Powerful Set-Based Gene-Environment Interaction Testing Framework for Complex Diseases.

Author(s): Jiao S,  Peters U,  Berndt S,  Bézieau S,  Brenner H,  Campbell PT,  Chan AT,  Chang-Claude J,  Lemire M,  Newcomb PA,  Potter JD,  Slattery ML,  Woods MO,  Hsu L

Journal: Genet Epidemiol

Date: 2015 Dec

Major Program(s) or Research Group(s): PLCO

PubMed ID: 26095235

PMC ID: PMC4675704

Abstract: Identification of gene-environment interaction (G × E) is important in understanding the etiology of complex diseases. Based on our previously developed Set Based gene EnviRonment InterAction test (SBERIA), in this paper we propose a powerful framework for enhanced set-based G × E testing (eSBERIA). The major challenge of signal aggregation within a set is how to tell signals from noise. eSBERIA tackles this challenge by adaptively aggregating the interaction signals within a set weighted by the strength of the marginal and correlation screening signals. eSBERIA then combines the screening-informed aggregate test with a variance component test to account for the residual signals. Additionally, we develop a case-only extension for eSBERIA (coSBERIA) and an existing set-based method, which boosts the power not only by exploiting the G-E independence assumption but also by avoiding the need to specify main effects for a large number of variants in the set. Through extensive simulation, we show that coSBERIA and eSBERIA are considerably more powerful than existing methods within the case-only and the case-control method categories across a wide range of scenarios. We conduct a genome-wide G × E search by applying our methods to Illumina HumanExome Beadchip data of 10,446 colorectal cancer cases and 10,191 controls and identify two novel interactions between nonsteroidal anti-inflammatory drugs (NSAIDs) and MINK1 and PTCHD3.