QTLRel is an R package for mapping quantitative trait loci (QTLs) in experimental crosses such as advanced intercross lines (AILs) where relatedness among individuals should not be ignored. QTLRel includes functions to estimate background genetic variance components, impute missing genotypes, simulate genotypes, perform a genome scan for quantitative trait loci, and plot the mapping results. QTLRel also includes functions to efficiently calculate Jacquard condensed identity coefficients. Many of these functions are similar to the functions in R/qtl, although many of the functions with similar names have different usage, and may generate different results.
This R package implements the methods described in
Cheng R, Lim J E, Samocha K E, Sokoloff G, Abney M, Skol A D, Palmer A A (2010). Genome-wide association studies and the problem of relatedness among advanced intercross lines and other highly recombinant populations Genetics 185(3): 1033–1044.
If you find this software useful for your project, we request that you cite the Genetics (2010) paper above, and the more recent paper published in BMC Genetics:
Cheng R, Abney M, Palmer A A, Skol A D (2011). QTLRel: an R package for genome-wide association studies in which relatedness is a concern. BMC Genetics 12(1): 66.
QTLRel is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability of fitness for a particular purpose. See LICENSE for more details.
There are two ways to install the QTLRel package for R.
The easiest way is to use the R command line. This installs QTLRel stored at CRAN. Simply enter install.packages("QTLRel") in R, and once the package is successfully installed on your computer, load the package using command library(QTLRel). Bear in mind that the version of the package kept on CRAN may not be completely up-to-date.
Alternatively, you may download the source code directly from github, and install the package from the source code. Installing QTLRel in this way involves a couple more steps (unless you happen to have devtools), but ensures that you have the most recent version. First, fork or clone the repository on your computer, or download the repository as a ZIP archive. Once you have expanded the files in the ZIP archive, build the package from the command line on your computer (not the R shell) with the following two commands:
R CMD check qtlreldir
R CMD build qtlreldir
where qtlreldir is the folder containing the files you downloaded from github. (To complete these steps successfully, you may have to install the gdata package first.) Once the package is built, a file will be created with a name something like QTLRel_0.2-12.tar.gz. Finally, to install the package, run the following command in the console:
R CMD INSTALL QTLRel_0.2-12.tar.gz
You will now be able to load the QTLRel library in R.
All the R functions in the QTLRel package are documented; for example, to get a description of the scanOne function, type help(scanOne) in R. To get a list of all the available functions in QTLRel, type either library(help=QTLRel) or help(package=QTLRel) in R.
See here for a tutorial explaining how to use QTLRel for QTL mapping in experimental crosses.
Also, the lgsmfear project repository contains a detailed working example showing how QTLRel can be used to map quantitative trait loci in an advanced intercross line.
Note that the code was recently modified to allow individuals in the pedigree to have only one parent (this corresponds to self-fertilization, or selfing). Since calculation of the identity coefficients assumes that the founders are not inbred, removing the constraint that an individual must have two parents permits inbred founders to be defined artificially through iterated selfing. This is demonstrated in the lgsmfear project.
The QTLRel package for R was originally developed by Riyan Cheng for Abraham Palmer's lab at the University of Chicago.