Comparison of statistical tests for disease association with rare variants.
Comparison of statistical tests for disease association with rare variants.
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DOI：
10.1002/gepi.20609
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发表时间：
2011-11
影响因子：
2.1
通讯作者：
Pan, Wei
中科院分区：
文献类型：
--
作者：
Basu, Saonli;Pan, Wei
关键词：
由于预期下一代测序数据的可用性，人们对研究复杂性状和罕见变异（RV）之间的关联越来越感兴趣。与常见变异 (CV) 的关联研究相反，由于 RV 出现频率较低，常识表明现有的 CV 统计测试可能不起作用，这促使最近开发了几种用于分析 RV 的新测试，其中大多数基于池化/折叠 RV 的想法。然而，缺乏对现有测试的评估，也缺乏对现有测试使用的指导。在这里，我们使用模拟数据提供了各种统计测试的全面比较。我们考虑独立和相关的罕见突变，以及 CV 和 RV 的代表性测试。正如预期的那样，如果感兴趣的位点中没有或很少有非因果（即中性或非关联）RV，而因果RV对性状的影响全部（或大部分）处于同一方向（即保护性或有害，但不是两者），则简单的汇总关联测试（不选择RV及其关联方向）和称为基于内核的自适应聚类（KBAC）的新测试表现相似并且最强大；在存在非因果 RV 的情况下，KBAC 比简单的汇总关联测试更稳健；然而，随着非因果 CV 数量的增加和/或存在相反的关联方向，获胜者是最初为 CV 提出的两种方法和为 RV 提出的一种称为 C-alpha 测试的新测试，每种方法都可以视为对随机效应模型中方差分量的测试。有趣的是，基于顺序模型选择（即选择因果 RV 及其关联方向）的几种方法，包括这里提出的两种新方法，表现稳健，并且通常具有上述两类方法之间的统计功效。
In anticipation of the availability of next-generation sequencing data, there is increasing interest in investigating association between complex traits and rare variants (RVs). In contrast to association studies for common variants (CVs), due to the low frequencies of RVs, common wisdom suggests that existing statistical tests for CVs might not work, motivating the recent development of several new tests for analyzing RVs, most of which are based on the idea of pooling/collapsing RVs. However, there is a lack of evaluations of, and thus guidance on the use of, existing tests. Here we provide a comprehensive comparison of various statistical tests using simulated data. We consider both independent and correlated rare mutations, and representative tests for both CVs and RVs. As expected, if there are no or few non-causal (i.e. neutral or non-associated) RVs in a locus of interest while the effects of causal RVs on the trait are all (or mostly) in the same direction (i.e. either protective or deleterious, but not both), then the simple pooled association tests (without selecting RVs and their association directions) and a new test called kernel-based adaptive clustering (KBAC) perform similarly and are most powerful; KBAC is more robust than simple pooled association tests in the presence of non-causal RVs; however, as the number of non-causal CVs increases and/or in the presence of opposite association directions, the winners are two methods originally proposed for CVs and a new test called C-alpha test proposed for RVs, each of which can be regarded as testing on a variance component in a random-effects model. Interestingly, several methods based on sequential model selection (i.e. selecting causal RVs and their association directions), including two new methods proposed here, perform robustly and often have statistical power between those of the above two classes.
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