Accuracy and power of the likelihood ratio test in detecting adaptive molecular evolution
Accuracy and power of the likelihood ratio test in detecting adaptive molecular evolution
复制标题
DOI：
10.1093/oxfordjournals.molbev.a003945
复制
发表时间：
2001-08-01
影响因子：
10.7
通讯作者：
Yang, ZH
中科院分区：
文献类型：
--
作者：
Anisimova, M;Bielawski, JP;Yang, ZH
蛋白质水平的选择压力通常通过非同义/同义速率比（omega = d(N)/d(s)）来衡量，其中 omega < 1、= 1 和 omega > 1 分别表示纯化（或负）选择、中性进化和多样化（或正）选择。 w 比率通常计算为各个站点的平均值。由于每种功能蛋白都有一些受到选择性限制的氨基酸位点，因此跨位点的平均速率会导致检测正选择的能力较低。最近开发的密码子替换模型允许 co 比率在位点之间变化，并且在经验数据分析中检测正选择方面似乎非常有效。在本研究中，我们使用计算机模拟来研究似然比检验 (LRT) 在检测氨基酸位点正选择方面的准确性和功效。该测试比较了两种嵌套模型：一种允许正选择位点（omega > 1），另一种则不允许，并使用 chi (2) 分布进行显着性测试。我们发现使用 chi (2) 分布使检验变得保守，尤其是当数据包含非常短且高度相似的序列时。尽管如此，轻轨还是很强大的。虽然数据中只有 5 或 6 个序列的功效可能较低，但在 17 个序列的数据集中，功效几乎为 100%。序列长度、序列分歧和正选择的强度也被发现会影响 LRT 的功效。研究发现，站点间欧米伽比率的精确假设分布不会影响轻轨的有效性。
The selective pressure at the protein level is usually measured by the nonsynonymous/synonymous rate ratio (omega = d(N)/d(s)), with omega < 1, = 1, and omega > 1 indicating purifying (or negative) selection, neutral evolution, and diversifying (or positive) selection, respectively. The w ratio is commonly calculated as an average over sites. As every functional protein has some amino acid sites under selective constraints, averaging rates across sites leads to low power to detect positive selection. Recently developed models of codon substitution allow the co ratio to vary among sites and appear to be powerful in detecting positive selection in empirical data analysis. In this study, we used computer simulation to investigate the accuracy and power of the likelihood ratio test (LRT) in detecting positive selection at amino acid sites. The test compares two nested models: one that allows for sites under positive selection (with omega > 1), and another that does not, with the chi (2) distribution used for significance testing. We found that use of the chi (2) distribution makes the test conservative, especially when the data contain very short and highly similar sequences. Nevertheless, the LRT is powerful. Although the power can be low with only 5 or 6 sequences in the data, it was nearly 100% in data sets of 17 sequences. Sequence length, sequence divergence, and the strength of positive selection also were found to affect the power of the LRT. The exact distribution assumed for the omega ratio over sites was found not to affect the effectiveness of the LRT.