EPFL, MIT, and Yale scientists have introduced a groundbreaking method called Secure Federated Genome-Wide Association Studies (SF-GWAS) that allows for efficient and accurate genetic research while keeping data confidential. This new method combines secure computation and distributed algorithms to enable collaborative analysis in medical research.
Data-sharing for genome-wide association studies (GWAS) is crucial for finding genetic links to health and disease, but current rules limit sharing across institutions. SF-GWAS solves these challenges by using cryptographic tools to enhance data collaborations. It has been successfully tested on a large scale and is now being implemented across Europe.
Key features of SF-GWAS include a federated approach to keep data at its original site and efficient algorithms to support different GWAS pipelines. The method has shown a significant improvement in runtime compared to previous methods in a survey of five datasets, including a UK Biobank cohort of 410,000 individuals.
SF-GWAS has been installed in Swiss university hospitals and is being rolled out in Italian hospitals and European cancer networks. The EPFL spin-off Tune Insight is leading this work and is also in talks with medical institutions in other countries.
The goal of SF-GWAS is to optimize public healthcare policy by unlocking large-scale medical research currently hindered by data silos. By encouraging a cultural shift towards rigorous data storage and structure, SF-GWAS aims to improve the overall quality of health and medical data. The method is described in a study published in Nature Genetics.
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