">"qtl2data/master/DO_Recla/recla.zip"</span>)</span>
<span id="cb3-3"><a href="#cb3-3"></a>recla <-<span class="st"> </span><span class="kw">read_cross2</span>(file)</span>
<span id="cb3-4"><a href="#cb3-4"></a><span class="co"># make sex a covariate for use in qtl2pleio::scan_pvl</span></span>
<span id="cb3-5"><a href="#cb3-5"></a>recla[[<span class="dv">6</span>]][ , <span class="dv">1</span>, drop =<span class="st"> </span><span class="ot">FALSE</span>] -><span class="st"> </span>sex</span>
<span id="cb3-6"><a href="#cb3-6"></a><span class="co"># insert pseudomarkers</span></span>
<span id="cb3-7"><a href="#cb3-7"></a><span class="kw">insert_pseudomarkers</span>(recla, <span class="dt">step =</span> <span class="fl">0.10</span>) -><span class="st"> </span>pseudomap</span>
<span id="cb3-8"><a href="#cb3-8"></a>gm <-<span class="st"> </span>pseudomap<span class="op">$</span><span class="st">`</span><span class="dt">8</span><span class="st">`</span></span></code></pre></div>
<p>We use the hidden Markov model from <span class="citation">Broman (2012a)</span> and <span class="citation">Broman (2012b)</span> (as implemented in <span class="citation">Broman et al. (2019)</span>) to calculate 36-state genotype probabilities for autosomal markers.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a>probs <-<span class="st"> </span><span class="kw">calc_genoprob</span>(recla, <span class="dt">map =</span> pseudomap, <span class="dt">cores =</span> <span class="dv">1</span>)</span></code></pre></div>
<p>We then convert the genotype probabilities to haplotype dosages.</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1"></a>aprobs <-<span class="st"> </span><span class="kw">genoprob_to_alleleprob</span>(probs)</span></code></pre></div>
<p>We calculate kinship matrices, by the “leave one chromosome out (loco)” method.</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a>kinship <-<span class="st"> </span><span class="kw">calc_kinship</span>(aprobs, <span class="st">"loco"</span>)</span></code></pre></div>
<p>Before performing our QTL mapping, we transform the phenotypes.</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a>recla<span class="op">$</span>pheno -><span class="st"> </span>ph</span>
<span id="cb7-2"><a href="#cb7-2"></a><span class="kw">log</span>(ph) -><span class="st"> </span>lph</span>
<span id="cb7-3"><a href="#cb7-3"></a><span class="kw">apply</span>(<span class="dt">FUN =</span> broman<span class="op">::</span>winsorize, <span class="dt">X =</span> lph, <span class="dt">MARGIN =</span> <span class="dv">2</span>) -><span class="st"> </span>wlph</span>
<span id="cb7-4"><a href="#cb7-4"></a><span class="kw">as_tibble</span>(wlph) -><span class="st"> </span>wlph_tib</span></code></pre></div>
<p>We next perform the univariate QTL scan for all phenotypes.</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a>sex2 <-<span class="st"> </span><span class="kw">matrix</span>(<span class="kw">as.numeric</span>(sex <span class="op">==</span><span class="st"> "female"</span>), <span class="dt">ncol =</span> <span class="dv">1</span>)</span>
<span id="cb8-2"><a href="#cb8-2"></a><span class="kw">colnames</span>(sex2) <-<span class="st"> "female"</span></span>
<span id="cb8-3"><a href="#cb8-3"></a><span class="kw">rownames</span>(sex2) <-<span class="st"> </span><span class="kw">rownames</span>(aprobs[[<span class="dv">1</span>]])</span>
