Optimized homomorphic encryption solution for secure genome-wide association studies

Abstract

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.

iDASH secure genome analysis competition 2018: blockchain genomic data access logging, homomorphic encryption on GWAS, and DNA segment searching

“…We received 13 solutions, submitted from 7 teams by the deadline. The research papers from the participating teams to describe the methods are included in this special issue [26][27][28][29][30]. to obtain mean and standard deviation.…”

Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

“…Fast polynomial approximation in CKKS can evaluate the multiplicative inverse, exponential, and logistic functions, as well as the discrete Fourier transform [197]. Additional works provide bootstrapping improvements [52,56] and RNS variants [29,57,184].…”

“…Crawford et al approximate the log-likelihood with a Taylor expansion and evaluate a closed-form formula approximation of the logistic regression parameters using HElib [74]. Blatt et al approximate the sigmoid function with Chebyshev polynomials for an implementation of logistic regression in PALISADE along with a custom variant of the CKKS scheme [29]. Carpov et al perform logistic regression over data encrypted using TFHE and HEAAN [48].…”

“…In 2015 and again in 2018, the iDASH competition called for development of secure outsourcing of GΩAS via homomorphic encryption. Logistic regression (Section 4.1), a common approach in genome-wide association studies, is used in several solutions [29,48]. Others use the χ 2 statistic [33,136,146,181,208], or statistical algorithms including the D ′ and r 2 measures of linkage disequilibrium [141,181], the estimation-maximization (EM) algorithm for halotyping [141], and Fisher's exact test [171].…”

“…Blatt et al provide the winning solution to the 2018 challenge using their own RNS-variant of the CKKS scheme [29]. Their experiments are performed using PALISADE, modiied to implement their RNS-variant.…”