An enhanced framework for local genetic correlation analysis

An enhanced framework for local genetic correlation analysis | 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 Brief Communication An enhanced framework for local genetic correlation analysis Xia Shen, Yuying Li, Yudi Pawitan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4568593/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Mar, 2025 Read the published version in Nature Genetics → Version 1 posted You are reading this latest preprint version Abstract Genetic correlation is a key parameter in the joint genetic model of complex traits, but it is usually estimated on a global genomic scale. Understanding local genetic correlations provides more detailed insights into the shared genetic architecture of complex traits. However, LAVA, as the state-of-the-art tool for local genetic correlation analysis, reports biased statistics and, therefore, is prone to false inference. We extend the high-definition likelihood (HDL) method to a local version, HDL-L (HDL-Local), which divides the genetic correlation analysis into semi-independent loci. HDL-L allows for a more granular estimation of genetic variances and covariances. Simulations show that HDL-L offers more consistent heritability estimates and more efficient genetic correlation estimates compared to LAVA. Across extensive simulations under different heritability settings, HDL-L maintained robust performance. In the analysis of 30 phenotypes from the UK Biobank, HDL-L identified 889 significant local genetic correlations across 658 loci, while LAVA identified 696 significant estimates across 441 loci. Furthermore, HDL-L demonstrated a significant computational advantage, being around 50 times faster than LAVA in the simulations. HDL-L proves to be a powerful tool for uncovering the detailed genetic landscape that underlies complex human traits, offering both superior accuracy and computational efficiency. Biological sciences/Genetics/Genetic association study/Genome-wide association studies Biological sciences/Computational biology and bioinformatics/High-throughput screening Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryTable1.xlsx Supplementary Table 1 Cite Share Download PDF Status: Published Journal Publication published 10 Mar, 2025 Read the published version in Nature Genetics → 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-4568593","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Brief Communication","associatedPublications":[],"authors":[{"id":320294076,"identity":"3a187950-8a7c-4a15-bf30-11bdafd54376","order_by":0,"name":"Xia Shen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDACZiBmbGBg4GdgMAALSBCtRbKBaC0MUC0GB4jVYnCc+QHjzx02ecbHmzcw/KhhSJzZQECLZDObATPvmbRiszPHChh7jjEkziZkCz8zgwEzY9vhxG03cgwYeBsYEucR0sLGzP6B8Wfb/8TN898YMP4lRgs/Mw/Q8LYDiRskeIAubCDCYZLNPAWHeduSE2ecSSs4LHNMwpig9w3OH9/48GebXWJ/++GND9/U2MjOOEDIGiA4gMQgLiJHwSgYBaNgFBAAAPhoPWNIfQobAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-4390-1979","institution":"University of Edinburgh","correspondingAuthor":true,"prefix":"","firstName":"Xia","middleName":"","lastName":"Shen","suffix":""},{"id":320294077,"identity":"90164869-f0fd-4b98-a9b5-ff7848cf45a7","order_by":1,"name":"Yuying Li","email":"","orcid":"https://orcid.org/0000-0002-3231-7542","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Yuying","middleName":"","lastName":"Li","suffix":""},{"id":320294078,"identity":"f25cdc0b-15cb-4f4e-9f5c-9ea23695184d","order_by":2,"name":"Yudi Pawitan","email":"","orcid":"https://orcid.org/0000-0003-0324-7052","institution":"Karolinska Institute","correspondingAuthor":false,"prefix":"","firstName":"Yudi","middleName":"","lastName":"Pawitan","suffix":""}],"badges":[],"createdAt":"2024-06-12 08:16:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4568593/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4568593/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41588-025-02123-3","type":"published","date":"2025-03-10T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78228076,"identity":"b5075f9d-c0fb-46c8-b9d7-f6f011922cd2","added_by":"auto","created_at":"2025-03-11 07:09:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3723363,"visible":true,"origin":"","legend":"","description":"","filename":"HDLLpaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4568593/v1_covered_84ffb081-6e24-4d75-9920-6155c10aa118.pdf"},{"id":59452162,"identity":"00ac4a1f-5db1-45f9-8f90-acba75f635f4","added_by":"auto","created_at":"2024-07-02 02:30:28","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":307850,"visible":true,"origin":"","legend":"Supplementary Table 1","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4568593/v1/b41f99bc9b9ef8ebcd9230a1.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"An enhanced framework for local genetic correlation analysis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"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-4568593/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4568593/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Genetic correlation is a key parameter in the joint genetic model of complex traits, but it is usually estimated on a global genomic scale. Understanding local genetic correlations provides more detailed insights into the shared genetic architecture of complex traits. However, LAVA, as the state-of-the-art tool for local genetic correlation analysis, reports biased statistics and, therefore, is prone to false inference. We extend the high-definition likelihood (HDL) method to a local version, HDL-L (HDL-Local), which divides the genetic correlation analysis into semi-independent loci. HDL-L allows for a more granular estimation of genetic variances and covariances. Simulations show that HDL-L offers more consistent heritability estimates and more efficient genetic correlation estimates compared to LAVA. Across extensive simulations under different heritability settings, HDL-L maintained robust performance. In the analysis of 30 phenotypes from the UK Biobank, HDL-L identified 889 significant local genetic correlations across 658 loci, while LAVA identified 696 significant estimates across 441 loci. 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