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Abstract Details

Activity Number: 131
Type: Contributed
Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #306677
Title: A Nonlinear Investigation for Gene-Environment Interaction in Case-Control Association Studies
Author(s): Cen Wu*+ and Yuehua Cui
Companies: Michigan State University and Michigan State University
Address: Department of Statistics and Probability, East Lansing, MI, 48824, United States
Keywords: varying coefficient model ; non-linear gene-environment interaction ; type 2 diabetes
Abstract:

The influences of genetic variants on the complex disease traits are generally dependent on the joint effects of variants , environment factors , as well as their interplays. The gene-environment (G×E) interaction plays a vital role for complex diseases, however, its mechanisms are poorly understood. The linear assumption for the relationship among genetic and environmental factors, along with their interactions, prevails in the current regression based framework to examine the G×E interaction, but this assumption couldn't be justified by the non-linear nature of the G×E interaction. Based on our previous work for the continuous traits, the linear G×E interaction assumption has been extended to the nonlinear version within the framework of varying coefficient model for the binary traits . The varying coefficients are estimated through the regression spline approach. A group of statistical tests have been proposed to elucidate the machinery of G×E interaction. The utility of the proposed framework is demonstrated through extensive simulation studies and a real data analysis on type 2 diabetes.


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