{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T06:31:14Z","timestamp":1774679474232,"version":"3.50.1"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"20","license":[{"start":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T00:00:00Z","timestamp":1621900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 HL145025"],"award-info":[{"award-number":["R01 HL145025"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01 HL131136"],"award-info":[{"award-number":["R01 HL131136"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Gene\u2013environment 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we develop a new software program, GEM (Gene\u2013Environment 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\u00a0768 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>GEM is freely available as an open source project at https:\/\/github.com\/large-scale-gxe-methods\/GEM.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab223","type":"journal-article","created":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T15:56:19Z","timestamp":1617810979000},"page":"3514-3520","source":"Crossref","is-referenced-by-count":45,"title":["GEM: scalable and flexible gene\u2013environment interaction analysis in millions of samples"],"prefix":"10.1093","volume":"37","author":[{"given":"Kenneth E","family":"Westerman","sequence":"first","affiliation":[{"name":"Department of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital , Boston, MA 02114, USA"},{"name":"Metabolism Program, Broad Institute of MIT and Harvard , Cambridge, MA 02142, USA"},{"name":"Department of Medicine, Harvard Medical School , Boston, MA 02115, USA"}]},{"given":"Duy T","family":"Pham","sequence":"additional","affiliation":[{"name":"Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston , Houston, TX 77030, USA"}]},{"given":"Liang","family":"Hong","sequence":"additional","affiliation":[{"name":"Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston , Houston, TX 77030, USA"}]},{"given":"Ye","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital , Boston, MA 02114, USA"}]},{"given":"Magdalena","family":"Sevilla-Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Department of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital , Boston, MA 02114, USA"},{"name":"Metabolism Program, Broad Institute of MIT and Harvard , Cambridge, MA 02142, USA"},{"name":"Department of Medicine, Harvard Medical School , Boston, MA 02115, USA"}]},{"given":"Yun Ju","family":"Sung","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Washington University School of Medicine , St. Louis, MO 63130, USA"},{"name":"Division of Biostatistics, Washington University School of Medicine , St. Louis, MO 63130, USA"}]},{"given":"Yan V","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Emory University Rollins School of Public Health , Atlanta, GA 30322, USA"},{"name":"Department of Biomedical Informatics, Emory University School of Medicine , Atlanta, GA 30322, USA"}]},{"given":"Alanna C","family":"Morrison","sequence":"additional","affiliation":[{"name":"Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston , Houston, TX 77030, USA"}]},{"given":"Han","family":"Chen","sequence":"additional","affiliation":[{"name":"Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston , Houston, TX 77030, USA"},{"name":"Center for Precision Health, School of Public Health and School of Biomedical Informatics, The University of Texas Health Science Center at Houston , Houston, TX 77030, USA"}]},{"given":"Alisa K","family":"Manning","sequence":"additional","affiliation":[{"name":"Department of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital , Boston, MA 02114, USA"},{"name":"Metabolism Program, Broad Institute of MIT and Harvard , Cambridge, MA 02142, USA"},{"name":"Department of Medicine, Harvard Medical School , Boston, MA 02115, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,5,25]]},"reference":[{"key":"2023051608585775500_btab223-B1","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1186\/1471-2105-11-134","article-title":"ProbABEL package for genome-wide association analysis of imputed data","volume":"11","author":"Aulchenko","year":"2010","journal-title":"BMC 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