Robust inference with GhostKnockoffs in genome-wide association studies

Robust inference with GhostKnockoffs in genome-wide association 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 Article Robust inference with GhostKnockoffs in genome-wide association studies Zihuai He, Xinran Qi, Michael Belloy, Jiaqi Gu, Xiaoxia Liu, Hua Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6396196/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Genome-wide association studies (GWASs) have been extensively adopted to depict the underlying genetic architecture of complex traits. Recent studies have demonstrated that for feature selection in GWASs data, in addition to controlling the familywise error rate (FWER), the false discovery rate (FDR) serves as an appealing alternative for detecting small effect loci associated with polygenic traits. However, the presence of correlations among genetic variants makes direct application of usual FDR-controlling procedures to marginal association tests ineffective. The knockoffs-based methods have shown guarantee in FDR control in GWASs, but their statistical validity and effectiveness in studies with related individuals remain unexplored. In this paper, we propose a knockoff-based approach by integrating recently proposed GhostKnockoffs and state-of-the-art marginal association tests. We show that GhostKnockoffs, which only requires GWAS Z-scores as input, is robust to arbitrary relatedness structure as long as the input Z-scores are derived from valid generalized linear mixed models. Therefore, it can be flexibly applied on top of the standard GWASs pipeline that accounts for relatedness to enhance the discovery of small effect loci. This robustness also generalizes GhostKnockoffs to other GWASs settings, such as the meta-analysis of multiple overlapping studies and studies based on association test statistics deviated from score tests. We demonstrate the method’s performance using simulation studies and a meta-analysis of nine European ancestral genome-wide association studies and whole exome/genome sequencing studies for the Alzheimer's disease. Health sciences/Biomarkers/Predictive markers Health sciences/Medical research/Genetics research Full Text Additional Declarations There is no duality of interest Cite Share Download PDF Status: Posted 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. 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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-6396196","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":449674418,"identity":"923c15f6-b2fa-4ed9-82bd-cda01e7af462","order_by":0,"name":"Zihuai He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACZh4gYWADZn8AcoFUAlFa0hiAFOMM4rSA1DIwHCZBi3w77zGJHwXn7e3Zzx5s+LjHmoGfPccArxbGZr40yR6D24k9PHmJjTOepTNI9rzBr4WZmcdMgsfgdgIPQ475Y54DhxkMbhCwhQ2oRfKPwTl7Hv43hs0gLfaEtPAAtUjzGBxg7JHIgWgxkCCgRYKZx9haxiA5sefGG8PGGQfSeSTOPCvAq0W+/4zhzTd/7OzZ+3MMGz4csJbjb0/egFcLpktJUz4KRsEoGAWjACsAAK50PbvAFQVjAAAAAElFTkSuQmCC","orcid":"","institution":"Stanford University","correspondingAuthor":true,"prefix":"","firstName":"Zihuai","middleName":"","lastName":"He","suffix":""},{"id":449674419,"identity":"c0b8aa37-fce0-4ad9-89dc-73cc71f7911c","order_by":1,"name":"Xinran Qi","email":"","orcid":"https://orcid.org/0000-0002-5263-2419","institution":"City of Hope","correspondingAuthor":false,"prefix":"","firstName":"Xinran","middleName":"","lastName":"Qi","suffix":""},{"id":449674420,"identity":"aa7c9562-6d8a-48de-bfcb-c522259c15ab","order_by":2,"name":"Michael Belloy","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Belloy","suffix":""},{"id":449674421,"identity":"2388603d-3039-4f06-8a6c-e3e2d4ecfe7d","order_by":3,"name":"Jiaqi Gu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Gu","suffix":""},{"id":449674422,"identity":"7250b2aa-6f1f-4a58-a48a-2294afd8bfce","order_by":4,"name":"Xiaoxia Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxia","middleName":"","lastName":"Liu","suffix":""},{"id":449674423,"identity":"2a6415e1-fbbd-41fd-8e3a-10832b4fa696","order_by":5,"name":"Hua Tang","email":"","orcid":"https://orcid.org/0000-0002-0177-8864","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2025-04-07 16:36:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6396196/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6396196/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87728433,"identity":"8a7b75d2-b625-42ae-a008-83722f9e2cbe","added_by":"auto","created_at":"2025-07-28 11:08:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2753411,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6396196/v1_covered_386acf38-bc63-4bdd-8181-3803c40ed19b.pdf"}],"financialInterests":"There is no duality of interest","formattedTitle":"Robust inference with GhostKnockoffs in genome-wide association studies","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6396196/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6396196/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGenome-wide association studies (GWASs) have been extensively adopted to depict the underlying genetic architecture of complex traits. Recent studies have demonstrated that for feature selection in GWASs data, in addition to controlling the familywise error rate (FWER), the false discovery rate (FDR) serves as an appealing alternative for detecting small effect loci associated with polygenic traits. However, the presence of correlations among genetic variants makes direct application of usual FDR-controlling procedures to marginal association tests ineffective. The knockoffs-based methods have shown guarantee in FDR control in GWASs, but their statistical validity and effectiveness in studies with related individuals remain unexplored. In this paper, we propose a knockoff-based approach by integrating recently proposed GhostKnockoffs and state-of-the-art marginal association tests. We show that GhostKnockoffs, which only requires GWAS Z-scores as input, is robust to arbitrary relatedness structure as long as the input Z-scores are derived from valid generalized linear mixed models. 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