<span id="cb8-4"><a href="#cb8-4"></a>out <-<span class="st"> </span><span class="kw">scan1</span>(<span class="dt">genoprobs =</span> aprobs, </span>
<span id="cb8-5"><a href="#cb8-5"></a> <span class="dt">pheno =</span> wlph, </span>
<span id="cb8-6"><a href="#cb8-6"></a> <span class="dt">kinship =</span> kinship, </span>
<span id="cb8-7"><a href="#cb8-7"></a> <span class="dt">addcovar =</span> sex2, </span>
<span id="cb8-8"><a href="#cb8-8"></a> <span class="dt">reml =</span> <span class="ot">TRUE</span></span>
<span id="cb8-9"><a href="#cb8-9"></a> )</span></code></pre></div>
<p>We want to look closely at those peaks on Chromosome 8. We’ll save the positions of peaks for our two traits of interest.</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1"></a>(peaks <-<span class="st"> </span><span class="kw">find_peaks</span>(out, pseudomap, <span class="dt">threshold =</span> <span class="dv">5</span>) <span class="op">%>%</span></span>
<span id="cb9-2"><a href="#cb9-2"></a><span class="st"> </span>dplyr<span class="op">::</span><span class="kw">arrange</span>(chr, pos) <span class="op">%>%</span></span>
<span id="cb9-3"><a href="#cb9-3"></a><span class="st"> </span>dplyr<span class="op">::</span><span class="kw">select</span>(<span class="op">-</span><span class="st"> </span>lodindex))</span></code></pre></div>
<pre><code>## lodcolumn chr pos lod
## 1 bw 1 23.9075 5.471580
## 2 OF_distance_first4 1 43.2385 5.772977
## 3 LD_transitions 1 95.8075 5.028258
## 4 OF_distance 2 49.9770 5.544458
## 5 bw 2 52.3932 7.352334
## 6 OF_immobile_pct 2 53.2646 9.771814
## 7 VC_bottom_distance_first4 2 71.0160 6.531654
## 8 OF_distance_first4 3 10.7360 5.541166
## 9 VC_bottom_distance_first4 3 16.3700 5.518637
## 10 VC_top_time_pct 3 17.9390 5.951067
## 11 LD_distance_light 3 23.4390 5.203246
## 12 bw 3 24.8390 5.632714
## 13 VC_top_time_first4 3 48.1280 6.144073
## 14 VC_top_velocity 3 48.5630 6.264824
## 15 VC_top_time_first4 4 3.5340 5.016970
## 16 OF_corner_pct 4 9.0111 6.272996
## 17 OF_immobile_pct 4 37.5206 5.529985
## 18 LD_distance_light 4 71.2992 5.040185
## 19 bw 5 10.0740 5.728865
## 20 VC_bottom_distance_first4 5 19.9741 5.419061
## 21 VC_top_time_first4 5 20.0741 6.075282
## 22 VC_bottom_distance 5 20.5930 5.691913
## 23 VC_bottom_time_pct 5 20.5930 6.360001
## 24 TS_latency_immobile 5 43.3504 5.995115
## 25 OF_corner_pct 5 64.2551 5.665658
## 26 OF_immobile_pct 6 53.4292 6.968771
## 27 TS_frequency_climbing 6 57.0362 5.361091
## 28 bw 7 9.1778 6.057098
## 29 TS_time_immobile 7 49.4778 8.067612
## 30 VC_bottom_distance 7 54.5591 5.011113
## 31 OF_distance_first4 7 57.9454 5.327302
## 32 VC_top_distance 7 83.8778 5.724788
## 33 OF_immobile_pct 7 83.9778 5.823555
## 34 TS_frequency_climbing 8 48.1732 5.483064
## 35 LD_distance_light 8 55.2762 5.323391
## 36 LD_light_pct 8 55.2762 5.274185
## 37 HP_latency 8 57.7732 6.223739
## 38 LD_distance_light 9 36.6965 5.196968
## 39 LD_light_pct 9 36.6965 5.417419
## 40 VC_top_time_first4 9 38.4834 5.109813
## 41 VC_top_time_pct 9 39.2680 6.356432
## 42 HP_latency 9 46.8502 5.222074
## 43 bw 10 3.7781 6.526199
## 44 OF_distance_first4 10 29.6698 5.462975
## 45 VC_bottom_time_pct 10 32.5438 5.432804
## 46 OF_periphery_pct 10 74.8530 5.246487
## 47 VC_top_distance 11 7.8200 6.245803
## 48 VC_top_distance_first4 11 11.6236 5.486915
## 49 VC_bottom_distance_first4 11 54.3420 5.367052
## 50 LD_transitions 11 58.9000 5.903217
## 51 VC_bottom_transitions 11 60.5984 5.114051
## 52 LD_light_pct 11 63.3943 6.464176
## 53 LD_distance_light 