期刊
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
卷 103, 期 482, 页码 534-546出版社
AMER STATISTICAL ASSOC
DOI: 10.1198/016214507000000554
关键词
Bayesian; clustering; Dirichlet process; genetic association; hierarchical regression; multiple testing; nonparametric Bayes; single nucleotide polymorphisms; sparse regression
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.
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