Secure Discovery of Genetic Relatives across Large-Scale and Distributed Genomic Datasets.

ABSTRACT: Finding relatives within a study cohort is a necessary step in many genomic studies. However, when the cohort is distributed across multiple entities subject to data-sharing restrictions, performing this step often becomes infeasible. Developing a privacy-preserving solution for this task is challenging due to the significant burden of estimating kinship between all pairs of individuals across datasets. We introduce SF-Relate, a practical and secure federated algorithm for identifying genetic relatives across data silos. SF-Relate vastly reduces the number of individual pairs to compare while maintaining accurate detection through a novel locality-sensitive hashing approach. We assign individuals who are likely to be related together into buckets and then test relationships only between in

SUBMITTER: Hong MM 

PROVIDER: S-EPMC11257153 | biostudies-literature | 2024 Apr-May

REPOSITORIES: biostudies-literature