A Novel Kernel-Based Statistical Approach to Testing Association with Body Mass Index in a Longitudinal Genetic Study

• Activity Number: 651
• Type: Contributed
• Date/Time: Thursday, August 13, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Zuoheng Wang* and Zhong Wang and Xinyu Zhang and Ke Xu
• Companies: Yale University and Cornell University and Yale University and Yale University

Abstract

Obesity is a major public health concern in the United States and contributes to the pathogenesis of many diseases. Body mass index (BMI) serves as a proxy for body fat percentage and an indication for obesity. Currently, the confirmed associations account for a small proportion of overall genetic risks for obesity. Multiple measurements in longitudinal genetic studies provide a route to reduce noise and correspondingly increase the strength of signals in genome-wide association studies (GWAS). In this study, we developed a powerful kernel-based statistical method for testing the joint effect of gene variants with a gene region on disease outcomes measured over multiple time points. We applied the new method to a longitudinal study to understand the genetic risk underlying BMI. We also conducted simulation studies to access the performance of the new statistical methods and demonstrated a power gain by taking advantage of repeated measurements and aggregating information across a biological region. This study not only contributes to the statistical toolbox in the current GWAS, but also potentially advances our understanding of the etiology of obesity.