11 63.4514 6.373437
## 54 VC_top_time_pct 12 20.5776 6.950143
## 55 VC_bottom_velocity 12 21.7760 5.653292
## 56 OF_center_pct 12 35.5140 6.399422
## 57 HP_latency 12 43.5150 5.131074
## 58 OF_periphery_pct 12 53.5776 7.240951
## 59 OF_corner_pct 13 59.7966 6.594594
## 60 VC_bottom_time_first4 14 11.9183 5.204368
## 61 VC_bottom_distance_first4 14 12.5316 5.763233
## 62 VC_bottom_transitions 14 12.5316 6.478533
## 63 VC_top_velocity 14 12.7819 6.840412
## 64 VC_bottom_distance 14 14.5316 5.592029
## 65 TS_frequency_climbing 14 21.1141 5.372594
## 66 OF_center_pct 14 53.7316 5.377182
## 67 TS_frequency_climbing 15 12.6680 6.043265
## 68 LD_light_pct 15 15.2374 5.674921
## 69 OF_distance_first4 16 23.2656 5.242102
## 70 VC_top_distance 17 15.6390 6.669277
## 71 VC_top_velocity 18 8.3750 5.558628
## 72 VC_top_time_first4 18 17.8068 6.246206
## 73 LD_transitions 18 37.4182 5.090332
## 74 VC_bottom_distance_first4 19 24.9615 7.362557
## 75 VC_bottom_time_first4 19 24.9615 7.499959
## 76 OF_immobile_pct 19 31.9505 5.639812
## 77 HP_latency 19 47.7977 5.485000</code></pre>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a>peaks8 <-<span class="st"> </span>peaks <span class="op">%>%</span></span>
<span id="cb11-2"><a href="#cb11-2"></a><span class="st"> </span>dplyr<span class="op">::</span><span class="kw">filter</span>(chr <span class="op">==</span><span class="st"> </span><span class="dv">8</span>, pos <span class="op">></span><span class="st"> </span><span class="dv">50</span>, pos <span class="op"><</span><span class="st"> </span><span class="dv">60</span>)</span>
<span id="cb11-3"><a href="#cb11-3"></a>pos_LD_light_pct <-<span class="st"> </span>peaks8 <span class="op">%>%</span></span>
<span id="cb11-4"><a href="#cb11-4"></a><span class="st"> </span>dplyr<span class="op">::</span><span class="kw">filter</span>(lodcolumn <span class="op">==</span><span class="st"> "LD_light_pct"</span>) <span class="op">%>%</span></span>
<span id="cb11-5"><a href="#cb11-5"></a><span class="st"> </span>dplyr<span class="op">::</span><span class="kw">select</span>(pos)</span>
<span id="cb11-6"><a href="#cb11-6"></a>pos_HP_latency <-<span class="st"> </span>peaks8 <span class="op">%>%</span></span>
<span id="cb11-7"><a href="#cb11-7"></a><span class="st"> </span>dplyr<span class="op">::</span><span class="kw">filter</span>(lodcolumn <span class="op">==</span><span class="st"> "HP_latency"</span>) <span class="op">%>%</span></span>
<span id="cb11-8"><a href="#cb11-8"></a><span class="st"> </span>dplyr<span class="op">::</span><span class="kw">select</span>(pos)</span></code></pre></div>
</div>
<div id="correlation" class="section level2">
<h2>Correlation</h2>
<p>Given that the two traits “percent time in light” and “distance traveled in light” share a peak, we want to ask how correlated they are.</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1"></a><span class="kw">cor</span>(wlph[ , <span class="dv">7</span>], wlph[ , <span class="dv">10</span>], <span class="dt">use =</span> <span class="st">"complete.obs"</span>)</span></code></pre></div>
<pre><code>## [1] 0.8859402</code></pre>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1"></a><span class="kw">cor</span>(wlph[ , <span class="dv">22</span>], wlph[ , <span class="dv">10</span>], <span class="dt">use =</span> <span class="st">"complete.obs"</span>)</span></code></pre></div>
