Enabling Privacy-Preserving GWAS in Heterogeneous Human Populations

TL;DR

This paper introduces a new computational framework that enables privacy-preserving genome-wide association studies (GWAS) in diverse human populations by correcting for population stratification while protecting sensitive data using differential privacy.

Contribution

It presents PrivSTRAT and PrivLMM, novel methods that adapt differential privacy to GWAS, addressing population stratification issues in privacy-preserving analysis.

Findings

Successfully protects privacy in GWAS data

Produces meaningful GWAS results with privacy guarantees

Applicable to both simulated and real datasets

Abstract

The projected increase of genotyping in the clinic and the rise of large genomic databases has led to the possibility of using patient medical data to perform genomewide association studies (GWAS) on a larger scale and at a lower cost than ever before. Due to privacy concerns, however, access to this data is limited to a few trusted individuals, greatly reducing its impact on biomedical research. Privacy preserving methods have been suggested as a way of allowing more people access to this precious data while protecting patients. In particular, there has been growing interest in applying the concept of differential privacy to GWAS results. Unfortunately, previous approaches for performing differentially private GWAS are based on rather simple statistics that have some major limitations. In particular, they do not correct for population stratification, a major issue when dealing with the genetically diverse populations present in modern GWAS. To address this concern we introduce a novel computational framework for performing GWAS that tailors ideas from differential privacy to protect private phenotype information, while at the same time correcting for population stratification. This framework allows us to produce privacy preserving GWAS results based on two of the most commonly used GWAS statistics: EIGENSTRAT and linear mixed model (LMM) based statistics. We test our differentially private statistics, PrivSTRAT and PrivLMM, on both simulated and real GWAS datasets and find that they are able to protect privacy while returning meaningful GWAS results.