Path-Probability Models Outperform Point-Estimate Scores for Noncoding GWAS Gene Prioritization

Path-Probability Models Outperform Point-Estimate Scores for Noncoding GWAS Gene Prioritization | 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 Path-Probability Models Outperform Point-Estimate Scores for Noncoding GWAS Gene Prioritization Abduxoliq Ashuraliyev This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8270134/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 identify thousands of disease-associated loci, but translating these to causal genes remains challenging because existing methods collapse complex regulatory pathways into single point-estimate scores. Here we introduce \textbf{mechanism graphs}---a new probabilistic inference object that explicitly represents causal chains from variants through regulatory elements to genes, tissues, and traits, while propagating calibrated uncertainty at each step. We combine Sum of Single Effects (SuSiE) fine-mapping with multi-causal colocalization (coloc.susie) and ensemble enhancer--gene linking using Activity-by-Contact (ABC) and promoter capture Hi-C (PCHi-C). On anti-leak holdout benchmarks, path-probability models achieve 76% recall at rank 20 [95% CI: 71--81%] versus 58% [52--64%] for Open Targets Genetics locus-to-gene scores. All modules maintain Expected Calibration Error below 0.05 on held-out benchmarks, enabling principled decision-making under our evaluation protocol. Colocalization signals replicate across independent eQTL studies ($r = 0.89$ effect size correlation). We demonstrate generalizability beyond cardiometabolic traits to neurological, immune, and cancer phenotypes, though calibration may shift with ancestry or tissue coverage changes. Biological sciences/Genetics/Genetic association study/Genome-wide association studies Biological sciences/Genetics/Gene regulation Biological sciences/Genetics/Gene expression Biological sciences/Computational biology and bioinformatics/Software Full Text Additional Declarations The authors declare no competing interests. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-8270134","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":562432027,"identity":"63b34bbb-0081-41f2-9481-56021d69e4e3","order_by":0,"name":"Abduxoliq Ashuraliyev","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie3RMQrCMBSA4RcC7aIWt0gvEQkIYm/iEinUJe4dRCtCXQquPYZHEAKdegBHoeDk0NHRZxEHIaVuDvnHkI/kJQA22x/mJkBBckYdSnbXGlcGgCttkRep48D13L0e5wDM6UJIXkbeKCsiv9eJHHQl+qn2+UUJEcR64+BxHNbB3EiySIRIBJJxpUrNnF4RSiiiVWK8mKIaSdicskqRMCXOJNFmcrw3ZHu6qIk/7UKGuaILHJ8245M3kW3EZzfB8ZFp88hZuRylOAuXLbMMvLBin698xDPvZVm9DozEkPxtu81ms9m+egKLP1cOfZ02dgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0003-5482-5526","institution":"Independent researcher","correspondingAuthor":true,"prefix":"","firstName":"Abduxoliq","middleName":"","lastName":"Ashuraliyev","suffix":""}],"badges":[],"createdAt":"2025-12-03 12:21:49","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8270134/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8270134/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98778238,"identity":"96ac9df0-fe65-447c-859f-c482e71a50ae","added_by":"auto","created_at":"2025-12-22 12:29:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1029337,"visible":true,"origin":"","legend":"Article File","description":"","filename":"main.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8270134/v1_covered_367ac20e-f5ae-43dc-9fc7-29d247a5b1f0.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Path-Probability Models Outperform Point-Estimate Scores for Noncoding GWAS Gene Prioritization","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Independent Research","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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-8270134/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8270134/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Genome-wide association studies identify thousands of disease-associated loci,\r\nbut translating these to causal genes remains challenging because existing methods\r\ncollapse complex regulatory pathways into single point-estimate scores.\r\nHere we introduce \\textbf{mechanism graphs}---a new probabilistic inference object\r\nthat explicitly represents causal chains from variants through regulatory elements\r\nto genes, tissues, and traits, while propagating calibrated uncertainty at each step.\r\nWe combine Sum of Single Effects (SuSiE) fine-mapping with multi-causal\r\ncolocalization (coloc.susie) and ensemble enhancer--gene linking using\r\nActivity-by-Contact (ABC) and promoter capture Hi-C (PCHi-C).\r\nOn anti-leak holdout benchmarks, path-probability models achieve 76\\% recall\r\nat rank 20 [95\\% CI: 71--81\\%] versus 58\\% [52--64\\%] for Open Targets Genetics\r\nlocus-to-gene scores.\r\nAll modules maintain Expected Calibration Error below 0.05 on held-out benchmarks,\r\nenabling principled decision-making under our evaluation protocol.\r\nColocalization signals replicate across independent eQTL studies\r\n($r = 0.89$ effect size correlation). 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