Integrating network annotation from multiple correlated traits to improve polygenic risk scores based on GWAS summary statistics

Integrating network annotation from multiple correlated traits to improve polygenic risk scores based on GWAS summary statistics | 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 Integrating network annotation from multiple correlated traits to improve polygenic risk scores based on GWAS summary statistics Qiuying Sha, Lirong Zhu, Xuewei Cao, Shuanglin Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9073777/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Polygenic risk scores (PRS) are valuable tools for predicting disease risk based on genetic information, with potential impacts on disease prevention and early treatment strategies. Although thousands of disease-associated genetic variants have been identified through genome-wide association studies (GWAS), the accuracy of genetic risk prediction for most diseases remains moderate and challenging. In this paper, we introduce NetPRS, a novel method that utilizes a penalized regression model and leverages network annotation information to enhance PRS prediction. This network annotation is obtained from a genotype-phenotype bipartite network (GPN), where multiple SNPs and traits are linked based on association strengths obtained from GWAS summary statistics. The network annotation allows for the incorporation of information from relevant traits into the PRS prediction for the target trait. Compared to state-of-the-art risk prediction methods, NetPRS consistently achieves improved prediction accuracy in both simulation studies and real data analysis. Biological sciences/Genetics/Genetic association study/Genome-wide association studies Biological sciences/Genetics/Genetic markers Network annotation Polygenic risk score Multiple correlated traits GWAS summary statistics Full Text Additional Declarations There is no duality of interest Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: revise 05 May, 2026 Review # 2 received at journal 16 Apr, 2026 Review # 1 received at journal 16 Apr, 2026 Reviewer # 2 agreed at journal 07 Apr, 2026 Reviewer # 1 agreed at journal 07 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 02 Apr, 2026 Unknown event 10 Mar, 2026 Editor assigned by journal 09 Mar, 2026 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-9073777","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":618863218,"identity":"7ae7adb1-dac7-401b-967e-0b55f2e1ec27","order_by":0,"name":"Qiuying Sha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAmklEQVRIiWNgGAWjYFACHgaGDxCWAfFaGGdAVJOghZmHJC18588e/Gyb8yexgb15mwRRWiRv5CVL524zSGzgOVZGnBaDGzwGEC0SOWZEajl/xvi3JUiL/BtitRzIMZNmBNvCQ6QWyRs5Zpa924yN23jSii2I0sIHdNiNn9vkZPvZD2+8QZQWhgNQmo045chaRsEoGAWjYBTgBAB+KiwWdL08VQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-9342-3269","institution":"Michigan Technological University","correspondingAuthor":true,"prefix":"","firstName":"Qiuying","middleName":"","lastName":"Sha","suffix":""},{"id":618863219,"identity":"ebe25dd9-0baf-4b0d-8885-0b7338da98ba","order_by":1,"name":"Lirong Zhu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lirong","middleName":"","lastName":"Zhu","suffix":""},{"id":618863220,"identity":"568a33d5-6e15-469d-bb2f-0fc00187b611","order_by":2,"name":"Xuewei Cao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xuewei","middleName":"","lastName":"Cao","suffix":""},{"id":618863221,"identity":"845ae51d-4587-4bfb-8a0a-f3afc8f66db8","order_by":3,"name":"Shuanglin Zhang","email":"","orcid":"","institution":"Michigan Technological University","correspondingAuthor":false,"prefix":"","firstName":"Shuanglin","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-03-09 13:43:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9073777/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9073777/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106960475,"identity":"c886d523-a780-4851-8c33-0de22172b399","added_by":"auto","created_at":"2026-04-15 09:21:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":979130,"visible":true,"origin":"","legend":"","description":"","filename":"NetPRSfinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9073777/v1_covered_adb53549-d2fd-4518-9bcf-24f5aa45615f.pdf"}],"financialInterests":"There is no duality of interest","formattedTitle":"Integrating network annotation from multiple correlated traits to improve polygenic risk scores based on GWAS summary statistics","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"european-journal-of-human-genetics","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejhg","sideBox":"Learn more about [European Journal of Human Genetics](http://www.nature.com/ejhg/)","snPcode":"41431","submissionUrl":"https://mts-ejhg.nature.com/cgi-bin/main.plex","title":"European Journal of Human Genetics","twitterHandle":"@ejhg_journal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Network annotation, Polygenic risk score, Multiple correlated traits, GWAS summary statistics","lastPublishedDoi":"10.21203/rs.3.rs-9073777/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9073777/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePolygenic risk scores (PRS) are valuable tools for predicting disease risk based on genetic information, with potential impacts on disease prevention and early treatment strategies. 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