title: Exploring data from genetic association studies using Bayesian variable selection and the Dirichlet process: application to searching for gene × gene patterns.
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.
authors.affiliation: School of Mathematics and Statistics, University of St Andrews, Scotland, United Kingdom. michail@mcs.st-and.ac.uk
authors.forename: Michail
authors.initials: M
authors.lastname: Papathomas
authors.forename: John
authors.initials: J
authors.lastname: Molitor
authors.forename: Clive
authors.initials: C
authors.lastname: Hoggart
authors.forename: David
authors.initials: D
authors.lastname: Hastie
authors.forename: Sylvia
authors.initials: S
authors.lastname: Richardson
mesh_terms: D001499:Bayes Theorem; D016000:Cluster Analysis; D003198:Computer Simulation; D056726:Genetic Association Studies / Q000379:methods*; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide