{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Cho H"],"funding":["ODCDC CDC HHS","NHGRI NIH HHS","U.S. Department of Health &amp; Human Services | NIH | National Human Genome Research Institute","U.S. Department of Health &amp; Human Services | NIH | NIH Office of the Director","NIH HHS"],"pagination":["809-814"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11985345"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["57(4)"],"pubmed_abstract":["Sharing data across institutions for genome-wide association studies (GWAS) would enhance the discovery of genetic variation linked to health and disease<sup>1,2</sup>. However, existing data-sharing regulations limit the scope of such collaborations<sup>3</sup>. Although cryptographic tools for secure computation promise to enable collaborative analysis with formal privacy guarantees, existing approaches either are computationally impractical or do not implement current state-of-the-art methods<sup>4-6</sup>. We introduce secure federated genome-wide association studies (SF-GWAS), a combination of secure computation frameworks and distributed algorithms that empowers efficient and accurate GWAS on private data held by multiple entities while ensuring data confidentiality. SF-GWAS supports"],"journal":["Nature genetics"],"pubmed_title":["Secure and federated genome-wide association studies for biobank-scale datasets."],"pmcid":["PMC11985345"],"funding_grant_id":["DP5 OD029574","R01 HG010959","RM1 HG011558"],"pubmed_authors":["Pyrgelis A","Berger B","Cho H","Chen J","Edupalli M","Troncoso-Pastoriza JR","Froelicher D","Hubaux JP"],"additional_accession":[]},"is_claimable":false,"name":"Secure and federated genome-wide association studies for biobank-scale datasets.","description":"Sharing data across institutions for genome-wide association studies (GWAS) would enhance the discovery of genetic variation linked to health and disease<sup>1,2</sup>. However, existing data-sharing regulations limit the scope of such collaborations<sup>3</sup>. Although cryptographic tools for secure computation promise to enable collaborative analysis with formal privacy guarantees, existing approaches either are computationally impractical or do not implement current state-of-the-art methods<sup>4-6</sup>. We introduce secure federated genome-wide association studies (SF-GWAS), a combination of secure computation frameworks and distributed algorithms that empowers efficient and accurate GWAS on private data held by multiple entities while ensuring data confidentiality. SF-GWAS supports","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Apr","modification":"2026-06-01T23:03:38.151Z","creation":"2026-05-25T03:06:48.308Z"},"accession":"S-EPMC11985345","cross_references":{"pubmed":["39994472"],"doi":["10.1038/s41588-025-02109-1"]}}