Deriving genomic diagnoses without revealing patient genomes

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