Incorporating Biological Pathways via a Markov Random Field Model in Genome-Wide Association Studies

• Activity Number: 28
• Type: Contributed
• Date/Time: Sunday, August 1, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Min Chen*+ and Judy Cho and Hongyu Zhao
• Companies: Yale University and Yale University and Yale University
• Keywords: Genome-wide association studies ; Markov Random Field ; Multiple marker ; Biological pathways

Abstract

Genome-wide association studies (GWAS) examine a large number of markers across the genome to identify associations between genetic variants and disease. Most published studies examine only single markers, which may be less informative than considering multiple markers jointly because genes interact with each other to affect disease risk. In addition, much knowledge has been accumulated in the literature on biological pathways and interactions. We propose a Markov Random Field (MRF) model to incorporate pathway topology in GWAS data analysis. The conditional distribution of our MRF model takes on a simple logistic regression form. Samples from the posterior distributions can be effectively drawn via a Gibbs sampler. Simulation studies and real data examples show that our proposed framework is more effective to identify genes associated with disease than single gene based method.