Homomorphic Encryption: An Application to Polygenic Risk Scores

Homomorphic Encryption: An Application to Polygenic Risk Scores | 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 Research Article Homomorphic Encryption: An Application to Polygenic Risk Scores elizabeth knight, Jiaqi Li, Mathew Jensen, Israel Yolou, Can Kockan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4565846/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 Background: Polygenic risk scores (PRS) have emerged as a powerful tool in precision medicine, enabling personalized risk assessments for complex diseases. However, using sensitive genomic data in PRS calculations raises concerns about privacy and security. Fully Homomorphic Encryption (FHE) offers a promising solution by allowing computations on encrypted data, preserving the privacy of both genomic information and PRS models. Methods: In this study, we present a novel application of FHE for secure and private PRS calculations using the CKKS protocol within the Lattigo library. Our approach involves a threeparty system: clients (doctors with sensitive genetic data), modelers developing a PRS (academics or a company), and evaluators (a ”local hospital” running the models while maintaining data confidentiality). We demonstrate the feasibility and accuracy of our protocol by applying it to synthetic datasets of various sizes and a robust 110k-SNP model for schizophrenia. Results: The normal PRS calculation results are essentially identical to the encrypted calculation: between the two results R2 is .999 & MSE is 2.27 × 10−6. Moreover, while the encrypted calculation is roughly 1000 times slower than conventional non-encrypted ones (when only considering the core PRS calculation), it is quite feasible on a single-CPU node - e.g. running on ∼1100 individuals with ∼110k SNPS took six minutes and ∼65G memory on a laptop computer. In addition, we investigate the impact of encryption parameters (modulus) on this computational time and accuracy in detail. Conclusion: Our approach enables secure PRS calculations on encrypted genomic data, addressing the pressing need for privacy-preserving solutions in the era of precision medicine. The ability to perform accurate risk assessments while maintaining patient confidentiality paves the way for broader adoption of PRS and personalized medicine in healthcare, particularly with the advent of large-scale computing power. Full Text Additional Declarations No competing interests reported. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4565846","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":318498128,"identity":"f3aec613-8d66-482c-bf6f-8159542c5949","order_by":0,"name":"elizabeth knight","email":"data:image/png;base64,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","orcid":"","institution":"Yale University","correspondingAuthor":true,"prefix":"","firstName":"elizabeth","middleName":"","lastName":"knight","suffix":""},{"id":318498129,"identity":"db3d62fc-60ab-4527-b080-fc7934ac72bf","order_by":1,"name":"Jiaqi Li","email":"","orcid":"","institution":"Yale University","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Li","suffix":""},{"id":318498130,"identity":"2ff020b2-c6ce-4d5b-99ca-bc928c441789","order_by":2,"name":"Mathew Jensen","email":"","orcid":"","institution":"Yale University","correspondingAuthor":false,"prefix":"","firstName":"Mathew","middleName":"","lastName":"Jensen","suffix":""},{"id":318498131,"identity":"f71aac26-400f-42f7-ab17-11547b5cbc0e","order_by":3,"name":"Israel Yolou","email":"","orcid":"","institution":"Yale University","correspondingAuthor":false,"prefix":"","firstName":"Israel","middleName":"","lastName":"Yolou","suffix":""},{"id":318498132,"identity":"1d2a7ffa-04f1-4a8f-9fbb-17ef06d70b0b","order_by":4,"name":"Can Kockan","email":"","orcid":"","institution":"Broad Institute","correspondingAuthor":false,"prefix":"","firstName":"Can","middleName":"","lastName":"Kockan","suffix":""},{"id":318498133,"identity":"ba2e8605-94e8-48e8-948c-698f9079958b","order_by":5,"name":"Mark Gerstein","email":"","orcid":"","institution":"Yale University","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Gerstein","suffix":""}],"badges":[],"createdAt":"2024-06-11 18:01:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4565846/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4565846/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67667222,"identity":"03dabbee-5b56-4c7c-aaec-8169f7c6609a","added_by":"auto","created_at":"2024-10-28 13:47:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":878792,"visible":true,"origin":"","legend":"","description":"","filename":"HEPRSBMC2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4565846/v1_covered_fd190fb0-b17f-4a3c-91dd-fbff8dc712e8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Homomorphic Encryption: An Application to Polygenic Risk Scores","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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-4565846/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4565846/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Polygenic risk scores (PRS) have emerged as a powerful tool in precision medicine, enabling personalized risk assessments for complex diseases. However, using sensitive genomic data in PRS calculations raises concerns about privacy and security. Fully Homomorphic Encryption (FHE) offers a promising solution by allowing computations on encrypted data, preserving the privacy of both genomic information and PRS models.\u003c/p\u003e\n\u003cp\u003eMethods: In this study, we present a novel application of FHE for secure and private PRS calculations using the CKKS protocol within the Lattigo library. Our approach involves a threeparty system: clients (doctors with sensitive genetic data), modelers developing a PRS (academics or a company), and evaluators (a ”local hospital” running the models while maintaining data confidentiality). We demonstrate the feasibility and accuracy of our protocol by applying it to synthetic datasets of various sizes and a robust 110k-SNP model for schizophrenia.\u003c/p\u003e\n\u003cp\u003eResults: The normal PRS calculation results are essentially identical to the encrypted calculation: between the two results R2 is .999 \u0026amp; MSE is 2.27 × 10−6. Moreover, while the encrypted calculation is roughly 1000 times slower than conventional non-encrypted ones (when only considering the core PRS calculation), it is quite feasible on a single-CPU node - e.g. running on ∼1100 individuals with ∼110k SNPS took six minutes and ∼65G memory on a laptop computer. In addition, we investigate the impact of encryption parameters (modulus) on this computational time and accuracy in detail.\u003c/p\u003e\n\u003cp\u003eConclusion: Our approach enables secure PRS calculations on encrypted genomic data, addressing the pressing need for privacy-preserving solutions in the era of precision medicine. 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