Bayesian Detection of Causal Rare Variants under Posterior Consistency
Bayesian Detection of Causal Rare Variants under Posterior Consistency
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DOI：
10.1371/journal.pone.0069633
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发表时间：
2013-07-26
期刊：
影响因子：
3.7
通讯作者：
Xiong, Momiao
中科院分区：
文献类型：
--
作者：
Liang, Faming;Xiong, Momiao
鉴定与复杂性状相关的因果罕见变异对全基因组关联研究构成了核心挑战。然而，目前大多数研究仅关注于测试给定基因组区域中的罕见变异是否与性状共同相关。虽然最近的一些工作，E。例如，在一个实施例中，贝叶斯风险指数方法试图解决这一问题，但尚不清楚在小N大P情况下它们是否能一致地识别因果罕见变异。我们开发了一种新的贝叶斯方法，即所谓的贝叶斯罕见变异检测器（BRVD），来解决这个问题。新方法同时解决了两个问题：（i）（全局关联测试）是否存在与疾病相关的任何变体，以及（ii）（因果变体检测）哪些变体（如果有的话）正在驱动关联。BRVD通过对模型和模型特定参数施加一些适当的先验分布，确保在小n大P情况下一致地识别因果罕见变异。数值结果表明，BRVD方法在检验全局关联性方面比现有的多变量和塌陷检验、加权和检验、RARECOVER、序列核关联检验、贝叶斯风险指数等方法更有效，在识别因果罕见变异方面也比贝叶斯风险指数方法更有效. BRVD也已成功应用于早发性心肌梗死（EOMI）外显子组序列数据。它确定了一些已在文献中验证的因果罕见变异。
Identification of causal rare variants that are associated with complex traits poses a central challenge on genome-wide association studies. However, most current research focuses only on testing the global association whether the rare variants in a given genomic region are collectively associated with the trait. Although some recent work, e. g., the Bayesian risk index method, have tried to address this problem, it is unclear whether the causal rare variants can be consistently identified by them in the small-n-large-P situation. We develop a new Bayesian method, the so-called Bayesian Rare Variant Detector (BRVD), to tackle this problem. The new method simultaneously addresses two issues: (i) (Global association test) Are there any of the variants associated with the disease, and (ii) (Causal variant detection) Which variants, if any, are driving the association. The BRVD ensures the causal rare variants to be consistently identified in the small-n-large-P situation by imposing some appropriate prior distributions on the model and model specific parameters. The numerical results indicate that the BRVD is more powerful for testing the global association than the existing methods, such as the combined multivariate and collapsing test, weighted sum statistic test, RARECOVER, sequence kernel association test, and Bayesian risk index, and also more powerful for identification of causal rare variants than the Bayesian risk index method. The BRVD has also been successfully applied to the Early-Onset Myocardial Infarction (EOMI) Exome Sequence Data. It identified a few causal rare variants that have been verified in the literature.