<pre><code>## [1] -0.1507317</code></pre>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1"></a><span class="kw">cor</span>(wlph[ , <span class="dv">7</span>], wlph[ , <span class="dv">22</span>], <span class="dt">use =</span> <span class="st">"complete.obs"</span>)</span></code></pre></div>
<pre><code>## [1] -0.143598</code></pre>
<p>Since “percent time in light” and “distance traveled in light” are very highly correlated, we’ll discard “distance traveled in light” and perform subsequent analyses with only “percent time in light” and the second trait, “hot plate latency”.</p>
</div>
<div id="scatter-plot-of-phenotypes" class="section level2">
<h2>Scatter plot of phenotypes</h2>
<p>We create a scatter plot for the two phenotypes, “hot plate latency” and “percent time in light”.</p>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1"></a><span class="kw">ggplot</span>() <span class="op">+</span><span class="st"> </span></span>
<span id="cb18-2"><a href="#cb18-2"></a><span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">data =</span> wlph_tib, <span class="kw">aes</span>(<span class="dt">y =</span> HP_latency, <span class="dt">x =</span> LD_light_pct)) <span class="op">+</span></span>
<span id="cb18-3"><a href="#cb18-3"></a><span class="st"> </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">"Percent time in light"</span>, <span class="dt">y =</span> <span class="st">"Hot plate latency"</span>)</span></code></pre></div>
<pre><code>## Warning: Removed 3 rows containing missing values (geom_point).</code></pre>
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" /><!-- --></p>
</div>
<div id="genome-wide-lod-plots-for-the-traits-from-recla" class="section level2">
<h2>Genome-wide LOD plots for the traits from Recla</h2>
<p>Let’s plot the results of the univariate QTL scans for our two traits.</p>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1"></a><span class="kw">plot</span>(out, <span class="dt">map =</span> pseudomap, </span>
<span id="cb20-2"><a href="#cb20-2"></a> <span class="dt">lodcolumn =</span> <span class="dv">10</span>, </span>
<span id="cb20-3"><a href="#cb20-3"></a> <span class="dt">main =</span> <span class="st">"percent time in light"</span></span>
<span id="cb20-4"><a href="#cb20-4"></a> )</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1"></a><span class="kw">plot</span>(out, <span class="dt">map =</span> pseudomap, </span>
<span id="cb21-2"><a href="#cb21-2"></a> <span class="dt">lodcolumn =</span> <span class="dv">22</span>, </span>
<span id="cb21-3"><a href="#cb21-3"></a> <span class="dt">main =</span> <span class="st">"hot plate latency"</span></span>
<span id="cb21-4"><a href="#cb21-4"></a> )</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="allele-effects-plots-on-chr-8-for-each-of-the-three-recla-traits" class="section level2">
<h2>Allele effects plots on Chr 8 for each of the three Recla traits</h2>
<p>We examine the allele effects plots for our two traits, in the region of interest on Chromosome 8.</p>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1"></a><span class="kw">scan1coef</span>(aprobs[ , <span class="dv">8</span>], <span class="dt">pheno =</span> wlph[ , <span class="dv">10</span>], <span class="dt">kinship =</span> kinship<span class="op">$</span><span class="st">`</span><span class="dt">8</span><span class="st">`</span>, </span>
<span id="cb22-2"><a href="#cb22-2"></a> <span class="dt">reml =</span> <span class="ot">TRUE</span>,</span>
