Multiple hypothesis testing to detect lineages under positive selection that affects only a few sites
Multiple hypothesis testing to detect lineages under positive selection that affects only a few sites
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DOI：
10.1093/molbev/msm042
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发表时间：
2007-05-01
影响因子：
10.7
通讯作者：
Yang, Ziheng
中科院分区：
文献类型：
--
作者：
Anisimova, Maria;Yang, Ziheng
随着基因组数据集的快速增长，检测正达尔文选择变得愈发重要。近期的密码子替换分支 - 位点模型考虑了树上分支间以及序列位点间选择压力的变化，并提供了一种检测仅影响少数位点的分子适应短期事件的方法。在基于此类模型的似然比检验中，必须事先指定要检验正选择的分支。在没有生物学假设来指定所谓的前景分支时，可以对许多分支进行检验，但有必要对多重检验进行校正。在本文中，我们利用计算机模拟来评估当使用分支 - 位点模型对系统发育树上的每个分支进行正选择检验时，6种多重检验校正程序的性能。其中4种方法控制族错误率（FWER），另外2种控制错误发现率（FDR）。我们发现，除了序列差异极大和严重违反模型的情况（此时检验可能不可靠）外，所有校正程序都能达到可接受的FWER。检验检测正选择的能力受选择强度和序列差异的影响，在中等差异时能力最强。控制FWER的4种校正程序具有相似的能力。我们推荐Rom程序，因为它的能力略高，但简单的Bonferroni校正也可用。控制FDR的2种校正程序能力略强，但FWER也较高。我们通过分析来自10种哺乳动物的分化簇2（CD2）基因胞外域的基因序列来展示多重检验程序。我们的模拟和实际数据分析都表明，当必须在同一数据集上对多个分支进行检验时，多重检验程序是有用的。
Detection of positive Darwinian selection has become ever more important with the rapid growth of genomic data sets. Recent branch-site models of codon substitution account for variation of selective pressure over branches on the tree and across sites in the sequence and provide a means to detect short episodes of molecular adaptation affecting just a few sites. In likelihood ratio tests based on such models, the branches to be tested for positive selection have to be specified a priori. In the absence of a biological hypothesis to designate so-called foreground branches, one may test many branches, but a correction for multiple testing becomes necessary. In this paper, we employ computer simulation to evaluate the performance of 6 multiple test correction procedures when the branch-site models are used to test every branch on the phylogeny for positive selection. Four of the methods control the familywise error rates (FWERs), whereas the other 2 control the false discovery rate (FDR). We found that all correction procedures achieved acceptable FWER except for extremely divergent sequences and serious model violations, when the test may become unreliable. The power of the test to detect positive selection is influenced by the strength of selection and the sequence divergence, with the highest power observed at intermediate divergences. The 4 correction procedures that control the FWER had similar power. We recommend Rom's procedure for its slightly higher power, but the simple Bonferroni correction is useable as well. The 2 correction procedures that control the FDR had slightly more power and also higher FWER, We demonstrate the multiple test procedures by analyzing gene sequences from the extracellular domain of the cluster of differentiation 2 (CD2) gene from 10 mammalian species. Both our simulation and real data analysis suggest that the Multiple test procedures are useful when multiple branches have to be tested on the same data set.