Secure large-scale genome-wide association studies using homomorphic encryption

Abstract

<jats:p>Genome-wide association studies (GWASs) seek to identify genetic variants associated with a trait, and have been a powerful approach for understanding complex diseases. A critical challenge for GWASs has been the dependence on individual-level data that typically have strict privacy requirements, creating an urgent need for methods that preserve the individual-level privacy of participants. Here, we present a privacy-preserving framework based on several advances in homomorphic encryption and demonstrate that it can perform an accurate GWAS analysis for a real dataset of more than 25,000 individuals, keeping all individual data encrypted and requiring no user interactions. Our extrapolations show that it can evaluate GWASs of 100,000 individuals and 500,000 single-nucleotide polymorphisms (SNPs) in 5.6 h on a single server node (or in 11 min on 31 server nodes running in parallel). Our performance results are more than one order of magnitude faster than prior state-of-the-art results using secure multiparty computation, which requires continuous user interactions, with the accuracy of both solutions being similar. Our homomorphic encryption advances can also be applied to other domains where large-scale statistical analyses over encrypted data are needed.</jats:p>

DyPS: Dynamic, Private and Secure GWAS

“…More recently, Bonte et al [55] introduced a distributed GWAS system, which uses HE and MPC methods, and answers yes/no responses for putative markers SNPs (rather than releasing χ 2 values). More recently, yet another HE-based approach [56] offers complementary mechanisms to accelerate and increase the performance of GWAS analysis, such as parallelization and encoding techniques. Kamm et al [57] a secure MPC framework where institutes share their genome dataset to a thirdparty data storage for χ 2 tests computing.…”

“…In this context, several works have presented methods that enable the distributed computation of aggregate statistics, relying either on homomorphic encryption (HE) [16][17][18][19], secure multi-party computations (MPCs) [20][21][22], or secret-sharing [23]. However, these works do not consider adversarial environment where GWAS results might be publicly released and potentially attacked by an adversary who might control one or several of the biocenters.…”

Interdisciplinary strategies to enable data-driven plant breeding in a changing climate

“…100 In parallel, investments in computational infrastructure to store and analyze these data have been, and will continue to be, made. [101][102][103][104] Recent research on cryptographic protocols for genomic data sharing and analysis 105,106 may further collaboration between the public and the private sectors to leverage the extensive databases of genotypic and phenotypic data warehoused by industry while preserving proprietary information. Because a limited number of candidate cultivars can be field tested, genotypes serve as a critical link between field-tested materials and untested materials held by breeding companies and in international gene banks.…”

Privacy considerations for sharing genomics data

“…This allows for secure computations on cloud systems even if the system itself is not. While this approach was long assumed too computationally expensive to be reasonable in the context of genomic data, Blatt et al recently presented an improvement in run-time of FHE for Genome Wide Association Studies (GWAS) by introducing parallelization and crypto-engineering optimizations, which allegedly outperforms secure multiparty computations (SMPC) (Blatt et al, 2020[ 7 ]). SMPC is another approach to secure computation, in which two or more parties that hold private data can compare and perform computations on the data without ever revealing the data itself to the other party or another third party (Erlich and Narayanan, 2014[ 26 ]).…”