Bayesian selection and clustering of polymorphisms in functionally related genes

Published

Journal Article

In epidemiologic studies, there is often interest in assessing the relationship between polymorphisms in functionally related genes and a health outcome. For each candidate gene, single nucleotide polymorphism (SNP) data are collected at a number of locations, resulting in a large number of possible genotypes. Because instabilities can result in analyses that include all the SNPs, dimensionality is typically reduced by conducting single SNP analyses or attempting to identify haplotypes. This article proposes an alternative Bayesian approach for reducing dimensionality. A multilevel Dirichlet process prior is used for the distribution of the SNP-specific regression coefficients within genes, incorporating a variable selection-type mixture structure to allow SNPs with no effect. This structure allows simultaneous selection of important SNPs and soft clustering of SNPs having similar impact on the health outcome. The methods are illustrated using data from a study of pro- and anti-inflammatory cytokine polymorphisms and spontaneous preterm birth.

Full Text

Duke Authors

Cited Authors

  • Dunson, DB; Herring, AH; Engel, SM

Published Date

  • June 1, 2008

Published In

Volume / Issue

  • 103 / 482

Start / End Page

  • 534 - 546

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1198/016214507000000554

Citation Source

  • Scopus