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International Journal of Genomics
Volume 2013, Article ID 406217, 10 pages
Research Article

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

Division of Biostatistics, Department of Preventive Medicine, University of Southern California (USC), 2001 N. Soto Street, Los Angeles, CA, USA

Received 30 May 2013; Revised 3 September 2013; Accepted 9 September 2013

Academic Editor: Soraya E. Gutierrez

Copyright © 2013 Lewei Duan and Duncan C. Thomas. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 method is applied to data on 504 SNPs in 38 candidate genes involved in DNA damage response in the WECARE study of second breast cancers in relation to radiotherapy exposure.