Paper 2020/563

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

Marcelo Blatt, Alexander Gusev, Yuriy Polyakov, and Shafi Goldwasser

Abstract

Genome-Wide Association Studies (GWAS) seek to identify genetic variants associated with a trait, and have been a powerful approach for understanding complex diseases. A critical challenge for GWAS 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 GWAS of 100,000 individuals and 500,000 SNPs in 5.6 hours on a single server node (or in 11 minutes 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 multi-party 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.