Optimized homomorphic encryption solution for secure genome-wide association studies
Name
s12920-020-0719-9.pdf
Description
Published version
Size
1.24 MB
Format
Unknown
Checksum (MD5)
50c225816103e2dea4e8626ef3bc94a7
Author(s)
Blatt, Marcelo
Gusev, Alexander
Polyakov, Yuriy
Rohloff, Kurt
Vaikuntanathan, Vinod
Date Issued
2020
Journal
BMC Medical Genomics
Publisher
Springer Science and Business Media LLC
Version
Final published version
Abstract
© 2020 The Author(s). Background: Genome-Wide Association Studies (GWAS) refer to observational studies of a genome-wide set of genetic variants across many individuals to see if any genetic variants are associated with a certain trait. A typical GWAS analysis of a disease phenotype involves iterative logistic regression of a case/control phenotype on a single-neuclotide polymorphism (SNP) with quantitative covariates. GWAS have been a highly successful approach for identifying genetic-variant associations with many poorly-understood diseases. However, a major limitation of GWAS is the dependence on individual-level genotype/phenotype data and the corresponding privacy concerns. Methods: We present a solution for secure GWAS using homomorphic encryption (HE) that keeps all individual data encrypted throughout the association study. Our solution is based on an optimized semi-parallel GWAS compute model, a new Residue-Number-System (RNS) variant of the Cheon-Kim-Kim-Song (CKKS) HE scheme, novel techniques to switch between data encodings, and more than a dozen crypto-engineering optimizations. Results: Our prototype can perform the full GWAS computation for 1,000 individuals, 131,071 SNPs, and 3 covariates in about 10 minutes on a modern server computing node (with 28 cores). Our solution for a smaller dataset was awarded co-first place in iDASH'18 Track 2: "Secure Parallel Genome Wide Association Studies using HE". Conclusions: Many of the HE optimizations presented in our paper are general-purpose, and can be used in solving challenging problems with large datasets in other application domains.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/S12920-020-0719-9
