Joint Regression Models for Pleiotropic Genetic Association Studies

• Activity Number: 159
• Type: Topic Contributed
• Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Colin O. Wu*+ and Minjung Kwak
• Companies: NHLBI/NIH and Yeungnam University
• Keywords: Genetic Association Study ; Joint Regression Models ; Pleiotropic Genetic Analysis ; Qualitative Trait ; Quantitative Trait ; Stratified Sample

Abstract

Genetic association studies in practice often involve multiple traits resulting from a common disease mechanism, and samples for such studies are often stratified based on some trait outcomes. In such situations, statistical methods using only one of these traits may be inadequate and lead to under-powered tests for detecting genetic associations. We propose an estimation and testing procedure for evaluating the shared-association of a genetic marker on the joint distribution of multiple traits of a common disease. Specifically, we assume that the disease mechanism involves both quantitative and qualitative traits, and our samples could be stratified based on the qualitative trait. Through a joint likelihood function, we derive a class of estimators and test statistics for evaluating the shared genetic association on both the quantitative and qualitative traits. Our simulation study shows that the joint likelihood test procedure is potentially more powerful than association tests based on separate traits. Application of our proposed procedure is demonstrated through the rheumatoid arthritis data provided by the Genetic Analysis Workshop 16 (GAW16).