Functional Logistic Regression Approach to Detecting Gene by Longitudinal Environmental Exposure Interaction

• Activity Number: 350
• Type: Topic Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: International Chinese Statistical Association
• Author(s): Peng Wei*
• Companies: The University of Texas School of Public Health
• Keywords: gene-environment interaction ; GWAS ; functional data analysis ; longitudinal exposure ; measurement error ; GxE

Abstract

Most complex human diseases are likely the consequence of the joint actions of genetic and environmental factors. Identification of gene-environment (GxE) interactions not only contributes to a better understanding of the disease mechanisms, but also improves disease risk prediction and targeted intervention. While most existing statistical methods only consider interactions between genes and static environmental exposures, many environmental factors, such as air pollution and diet, change over time, and cannot be accurately captured at one measurement time point or by simply categorizing into static exposure categories. Here we propose a powerful functional logistic regression (FLR) approach to model the time-varying effect of longitudinal environmental exposure and its interaction with genetic factors on disease risk. Our proposed FLR model is capable of accommodating longitudinal exposures measured at irregular time points and contaminated by measurement errors. We show the statistical power gains of proposed method using extensive simulations, and demonstrate its utility using a case-control study of pancreatic cancer.