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

Marcelo Blatt, Alexander Gusev, Yuriy Polyakov, Shafi Goldwasser

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number11608
JournalProceedings of the National Academy of Sciences of the United States of America
Volume117
Issue number21
DOIs
StatePublished - May 26 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General

Keywords

  • Encrypted computing
  • Genome-wide association studies
  • Homomorphic encryption

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