Achieving GWAS with homomorphic encryption.

BMC Medical Genomics
Jun Jie SimKhin Mi Mi Aung

Abstract

One way of investigating how genes affect human traits would be with a genome-wide association study (GWAS). Genetic markers, known as single-nucleotide polymorphism (SNP), are used in GWAS. This raises privacy and security concerns as these genetic markers can be used to identify individuals uniquely. This problem is further exacerbated by a large number of SNPs needed, which produce reliable results at a higher risk of compromising the privacy of participants. We describe a method using homomorphic encryption (HE) to perform GWAS in a secure and private setting. This work is based on a proposed algorithm. Our solution mainly involves homomorphically encrypted matrix operations and suitable approximations that adapts the semi-parallel GWAS algorithm for HE. We leverage upon the complex space of the CKKS encryption scheme to increase the number of SNPs that can be packed within a ciphertext. We have also developed a cache module that manages ciphertexts, reducing the memory footprint. We have implemented our solution over two HE open source libraries, HEAAN and SEAL. Our best implementation took 24.70 minutes for a dataset with 245 samples, over 4 covariates and 10643 SNPs. We demonstrate that it is possible to achieve GWAS wit...Continue Reading

References

Jul 13, 2004·Science·Zhen LinRuss B Altman
Apr 19, 2018·JMIR Medical Informatics·Miran KimXiaoqian Jiang
Oct 13, 2018·BMC Medical Genomics·Andrey KimJung Hee Cheon
Oct 13, 2018·BMC Medical Genomics·Hao ChenKristin Lauter

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Citations

Dec 4, 2020·Computers in Biology and Medicine·Chandra Thapa, Seyit Camtepe

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Methods Mentioned

BETA
iDASH

Software Mentioned

mathcal
HEAAN
MatMult
DotProd
HomLogisticRegression
iDASH
numpy
Eigen
SEAL
REP

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