vqtl2

Overview

vqtl2 is a drop-in extension of qtl2, providing the functions to accommodate variance heterogeneity arising from background factors and detect vQTL, as described in the literature:

• QTL Mapping on a Background of Variance Heterogeneity
• Mean-Variance QTL Mapping Identifies Novel QTL for Circadian Activity and Exploratory Behavior in Mice
• vqtl: an R package for Mean-Variance QTL Mapping (version 1)

The central functions of package vqtl2 are:

• scan1var() conducts a genome scan
• scan1var_perm() conducts permutations to assess the statistical significance of findings
• plot_scan1var() plots the association statistic(s) at each genetic locus
• plot_allele_effects() plots the genetic and non-genetic effects at a given locus

These all inter-operate with package qtl and follow most of the same paradigms.

If you are new to QTL mapping, the best place to start is the Karl Broman's website.

Installation

I plan to put vqtl2 on CRAN, but until then, the easiest way to get vqtl2 is from github. The easiest way to download and install an R package from github is with the devtools package, which is available on CRAN. Here's the R code to do that:

install.packages('devtools')
devtools::install_github(repo = 'rcorty/vqtl2')

Usage (incomplete)

library(qtl2)
library(vqtl2)

# read in cross per qtl2
# my_DO_cross <- read_cross2(file = 'path_to_my_cross')

my_s1v <- scan1var(pheno_name = 'WBC',
                   mean_covar_names = c('NEUT', 'Sex'),
                   var_covar_names = c('Sex', 'Batch'),
                   alleleprobs = tiny_DO_ap,
                   non_genetic_data = cbind(tiny_DO_cross$pheno,
                                            tiny_DO_cross$covar))

# simplest call -- uses lots of defaults
# need to change the plot function to use generic?
plot_scan1var(s1v = my_s1v, genetic_map = tiny_DO_cross$gmap)
# what = 'association', stat = empirical_p if available, LR otherwise
# meanorvar = 'both'

# here we get allele effects on mean and variance
plot(x = my_s1v, what = 'allele_effects')

# here we get covariate effects on mean and variance
plot(x = my_s1v, what = c('covar_effects'))

# this plotting function uses ggplot2, so you can change it before you "render"
plot(x = my_s1v) + geom_hline(yintercept = 2) + theme_dark()

Getting help

If you encounter a bug, please post a minimal reproducible example on the github issues page. For notes on how to produce a minimal reproducible example, check out this StackOverflow post.

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Please note that the 'vqtl2' project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.