Originally published as Genetics Published Articles Ahead of Print on May 27, 2008.

Genetics, Vol. 179, 1045-1055, June 2008, Copyright © 2008
doi:10.1534/genetics.107.085589

Bayesian LASSO for Quantitative Trait Loci Mapping

Nengjun Yi*,1 and Shizhong Xu{dagger}

* Department of Biostatistics, Section on Statistical Genetics, University of Alabama, Birmingham, Alabama 35294 and {dagger} Department of Botany and Plant Sciences, University of California, Riverside, California 92521

1 Corresponding author: Department of Biostatistics, University of Alabama, Birmingham, AL 35294-0022.
E-mail: nyi{at}ms.soph.uab.edu

The mapping of quantitative trait loci (QTL) is to identify molecular markers or genomic loci that influence the variation of complex traits. The problem is complicated by the facts that QTL data usually contain a large number of markers across the entire genome and most of them have little or no effect on the phenotype. In this article, we propose several Bayesian hierarchical models for mapping multiple QTL that simultaneously fit and estimate all possible genetic effects associated with all markers. The proposed models use prior distributions for the genetic effects that are scale mixtures of normal distributions with mean zero and variances distributed to give each effect a high probability of being near zero. We consider two types of priors for the variances, exponential and scaled inverse-{chi}2 distributions, which result in a Bayesian version of the popular least absolute shrinkage and selection operator (LASSO) model and the well-known Student's t model, respectively. Unlike most applications where fixed values are preset for hyperparameters in the priors, we treat all hyperparameters as unknowns and estimate them along with other parameters. Markov chain Monte Carlo (MCMC) algorithms are developed to simulate the parameters from the posteriors. The methods are illustrated using well-known barley data.