Enabling Privacy-Preserving GWASs in Heterogeneous Human Populations
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1.22 MB
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Author(s)
Sahinalp, Cenk
Simmons, Sean Kenneth
Berger Leighton, Bonnie
Date Issued
July 2016
Journal
Cell Systems
Publisher
Elsevier
Citation
Simmons, Sean et al. “Enabling Privacy-Preserving GWASs in Heterogeneous Human Populations.” Cell Systems 3, 1 (July 2016): 54–61
Version
Final published version
Abstract
The proliferation of large genomic databases offers the potential to perform increasingly larger-scale genome-wide association studies (GWASs). Due to privacy concerns, however, access to these data is limited, greatly reducing their usefulness for research. Here, we introduce a computational framework for performing GWASs that adapts principles of differential privacy-a cryptographic theory that facilitates secure analysis of sensitive data-to both protect private phenotype information (e.g., disease status) and correct for population stratification. This framework enables us to produce privacy-preserving GWAS results based on EIGENSTRAT and linear mixed model (LMM)-based statistics, both of which correct for population stratification. We test our differentially private statistics, PrivSTRAT and PrivLMM, on simulated and real GWAS datasets and find they are able to protect privacy while returning meaningful results. Our framework can be used to securely query private genomic datasets to discover which specific genomic alterations may be associated with a disease, thus increasing the availability of these valuable datasets.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Creative Commons Attribution 4.0 International License
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DOI of Published Version
https://doi.org/10.1016/J.CELS.2016.04.013
