vqtl: An R Package for Mean-Variance QTL Mapping

Abstract

We present vqtl, an R package for mean-variance QTL mapping. This QTL mapping approach tests for genetic loci that influence the mean of the phenotype, termed mean QTL, the variance of the phenotype, termed variance QTL, or some combination of the two, termed mean-variance QTL. It is unique in its ability to correct for variance heterogeneity arising not only from the QTL itself but also from nuisance factors, such as sex, batch, or housing. This package provides functions to conduct genome scans, run permutations to assess the statistical significance, and make informative plots to communicate results. Because it is inter-operable with the popular qtl package and uses many of the same data structures and input patterns, it will be straightforward for geneticists to analyze future experiments with vqtl as well as re-analyze past experiments, possibly discovering new QTL.

R/qtl2: Software for Mapping Quantitative Trait Loci with High-Dimensional Data and Multiparent Populations

“…Further, users and developers both benefit by it being an add-on package for the general statistical software, R (R Core Team 2018). A number of other R packages have been written to work in concert with R/qtl1, including ASMap (Taylor and Butler 2017), ctl (Arends et al 2016), dlmap (Huang et al 2012b), qtlcharts (Broman 2015), vqtl (Corty and Valdar 2018), and wgaim (Taylor and Verbyla 2011).…”

Mean-Variance QTL Mapping Identifies Novel QTL for Circadian Activity and Exploratory Behavior in Mice

“…This approach should not be too alien — when variance heterogeneity is absent, it simplifies to the well-known SLM-based approach. Full-featured software that implements this approach is described in a companion article (Corty and Valdar 2018b).…”

“…Here we demonstrate, with two real data examples available from the Mouse Phenome Database (Bogue et al 2017), that QTL mapping using the DGLM, which we term “mean-variance QTL mapping”, largely replicates the results of standard QTL mapping and detects additional QTL that the traditional analysis does not. In two companion articles, we demonstrate typical usage of R package vqtl, which implements mean-variance QTL mapping (Corty and Valdar 2018b), and describe the how mean-variance QTL mapping and its associated permutation procedures reliably detects QTL in the face of variance heterogeneity arising from background factors ( i.e. , genetic or non-genetic factors outside the targeted QTL) (Corty and Valdar 2018a).…”

Genome-to-phenome research in rats: progress and perspectives

“…The team observed that there was a significant enrichment (p < 10 -10 ) of SNPs, large deletions, and INDELs in cis -eQTL gene regions as opposed to non- cis -eQTL gene regions as well as a significant enrichment (p < 2.2 × 10 -16 ) of SNPs in the promoter region of cis -eQTLs compared to non- cis -eQTLs. Due to the success of eQTL mapping, QTL mapping has been further explored in DNA methylation (mQTLs), alternative splicing (sQTLs), chromatin accessibility (caQTLs), protein expression (pQTLs), cell metabolism (metaQTLs), ribosome occupancy (riboQTLs), histone (hQTLs), microRNA QTLs (miQTLs), and variance analysis (vQTL) 61 - 73 .…”