GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy

TL;DR

This paper introduces optimization-based differential privacy mechanisms for GWAS that improve the accuracy of released genetic association statistics while ensuring participant privacy, enabling comprehensive and private data sharing.

Contribution

It proposes novel DP techniques using optimization and personalized priors to enhance utility in GWAS data release, addressing limitations of previous methods.

Findings

High accuracy in privacy-preserving GWAS statistics on UK Biobank data

Effective reduction of noise through optimization-based randomization

Sample-specific privacy optimization improves utility

Abstract

Genome-wide association studies (GWAS) are an essential tool in biomedical research for identifying genetic factors linked to health and disease. However, publicly releasing GWAS summary statistics poses well-recognized privacy risks, including the potential to infer an individual's participation in the study or to reveal sensitive phenotypic information (e.g., disease status). While differential privacy (DP) offers a rigorous mathematical framework for mitigating these risks, existing DP techniques for GWAS either introduce excessive noise or restrict the release to a limited set of results. In this work, we present practical DP mechanisms for releasing the complete set of genome-wide association statistics with privacy guarantees. We demonstrate the accuracy of the privacy-preserving statistics released by our mechanisms on a range of GWAS datasets from the UK Biobank, utilizing both real and simulated phenotypes. We introduce two key techniques to overcome the limitations of prior approaches: (1) an optimization-based randomization mechanism that directly minimizes the expected error in GWAS results to enhance utility, and (2) the use of personalized priors, derived from predictive models privately trained on a subset of the dataset, to enable sample-specific optimization which further reduces the amount of noise introduced by DP. Overall, our work provides practical tools for accurately releasing comprehensive GWAS results with provable protection of study participants.