<span id="cb22-3"><a href="#cb22-3"></a> <span class="dt">addcovar =</span> sex2) -><span class="st"> </span>s1c_<span class="dv">10</span></span>
<span id="cb22-4"><a href="#cb22-4"></a><span class="kw">scan1coef</span>(aprobs[ , <span class="dv">8</span>], <span class="dt">pheno =</span> wlph[ , <span class="dv">22</span>], <span class="dt">kinship =</span> kinship<span class="op">$</span><span class="st">`</span><span class="dt">8</span><span class="st">`</span>, </span>
<span id="cb22-5"><a href="#cb22-5"></a> <span class="dt">reml =</span> <span class="ot">TRUE</span>,</span>
<span id="cb22-6"><a href="#cb22-6"></a> <span class="dt">addcovar =</span> sex2) -><span class="st"> </span>s1c_<span class="dv">22</span></span></code></pre></div>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1"></a><span class="co"># subset scan1output objects</span></span>
<span id="cb23-2"><a href="#cb23-2"></a>s1c_10s <-<span class="st"> </span>s1c_<span class="dv">10</span>[<span class="dv">650</span><span class="op">:</span><span class="dv">999</span>, ] </span>
<span id="cb23-3"><a href="#cb23-3"></a><span class="co"># 650:999 is the same as the interval for the two-dimensional scan.</span></span>
<span id="cb23-4"><a href="#cb23-4"></a>s1c_22s <-<span class="st"> </span>s1c_<span class="dv">22</span>[<span class="dv">650</span><span class="op">:</span><span class="dv">999</span>, ]</span></code></pre></div>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1"></a><span class="kw">plot_coefCC</span>(s1c_10s, <span class="dt">scan1_output =</span> out[ , <span class="dv">10</span>, <span class="dt">drop =</span> <span class="ot">FALSE</span>], <span class="dt">map =</span> pseudomap, <span class="dt">main =</span> <span class="st">"percent time in light"</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1"></a><span class="kw">plot_coefCC</span>(s1c_22s, <span class="dt">scan1_output =</span> out[ , <span class="dv">22</span>, <span class="dt">drop =</span> <span class="ot">FALSE</span>], <span class="dt">map =</span> pseudomap, <span class="dt">main =</span> <span class="st">"hot plate latency"</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="two-dimensional-scan-results-from-github" class="section level2">
<h2>Two-dimensional scan results from github</h2>
<p>We present the code that we ran to perform the two-dimensional scan.</p>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1"></a><span class="kw">scan_pvl</span>(<span class="dt">probs =</span> aprobs<span class="op">$</span><span class="st">`</span><span class="dt">8</span><span class="st">`</span>, </span>
<span id="cb26-2"><a href="#cb26-2"></a> <span class="dt">pheno =</span> wlph[, <span class="kw">c</span>(<span class="dv">10</span>, <span class="dv">22</span>)], </span>
<span id="cb26-3"><a href="#cb26-3"></a> <span class="dt">addcovar =</span> sex2, </span>
<span id="cb26-4"><a href="#cb26-4"></a> <span class="dt">kinship =</span> kinship<span class="op">$</span><span class="st">`</span><span class="dt">8</span><span class="st">`</span>, </span>
<span id="cb26-5"><a href="#cb26-5"></a> <span class="dt">start_snp =</span> <span class="dv">650</span>, </span>
<span id="cb26-6"><a href="#cb26-6"></a> <span class="dt">n_snp =</span> <span class="dv">350</span>) -><span class="st"> </span>pvl1022</span></code></pre></div>
