Bayesian selection and clustering of polymorphisms in functionally related genes
Bayesian selection and clustering of polymorphisms in functionally related genes
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DOI：
10.1198/016214507000000554
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发表时间：
2008-06-01
影响因子：
3.7
通讯作者：
Engel, Stephanie M.
中科院分区：
文献类型：
--
作者：
Dunson, David B.;Herring, Amy H.;Engel, Stephanie M.
在流行病学研究中，经常有兴趣评估功能相关基因的多态与健康结果之间的关系。对于每个候选基因，在多个位置收集单核苷酸多态(SNP)数据，从而产生大量可能的基因类型。由于不稳定性可能导致分析包括所有SNP，因此通常通过进行单个SNP分析或尝试识别单倍型来降低维度。本文提出了一种贝叶斯降维方法。多水平Dirichlet过程先验被用于基因内SNP特有的回归系数的分布，结合了变量选择型混合结构以允许SNP不起作用。这种结构允许同时选择重要的SNPs，并对对健康结果有类似影响的SNPs进行软聚类。这些方法是使用促炎和抗炎细胞因子多态和自发早产研究的数据来说明的。
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.