Bayesian selection and clustering of polymorphisms in functionally-related genes

In epidemiologic studies, there is often interest in assessing the relationship between polymorphisms in functionally-related genes and a health outcome. For each candi-date gene, single nucleotide polymorphism (SNP) data are collected at a number of locations, resulting in a large number of possible genotypes. Because instabilities can result in analy-ses that include all the SNPs, dimensionality is typically reduced by conducting single SNP analyses or attempting to identify haplotypes. This article proposes an alternative Bayesian approach for reducing dimensionality. A multi-level Dirichlet process prior is used for the distribution of the SNP-specific regression coefficients within genes, incorporating a variable selection-type mixture structure in the base measure to allow SNPs with no effect. This structure allows simultaneous selection of important SNPs and clustering of SNPs having similar impact on the health outcome. The methods are illustrated using data from a stud