<p>To save computing time, we read the two-dimensional scan results from Zenodo. Because the Zenodo file was created with an older version of <code>scan_pvl</code>, it contains the log (natural base) likelihoods, rather than log10 likelihoods. Thus, we’ll want to rectify this, which we do with code below.</p>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1"></a><span class="kw">as_tibble</span>(<span class="kw">read.table</span>(<span class="st">"https://zenodo.org/record/3210710/files/recla-10-22.txt?download=1"</span>, <span class="dt">stringsAsFactors =</span> <span class="ot">FALSE</span>)) -><span class="st"> </span>pvl1022</span></code></pre></div>
<p>We then calculate the likelihood ratio test statistic.</p>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1"></a>(mylrt <-<span class="st"> </span><span class="kw">calc_lrt_tib</span>(pvl1022))</span></code></pre></div>
<pre><code>## [1] 2.771408</code></pre>
</div>
<div id="profile-lod-plot" class="section level2">
<h2>Profile LOD Plot</h2>
<p>We create a profile LOD plot.</p>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1"></a><span class="kw">colnames</span>(recla<span class="op">$</span>pheno)[<span class="kw">c</span>(<span class="dv">10</span>, <span class="dv">22</span>)] <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Percent time in light"</span>, <span class="st">"Hot plate latency"</span>)</span>
<span id="cb30-2"><a href="#cb30-2"></a>pvl1022 <span class="op">%>%</span></span>
<span id="cb30-3"><a href="#cb30-3"></a><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">log10lik =</span> loglik <span class="op">/</span><span class="st"> </span><span class="kw">log</span>(<span class="dv">10</span>)) <span class="op">%>%</span></span>
<span id="cb30-4"><a href="#cb30-4"></a><span class="st"> </span>dplyr<span class="op">::</span><span class="kw">select</span>(<span class="op">-</span><span class="st"> </span>loglik) <span class="op">%>%</span></span>
<span id="cb30-5"><a href="#cb30-5"></a><span class="st"> </span><span class="kw">calc_profile_lods</span>() <span class="op">%>%</span></span>
<span id="cb30-6"><a href="#cb30-6"></a><span class="st"> </span><span class="kw">add_pmap</span>(<span class="dt">pmap =</span> recla<span class="op">$</span>pmap<span class="op">$</span><span class="st">`</span><span class="dt">8</span><span class="st">`</span>) <span class="op">%>%</span></span>
<span id="cb30-7"><a href="#cb30-7"></a><span class="st"> </span><span class="kw">ggplot</span>() <span class="op">+</span><span class="st"> </span><span class="kw">geom_line</span>(<span class="kw">aes</span>(<span class="dt">x =</span> marker_position, <span class="dt">y =</span> profile_lod, <span class="dt">colour =</span> trait))</span></code></pre></div>
<pre><code>## Joining, by = "marker"</code></pre>
<p><img 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" /><!-- --></p>
</div>
<div id="bootstrap-analyses" class="section level2">
<h2>Bootstrap analyses</h2>
<p>First, we find the <em>pleiotropy peak</em> marker. This is the marker for which the log likelihood is maximized under the constraint of pleiotropy.</p>
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1"></a><span class="kw">find_pleio_peak_tib</span>(pvl1022, <span class="dt">start_snp =</span> <span class="dv">650</span>)</span></code></pre></div>
<pre><code>## loglik139
## 788</code></pre>
<p>We need the <em>pleiotropy peak</em> marker in the bootstrap analyses because it is the marker used in drawing bootstrap samples.</p>
