The goal of qtlbim is to perform multivariate bayesian QTL mapping after the methods developed in several journal articles by Yi, et al. Currently, it works only with two-parent crosses, but we’d like to develop methods for multiparent crosses like the Diversity Outbred mice.

This particular repository was forked from the CRAN Github repository and subsequently updated to work with new versions of gcc.

qtlbim isn’t on CRAN right now.

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("fboehm/qtlbim")

Detailed use instructions are available in the vignettes. We reproduce below part of the text from one vignette.

This is a basic example:

library(qtlbim)
#> Loading required package: qtl
#> Loading required package: lattice
#> Loading required package: coda
#> Loading required package: tools
#> Loading required package: MASS
## basic example code

This document focuses on the hyper dataset from the qtl (Broman et al. 2003) R package, which was initially studied in Sugiyama et al. (2001). The hyper dataset is stored in qtl as a cross object. The R/qtlbim package processes this cross object to create a qb object called qbHyper, containing the MCMC samples. The hyper demo shows how this is done.

It is possible to directly load the already saved qb object with the data command. Following this by a call to qb.cross extracts a version of the cross object used to create the qb object.

data(qbHyper)
hyper <- qb.cross(qbHyper)

Alternatively, a object can be created by the following sequence of commands. First load the hyper data set from R/qtl, and subset on the autosomes, as R/qtlbim does not yet handle the X chromosome properly.

data(hyper)
hyper <- subset(hyper, chr=1:19)

To run the MCMC sampler on the hyper data we use the command

hyper <- qb.genoprob(hyper, step=2) 
# Now run the MCMC model selection algorithm.
# This can take several minutes.
qbHyper <- qb.mcmc(hyper, pheno.col = 1, seed = 1616)
#> qb.mcmc is running 60,600 iterations. The current iterations are saved: 
#> 200
#> 400
#> 600
#> 800
#> 1000
#> 1200
#> 1400
#> 1600
#> 1800
#> 2000
#> 2200
#> 2400
#> 2600
#> 2800
#> 3000
#> MCMC sample has been saved to: ./bp_Feb-14-190121.
#> Bayesian MCMC took 0.74 minutes.
#> Warning: Removing external directory ./bp_Feb-14-190121

The option seed sets the pseudorandom number seed so that this run can be repeated exactly. The qb object called qbHyper is used throughout this vignette.

This section describes in more detail how to create Markov chain Monte Carlo (MCMC) samples from the Bayesian posterior to be used for QTL mapping. The next step to mapping with the R/qtl package would be to use the function calc.genoprob to create genotype probabilities based on a Hidden Markov model. However, for Bayesian model selection, we replace calc.genoprob with the R/qtlbim function qb.genoprob. The function qb.genoprob performs some bookkeeping before calling calc.genoprob with the variable stepwidth option for pseudomarker positions. The probabilities for genotypes at pseudomarkers and at markers with missing data are calculated by calc.genoprob from the observed marker data using the multipoint method (Jiang and Zeng, 1997).

The MCMC samples are created by qb.mcmc after running qb.genoprob. In the simplest case, MCMC samples are created with the following two calls:

hyper <- qb.genoprob(hyper, step=2) 
qbHyper <- qb.mcmc(hyper, pheno.col = 1)
#> qb.mcmc is running 60,600 iterations. The current iterations are saved: 
#> 200
#> 400
#> 600
#> 800
#> 1000
#> 1200
#> 1400
#> 1600
#> 1800
#> 2000
#> 2200
#> 2400
#> 2600
#> 2800
#> 3000
#> MCMC sample has been saved to: ./bp_Feb-14-190206.
#> Bayesian MCMC took 0.75 minutes.
#> Warning: Removing external directory ./bp_Feb-14-190206

By default the qb.mcmc function prints out progress messages of the number of iterations completed. These progress messages can be suppressed by setting verbose=FALSE. Arguments for the routines qb.data and qb.model, described below, can be passed through qb.mcmc. Otherwise, default values are used. The detail below for qb.data.qb.model, andqb.mcmc` routines could be skipped in favor of default settings.

The function qb.data specifies the traits to be analyzed, their underlying distribution, the random and/or fixed covariates and whether to standardize or to use a boxcox transformation. Note that, the cross object can have several phenotypes and some of which could be used as covariates.

qbData <- qb.data(hyper, pheno.col = 1, trait = "normal", fixcov = 0, rancov = 0)

The R/qtlbim routines handle normal, binary and ordinal data. In addition, the user can specify fixed (fixcov) and random (rancov) covariate(s). [The pheno.col, fixcov, and rancov values can be numeric indices to the phenotype names, or character strings with exact phenotype names.] Fixed covariates can be included as interacting covariates with the intcov option to qb.model (see below).

The function qb.model defines the model parameters, using defaults that work well in most settings. Users are probably most interested in specifying if epistasis is considered, the prior expected number of main effect QTLs (main.nqtl), and the prior expected total number of QTLs (mean.nqtl), which includes additional QTLs with only epistatic effects. A user may set main.nqtl and mean.nqtl based on previous QTL analysis, for example using R/qtl. Setting the maximum number of QTLs overall (max.nqtl) or per chromosome (chr.nqtl), and setting the minimum interval between linked QTL, can be used to restrict sampling as needed.

Typically a real data set has several traits which can be considered as covariates. The intcov option specifies which covariate(s) can interact with QTLs, or equivalently, which environmental factors may have GxE interactions. The intcov should be a vector of 0s and 1s of the same length as the fixcov option specified for qb.data (see above).

qbModel <- qb.model(hyper, epistasis = TRUE, main.nqtl = 3, 
interval = rep(5,nchr(hyper)), chr.nqtl = rep(2,nchr(hyper)), 
depen = FALSE, prop = c(0.5, 0.1, 0.05))

The function qb.mcmc creates MCMC samples on the data and model specified. The results are initially saved in a unique directory under mydir, which is removed at completion of the command. Options for qb.data and qb.model can be passed directly to qb.mcmc, or as the objects created above.

qb <- qb.mcmc(hyper, data = qbData, model = qbModel, mydir = ".", 
              n.iter = 3000, n.thin = 20, genoupdate = TRUE)
#> qb.mcmc is running 60,600 iterations. The current iterations are saved: 
#> 200
#> 400
#> 600
#> 800
#> 1000
#> 1200
#> 1400
#> 1600
#> 1800
#> 2000
#> 2200
#> 2400
#> 2600
#> 2800
#> 3000
#> MCMC sample has been saved to: ./bp_Feb-14-190251.
#> Bayesian MCMC took 0.75 minutes.
#> Warning: Removing external directory ./bp_Feb-14-190251

The genoupdate option simulates pseudomarker and missing marker genotypes if TRUE, or uses a Haley-Knott (1992) type approach if FALSE; the latter is faster, but not generally recommended if there are many missing genotypes or selective genotyping. n.iter samples are saved, thinning to one in n.thin from the MCMC samples to reduce serial correlation. That is, n.iter * n.thin samples are drawn, after an initial n.burnin samples (1% of total by default) are discarded to allow the chain to converge closer to the posterior distribution.