Secure genome-wide association analysis using multiparty computation

Cho et al. ‘describe a protocol for large-scale genome-wide analysis that facilitates quality control and population stratification correction […] while maintaining the confidentiality of underlying genotypes and phenotypes. […] This approach may help to make currently restricted data available to the scientific community and could potentially enable secure genome crowdsourcing, allowing individuals to contribute their genomes to a study without compromising their privacy.’

  • Cho H, Wu DJ, Berger B
    Secure genome-wide association analysis using multiparty computation
    Nature Biotechnology. 2018. Online May 07.
    (Abstract, PDF, Source Code)

Privacy-preserving GWAS is practical

In this preprint, Bonte et al. describe both a homomorphic encryption approach and a secure multiparty computation approach and provide efficient implementations.

  • Bonte C, Makri E, Ardeshirdavani A, Simm J, Moreau Y, Vercauteren F
    Privacy-Preserving Genome-Wide Association Study is Practical
    Cryptology ePrint Archive: Report 2017/955. Revision of 2017-11-20.
    (Abstract, PDF)

Deriving genomic diagnoses without revealing patient genomes

Jagadeesh et al. encode an individuals functional variants as a binary vector. They then use Yao’s protocol to identify relevant coincidences between pools of such vectors engaging in secure multiparty computation.

  • Jagadeesh KA, Wu DJ, Birgmeier JA, Boneh D, Bejerano G
    Deriving genomic diagnoses without revealing patient genomes
    Science. 2017. Volume 357. Issue 6352. Pages 692–695.
    (Abstract, PDF, Source Code)