Reinventing dN/dS and the study of natural selection

重塑 dN/dS 和自然选择的研究

• 批准号: 10535021
• 负责人: Emanuel Hey
• 金额: $ 31.28万
• 依托单位: TEMPLE UNIV OF THE COMMONWEALTH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10535021
• 关键词: Affect; Amino Acid Substitution; Amino Acids; Back; Biological; Code; Codon Nucleotides; Computer software; Control Groups; Data; Factor Analysis; Failure; Frequencies; Genes; Genetic; Genetic Programming; Genetic Techniques; Genetic Variation; Genomics; Individual; Lead; Learning; Life; Measures; Methods; Modeling; Natural Selections; Pattern; Phylogenetic Analysis; Population; Population Genetics; Population Sizes; Proteins; Publications; Research; Research Personnel; Sampling; Sequence Alignment; Sequence Homologs; Site; Speed; Taxonomy; Testing; Validation; Variant; base; comparative; fitness; genetic testing; genomic data; improved; internal control; model development; molecular clock; news; novel; novel strategies; predictive modeling; tool; web app

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.

对遗传变异的自然选择是适应和新物种形成的最终原因。全部 基因组水平的自然选择研究需要一个对照组：一组变异或替代， 没有经过选择。迄今为止，为此目的使用最广泛的变体类别 是蛋白质编码基因内的同义变化。因为这些变体散布在 非同义变体，它们是自然的对照组。然而，大量证据表明同义 过去三十年积累的变化往往不是严格中立的。在许多情况下明确 选择证据为研究人员提供了确定哪些同义变化最有可能的方法 受到选择的影响，并且更有可能是严格中立的。缺少的是包含此内容的方法 定量进化模型中有关分类同义变化（中性和选定）的信息 这使得我们能够对选择在何时、何地以及如何运作进行广泛的推断。拟议的 研究基于一类新的进化模型（多重同义替换或 MSS 模型） 其中包括进化变化估计中的选定和中性同义变化。这些型号 可以拟合同源序列比对来推断氨基酸替换变化的相对速率，并且 选择同义变化时，要严格中性同义变化。这些类型的 (dN/dS) 分析适用于 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，当前对中立进化站点构成的定义是严重错误的，因为 它使选择性力量的估计产生偏差（通过低估中性变化的速率），并且改善了 没有这种偏见的估计将提供对行为的更好和更准确的理解 广泛的进化背景中的自然选择。我们将开发并测试一种新的替代品 使用多组同义替换的模型（MSS模型），我们将它们应用到不同的 从生命王国中采样的一组类群。使用遗传算法发现的估计 MSS 模型 搜索所有可能的 MSS 模型的空间，将使用内部控制进行验证，基于 替代率速度和稳定性的中性模型预测，并使用外部控制，基于 预测群体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，利用 MSS 模型，开发新的 寻求识别同义变化选择的方法。我们的模型将广泛应用于 基因组数据的分类范围，并作为流行软件包 HyPhy 和 web 的一部分提供 应用程序 Datamonkey。