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<h1 class="title toc-ignore">Prj01_RL_pheno_qtl2_DO_11092020_69k</h1>
<h4 class="author">Hao He</h4>
<h4 class="date">2022-08-29</h4>
</div>
<p>
<button type="button" class="btn btn-default btn-workflowr btn-workflowr-report" data-toggle="collapse" data-target="#workflowr-report">
<span class="glyphicon glyphicon-list" aria-hidden="true"></span> workflowr <span class="glyphicon glyphicon-exclamation-sign text-danger" aria-hidden="true"></span>
</button>
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<div id="workflowr-report" class="collapse">
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<a data-toggle="tab" href="#checks"> Checks <span class="glyphicon glyphicon-exclamation-sign text-danger" aria-hidden="true"></span> </a>
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<p>
<strong>Last updated:</strong> 2022-08-29
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<p>
<strong>Checks:</strong> <span class="glyphicon glyphicon-ok text-success" aria-hidden="true"></span> 6 <span class="glyphicon glyphicon-exclamation-sign text-danger" aria-hidden="true"></span> 1
</p>
<p>
<strong>Knit directory:</strong> <code>csna_workflow/</code> <span class="glyphicon glyphicon-question-sign" aria-hidden="true" title="This is the local directory in which the code in this file was executed."> </span>
</p>
<p>
This reproducible <a href="http://rmarkdown.rstudio.com">R Markdown</a> analysis was created with <a
href="https://github.com/jdblischak/workflowr">workflowr</a> (version 1.6.2). The <em>Checks</em> tab describes the reproducibility checks that were applied when the results were created. The <em>Past versions</em> tab lists the development history.
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<a data-toggle="collapse" data-parent="#workflowr-checks" href="#strongRMarkdownfilestronguptodate"> <span class="glyphicon glyphicon-ok text-success" aria-hidden="true"></span> <strong>R Markdown file:</strong> up-to-date </a>
</p>
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<div id="strongRMarkdownfilestronguptodate" class="panel-collapse collapse">
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<p>Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.</p>
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<a data-toggle="collapse" data-parent="#workflowr-checks" href="#strongEnvironmentstrongempty"> <span class="glyphicon glyphicon-ok text-success" aria-hidden="true"></span> <strong>Environment:</strong> empty </a>
</p>
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<div id="strongEnvironmentstrongempty" class="panel-collapse collapse">
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<p>Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.</p>
</div>
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<a data-toggle="collapse" data-parent="#workflowr-checks" href="#strongSeedstrongcodesetseed20190922code"> <span class="glyphicon glyphicon-ok text-success" aria-hidden="true"></span> <strong>Seed:</strong> <code>set.seed(20190922)</code> </a>
</p>
</div>
<div id="strongSeedstrongcodesetseed20190922code" class="panel-collapse collapse">
<div class="panel-body">
<p>The command <code>set.seed(20190922)</code> was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.</p>
</div>
</div>
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<div class="panel-heading">
<p class="panel-title">
<a data-toggle="collapse" data-parent="#workflowr-checks" href="#strongSessioninformationstrongrecorded"> <span class="glyphicon glyphicon-ok text-success" aria-hidden="true"></span> <strong>Session information:</strong> recorded </a>
</p>
</div>
<div id="strongSessioninformationstrongrecorded" class="panel-collapse collapse">
<div class="panel-body">
<p>Great job! Recording the operating system, R version, and package versions is critical for reproducibility.</p>
</div>
</div>
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<div class="panel-heading">
<p class="panel-title">
<a data-toggle="collapse" data-parent="#workflowr-checks" href="#strongCachestrongnone"> <span class="glyphicon glyphicon-ok text-success" aria-hidden="true"></span> <strong>Cache:</strong> none </a>
</p>
</div>
<div id="strongCachestrongnone" class="panel-collapse collapse">
<div class="panel-body">
<p>Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.</p>
</div>
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</div>
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<p class="panel-title">
<a data-toggle="collapse" data-parent="#workflowr-checks" href="#strongFilepathsstrongabsolute"> <span class="glyphicon glyphicon-exclamation-sign text-danger" aria-hidden="true"></span> <strong>File paths:</strong> absolute </a>
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</div>
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Using absolute paths to the files within your workflowr project makes it difficult for you and others to run your code on a different machine. Change the absolute path(s) below to the suggested relative path(s) to make your code more reproducible.
