GEM: scalable and flexible gene-environment interaction analysis in millions of samples

• Type of Material: Article
• Authors: Kenneth E Westerman, Massachusetts General Hospital; Duy T Pham, The University of Texas Health Science Center at Houston; Liang Hong, The University of Texas Health Science Center at Houston; Ye Chen, Massachusetts General Hospital; Magdaalena Sevilla-Gonzalez, Massachusetts General Hospital; Yun Ju Sung, Washington Univ; Yan Sun, Emory University; Alanna C Morrison, The University of Texas Health Science Center at Houston; Han Chen, The University of Texas Health Science Center at Houston; Alisa K Manning, Massachusetts General Hospital
• Language: English
• Date: 2021-05-25
• Publisher: OXFORD UNIV PRESS
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2021. Published by Oxford University Press.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1093/bioinformatics/btab223
• Title of Journal or Parent Work: BIOINFORMATICS
• Volume: 37
• Issue: 20
• Start Page: 3514
• End Page: 3520
• Grant/Funding Information: This work was supported by National Institutes of Health (NIH) [grant number R01 HL145025].; The Analysis Commons was funded by NIH [grant number R01 HL131136].
• Keywords: Life Sciences & Biomedicine; ASSOCIATION ANALYSIS; Science & Technology; Mathematical & Computational Biology; Mathematics; Biotechnology & Applied Microbiology; GENOME; Technology; Physical Sciences; Computer Science, Interdisciplinary Applications; Statistics & Probability; Biochemical Research Methods; Computer Science; Biochemistry & Molecular Biology

Abstract

Motivation: Gene-environment interaction (GEI) studies are a general framework that can be used to identify genetic variants that modify the effects of environmental, physiological, lifestyle or treatment effects on complex traits. Moreover, accounting for GEIs can enhance our understanding of the genetic architecture of complex diseases and traits. However, commonly used statistical software programs for GEI studies are either not applicable to testing certain types of GEI hypotheses or have not been optimized for use in large samples. Results: Here, we develop a new software program, GEM (Gene-Environment interaction analysis in Millions of samples), which supports the inclusion of multiple GEI terms, adjustment for GEI covariates and robust inference, while allowing multi-threading to reduce computation time. GEM can conduct GEI tests as well as joint tests of genetic main and interaction effects for both continuous and binary phenotypes. Through simulations, we demonstrate that GEM scales to millions of samples while addressing limitations of existing software programs. We additionally conduct a gene-sex interaction analysis on waist-hip ratio in 352 768 unrelated individuals from the UK Biobank, identifying 24 novel loci in the joint test that have not previously been reported in combined or sex-specific analyses. Our results demonstrate that GEM can facilitate the next generation of large-scale GEI studies and help advance our understanding of the genetic architecture of complex diseases and traits.