A Bayesian Hierarchical Model for Detecting Haplotype-Haplotype and Haplotype-Environment Interactions in Genetic Association Studies

• Activity Number: 233
• Type: Contributed
• Date/Time: Monday, August 1, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Jun Li*+ and Kui Zhang and Nengjun Yi
• Companies: University of Alabama at Birmingham and University of Alabama at Birmingham and University of Alabama at Birmingham
• Keywords: Bayesian methods ; Generalized linear models ; Genetic association ; Hierarchical models ; Haplotype-haplotype interactions ; Haplotype-environment interactions

Abstract

Genetic association studies based on haplotypes are powerful in the discovery and characterization of the genetic basis of complex human diseases. However, statistical methods for detecting haplotype-haplotype (HH) and haplotype-environment (HE) interactions have not yet been fully developed. Furthermore, methods for detecting the association between rare haplotypes and disease have not kept pace with their counterpart of common haplotypes. We propose an efficient and robust method to tackle these problems based on a Bayesian hierarchical generalized linear model. Our model simultaneously fits environmental effects, main effects of numerous common and rare haplotypes, and HH and HE interactions. The key to the approach is the use of a continuous prior distribution on coefficients that favors sparseness and facilitates computation. We develop a fast expectation-maximization algorithm to fit models by estimating posterior modes of coefficients. We evaluate the proposed method and compare its performance to existing methods on extensive simulated data. The results show that the proposed method performs well under all situations and is more powerful than existing approaches.