Powerful Set‐Based Gene‐Environment Interaction Testing Framework for Complex Diseases

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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 .
Funding Information
  • GECCO: National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services (U01 CA137088; R01 CA059045; U01 CA164930)

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