On the varied pattern of evolution of 2 fungal genomes: A critique of Hughes and Friedman
On the varied pattern of evolution of 2 fungal genomes: A critique of Hughes and Friedman
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DOI：
10.1093/molbev/msl122
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发表时间：
2006-12-01
影响因子：
10.7
通讯作者：
Yang, Ziheng
中科院分区：
文献类型：
--
作者：
Yang, Ziheng
人们提出了许多统计测试来检测影响蛋白质中少数氨基酸位点的阳性达尔文选择，例如过量的非同义核苷酸取代。这些测试通常比成对序列比较更强大，成对序列比较在整个基因上平均同义（d（S））和非同义（d（N））率。然而，在最近的一项研究中，Hughes AL和Friedman R（2005年）。两种真菌基因组之间同义和非同义差异模式的变异。分子生物进化二十二：1320-1324）认为d（S）和d（N）预期会随序列随机沿着波动，并且个体密码子中过多的非同义差异不是正选择的证据。作者比较了两种酵母菌（酿酒酵母和奇异酵母）基因组中蛋白质编码基因的密码子。他们计算了每个密码子中每个位点的同义和非同义差异的比例（p（S）和p（N）），发现p（N）通常大于p（S），并且在某些密码子中p（S）和p（N）呈负相关。作者认为，这些结果推翻了以前在正选择下对密码子的测试。在这里，我讨论了休斯和弗里德曼分析中的几个统计错误，包括将统计与参数混淆，任意的数据过滤，以及从数据中推导假设。我也适用于似然比测试的积极选择的酵母数据和经验说明休斯和弗里德曼的批评，这样的测试是无效的。
A number of statistical tests have been proposed to detect positive Darwinian selection affecting a few amino acid sites in a protein, exemplified by an excess of nonsynonymous nucleotide substitutions. These tests are often more powerful than pairwise sequence comparison, which averages synonymous (d(S)) and nonsynonymous (d(N)) rates over the whole gene. In a recent study, however, Hughes AL and Friedman R (2005. Variation in the pattern of synonymous and nonsynonymous difference between two fungal genomes. Mol Bio Evol. 22: 1320-1324) argue that d(S) and d(N) are expected to fluctuate along the sequence by chance and that an excess of nonsynonymous differences in individual codons is no evidence for positive selection. The authors compared codons in protein-coding genes from the genomes of 2 yeast species, Saccharomyces cerevisiae and Saccharomyces paradoxus. They calculated the proportions of synonymous and nonsynonymous differences per site (p(S) and p(N)) in every codon and discovered that p(N) is often greater than p(S) and that among some codons p(S) and p(N) are negatively correlated. The authors argued that these results invalidate previous tests of codons under positive selection. Here I discuss several errors of statistics in the analysis of Hughes and Friedman, including confusion of statistics with parameters, arbitrary data filtering, and derivation of hypotheses from data. I also apply likelihood ratio tests of positive selection to the yeast data and illustrate empirically that Hughes and Friedman's criticisms on such tests are not valid.