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.

journal_name

Genet Epidemiol

journal_title

Genetic epidemiology

authors

Papathomas M,Molitor J,Hoggart C,Hastie D,Richardson S

doi

10.1002/gepi.21661

subject

Has Abstract

pub_date

2012-09-01 00:00:00

pages

663-74

issue

6

eissn

0741-0395

issn

1098-2272

journal_volume

36

pub_type

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