title: Stochastic search variable selection for identifying multiple quantitative trait loci.
abstract: In this article, we utilize stochastic search variable selection methodology to develop a Bayesian method for identifying multiple quantitative trait loci (QTL) for complex traits in experimental designs. The proposed procedure entails embedding multiple regression in a hierarchical normal mixture model, where latent indicators for all markers are used to identify the multiple markers. The markers with significant effects can be identified as those with higher posterior probability included in the model. A simple and easy-to-use Gibbs sampler is employed to generate samples from the joint posterior distribution of all unknowns including the latent indicators, genetic effects for all markers, and other model parameters. The proposed method was evaluated using simulated data and illustrated using a real data set. The results demonstrate that the proposed method works well under typical situations of most QTL studies in terms of number of markers and marker density.
authors.affiliation: Department of Biostatistics, University of Alabama, Birmingham 35294-0022, USA. nyi@ms.soph.uab.edu
authors.forename: Nengjun
authors.initials: N
authors.lastname: Yi
authors.forename: Varghese
authors.initials: V
authors.lastname: George
authors.forename: David B
authors.initials: DB
authors.lastname: Allison
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D005819:Genetic Markers; D001467:Hordeum / Q000235:genetics; D008957:Models, Genetic; D040641:Quantitative Trait Loci; D013269:Stochastic Processes
chemical_list: D005819:Genetic Markers