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

Shuo Jiao, Ulrike Peters, Sonja Berndt, Stéphane Bézieau, Hermann Brenner, Peter T. Campbell, Andrew T. Chan, Jenny Chang-Claude, Mathieu Lemire, Polly A. Newcomb, John D. Potter, Martha L. Slattery, Michael O. Woods, Li Hsu

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)609-618
Number of pages10
JournalGenetic Epidemiology
Issue number8
StatePublished - Dec 1 2015
Externally publishedYes


  • G × E screening statistics
  • GWAS
  • Rare variants

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)


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