</p>
<table class="table table-condensed table-hover">
<thead>
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<th style="text-align:left;">
absolute
</th>
<th style="text-align:left;">
relative
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</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
/projects/csna/csna_workflow/
</td>
<td style="text-align:left;">
.
</td>
</tr>
<tr>
<td style="text-align:left;">
/projects/csna/csna_workflow/data/69k_grid_pgmap.RData
</td>
<td style="text-align:left;">
data/69k_grid_pgmap.RData
</td>
</tr>
<tr>
<td style="text-align:left;">
/projects/csna/csna_workflow/data/cc_variants.sqlite
</td>
<td style="text-align:left;">
data/cc_variants.sqlite
</td>
</tr>
<tr>
<td style="text-align:left;">
/projects/csna/csna_workflow/data/mouse_genes_mgi.sqlite
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<td style="text-align:left;">
data/mouse_genes_mgi.sqlite
</td>
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data/GCTA/Prj01_RL_pheno_qttl2_DO_11092020/update_id.txt
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/projects/csna/csna_workflow/data/GCTA/Prj01_RL_pheno_qttl2_DO_11092020/update_sex.txt
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data/GCTA/Prj01_RL_pheno_qttl2_DO_11092020/update_sex.txt
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/projects/csna/csna_workflow/data/GCTA/Prj01_RL_pheno_qttl2_DO_11092020/update_covar.txt
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/projects/csna/csna_workflow/data/GCTA/Prj01_RL_pheno_qttl2_DO_11092020/update.pheno.txt
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data/GCTA/Prj01_RL_pheno_qttl2_DO_11092020/update.pheno.txt
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data/GCTA
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/projects/csna/csna_workflow/data/GCTA/Prj01_RL_pheno_qttl2_DO_11092020/
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data/GCTA/Prj01_RL_pheno_qttl2_DO_11092020
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/projects/csna/csna_workflow/data/GCTA/Prj01_RL_pheno_qttl2_DO_11092020/h2.csv
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data/GCTA/Prj01_RL_pheno_qttl2_DO_11092020/h2.csv
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</div>
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<pre><code>
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</code></pre>
<p>
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
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These are the previous versions of the repository in which changes were made to the R Markdown (<code>analysis/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd</code>) and HTML (<code>docs/Prj01_RL_pheno_qtl2_DO_11092020_69k.html</code>) files. If you’ve configured a remote Git repository (see <code>?wflow_git_remote</code>), click on the hyperlinks in the table below to view the files as they were in that past version.
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Rmd
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<a href="https://github.com/TheJacksonLaboratory/CSNA/blob/8541f24e3439709e29ad28072f9e2d85a82452b0/analysis/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd" target="_blank">8541f24</a>
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<td>
xhyuo
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2022-08-29
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Prj01_RL_pheno_qtl2_DO_11092020_69k
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<p><strong>Last update:</strong> 2022-08-29</p>
<p>###loading libraries</p>
<pre class="r"><code>library(ggplot2)
library(gridExtra)
library(GGally)
library(parallel)
library(qtl2)
library(parallel)
library(survival)
library(regress)
library(abind)
library(openxlsx)
library(tidyverse)
rz.transform <- function(y) {
rankY=rank(y, ties.method="average", na.last="keep")
rzT=qnorm(rankY/(length(na.exclude(rankY))+1))
return(rzT)
}
setwd("/projects/csna/csna_workflow/")</code></pre>
<p>###Read phenotype data</p>
<pre class="r"><code>#Prj01_RL_pheno_qttl2_DO_11092020
pheno <- readr::read_csv("data/pheno/Prj01_RL_pheno_qttl2_DO_11092020.csv", na = c("N/A", "NA"))
pheno <- pheno %>%
filter(apply(pheno, 1, function(x) sum(is.na(x))) < 5) %>% # remove rows with 5 NAs
mutate(
Acq.Anticipatory.Correct.Responses.nooutlier = case_when(
(Acq.Anticipatory.Correct.Responses > 5 | is.na(Acq.Anticipatory.Correct.Responses)) ~ NA_real_,
TRUE ~ Acq.Anticipatory.Correct.Responses),
Rev.Anticipatory.Incorrect.Responses.nooutlier = case_when(
(Rev.Anticipatory.Incorrect.Responses > 6) ~ NA_real_,
TRUE ~ Rev.Anticipatory.Incorrect.Responses
)) %>% #boxcoxtransformation
mutate(across(c(Rev.Anticipatory.Incorrect.Responses), ~ EnvStats::boxcoxTransform(.x, lambda = EnvStats::boxcox(.x, optimize = TRUE)$lambda),
.names ="{.col}.boxcox.trans")) %>% #omit values >6 for Acq.Anticipatory.Correct.Responses and Rev.Anticipatory.Incorrect.Responses variables.
