Reinventing dN/dS and the study of natural selection

对遗传变异的自然选择是适应和形成新物种的根本原因。全 在基因组水平上研究自然选择需要一个对照组：一组变异或替换， 没有受到选择的影响。到目前为止，用于这一目的最广泛的变体类别 是蛋白质编码基因中的同义词变化。因为这些变种夹杂着 非同义变体，它们是一个自然对照组。然而，压倒性的证据表明， 变化往往不是严格意义上的中立，而是过去三十年积累起来的。在许多情况下，明确 选择的证据为调查人员提供了确定哪些同义变化最有可能 受到选择的影响，而且更有可能是严格中立的。缺少的是包括这一点的方法 关于量化进化模型中分类的同义变化(中性和选定的)的信息 这使得能够对在哪里、何时以及在什么情况下进行选择进行广泛的推断。建议数 研究基于一类新的进化模型(多重同义替代或MSS模型) 这既包括进化变化估计中的选定同义变化，也包括中性同义变化。这些型号 可以与同源序列比对相匹配，以推断氨基酸替换变化的相对速率，以及 选择同义词更改，以严格中立同义词更改。这些类型的(dN/ds)分析 回到进化遗传学的起源，今天它们仍然是最常用的工具之一。我们的 假设是，目前对中性进化站点的定义是严重错误的，因为 它偏向于对选择性力量的估计(通过低估中性变化的速度)，这一点得到了改善 没有这种偏见的估计将大大更好和更准确地理解 在广泛的进化背景下的自然选择。我们将开发和测试一种新的替代类 使用多组同义替换的模型(MSS模型)，我们将把它们应用于不同的 从生活中的各个王国采集的一套分类群。估计的MSS模型，使用遗传算法发现 对所有可能的MSS型号进行搜索，将使用内部控制进行验证，基于 中性模型对替代率速度和稳定性的预测，并使用外部控制，基于 预测种群基因组和功能效应的协变量。我们将修改一系列广泛使用的 比较和群体遗传学技术，以利用MSS模型，开发新的 寻求识别对同义变化的选择的方法。我们的模型将广泛应用于 基因组数据的分类范围，并作为流行的软件包hyphy和web的一部分提供 应用程序数据关键字。

Natural selection on genetic variation is the ultimate cause of adaptation and the formation of new species. All studies of natural selection at the genomic level require a control group: a set of variants or substitutions that have not been subject to selection. The class of variants that, by far, are the most widely used for this purpose are synonymous changes within protein coding genes. Because these variants are interspersed with nonsynonymous variants, they are a natural control group. However, overwhelming evidence that synonymous changes are often not strictly neutral has accumulated over the past three decades. In many contexts the clear evidence for selection provides investigators with ways to identify which synonymous changes are most likely to be affected by selection and which are more likely to be strictly neutral. What is missing are ways to include this information, on classified synonymous changes (neutral and selected), within quantitative evolutionary models that enable a broad range of inferences about where, when, and on what selection operates. The proposed research is based on a new class of evolutionary models (Multiple Synonymous Substitution or MSS models) that include both selected and neutral synonymous changes in estimates of evolutionary change. These models can be fitted to homologous sequence alignments to infer relative rates of amino-acid replacement changes, and selected synonymous changes, to strictly neutral synonymous changes. These types of (dN/dS) analyses go back to the origins of evolutionary genetics, and today they remain among the most commonly used tools. Our hypothesis is that the current definition of what constitutes neutrally evolving sites is importantly wrong in that it biases the estimation of selective forces (by underestimating the rates of neutral changes), and that improved estimates without that bias will provide significantly better and more accurate understanding of the action of natural selection in a broad array of evolutionary contexts. We will develop and test a new class of substitution model that uses multiple sets of synonymous substitutions (MSS models), and we will apply them to a diverse set of taxa sampled from across life's kingdoms. Estimated MSS models, discovered using a genetic algorithm that searches over the space of all possible MSS models, will be validated using internal controls, based on neutral model predictions of substitution rate speed and constancy, and using external controls, based on predicted covariates of population genomic and functional effects. We will revise an array of widely-used comparative and population genetics techniques to take advantage of MSS models, and develop new approaches that seek to identify selection on synonymous changes. Our models will be applied to a wide taxonomic range of genomic data and made available as a part of popular software package HyPhy and web application Datamonkey.