Reliabilities of identifying positive selection by the branch-site and the site-prediction methods
Reliabilities of identifying positive selection by the branch-site and the site-prediction methods
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DOI：
10.1073/pnas.0901855106
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发表时间：
2009-04-21
影响因子：
11.1
通讯作者：
Nei, Masatoshi
中科院分区：
文献类型：
--
作者：
Nozawa, Masafumi;Suzuki, Yoshiyuki;Nei, Masatoshi
自然选择在蛋白质编码基因中的作用通常是通过检查非同义核苷酸替换与同义核苷酸替换的比率(Omega)来研究的。基于似然比检验的分支点法(BSM)是检测对系统发育树的预定分支的正选择的检验之一。然而，由于涉及的核苷酸替换次数往往很少，我们进行了计算机模拟，以检验BSM方法的可靠性，并与基于Fisher精确检验的小样本方法(SSM)进行比较。结果表明，当核苷酸替换数目接近于80个或更少时，BSM比SSM容易产生假阳性。因为omega值也被用来预测肯定选择的位点，所以我们使用脊椎动物的暗光和色觉基因的核苷酸序列数据来检验位置预测方法的可靠性。结果表明，位点预测方法识别实验确定的氨基酸的功能变化的概率很低，并且经常错误地识别氨基酸取代不太可能重要的其他位点。之所以出现这种低的可预测性，是因为目前大多数统计方法都是为了识别具有高omega值的密码子位点，这可能与功能变化没有任何关系。显示功能变化的密码子位置一般不显示高omega值。为了理解适应性进化，某种形式的实验验证是必要的。
Natural selection operating in protein-coding genes is often studied by examining the ratio (omega) of the rates of nonsynonymous to synonymous nucleotide substitution. The branch-site method (BSM) based on a likelihood ratio test is one of such tests to detect positive selection for a predetermined branch of a phylogenetic tree. However, because the number of nucleotide substitutions involved is often very small, we conducted a computer simulation to examine the reliability of BSM in comparison with the small-sample method (SSM) based on Fisher's exact test. The results indicate that BSM often generates false positives compared with SSM when the number of nucleotide substitutions is approximate to 80 or smaller. Because the omega value is also used for predicting positively selected sites, we examined the reliabilities of the site-prediction methods, using nucleotide sequence data for the dim-light and color vision genes in vertebrates. The results showed that the site-prediction methods have a low probability of identifying functional changes of amino acids experimentally determined and often falsely identify other sites where amino acid substitutions are unlikely to be important. This low rate of predictability occurs because most of the current statistical methods are designed to identify codon sites with high omega values, which may not have anything to do with functional changes. The codon sites showing functional changes generally do not show a high omega value. To understand adaptive evolution, some form of experimental confirmation is necessary.