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Nat. Genet., 24 February 2025 | https://doi.org/10.1038/s41588-025-02109-1
Secure and federated genome-wide association studies for biobank-scale datasets
Hyunghoon Cho 1,2,3,8,*, David Froelicher 3,4,8, Jeffrey Chen 3,4, Manaswitha Edupalli 3, Apostolos Pyrgelis 5, Juan R. Troncoso-Pastoriza 6, Jean-Pierre Hubaux 5,6,* & Bonnie Berger 3,4,7,*
1Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA.
2Department of Computer Science, Yale University, New Haven, CT, USA.
3Broad Institute of MIT and Harvard, Cambridge, MA, USA.
4Computer Science and AI Laboratory, MIT, Cambridge, MA, USA.
5School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland.
6Tune Insight SA, Lausanne, Switzerland.
7Department of Mathematics, MIT, Cambridge, MA, USA.
8These authors contributed equally: Hyunghoon Cho, David Froelicher.
2Department of Computer Science, Yale University, New Haven, CT, USA.
3Broad Institute of MIT and Harvard, Cambridge, MA, USA.
4Computer Science and AI Laboratory, MIT, Cambridge, MA, USA.
5School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland.
6Tune Insight SA, Lausanne, Switzerland.
7Department of Mathematics, MIT, Cambridge, MA, USA.
8These authors contributed equally: Hyunghoon Cho, David Froelicher.
*Corresponding authors: correspondence to Hyunghoon Cho, Jean-Pierre Hubaux or Bonnie Berger
Abstract
Sharing data across institutions for genome-wide association studies (GWAS) would enhance the discovery of genetic variation linked to health and disease. However, existing data-sharing regulations limit the scope of such collaborations. 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. 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 widely used GWAS pipelines based on principal-component analysis or linear mixed models. We demonstrate the accuracy and practical runtimes of SF-GWAS on five datasets, including a UK Biobank cohort of 410,000 individuals, showcasing an order-of-magnitude improvement in runtime compared to previous methods. Our work enables secure collaborative genomic studies at unprecedented scale.
논문정보
- Research article
- 2025년 02월 (BRIC 등록일 2025-04-01)
- 국외 연구진
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