Using Bayesian Hierarchical Model to Detect Related Multiple SNPs Within Multiple Genes to Disease Risk

• Activity Number: 374
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Lewei Duan*+
• Keywords: hierarchical Bayes models ; pathway models ; candidate genes ; DNA damage response ; breast cancer

Abstract

We are interested in exploring a novel statistical method to investigate the involvement of multiple genes thought to be part of a common pathway for a particular disease. If a gene is identified to be associated with the disease, we are also interested in discovering which SNPs within this gene are responsible for this association. Here we present an extension of Bayesian hierarchical modeling approach 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 good ability to recover the underlying model. 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 exposures.