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

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SF-Relate is a new federated algorithm for securely finding genetic relatives across distributed datasets. It uses privacy-preserving methods to accurately identify family connections without sharing sensitive data.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Privacy-Preserving Technologies

Background:

  • Identifying relatives in genomic studies is crucial but hindered by data silos and privacy concerns.
  • Existing methods struggle with distributed cohorts due to data-sharing restrictions and computational burden.
  • A secure and efficient solution is needed for cross-dataset kinship estimation.

Purpose of the Study:

  • To introduce SF-Relate, a practical and secure federated algorithm for identifying genetic relatives across distributed data.
  • To enable accurate kinship detection while preserving individual privacy.
  • To overcome the limitations of data-sharing restrictions in large-scale genomic studies.

Main Methods:

  • Developed SF-Relate, a federated algorithm utilizing locality-sensitive hashing to group potentially related individuals into buckets.
  • Implemented a novel hash function capturing identity-by-descent (IBD) segments for efficient and accurate bucketing.
  • Employed multiparty homomorphic encryption (MHE) for secure, collaborative computation of relatedness coefficients without data sharing.

Main Results:

  • SF-Relate significantly reduces the number of pairwise comparisons needed for kinship analysis.
  • On a 200K individual dataset split across two parties, SF-Relate achieved 94.9% detection of third-degree relatives and 99.9% for second-degree or closer relatives.
  • The algorithm demonstrated practical runtimes, completing analysis within 15 hours.

Conclusions:

  • SF-Relate provides an accurate, practical, and privacy-preserving solution for identifying genetic relatives across distributed datasets.
  • The novel hashing and MHE approach enables secure kinship estimation, facilitating large-scale genomic research.
  • This work overcomes significant hurdles in collaborative genomic studies by enabling secure cross-entity relationship discovery.