Quantitative Bayesian Lasso for Detecting Effects of Rare Haplotype Variants and Environmental Factors on Complex Diseases

• Activity Number: 609
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Han Zhang* and Shili Lin
• Companies: The Ohio State University and The Ohio State University
• Keywords: rare variants ; QBL ; MCMC ; GWAS ; GXE ; quantitative traits

Abstract

Rare variants and gene-environment interaction (GXE) are two potentially important contributors to the etiology of complex diseases. Since many diseases (e.g. dichotomous traits) are discretization of some underlying quantitative measurements, it is important to study such quantitative traits directly as they may contain greater amount of information. Obesity is such an example. In recent years, several methods have been proposed for detecting association of rare haplotype variants and GXE, where G is a rare haplotype variant, with complex diseases. However, the focus of most existing methods are on binary traits and case-control data. In this talk, we will present a Quantitative Bayesian Lasso (QBL) method for detecting rare haplotype effects and GXE on quantitative traits for cohort data. By appropriately setting the priors for the effect size parameters, we can increase statistical power for detecting main, and interacting, effects involving rare haplotype variants. We will present simulation results with both continuous and discrete environmental factors and a range of disease models and distributions. We will also demonstrate the utility of QBL in a real data application.