SecureMA: protecting participant privacy in genetic association meta-analysis.

Xie W, Kantarcioglu M, Bush WS, Crawford D, Denny JC, Heatherly R, Malin BA
Bioinformatics. 2014 30 (23): 3334-41

PMID: 25147357 · PMCID: PMC4296153 · DOI:10.1093/bioinformatics/btu561

MOTIVATION - Sharing genomic data is crucial to support scientific investigation such as genome-wide association studies. However, recent investigations suggest the privacy of the individual participants in these studies can be compromised, leading to serious concerns and consequences, such as overly restricted access to data.

RESULTS - We introduce a novel cryptographic strategy to securely perform meta-analysis for genetic association studies in large consortia. Our methodology is useful for supporting joint studies among disparate data sites, where privacy or confidentiality is of concern. We validate our method using three multisite association studies. Our research shows that genetic associations can be analyzed efficiently and accurately across substudy sites, without leaking information on individual participants and site-level association summaries.

AVAILABILITY AND IMPLEMENTATION - Our software for secure meta-analysis of genetic association studies, SecureMA, is publicly available at http://github.com/XieConnect/SecureMA. Our customized secure computation framework is also publicly available at http://github.com/XieConnect/CircuitService.

© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

MeSH Terms (10)

Genetic Association Studies Genetic Privacy Genome-Wide Association Study Genomics Genomics Humans Hypothyroidism Meta-Analysis as Topic Obesity Software

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