Abstract
R/qtl2 is an interactive software environment for mapping quantitative trait loci (QTL) in experimental populations. The R/qtl2 software expands the scope of the widely used R/qtl software package to include multiparent populations derived from more than two founder strains, such as the Collaborative Cross and Diversity Outbred mice, heterogeneous stocks, and MAGIC plant populations. R/qtl2 is designed to handle modern high-density genotyping data and high-dimensional molecular phenotypes, including gene expression and proteomics. R/qtl2 includes the ability to perform genome scans using a linear mixed model to account for population structure, and also includes features to impute SNPs based on founder strain genomes and to carry out association mapping. The R/qtl2 software provides all of the basic features needed for QTL mapping, including graphical displays and summary reports, and it can be extended through the creation of add-on packages. R/qtl2, which is free and open source software written in the R and C++ programming languages, comes with a test framework.
Highlights
R/qtl2 is an interactive software environment for mapping quantitative trait loci (QTL) in experimental populations
We have completed the core of the R/qtl2 software package, which is a reimplementation of the widely used software
R/qtl, to better handle high-dimensional genotypes and phenotypes, and modern cross designs including Multiparent populations (MPPs). This software forms a key computational platform for QTL analysis in MPPs, and includes genotype reconstruction for a variety of MPP designs, numerous facilities for quality-control assessments, QTL genome scans by Haley-Knott regression (Haley and Knott 1992) and linear mixed models to account for population structure, and Best Linear Unbiased Predictors (BLUPs)-based estimates of QTL effects
Summary
R/qtl input files for both datasets are available at GitHub (https:// github.com/rqtl/qtl2data). The R/qtl software is available from its web site (https:// kbroman.org/qtl2) as well as GitHub (https://github.com/ rqtl/qtl). (Computationally intensive aspects of R/qtl are in C.) We use Rcpp (Eddelbuettel and François 2011; Eddelbuettel 2013) for the interface between R and C++, to simplify code and reduce the need for copying. We use roxygen (Wickham et al 2017) to develop the R package documentation. Linear algebra calculations, such as matrix decomposition and linear regression, are a central part of QTL analysis. The use of unit tests helps us to catch bugs earlier, and revealed several bugs in R/qtl
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