mutate(across(Acq.Total.Trials:Rev.Anticipatory.Incorrect.Responses.nooutlier, ~ rz.transform(.x),
.names ="{.col}.rz"))
#load gm data
load("data/Jackson_Lab_12_batches/gm_DO3173_qc_newid.RData")
#subset
overlap.id <- intersect(pheno$mouseID, ind_ids(gm_after_qc))
load("data/Jackson_Lab_12_batches/qc_info.RData")
#merge pheno with covar
pheno <- pheno %>%
left_join(qc_info) %>%
filter(mouseID %in% overlap.id) %>%
filter(bad.sample == FALSE) %>%
mutate(sex = case_when(
sex == "M" ~ 1,
sex == "F" ~ 0
)) %>%
mutate_at(vars(sex,
ngen),
list(factor))
#pgmap
load("/projects/csna/csna_workflow/data/69k_grid_pgmap.RData")
query_variants <- create_variant_query_func("/projects/csna/csna_workflow/data/cc_variants.sqlite")
query_genes <- create_gene_query_func("/projects/csna/csna_workflow/data/mouse_genes_mgi.sqlite")</code></pre>
<p>###Plot qtl mapping on 69k Plot by using its output Prj01_RL_11092020_cutoff6.qtlout.69k.RData</p>
<pre class="r"><code>load("output/Prj01_RL_11092020_cutoff6.qtlout.69k.RData")
#genome-wide plot
for(i in names(m2.qtl.out)){
par(mar=c(5.1, 4.1, 1.1, 1.1))
ymx <- maxlod(m2.qtl.out[[i]]) # overall maximum LOD score
plot(m2.qtl.out[[i]], map=pmap, lodcolumn=1, col="slateblue", ylim=c(0, 12))
abline(h=summary(m2.permu[[i]], alpha=c(0.10, 0.05, 0.01))[[1]], col="red")
abline(h=summary(m2.permu[[i]], alpha=c(0.10, 0.05, 0.01))[[2]], col="red")
abline(h=summary(m2.permu[[i]], alpha=c(0.10, 0.05, 0.01))[[3]], col="red")
title(main = paste0(i))
}</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-1.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-2.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-3.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-4.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-5.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-6.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-7.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-8.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-9.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-10.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-11.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-12.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-13.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-14.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-15.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>
pdf(file = paste0("output/RL_prj/Prj01_RL_11092020_cutoff6.qtlout.pdf"), width = 16, height =8)
#save genome-wide plo
for(i in names(m2.qtl.out)){
par(mar=c(5.1, 4.1, 1.1, 1.1))
ymx <- maxlod(m2.qtl.out[[i]]) # overall maximum LOD score
plot(m2.qtl.out[[i]], map=pmap, lodcolumn=1, col="slateblue", ylim=c(0, 10))
abline(h=summary(m2.permu[[i]], alpha=c(0.10, 0.05, 0.01))[[1]], col="red")
abline(h=summary(m2.permu[[i]], alpha=c(0.10, 0.05, 0.01))[[2]], col="red")
abline(h=summary(m2.permu[[i]], alpha=c(0.10, 0.05, 0.01))[[3]], col="red")
title(main = paste0(i))
}
dev.off()
# png
# 2
#peaks coeff plot
for(i in names(m2.qtl.out)){
print(i)
peaks <- find_peaks(m2.qtl.out[[i]], map=pmap, threshold=6, drop=1.5)
if(nrow(peaks) > 0) {
print(peaks)
for(p in 1:dim(peaks)[1]) {
print(p)
chr <-peaks[p,3]
#coeff plot
par(mar=c(4.1, 4.1, 0.6, 0.6))
plot_coefCC(m2.sigqtl.coef[[i]][[p]], pmap[chr], scan1_output=m2.qtl.out[[i]], bgcolor="gray95", legend=NULL)
plot_coefCC(m2.sigqtl.blup[[i]][[p]], pmap[chr], scan1_output=m2.qtl.out[[i]], bgcolor="gray95", legend=NULL)
#plot(m2.sigqtl.snps[[i]][[p]]$lod, m2.sigqtl.snps[[i]][[p]]$snpinfo, drop_hilit=1.5, genes=m2.sigqtl.genes[[i]][[p]])
}
}
}
# [1] "Acq.Total.Trials"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 6 88.69893 6.184173 3.77429 147.36991
# 2 1 pheno1 12 81.73145 6.640822 81.11268 99.75717
# [1] 1</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-16.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-17.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code># [1] 2</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-18.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-19.