Accounting for uncertainty in residual variances improves calibration for fine-mapping with small sample sizes

Accounting for uncertainty in residual variances improves calibration for fine-mapping with small sample sizes | 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 Accounting for uncertainty in residual variances improves calibration for fine-mapping with small sample sizes Gao Wang, William Denault, Peter Carbonetto, Ruixi Li, Matthew Stephens This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7490160/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 The Sum of Single Effects (SuSiE) model is a widely adopted method for genetic fine-mapping. We show that, in small-sample studies, the original SuSiE fitting procedure produces substantially higher rates of false positive findings. We show that a simple modification to SuSiE improves performance in small-sample studies. This modification is particularly important for emerging molecular QTL applications in rare cell types and primary tissues where sample sizes are inherently limited. Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Computational biology and bioinformatics/Software Biological sciences/Computational biology and bioinformatics/Statistical methods Full Text Additional Declarations There is NO Competing 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-7490160","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Brief Communication","associatedPublications":[],"authors":[{"id":529793994,"identity":"3e564875-c860-4aeb-bf5a-015ba6ce9c03","order_by":0,"name":"Gao Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYDADfgYGxgMMDAd4QBwJorRINgDVk6bF4ABECwNBLQbHzx5+wfDLTs74+OEDh3lz7sjwNzAfvM2DT8uZvDQLxr5kY7MzaQmHebc945E4wJZsjU+L2YEcMwPGHubEbTd4DIBaDvMAvWMmjVfL+TcgLfWJm2dAtcgf4P+GX8uNHOMHDD8OJ26QgGoxOMDDhleL/Y03ZgyJDceNJYB+OTgX6BfDw2zGlnPwaJHszzH+8OFPtRx/++GDD95uu2Mvd7z54Y03eLQAAZtEYhsynxm/crCSDwx/CKsaBaNgFIyCEQwAiVdSNIQFnVsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9336-402X","institution":"Columbia University","correspondingAuthor":true,"prefix":"","firstName":"Gao","middleName":"","lastName":"Wang","suffix":""},{"id":529793995,"identity":"95281b1d-60ce-4159-8774-db9a83e9b313","order_by":1,"name":"William Denault","email":"","orcid":"","institution":"The University of Chicago","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"","lastName":"Denault","suffix":""},{"id":529793996,"identity":"6e3644d0-cf6f-49be-b4a1-bfd97dad7bd0","order_by":2,"name":"Peter Carbonetto","email":"","orcid":"","institution":"The University of Chicago","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Carbonetto","suffix":""},{"id":529793997,"identity":"2223d5d0-159c-4193-bdf3-112ed08071c8","order_by":3,"name":"Ruixi Li","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Ruixi","middleName":"","lastName":"Li","suffix":""},{"id":529793998,"identity":"f386e713-0e09-4223-9c88-b19d24bea347","order_by":4,"name":"Matthew Stephens","email":"","orcid":"https://orcid.org/0000-0001-5397-9257","institution":"University of Chicago","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Stephens","suffix":""}],"badges":[],"createdAt":"2025-08-29 16:05:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7490160/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7490160/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98429232,"identity":"db6453a6-2199-475d-8b3b-7b9868cebd8d","added_by":"auto","created_at":"2025-12-17 16:43:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1116557,"visible":true,"origin":"","legend":"Article File","description":"","filename":"SuSiESSmanuscript20250924.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7490160/v1_covered_c920a575-e635-4572-9f96-04508846dad1.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Accounting for uncertainty in residual variances improves calibration for fine-mapping with small sample sizes","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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-7490160/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7490160/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The Sum of Single Effects (SuSiE) model is a widely adopted method for genetic fine-mapping. 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