Bayesian model comparison in genetic association analysis: linear mixed modeling and SNP set testing.
Bayesian model comparison in genetic association analysis: linear mixed modeling and SNP set testing.
复制标题
遗传关联分析中的贝叶斯模型比较：线性混合模型和 SNP 集测试。
DOI：
10.1093/biostatistics/kxv009
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Wen,Xiaoquan
中科院分区：
文献类型：
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作者：
Wen,Xiaoquan
研究了一种灵活的贝叶斯线性回归模型下的假设检验和模型比较问题，该模型的建立与遗传关联研究中的线性混合效应模型和单核苷酸多态(SNP)集分析的参数模型密切相关。我们导出了一类解析近似贝叶斯因子，并说明了它们与各种频率检验统计量的关系，包括Wald统计量和方差分量得分统计量。利用贝叶斯模型平均和分层建模的优点，我们展示了在遗传关联研究中利用导出的贝叶斯因子的方法的一些明显的优势和灵活性。在单SNP关联测试、多点精细映射和SNP集合关联测试的应用中，我们用真实或模拟的数值例子展示了我们提出的方法。
We consider the problems of hypothesis testing and model comparison under a flexible Bayesian linear regression model whose formulation is closely connected with the linear mixed effect model and the parametric models for Single Nucleotide Polymorphism (SNP) set analysis in genetic association studies. We derive a class of analytic approximate Bayes factors and illustrate their connections with a variety of frequentist test statistics, including the Wald statistic and the variance component score statistic. Taking advantage of Bayesian model averaging and hierarchical modeling, we demonstrate some distinct advantages and flexibilities in the approaches utilizing the derived Bayes factors in the context of genetic association studies. We demonstrate our proposed methods using real or simulated numerical examples in applications of single SNP association testing, multi-locus fine-mapping and SNP set association testing.