<p>To save computing time, we read the bootstrap results files from Github. For details of how we performed the bootstrap analyses on the University of Wisconsin-Madison Center for High-Throughput Computing, please see the documentation in the qtl2pleio-manuscript repository: <a href="https://github.com/fboehm/qtl2pleio-manuscript" class="uri">https://github.com/fboehm/qtl2pleio-manuscript</a>.</p>
<p>The code below creates a temporary directory “tmp” in the user’s working directory. We then read results files from Github. Each text file contains a single likelihood ratio test statistic from a bootstrap sample.</p>
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1"></a><span class="co">## read boot lrt files</span></span>
<span id="cb34-2"><a href="#cb34-2"></a>boot_lrt <-<span class="st"> </span><span class="kw">list</span>()</span>
<span id="cb34-3"><a href="#cb34-3"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="op">:</span><span class="dv">1000</span>){</span>
<span id="cb34-4"><a href="#cb34-4"></a> n <-<span class="st"> </span>i <span class="op">-</span><span class="st"> </span><span class="dv">1</span></span>
<span id="cb34-5"><a href="#cb34-5"></a> fn <-<span class="st"> </span><span class="kw">paste0</span>(<span class="st">"https://raw.githubusercontent.com/fboehm/qtl2pleio-manuscript-chtc/master/Recla-bootstrap/results/recla-boot-run561_"</span>, n, <span class="st">".txt"</span>)</span>
<span id="cb34-6"><a href="#cb34-6"></a> boot_lrt[i] <-<span class="st"> </span><span class="kw">read.table</span>(fn)</span>
<span id="cb34-7"><a href="#cb34-7"></a>}</span>
<span id="cb34-8"><a href="#cb34-8"></a><span class="co"># convert list to numeric vector</span></span>
<span id="cb34-9"><a href="#cb34-9"></a>boot_lrt <-<span class="st"> </span><span class="kw">unlist</span>(boot_lrt)</span></code></pre></div>
<p>We get a bootstrap p-value by comparing the above vector’s values to <code>mylrt</code>, the test statistic for the observed data.</p>
<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1"></a><span class="kw">sum</span>(boot_lrt <span class="op">>=</span><span class="st"> </span>mylrt) <span class="op">/</span><span class="st"> </span><span class="kw">length</span>(boot_lrt)</span></code></pre></div>
<pre><code>## [1] 0.109</code></pre>
</div>
<div id="session-info" class="section level2">
<h2>Session info</h2>
<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1"></a>devtools<span class="op">::</span><span class="kw">session_info</span>()</span></code></pre></div>
<pre><code>## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.0 (2020-04-24)
## os Ubuntu 20.04 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/New_York
## date 2020-06-29
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib source
## assertthat 0.2.1 2019-03-21 [2] CRAN (R 4.0.0)
## backports 1.1.8 2020-06-17 [2] CRAN (R 4.0.0)
## bit 1.1-15.2 2020-02-10 [2] CRAN (R 4.0.0)
## bit64 0.9-7 2017-05-08 [2] CRAN (R 4.0.0)
## blob 1.2.1 2020-01-20 [2] CRAN (R 4.0.0)
## broman 0.69-5 2019-04-11 [2] CRAN (R 4.0.0)
## callr 3.4.3 2020-03-28 [2] CRAN (R 4.0.0)
## cli 2.0.2 2020-02-28 [2] CRAN (R 4.0.0)
## codetools 0.2-16 2018-12-24 [2] CRAN (R 4.0.0)
## colorspace 1.4-1 2019-03-18 [2] CRAN (R 4.0.0)
## crayon 1.3.4 2017-09-16 [2] CRAN (R 4.0.0)
## data.table 1.12.8 2019-12-09 [2] CRAN (R 4.0.0)
## DBI 1.1.0 2019-12-15 [2] CRAN (R 4.0.0)
## desc 1.2.0 2018-05-01 [2] CRAN (R 4.0.0)
## devtools 2.3.0 2020-04-10 [2] CRAN (R 4.0.0)
## digest 0.6.25 2020-02-23 [2] CRAN (R 4.0.0)
## dplyr * 1.0.0 2020-05-29 [2] CRAN (R 4.0.0)
