Genome Scans to Accommodate and Target Genetic and Non-Genetic Effects on Trait Variance in Test Crosses

In recognition that there are many factors (genetic loci, macro- genetic factors such as sex, and environmental factors) that influence the extent of environmental variation, the 'vqtl' package conducts genome scans that accommodate and target these factors. The main functions of this package, scanonevar() and scanonevar.perm() take as input a cross object from the popular 'qtl' package.


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The 'vqtl' package conducts QTL mapping using an elaboration of the traditional Haley-Knott model. It uses the double generalized linear model (DGLM) to model systematic effects on mean and variance. These systematic effects can be both "nuisance effects" that are uninteresting per se, but are valuable to "correct for" as well as genetic effects which are of immediate interest.

Installation

You can install the current stable version of vqtl from CRAN with:

install.packages(pkgs = 'vqtl')

You can install newer version from github with:

install.packages("devtools")
devtools::install_github("rcorty/vqtl")

Typical usage

First we'll simulate an rross object, using a utility from qtl. Note that we load library qtl before library vqtl. This is necessary so that vqtl::scanonevar overrides qtl::scanonevar and not the other way around.

library(qtl)
library(vqtl)
#> Attaching package: 'vqtl'
#> The following object is masked from 'package:qtl':
#> 
#>     scanonevar
 
set.seed(27599)
 
test.cross <- qtl::sim.cross(map = qtl::sim.map(len = rep(20, 5), eq.spacing = FALSE))

We'll create two additional columns in the phenotype dataframe and calculate genotype probabilities at each pseudolocus using the Hidden Markov Model provided by qtl.

test.cross[['pheno']][['sex']] <- sample(x = c(0, 1),
                                         size = qtl::nind(test.cross),
                                         replace = TRUE)
test.cross[['pheno']][['sire']] <- factor(x = sample(x = 1:5,
                                                     size = qtl::nind(test.cross),
                                                     replace = TRUE))
test.cross <- qtl::calc.genoprob(cross = test.cross, step = 2)

Now that we have a cross object that's ready for analysis, we can use the scanonevar function to conduct a genome scan using the DGLM. Note that we use two formulas -- one for the mean and one for the variancee.

The mean formula must have one variable to the left of the ~ and that variable must be in cross$pheno. The mean and variance formula can have any number of variables to the right of the ~. Valid variables are (a) in cross$pheno, (b) the name of a marker, or (c) a special keyword.

The special keywords for mean.formula are mean.QTL.add and mean.QTL.dom. The special keywords for var.formula are var.QTL.add and var.QTL.dom.

sov <- scanonevar(cross = test.cross,
                  mean.formula = phenotype ~ sex + D1M2 + mean.QTL.add + mean.QTL.dom,
                  var.formula = ~ sire + D2M3 + var.QTL.add + var.QTL.dom)

News

Reference manual

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install.packages("vqtl")

2.0.0 by Robert Corty, 15 days ago


Browse source code at https://github.com/cran/vqtl


Authors: Robert Corty [aut, cre]


Documentation:   PDF Manual  


GPL-3 license


Imports doParallel, foreach, iterators, parallel, knitr, dplyr, qtl, dglm, evd, ggplot2, gtools, lazyeval, stringr, tidyr, testthat, hglm, purrr

Suggests covr


See at CRAN