Exploring Data From Genetic Association Studies Using Bayesian Variable Selection and the Dirichlet Process: Application to Searching for Gene × Gene Patterns

Michail Papathomas, John Molitor, Clive Hoggart, David Hastie, Sylvia Richardson

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)

Abstract

We construct data exploration tools for recognizing important covariate patterns associated with a phenotype, with particular focus on searching for association with gene-gene patterns. To this end, we propose a new variable selection procedure that employs latent selection weights and compare it to an alternative formulation. The selection procedures are implemented in tandem with a Dirichlet process mixture model for the flexible clustering of genetic and epidemiological profiles. We illustrate our approach with the aid of simulated data and the analysis of a real data set from a genome-wide association study.
Original languageEnglish
Pages (from-to)663-674
JournalGenetic Epidemiology
Volume36
Issue number6
Early online date31 Jul 2012
DOIs
Publication statusPublished - Sept 2012

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