## ellipsis 0.3.1 2020-05-15 [2] CRAN (R 4.0.0)
## evaluate 0.14 2019-05-28 [2] CRAN (R 4.0.0)
## fansi 0.4.1 2020-01-08 [2] CRAN (R 4.0.0)
## farver 2.0.3 2020-01-16 [2] CRAN (R 4.0.0)
## fs 1.4.1 2020-04-04 [2] CRAN (R 4.0.0)
## furrr 0.1.0 2018-05-16 [2] CRAN (R 4.0.0)
## future 1.17.0 2020-04-18 [2] CRAN (R 4.0.0)
## gemma2 0.1.1 2019-10-01 [2] CRAN (R 4.0.0)
## generics 0.0.2 2018-11-29 [2] CRAN (R 4.0.0)
## ggplot2 * 3.3.1 2020-05-28 [2] CRAN (R 4.0.0)
## globals 0.12.5 2019-12-07 [2] CRAN (R 4.0.0)
## glue 1.4.1 2020-05-13 [2] CRAN (R 4.0.0)
## gtable 0.3.0 2019-03-25 [2] CRAN (R 4.0.0)
## htmltools 0.5.0 2020-06-16 [2] CRAN (R 4.0.0)
## jsonlite 1.6.1 2020-02-02 [2] CRAN (R 4.0.0)
## knitr 1.28 2020-02-06 [2] CRAN (R 4.0.0)
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## lifecycle 0.2.0 2020-03-06 [2] CRAN (R 4.0.0)
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## Matrix 1.2-18 2019-11-27 [2] CRAN (R 4.0.0)
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## ps 1.3.3 2020-05-08 [2] CRAN (R 4.0.0)
## purrr 0.3.4 2020-04-17 [2] CRAN (R 4.0.0)
## qtl2 * 0.22-8 2020-06-26 [2] CRAN (R 4.0.0)
## qtl2pleio * 1.3.0 2020-06-29 [1] local
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## stringi 1.4.6 2020-02-17 [2] CRAN (R 4.0.0)
## stringr 1.4.0 2019-02-10 [2] CRAN (R 4.0.0)
## testthat 2.3.2 2020-03-02 [2] CRAN (R 4.0.0)
## tibble 3.0.1 2020-04-20 [2] CRAN (R 4.0.0)
## tidyselect 1.1.0 2020-05-11 [2] CRAN (R 4.0.0)
## usethis 1.6.1 2020-04-29 [2] CRAN (R 4.0.0)
## utf8 1.1.4 2018-05-24 [2] CRAN (R 4.0.0)
## vctrs 0.3.1 2020-06-05 [2] CRAN (R 4.0.0)
## withr 2.2.0 2020-04-20 [2] CRAN (R 4.0.0)
## xfun 0.15 2020-06-21 [2] CRAN (R 4.0.0)
## yaml 2.2.1 2020-02-01 [2] CRAN (R 4.0.0)
##
## [1] /tmp/RtmpdlvULy/temp_libpath1b4b6666b916
## [2] /home/fred/R/x86_64-pc-linux-gnu-library/4.0
## [3] /usr/local/lib/R/site-library
## [4] /usr/lib/R/site-library
## [5] /usr/lib/R/library</code></pre>
</div>
<div id="references" class="section level2 unnumbered">
<h2>References</h2>
<div id="refs" class="references">
<div id="ref-broman2012genotype">
<p>Broman, Karl W. 2012a. “Genotype Probabilities at Intermediate Generations in the Construction of Recombinant Inbred Lines.” <em>Genetics</em> 190 (2): 403–12.</p>
</div>
<div id="ref-broman2012haplotype">
<p>———. 2012b. “Haplotype Probabilities in Advanced Intercross Populations.” <em>G3: Genes, Genomes, Genetics</em> 2 (2): 199–202.</p>
</div>
<div id="ref-broman2019rqtl2">
<p>Broman, Karl W, Daniel M Gatti, Petr Simecek, Nicholas A Furlotte, Pjotr Prins, Śaunak Sen, Brian S Yandell, and Gary A Churchill. 2019. “R/Qtl2: Software for Mapping Quantitative Trait Loci with High-Dimensional Data and Multiparent Populations.” <em>Genetics</em> 211 (2): 495–502.</p>
</div>
<div id="ref-logan2013high">
<p>Logan, Ryan W, Raymond F Robledo, Jill M Recla, Vivek M Philip, Jason A Bubier, Jeremy J Jay, Carter Harwood, et al. 2013. “High-Precision Genetic Mapping of Behavioral Traits in the Diversity Outbred Mouse Population.” <em>Genes, Brain and Behavior</em> 12 (4): 424–37.</p>
</div>
<div id="ref-recla2014precise">
<p>Recla, Jill M, Raymond F Robledo, Daniel M Gatti, Carol J Bult, Gary A Churchill, and Elissa J Chesler. 2014. “Precise Genetic Mapping and Integrative Bioinformatics in Diversity Outbred Mice Reveals Hydin as a Novel Pain Gene.” <em>Mammalian Genome</em> 25 (5-6): 211–22.</p>
</div>
</div>
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