Novel Coalescent Approaches for Studying Evolutionary Processes

研究进化过程的新联合方法

• 批准号: 10552480
• 负责人: Julia Palacios
• 金额: $ 38.68万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-17 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10552480
• 关键词: Bayesian Analysis; Biological; Combinatorial Optimization; Data; Demography; Disease; Environment; Evolution; Genealogy; Genetic Variation; Genomic Segment; Health; Individual; Knowledge; Laboratories; Laboratory Research; Mathematics; Methods; Mission; Modeling; Molecular; Mutation; Organism; Pattern; Phylogenetic Analysis; Population; Population Genetics; Process; Property; Research; Shapes; Source; Testing; United States National Institutes of Health; Variant; genetic variant; insight; novel; novel strategies; pathogen; programs; theories; tool

Project Summary/Abstract: My laboratory research program in stochastic modeling and inference of evolutionary processes focuses on developing efficient methods for inference of evolutionary parameters from molecular data, and statistical tests for assessing evolutionary hypotheses. This proposal will focus on answering three fundamental questions in the study of evolutionary processes: Are the observed patters of genetic diversity the result of adaptive or non-adaptive evolution? What is the mode and strength of selection? How can we identify genomic regions undergoing selection? Whether adaptation, demography or local patterns of mutations are the sources of variation across populations, these forces influence the shape of the underlying genealogies and phylogenetic networks. Hence, assessing differences among genealogies provide information about differences in these forces, particularly among genealogies of different individuals, possibly living in different environments and times. We propose to approach these questions by defining new coalescent models of selection and exploiting a metric on the space of genealogies to define statistical tests. The computational advantage and the ease of biological interpretation, together with the mathematical properties of the proposed models and metric spaces, open the door to novel approaches for studying adaptation. Over the next five years, the Palacios laboratory will combine tools from combinatorial optimization, Bayesian inference, and coalescent theory to develop new coalescent models and tests applicable to studying the evolution of pathogens and other organisms.

项目概要/摘要： 我的实验室研究计划在随机建模和推理的进化过程的重点是 发展从分子数据推断进化参数的有效方法， 评估进化假说的测试。这一建议将重点回答三个基本问题 进化过程研究中的问题：观察到的遗传多样性模式是 适应性进化还是非适应性进化？选择的模式和力度如何？我们如何识别 基因组区域进行选择？无论是适应、人口统计学还是局部突变模式， 这些力量影响着潜在系谱的形状， 和系统发育网络。因此，评估家谱之间的差异提供了以下信息 这些力量的差异，特别是不同个体的家谱之间的差异，可能生活在 不同的环境和时间。我们建议通过定义新的聚结剂来解决这些问题 选择模型和利用谱系空间上的度量来定义统计测试。的 计算优势和生物学解释的容易性，以及数学性质， 所提出的模型和度量空间，为研究适应的新方法打开了大门。超过 在接下来的五年里，帕拉西奥斯实验室将联合收割机工具，从组合优化，贝叶斯 推理和聚结理论，开发新的聚结模型和适用于研究 病原体和其他生物的进化。