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

Authors

  • Michail Papathomas,

    Corresponding author
    1. Department of Epidemiology and Biostatistics, Imperial College London, United Kingdom
    • School of Mathematics and Statistics, University of St Andrews, Scotland, United Kingdom
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  • John Molitor,

    1. Department of Epidemiology and Biostatistics, Imperial College London, United Kingdom
    2. College of Health and Human Sciences, Oregon State University, Corvallis, Oregon
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  • Clive Hoggart,

    1. Department of Epidemiology and Biostatistics, Imperial College London, United Kingdom
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  • David Hastie,

    1. Department of Epidemiology and Biostatistics, Imperial College London, United Kingdom
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  • Sylvia Richardson

    1. Department of Epidemiology and Biostatistics, Imperial College London, United Kingdom
    2. MRC Biostatistics Unit, Institute of Public Health, Cambridge, United Kingdom
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Correspondence to: Michail Papathomas, The Observatory, University of St Andrews, Buchanan Gardens, St Andrews, Fife, Scotland, KY16 9LZ, UK. E-mail: michail@mcs.st-and.ac.uk

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.

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