title: 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, 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.
authors.affiliation: Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
authors.forename: Sean
authors.initials: S
authors.lastname: Simmons
authors.affiliation: School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada; School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA.
authors.forename: Cenk
authors.initials: C
authors.lastname: Sahinalp
authors.affiliation: Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Electronic address: bab@mit.edu.
authors.forename: Bonnie
authors.initials: B
authors.lastname: Berger
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D030661:Genetic Privacy; D015894:Genome, Human; D055106:Genome-Wide Association Study; D023281:Genomics; D006801:Humans; D018907:Privacy