Distributions of selection coefficients from phylogenomic data - modeling the time-heterogeneity of the substitution process

系统发育数据的选择系数分布 - 模拟替代过程的时间异质性

• 批准号: 435516-2013
• 负责人: Rodrigue, Nicolas
• 金额: $ 2.26万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=663563
• 关键词: Distributions; selection; coefficients; phylogenomic; data

Thanks to the availability of a broad spectrum of bio-molecular data, and the capabilities of modern computing machines, the field of statistical modeling of molecular evolution has been undergoing rapid advances in recent years. Among these advances are a class of substitution models known as mutation-selection models, in which distinct parameters are assigned to mutational features and selective effects. These models are inspired by population-genetics theory, yet are applicable to the analysis of homologous genes sampled across several different species. Using the formalisms of probability theory, one can quantify the evidence for a given model, and by comparing the evidence for several alternative forms of mutation-selection models, one can understand the relative importance of the different elements bearing on the overall, long-term evolutionary process.**Over the course of this research program, we will develop novel models of molecular evolution of protein-coding DNA sequences using the mutation-selection framework, which allow for different aspects of the evolutionary process to vary over time. These models entail non-analytical mathematical expressions, requiring the use of high-performance computing for obtaining numerical approximations of the desired probabilities. The program will enable a sound probabilistic exploration of fundamental questions in evolutionary genetics, regarding the relative importance of mutation, selection and drift, but also regarding how shifts of these features relate to shifts in species traits, population structures, and ecological features.****

由于广泛的生物分子数据的可用性，以及现代计算机的能力，分子进化的统计建模领域近年来一直在迅速发展。 在这些进展中，有一类被称为突变选择模型的替代模型，其中为突变特征和选择效应分配了不同的参数。 这些模型受到群体遗传学理论的启发，但适用于分析几个不同物种的同源基因。 使用概率论的形式主义，人们可以量化给定模型的证据，通过比较几种不同形式的突变选择模型的证据，人们可以理解影响整个长期进化过程的不同因素的相对重要性。在这项研究计划的过程中，我们将开发新的模型的蛋白质编码的DNA序列的分子进化使用突变选择框架，它允许进化过程的不同方面随着时间的推移而变化。 这些模型需要非分析数学表达式，需要使用高性能计算来获得所需概率的数值近似值。该计划将使进化遗传学中的基本问题的合理概率探索，关于突变，选择和漂移的相对重要性，以及这些特征的变化如何与物种特征，种群结构和生态特征的变化相关。