SNPs, Haplotypes, and Model Selection in Candidate Gene Regions

• Activity Number: 45
• Type: Topic Contributed
• Date/Time: Sunday, August 8, 2004 : 4:00 PM to 5:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): David Conti*+ and William Gauderman
• Companies: University of Southern California and University of Southern California
• Keywords: SNPs ; haplotypes ; association analysis ; Bayes model averaging

Abstract

Modern molecular techniques make discovery of numerous single nucleotide polymorphims (SNPs) in candidate gene regions feasible. Conventional analysis of multilocus data ranges from either independent tests with each variant or the use of haplotypes in association analysis. The first technique ignores the dependencies between SNPs, while the second, though it may increase power, often introduces uncertainty by estimating haplotypes from population data. Additionally, as the number of loci expands, ambiguity in haplotype estimation increases and resolution of the specific causal variant may become problematic. We present a genotype-level analysis to jointly model the SNPs and we introduce a modified SNP´SNP interaction term to capture the underlying haplotype phase information. By reparameterizing the information from multiple SNPs into linear combinations of SNP and phase terms, we frame the analysis of multilocus data into a model selection paradigm. Within this paradigm, we propose a Bayes model-averaging procedure, which highlights key SNPs and phase terms while incorporating uncertainty in model selection. Prior distributions are modified with genetic information.