Deriving genomic diagnoses without revealing patient genomes
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 18, 2017
- Source ID
- 10.1126/science.aam9710
Entities
People
- Dan Boneh
- David J Wu
- Gill Bejerano
- Johannes A. Birgmeier
- Karthik Jagadeesh
Organizations
- David and Lucile Packard Foundation
- Microsoft Research
- National Science Foundation
- Simons Foundation
- Stanford University
- Stanford University School of Medicine