Enabling Privacy-Preserving GWASs in Heterogeneous Human Populations.

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, PrivST

SUBMITTER: Simmons S 

PROVIDER: S-EPMC4994706 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature