Hierarchical Generalized Linear Models for Multiple Quantitative Trait Locus Mapping

We develop hierarchical generalized linear models and computationally efficient algorithms for genomewide analysis of quantitative trait loci (QTL) for various types of phenotypes ill experimental crosses. The proposed models can fit it large number of effects, including covariates, main effects of numerous loci, and gene-gene (epistasis) and gene-environment. (G X E) interactions. The key to the approach is the use of continuous prior distribution oil coefficients that favors sparseness ill the filled model and facilitates computation. We develop it fast expectation-maximization (EM) algorithm to fit models by estimating posterior modes of coefficients. We incorporate our algorithm into the iteratively weighted least squares For classical generalized linear models as implemented ill the package R. We propose a model search strategy, to build a parsimonious model. Our method takes advantage of the special correlation structure in QTL (lata. Simulation studies demonstrate reasonable power to detect true effects, while controlling the rate of false positives. We illustrate with three real data sets and compare our method to existing methods for multilple-QTL. mapping. Our method has been implemented in our freely available package R/qtlbim (www.qtlbim.org), providing a valuable addition to our previous Markov chain Monte Carlo (MCMC) approach.