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)

Privacy-preserving genome-wide association studies on cloud environment using fully homomorphic encryption

‘To maintain the privacy of subjects’, the authors ‘propose encryption of all genotype and phenotype data. To allow the cloud to perform meaningful computation in relation to the encrypted data, [they] use a fully homomorphic encryption scheme. Noting that [they] can evaluate typical statistics for GWAS from a frequency table, [their] solution evaluates frequency tables with encrypted genomic and clinical data as input. [They] propose to use a packing technique for efficient evaluation of these frequency tables.’

  • Lu WJ, Yamada Y, Sakuma J
    Privacy-preserving genome-wide association studies on cloud environment using fully homomorphic encryption
    BMC Medical Informatics and Decision Making 2015 15(Suppl 5):S1
    (Abstract, PDF)

Fully outsourced secure genome study based on homomorphic encryption

The authors ‘present a novel framework (FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption) to fully outsource GWAS (i.e., chi-square statistic computation) using homomorphic encryption. The proposed framework enables secure divisions over encrypted data. [They] introduce two division protocols (i.e., secure errorless division and secure approximation division) with a trade-off between complexity and accuracy in computing chi-square statistics.’

  • Zhang Y, Dai W, Jiang X, Xiong H, Wang S
    FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption
    BMC Medical Informatics and Decision Making 2015 15(Suppl 5):S5
    (Abstract, PDF)

Secure distributed genome analysis for GWAS and sequence comparison computation

The authors ‘propose techniques for securing computation with real-life genomic data for minor allele frequency and chi-squared statistics computation, as well as distance computation between two genomic sequences, as specified by the iDASH competition tasks. [They] put forward novel optimizations, including a generalization of a version of mergesort, which might be of independent interest.’

  • Zhang Y, Blanton M, Almashaqbeh G
    Secure distributed genome analysis for GWAS and sequence comparison computation
    BMC Medical Informatics and Decision Making 2015 15(Suppl 5):S4
    (Abstract, PDF)

Privacy-preserving GWAS analysis on federated genomic datasets

The authors ‘present a privacy-preserving GWAS framework on federated genomic datasets. [Their] method is to layer the GWAS computations on top of secure multi-party computation (MPC) systems. This approach allows two parties in a distributed system to mutually perform secure GWAS computations, but without exposing their private data outside.’

  • Constable SD, Tang Y, Wang S, Jiang X, Chapin S
    Privacy-preserving GWAS analysis on federated genomic datasets
    BMC Medical Informatics and Decision Making 2015 15(Suppl 5):S2
    (Abstract, PDF)

Privacy-preserving GWAS on cloud environment using fully homomorphic encryption

Lu et al. ‘propose encryption of all genotype and phenotype data. To allow the cloud to perform meaningful computation in relation to the encrypted data, [they] use a fully homomorphic encryption scheme. Noting that [they] can evaluate typical statistics for GWAS from a frequency table, [their] solution evaluates frequency tables with encrypted genomic and clinical data as input. [They] propose to use a packing technique for efficient evaluation of these frequency tables.’

  • Lu WJ, Yamada Y, Sakuma J
    Privacy-preserving genome-wide association studies on cloud environment using fully homomorphic encryption
    BMC Medical Informatics and Decision Making 2015 15(Suppl 5):S1
    (Abstract, PDF)

Scalable privacy-preserving data sharing methodology for genome-wide association studies

The authors ‘apply privacy-preserving methods that are adapted from Uhler et al. 2013 and Yu et al. 2014 to the [iDASH] challenge’s data and analyze the data utility after the data are perturbed by the privacy-preserving methods. Major contributions of this paper include new interpretation of the chi square statistic in a GWAS setting and new results about the Hamming distance score, a key component for one of the privacy-preserving methods.’

  • Yu F, Ji Z
    Scalable privacy-preserving data sharing methodology for genome-wide association studies: an application to iDASH healthcare privacy protection challenge
    BMC Medical Informatics and Decision Making 2014 14(Suppl 1):S3
    (Abstract, PDF)