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.

遗传变异的自然选择是适应和形成新物种的最终原因。全部 基因组水平上自然选择的研究需要一个对照组：一组变体或替代品 没有被选择。到目前为止，用于此目的最广泛使用的变体类别 是蛋白质编码基因中的同义变化。因为这些变体散布在 它们是一个自然对照组的非主义变体。但是，压倒性的证据表明同义词 在过去的三十年中，通常并不是严格的中性变化。在许多情况下 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的方法 受到选择的影响，并且更有可能严格中立。缺少的是包括此的方法 在定量进化模型中，有关机密的同义变化（中性和选定）的信息 这可以对选择在何处，何时和选择哪种运作的广泛推论。提议 研究基于一类新的进化模型（多个同义替代或MSS模型） 其中包括选定的和中性的同义变化，对进化变化的估计值进行了变化。这些模型 可以适合同源序列一致性，以推断氨基酸替代变化的相对速率，并 选定的同义更改，以严格中性的同义更改。这些类型的（DN/DS）分析进行 回到进化遗传学的起源，如今，它们仍然是最常用的工具之一。我们的 假设是，当前构成中性不断发展的网站的定义是重要的 它偏向选择性力的估计（通过低估中性变化速率），并改善 没有这种偏见的估计将为对动作的作用提供更好，更准确的理解 在各种进化环境中的自然选择。我们将开发和测试新的替代类别 使用多组同义替代（MSS模型）的模型，我们将它们应用于多样化 从整个生活的王国中取样了一套分类单元。使用遗传算法发现的估计MSS模型 在所有可能的MSS模型的空间上进行搜索，将使用内部控件（基于内部控件）验证 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 比较和人口遗传学技术可以利用MSS模型，并开发新的 试图确定选择同义变化的方法。我们的模型将应用于宽阔的 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。