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

Sui Pi Chen, Guan-Hua Huang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish
Pages (from-to)275-297
Number of pages23
JournalStatistical Applications in Genetics and Molecular Biology
Volume13
Issue number3
DOIs
StatePublished - 1 Jan 2014

Keywords

  • Dirichlet process mixtures
  • Epistasis
  • Permutation test
  • Stochastic search

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