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同义站点间替换率的变化极大地夸大了选择分析的假阳性率:忽视后果自负。

Synonymous Site-to-Site Substitution Rate Variation Dramatically Inflates False Positive Rates of Selection Analyses: Ignore at Your Own Peril.

机构信息

Bioinformatics Research Center, North Carolina State University, Raleigh, NC.

Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA.

出版信息

Mol Biol Evol. 2020 Aug 1;37(8):2430-2439. doi: 10.1093/molbev/msaa037.

Abstract

Most molecular evolutionary studies of natural selection maintain the decades-old assumption that synonymous substitution rate variation (SRV) across sites within genes occurs at levels that are either nonexistent or negligible. However, numerous studies challenge this assumption from a biological perspective and show that SRV is comparable in magnitude to that of nonsynonymous substitution rate variation. We evaluated the impact of this assumption on methods for inferring selection at the molecular level by incorporating SRV into an existing method (BUSTED) for detecting signatures of episodic diversifying selection in genes. Using simulated data we found that failing to account for even moderate levels of SRV in selection testing is likely to produce intolerably high false positive rates. To evaluate the effect of the SRV assumption on actual inferences we compared results of tests with and without the assumption in an empirical analysis of over 13,000 Euteleostomi (bony vertebrate) gene alignments from the Selectome database. This exercise reveals that close to 50% of positive results (i.e., evidence for selection) in empirical analyses disappear when SRV is modeled as part of the statistical analysis and are thus candidates for being false positives. The results from this work add to a growing literature establishing that tests of selection are much more sensitive to certain model assumptions than previously believed.

摘要

大多数自然选择的分子进化研究都保持了几十年来的假设,即在基因内部的位点上,同义替换率变异(SRV)不存在或可以忽略不计。然而,许多研究从生物学的角度挑战了这一假设,并表明 SRV 的幅度与非同义替换率变异相当。我们通过将 SRV 纳入一种现有的方法(BUSTED)来评估这一假设对分子水平选择推断方法的影响,该方法用于检测基因中爆发式分化选择的特征。使用模拟数据,我们发现,即使在选择测试中不考虑中等水平的 SRV,也可能产生无法容忍的高假阳性率。为了评估 SRV 假设对实际推断的影响,我们在对来自 Selectome 数据库的超过 13000 个硬骨鱼(有骨脊椎动物)基因比对的实证分析中,比较了有和没有该假设的测试结果。这项研究揭示,当将 SRV 建模为统计分析的一部分时,实证分析中近 50%的阳性结果(即选择的证据)消失了,因此它们可能是假阳性的候选。这项工作的结果增加了越来越多的文献,这些文献表明,选择测试对某些模型假设的敏感性比以前认为的要高得多。

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