Report

Deriving genomic diagnoses without revealing patient genomes

See allHide authors and affiliations

Science  18 Aug 2017:
Vol. 357, Issue 6352, pp. 692-695
DOI: 10.1126/science.aam9710

You are currently viewing the abstract.

View Full Text

Sharing data, protecting privacy

Although data-sharing is crucial for making the best use of genetic data in diagnosing disease, many individuals who might donate data are concerned about privacy. Jagadeesh et al. describe a solution that combines a protocol from modern cryptography with frequency-based clinical genetics used to diagnose causal disease mutations in patients with monogenic disorders. This framework correctly identified the causal gene in cases involving actual patients, while protecting more than 99% of individual participants' most private variants.

Science, this issue p. 692

Abstract

Patient genomes are interpretable only in the context of other genomes; however, genome sharing enables discrimination. Thousands of monogenic diseases have yielded definitive genomic diagnoses and potential gene therapy targets. Here we show how to provide such diagnoses while preserving participant privacy through the use of secure multiparty computation. In multiple real scenarios (small patient cohorts, trio analysis, two-hospital collaboration), we used our methods to identify the causal variant and discover previously unrecognized disease genes and variants while keeping up to 99.7% of all participants’ most sensitive genomic information private.

View Full Text