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code># [1] "Acq.Anticipatory.Correct.Responses"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 1 168.41339 11.416644 168.398842 168.52005
# 2 1 pheno1 2 83.81222 9.064175 83.529734 83.82600
# 3 1 pheno1 3 88.59530 7.550793 86.050147 89.39324
# 4 1 pheno1 4 85.44520 6.082516 3.945062 86.84903
# 5 1 pheno1 10 117.29331 6.097238 116.264165 118.73253
# 6 1 pheno1 17 82.91709 6.294987 81.121859 83.40925
# [1] 1</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-20.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-21.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code># [1] 2</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-22.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-23.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code># [1] 3</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-24.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-25.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code># [1] 4</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-26.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-27.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code># [1] 5</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-28.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-29.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code># [1] 6</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-30.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-31.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code># [1] "Rev.Total.Trials"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 14 121.29650 6.542708 121.11874 121.5717
# 2 1 pheno1 18 83.08088 6.553229 82.31526 90.6726
# [1] 1</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-32.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-33.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code># [1] 2</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-34.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-35.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code># [1] "Rev.Anticipatory.Incorrect.Responses"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 1 170.96387 6.571598 168.25531 171.06618
# 2 1 pheno1 6 103.68554 7.392701 101.08006 103.77874
# 3 1 pheno1 14 49.25670 6.252322 48.80084 54.24423
# 4 1 pheno1 16 56.17223 7.087466 54.37418 58.59104
# 5 1 pheno1 17 65.65263 9.228873 64.83160 66.34240
# [1] 1</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-36.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-37.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code># [1] 2</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-38.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-39.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code># [1] 3</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-40.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-41.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code># [1] 4</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-42.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-43.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code># [1] 5</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-44.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-45.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code># [1] "Diff.Total.Trials"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 7 80.80292 8.549071 80.09433 81.52958
# 2 1 pheno1 14 121.29944 6.649824 121.03534 123.78109
# [1] 1</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-46.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-47.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code># [1] 2</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-48.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-49.png" width="672" style="display: block; margin: auto;" /></p>
<pre><code># [1] "Acq.Anticipatory.Correct.Responses.nooutlier"
# lodindex lodcolumn chr pos lod ci_lo ci_hi
# 1 1 pheno1 1 168.40300 8.387058 168.22298 168.52005
# 2 1 pheno1 10 117.42104 6.828398 116.30325 117.97397
# 3 1 pheno1 17 82.87091 6.126227 81.12186 83.40925
# 4 1 pheno1 19 17.71985 6.578499 17.23709 18.07340
# [1] 1</code></pre>
<p><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-50.png" width="672" style="display: block; margin: auto;" /><img src="figure/Prj01_RL_pheno_qtl2_DO_11092020_69k.Rmd/Plot%20qtl%20mapping%20on%2069k-51.png" width="672" style="display: block; margin: auto;" /></p>