Network Construction Using Sparse Gaussian Graphical Model Based on GWAS Summary Statistics

Network Construction Using Sparse Gaussian Graphical Model 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 Network Construction Using Sparse Gaussian Graphical Model Based on GWAS Summary Statistics Megh Subedi, Xuewei Cao, Byung-Jun Kim, Qiuying Sha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6026484/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract In genome-wide association studies (GWAS), thousands of genetic variants are tested to identify the association of genetic variants with a phenotype. GWAS have identified many strongly associated genetic variants with phenotypes and have greatly enhanced our understanding of the genetic architecture of complex phenotypes and diseases. Joint analysis of multiple phenotypes can increase the overall statistical power to detect genetic associations and allow for the identification of pleiotropic loci. A phenotype-phenotype network (PPN) represents phenotypes as nodes and the relationship between them as edges, which allows for the visualization of complex relationships between phenotypes, making it easier to identify clusters and help us intuitively grasp how different phenotypes are related. In this study, we proposed a new method to construct a PPN using the sparse Gaussian Graphical Model (sGGM) based on GWAS summary statistics. This approach will isolate the direct relationship between phenotypes, making it easier to identify clusters of phenotypes conditional on other phenotypes that reflect the more meaningful biological or functional connections. Then we applied the community detection method to partition phenotypes into disjoint modules based on the partial correlation matrix of phenotypes. For each module, various multiple phenotype association tests can be employed to test the association between a SNP and phenotypes in that module. We conducted a comprehensive simulation study to compare the performance of several multiple phenotype association tests using sGGM, network modules obtained from the correlation matrix of phenotypes, and those based on all phenotypes, respectively. The simulation results demonstrated that most of the multiple phenotype association tests based on network modules from sGGM not only effectively control the Type I error rates but also exhibit higher power compared to network modules derived from the correlation matrix and association tests on all phenotypes without modular segmentation. We applied this method to the GWAS summary statistics of 92 phenotypes from chapter IX of the UK Biobank. The results showed that applying the multiple phenotype association tests using the network module from sGGM detects more significant SNPs than that of using the network module from the correlation matrix. Biological sciences/Genetics Biological sciences/Genetics/Genetic association study GWAS summary statistics sparse Gaussian graphical model phenotype-phenotype network multiple-phenotype association tests Full Text Additional Declarations No competing interests reported. Supplementary Files sGGMSupplementary.docx Cite Share Download PDF Status: Published Journal Publication published 04 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 17 Apr, 2025 Reviews received at journal 31 Mar, 2025 Reviews received at journal 30 Mar, 2025 Reviewers agreed at journal 19 Mar, 2025 Reviewers agreed at journal 10 Mar, 2025 Reviewers invited by journal 04 Mar, 2025 Editor assigned by journal 04 Mar, 2025 Editor invited by journal 28 Feb, 2025 Submission checks completed at journal 27 Feb, 2025 First submitted to journal 13 Feb, 2025 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-6026484","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":422430146,"identity":"3ea2021b-3731-4dc9-a114-1f1d807a119b","order_by":0,"name":"Megh Subedi","email":"","orcid":"","institution":"Michigan Technological University","correspondingAuthor":false,"prefix":"","firstName":"Megh","middleName":"","lastName":"Subedi","suffix":""},{"id":422430147,"identity":"de11fc10-e393-4911-9e2f-7f8d6e307cf7","order_by":1,"name":"Xuewei Cao","email":"","orcid":"","institution":"Michigan Technological University","correspondingAuthor":false,"prefix":"","firstName":"Xuewei","middleName":"","lastName":"Cao","suffix":""},{"id":422430148,"identity":"2fb3dfe8-32a5-40cf-a29d-95aacbc7b917","order_by":2,"name":"Byung-Jun Kim","email":"","orcid":"","institution":"Michigan Technological University","correspondingAuthor":false,"prefix":"","firstName":"Byung-Jun","middleName":"","lastName":"Kim","suffix":""},{"id":422430149,"identity":"2d7af447-936a-4362-8b5e-98ffec42052e","order_by":3,"name":"Qiuying Sha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAmklEQVRIiWNgGAWjYFACHgaGDxCWAfFaGGdAVJOghZmHJC18588e/Gyb8yexgb15mwRRWiRv5CVL524zSGzgOVZGnBaDGzwGEC0SOWZEajl/xvi3JUiL/BtitRzIMZNmBNvCQ6QWyRs5Zpa924yN23jSii2I0sIHdNiNn9vkZPvZD2+8QZQWhgNQmo045chaRsEoGAWjYBTgBAB+KiwWdL08VQAAAABJRU5ErkJggg==","orcid":"","institution":"Michigan Technological University","correspondingAuthor":true,"prefix":"","firstName":"Qiuying","middleName":"","lastName":"Sha","suffix":""}],"badges":[],"createdAt":"2025-02-14 01:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6026484/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6026484/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-22475-4","type":"published","date":"2025-11-04T15:57:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":95563927,"identity":"9614a9c6-65c7-4244-8e23-a79e22871d80","added_by":"auto","created_at":"2025-11-10 16:04:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1012961,"visible":true,"origin":"","legend":"","description":"","filename":"sGGMManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6026484/v1_covered_3a82267d-bf92-46e5-b42e-e6b66ccd7f18.pdf"},{"id":77578906,"identity":"5b64eb95-3f07-4b78-960d-dec04c829fca","added_by":"auto","created_at":"2025-03-03 09:34:37","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4020578,"visible":true,"origin":"","legend":"","description":"","filename":"sGGMSupplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6026484/v1/e68574bf7b34a57923d4dd50.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network Construction Using Sparse Gaussian Graphical Model Based on GWAS Summary Statistics","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"GWAS summary statistics, sparse Gaussian graphical model, phenotype-phenotype network, multiple-phenotype association tests ","lastPublishedDoi":"10.21203/rs.3.rs-6026484/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6026484/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In genome-wide association studies (GWAS), thousands of genetic variants are tested to identify the association of genetic variants with a phenotype. 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