biostudies-literatureCho HODCDC CDC HHSNHGRI NIH HHSU.S. Department of Health & Human Services | NIH | National Human Genome Research InstituteU.S. Department of Health & Human Services | NIH | NIH Office of the DirectorNIH HHS809-814https://www.ebi.ac.uk/biostudies/studies/S-EPMC11985345biostudies-literatureUnknown57(4)Sharing data across institutions for genome-wide association studies (GWAS) would enhance the discovery of genetic variation linked to health and disease^1,2. However, existing data-sharing regulations limit the scope of such collaborations³. 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^4-6. 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 supportsNature geneticsSecure and federated genome-wide association studies for biobank-scale datasets.PMC11985345DP5 OD029574R01 HG010959RM1 HG011558Pyrgelis ABerger BCho HChen JEdupalli MTroncoso-Pastoriza JRFroelicher DHubaux JPfalseSecure and federated genome-wide association studies for biobank-scale datasets.Sharing data across institutions for genome-wide association studies (GWAS) would enhance the discovery of genetic variation linked to health and disease^1,2. However, existing data-sharing regulations limit the scope of such collaborations³. 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^4-6. 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 supports2025-01-01T00:00:00Z2025 Apr2026-06-01T23:03:38.151Z2026-05-25T03:06:48.308ZS-EPMC119853453999447210.1038/s41588-025-02109-1