Detecting individual sites subject to episodic diversifying selection.
Detecting individual sites subject to episodic diversifying selection.
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DOI：
10.1371/journal.pgen.1002764
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发表时间：
2012
期刊：
影响因子：
4.5
通讯作者：
Kosakovsky Pond SL
中科院分区：
文献类型：
--
作者：
Murrell B;Wertheim JO;Moola S;Weighill T;Scheffler K;Kosakovsky Pond SL
自然选择在蛋白质编码基因上的印记通常很难识别，因为选择通常是短暂的或偶发的，即它只影响谱系的一个子集。现有的计算技术，旨在确定网站的普遍选择，可能无法识别网站的选择是偶发性的：一个积极选择的网站的很大一部分。我们提出了一个混合效应的进化模型（MEME），能够识别的情况下，情节和普遍的积极选择在一个单独的网站的水平。使用经验和模拟数据，我们证明了上级性能的MEME在广泛的场景下，比旧的模型。我们发现，情节选择是普遍的，并得出结论，网站的数量经历积极的选择可能被大大低估。识别经历适应性进化的蛋白质编码基因的区域对于回答进化生物学和遗传学中的许多问题非常重要。为了梳理出自然选择的遗传证据，必须分析来自不同分类群的基因，其中只有一个子集可能经历了适应性进化;同一个基因区域可能在导致其他分类群的谱系中处于稳定或放松选择之下。目前大多数用于检测蛋白质编码基因中某个位点的自然选择印记的计算方法都假设自然选择的强度和方向在所有谱系中是恒定的。在这里，我们提出了一种方法来检测适应性进化，即使选择力是不恒定的类群。使用各种特征良好的基因，我们发现的证据表明，自然选择通常是情节和建模，它揭示了更多的网站受到情节积极的选择比以前认识到。
The imprint of natural selection on protein coding genes is often difficult to identify because selection is frequently transient or episodic, i.e. it affects only a subset of lineages. Existing computational techniques, which are designed to identify sites subject to pervasive selection, may fail to recognize sites where selection is episodic: a large proportion of positively selected sites. We present a mixed effects model of evolution (MEME) that is capable of identifying instances of both episodic and pervasive positive selection at the level of an individual site. Using empirical and simulated data, we demonstrate the superior performance of MEME over older models under a broad range of scenarios. We find that episodic selection is widespread and conclude that the number of sites experiencing positive selection may have been vastly underestimated. Identifying regions of protein coding genes that have undergone adaptive evolution is important to answering many questions in evolutionary biology and genetics. In order to tease out genetic evidence for natural selection, genes from a diverse array of taxa must be analyzed, only a subset of which may have undergone adaptive evolution; the same gene region may be under stabilizing or relaxed selection in lineages leading to other taxa. Most current computational methods designed to detect the imprint of natural selection at a site in a protein coding gene assume the strength and direction of natural selection is constant across all lineages. Here, we present a method to detect adaptive evolution, even when the selective forces are not constant across taxa. Using a variety of well-characterized genes, we find evidence suggesting that natural selection is generally episodic and that modeling it as such reveals that many more sites are subject to episodic positive selection than previously appreciated.
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