Bayesian Variable and Model Selection Methods for Genetic Association Studies
Bayesian Variable and Model Selection Methods for Genetic Association Studies
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DOI：
10.1002/gepi.20353
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发表时间：
2009-01-01
影响因子：
2.1
通讯作者：
Fridley, Brooke L.
中科院分区：
文献类型：
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作者：
Fridley, Brooke L.
随着需要分析数百到数千个单核苷酸多态性（snp）的高通量基因分型方法的出现，以及利用这些基因研究更好地理解常见、复杂疾病的兴趣的增加，变量选择的重要性日益增加。到目前为止，标准的方法是单独分析每个SNP的基因型，以寻找与疾病的关联。或者，对snp或单倍型的组合进行关联分析。研究复杂疾病或表型的另一个额外的并发症是，疾病的遗传风险通常是由于染色体上不同位置的多个snp，个体影响很小，但可能对表型产生很大的集体影响。因此，与单SNP模型相比，多位点SNP模型可能更好地捕捉到真正潜在的基因型-表型关系。因此，需要创新的方法来确定模型中包含哪些snp。本文的目的是描述目前使用贝叶斯方法进行变量和模型选择的几种方法，并说明它们在使用真实和模拟的候选基因数据进行复杂疾病的遗传关联研究中的应用。特别地，贝叶斯模型平均（BMA）、随机搜索变量选择（SSVS）和贝叶斯变量选择（BVS）使用可逆跳跃马尔可夫链蒙特卡罗（MCMC）进行候选基因关联研究，使用年龄相关性黄斑变性（AMD）的研究和模拟数据进行了说明。麝猫。流行病学杂志。33:27-37,2009。(C) 2008 Wiley-Liss, Inc。
Variable selection is growing in importance with the advent of high throughput genotyping methods requiring analysis of hundreds to thousands of single nucleotide polymorphisms (SNPs) and the increased interest in using these genetic studies to better understand common, complex diseases. Up to now, the standard approach has been to analyze the genotypes for each SNP individually to look for an association with a disease. Alternatively, combinations of SNPs or haplotypes are analyzed for association. Another added complication in studying complex diseases or phenotypes is that genetic risk for the disease is often due to multiple SNPs in various locations on the chromosome with small individual effects that may have a collectively large effect on the phenotype. Hence, multi-locus SNP models, as opposed to single SNP models, may better capture the true underlying genotypic-phenotypic relationship. Thus, innovative methods for determining which SNPs to include in the model are needed. The goal of this article is to describe several methods currently available for variable and model selection using Bayesian approaches and to illustrate their application for genetic association studies using both real and simulated candidate gene data for a complex disease. In particular, Bayesian model averaging (BMA), stochastic search variable selection (SSVS), and Bayesian variable selection (BVS) using a reversible jump Markov chain Monte Carlo (MCMC) for candidate gene association studies are illustrated using a Study of age-related macular degeneration (AMD) and simulated data. Genet. Epidemiol. 33:27-37, 2009. (C) 2008 Wiley-Liss, Inc.