fastGxE: Powering genome-wide detection of genotype-environment interactions in biobank studies

fastGxE: Powering genome-wide detection of genotype-environment interactions in biobank studies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Technical Report fastGxE: Powering genome-wide detection of genotype-environment interactions in biobank studies Xiang Zhou, Chao Ning This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5952773/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Traditional genome-wide association studies (GWAS) have primarily focused on detecting main genotype effects, often overlooking genotype-environment interactions (GxE), which are essential for understanding context-specific genetic effects and refining disease etiology. Here, we present fastGxE, a scalable and effective genome-wide GxE method designed to identify genetic variants that interact with environmental factors to influence traits of interest. fastGxE controls for both polygenic effects and polygenic interaction effects, is robust to the number of environmental factors involved in GxE interactions, and ensures scalability for genome-wide GxE analysis in large biobank studies, achieving speed improvements of 32.98-126.49 times over existing approaches. We illustrate the benefits of fastGxE through extensive simulations and an in-depth analysis of 32 physical traits and 67 blood biomarkers from the UK Biobank. In real data applications, fastGxE identifies nine genomic loci associated with physical traits, including six novel ones, and 26 genomic loci associated with blood biomarkers, 19 of which are novel. The new discoveries highlight the dynamic interplay between genetics and the environment, uncovering potentially clinically significant pathways that could inform personalized interventions and treatment strategies. Biological sciences/Computational biology and bioinformatics/Software Biological sciences/Genetics/Genetic association study/Genome-wide association studies Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.docx Supplementary Information Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5952773","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Technical Report","associatedPublications":[],"authors":[{"id":430721853,"identity":"0733d4be-55a7-484a-ba4d-45133eca61b4","order_by":0,"name":"Xiang Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYFACHjCZwA8kDjA2gEhitUg2kKzFAKSSKC0G588efMzz506e8Y3sxAM/dzDI8d1IwK9FsuFcsjFv27Nisxu5Gw72nmEwliSkhZ+xx0yat+Fw4jaglsOMbQyJGwhpYWPmMZPm+XM4cfMMiJZ6glr42UBa2A4nbpCAaEkwIOiXHh5jw7ltzxJnnHkL9EubhOHMMw/wazE4f8bwwZs/dxL723M3f/jZZiPPd5yALVBwAMaQIEo5ipZRMApGwSgYBZgAAJs7ThbHfKQyAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-4331-7599","institution":"University of Michigan","correspondingAuthor":true,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Zhou","suffix":""},{"id":430721854,"identity":"5c905849-c8e7-489f-89d7-4f9c97236e8c","order_by":1,"name":"Chao Ning","email":"","orcid":"https://orcid.org/0000-0001-8247-1700","institution":"University of Michigan","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Ning","suffix":""}],"badges":[],"createdAt":"2025-02-03 17:07:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5952773/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5952773/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78868305,"identity":"abe65dd7-03ab-490c-89a8-5e130c79ca82","added_by":"auto","created_at":"2025-03-20 05:03:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1445453,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5952773/v1_covered_fc70608b-3a9a-4eb9-b681-dafe5ccdd939.pdf"},{"id":78867556,"identity":"0eddeeb2-a05a-4e12-a924-6fe720b383a6","added_by":"auto","created_at":"2025-03-20 04:39:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1053383,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5952773/v1/73199834897091a39f19d08d.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"fastGxE: Powering genome-wide detection of genotype-environment interactions in biobank studies","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5952773/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5952773/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTraditional genome-wide association studies (GWAS) have primarily focused on detecting main genotype effects, often overlooking genotype-environment interactions (GxE), which are essential for understanding context-specific genetic effects and refining disease etiology. 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