Secure Discovery of Genetic Relatives across Large-Scale and Distributed Genomic Datasets
Name
hong-matthong-sm-eecs-2024-thesis.pdf
Description
Thesis PDF
Size
1.62 MB
Format
Adobe PDF
Checksum (MD5)
d2e4d7ef1f7c00981abb12b996e1589a
Author(s)
Hong, Matthew M.
Advisor(s)
Berger, Bonnie
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
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. In this thesis, 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 individuals in matching buckets across parties. To this end, we construct an effective hash function that captures identity-by-descent (IBD) segments in genetic sequences, which, along with a new bucketing strategy, enable accurate and practical private relative detection. To guarantee privacy, we introduce an efficient algorithm based on multiparty homomorphic encryption (MHE) to allow data holders to cooperatively compute the relatedness coefficients between individuals, and to further classify their degrees of relatedness, all without sharing any private data. We demonstrate the accuracy and practical runtimes of SF-Relate on the UK Biobank and All of Us datasets. On a dataset of 200K individuals split between two parties, SF-Relate detects 94.9% of third-degree relatives, and 99.9% of second-degree or closer relatives, within 15 hours of runtime. Our work enables secure identification of relatives across large-scale genomic datasets, and thus a wide range of downstream privacy-preserving collaborative studies.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Copyright retained by author(s)
Persistent DSpace Link
