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A Bayesian clustering approach for detecting gene-gene interactions in high-dimensional genotype data

Author

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  • Chen Sui-Pi

    (Service System Technology Center, Industrial Technology Research Institute, Taiwan)

  • Huang Guan-Hua

    (Institute of Statistics, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu 30010, Taiwan, Phone: +886-3-513-1334, Fax: +886-3-572-8745)

Abstract

This paper uses a Bayesian formulation of a clustering procedure to identify gene-gene interactions under case-control studies, called the Algorithm via Bayesian Clustering to Detect Epistasis (ABCDE). The ABCDE uses Dirichlet process mixtures to model SNP marker partitions, and uses the Gibbs weighted Chinese restaurant sampling to simulate posterior distributions of these partitions. Unlike the representative Bayesian epistasis detection algorithm BEAM, which partitions markers into three groups, the ABCDE can be evaluated at any given partition, regardless of the number of groups. This study also develops permutation tests to validate the disease association for SNP subsets identified by the ABCDE, which can yield results that are more robust to model specification and prior assumptions. This study examines the performance of the ABCDE and compares it with the BEAM using various simulated data and a schizophrenia SNP dataset.

Suggested Citation

  • Chen Sui-Pi & Huang Guan-Hua, 2014. "A Bayesian clustering approach for detecting gene-gene interactions in high-dimensional genotype data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(3), pages 1-23, June.
    • Handle: RePEc:bpj:sagmbi:v:13:y:2014:i:3:p:23:n:1
    DOI: 10.1515/sagmb-2012-0074
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    References listed on IDEAS

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