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. 1. Introduction The Women’s Environment, Cancer And Radiation Epidemiology (WECARE) study [1] is aimed at a comprehensive examination of genes involved in particular functional pathways. The study is a population-based nested case-control study of 708 contralateral breast cancers (CBC) within a notional cohort of about 65,000 survivors of a first breast cancer, 1401 of whom serve as controls, and the primary exposure of interest is ionizing radiation dose to the contralateral breast from radiotherapy for treatment of the first cancer. Ionizing radiation is known to cause double strand breaks (DSBs) in DNA, which can invoke any of several DNA damage response mechanisms, primarily DSB repair via either homologous recombination or nonhomologous end joining, cell cycle checkpoint regulation, or apoptosis. The original study focused on mutations in the ATM gene, which plays a central role in the recognition of DSBs. The study was then extended to include BRCA1, BRCA2, and CHEK2, which are all involved in homologous recombination repair (HRR), and later still to include a broader set of 38 candidate genes involved in this and other pathways for DSB damage response. We have previously reported on the main effects of ionizing radiation [2, 3], ATM [4–6], BRCA1/2 [7–12], CHEK2 [13], and the interactions of radiation with ATM [14] and BRCA1/2 [15] as well as with other treatments and reproductive factors [16, 17], amongst other risk factors. The aim of this paper is to provide a comprehensive modeling strategy for examining the effects of all genes in a pathway and to apply the approach to candidate genes
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