Stochastic Search Gene Suggestion: A Bayesian Hierarchical Model for Gene-mapping

• 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): Michael D. Swartz*+ and Marek Kimmel and Peter Mueller and Christopher I. Amos
• Companies: Texas A&M University and U.T. M.D. Anderson Cancer Center and Rice University and University of Texas MD Anderson Cancer Center and University of Texas MD Anderson Cancer Center
• Keywords: Bayesian model selection ; gene mapping ; Markov chain Monte Carlo ; Bayesian hierarchical model

Abstract

Mapping the genes for a complex disease, such as Rheumatoid Arthritis (RA), involves finding multiple genetic loci that may contribute to the onset of the disease. Pairwise testing of the loci leads to the problem of multiple testing. To avoid multiple tests, we can look at haplotypes; but this results in a contingency table with sparse counts. Using case-parent triad data, we extend the Bayesian conditional logistic regression model developed by Thomas, et al., by defining prior distributions on the allele main effects that model the genetic dependencies present in the HLA region of Chromosome 6. We also added a hierarchical level for model selection that accounts for both locus and allele selection. Thus we cast the problem of identifying genetic loci relevant to the disease into a problem of Bayesian model selection. We evaluate the performance of the procedure with some simulated examples, and then apply our procedure to identifying genetic effects influencing susceptibility to RA. This research is supported by a Genetic Epidemiology Fellowship supported by NCI grant R25-CA57730, Robert Chamberlain, PhD, Principal Investigator.