A Bayesian Hierarchical Model for Relating Multiple SNPs within Multiple Genes to Disease Risk.

ABSTRACT: A variety of methods have been proposed for studying the association of multiple genes thought to be involved in a common pathway for a particular disease. Here, we present an extension of a Bayesian hierarchical modeling strategy that allows for multiple SNPs within each gene, with external prior information at either the SNP or gene level. The model involves variable selection at the SNP level through latent indicator variables and Bayesian shrinkage at the gene level towards a prior mean vector and covariance matrix that depend on external information. The entire model is fitted using Markov chain Monte Carlo methods. Simulation studies show that the approach is capable of recovering many of the truly causal SNPs and genes, depending upon their frequency and size of their effects. The m

SUBMITTER: Duan L 

PROVIDER: S-EPMC3892936 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature