Population genomic inferences of history and selection across populations and time

跨群体和时间的历史和选择的群体基因组推断

• 批准号: 10623079
• 负责人: Ryan Gutenkunst
• 金额: $ 15.16万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10623079
• 关键词: Affect; Architecture; Biology; Blinded; Communicable Diseases; Communities; Computer software; Data; Diffusion; Dimensions; Ecology; Evaluation; Evolution; Foundations; Future; Genetic; Genetic Diseases; Genomics; Goals; Human; Human Genetics; Life; Methodology; Methods; Modeling; Molecular; Mutation; Natural Selections; Outcome; Play; Population; Public Health; Recording of previous events; Research; Role; Shapes; System; Testing; Time; deep learning; fitness; genomic data; improved; innovation; insight; novel; pathogen; programs; tool

Project Summary/Abstract The growing abundance of population genomic data creates a critical need for inference approaches that can reveal evolutionary history. The PI's long-term goal is to understand how natural selection shapes the evolution and function of the molecular networks that comprise life. Toward that goal, the PI's group develops and applies methods for inferring the evolutionary past from population genomic data. The objectives of this application are to understand how context affects mutation fitness effects, to develop improved inference methods, and to support the population genomics research community. The rationale is that this research program will both reveal new insights into evolution and enhance the ability of colleagues to reveal complementary insights. The PI's research group has expanded the concept of a distribution of fitness effects to multiple dimensions, focusing on differences in mutation fitness effects among populations. The PI proposes to apply this approach to numerous systems, to elucidate the relative roles of genetic and environmental context in creating differences in fitness effects. The group will also extend this approach to consider differences in fitness effects over time. The PI developed and maintains the software dadi, among the most popular approaches for fitting population genomic models to data. The PI will continue to support and enhance dadi, while developing complementary inference approaches. These will include new diffusion methods based on pairs of loci and the linkage among them and a novel deep learning approach for inferring the distribution of fitness effects. The PI helped found the PopSim consortium, which aims to expand the rigor and transparency of population ge- nomic models for the scientific community. The PI's group will continue to be active in the consortium, particularly leading a new initiative to facilitate rigorous testing of population genomic methods via open competition. The proposed research program is innovative both conceptually and methodologically. The novel concept of a multidimensional distribution of fitness effects has many applications, and the group will develop novel method- ology for several population genomics inferences. The expected outcomes of the proposed research are new insights into the ecology and biology of mutation fitness effects, new population genomic inference tools, and a framework for blinded evaluation of such tools. These outcomes are expected to have important positive impact on the filed of population genomics. The methods will be widely applicable and well-supported, and the inferences will feed into approaches for inferring the evolutionary past and predicting the evolutionary future.

项目摘要/摘要 人口基因组数据的丰富度越来越多，对推理方法的迫切需求至关重要 揭示进化史。 PI的长期目标是了解自然选择如何塑造演变 和完整生命的分子网络的功能。为了实现这一目标，PI的小组发展并适用 从人群基因组数据中推断出进化过去的方法。此应用程序的目标是 了解上下文如何影响突变的效果，开发改进的推理方法并支持 人群基因组学研究界。理由是该研究计划都将揭示新的 洞察进化并增强同事揭示完整见解的能力。 PI的研究小组将拟合度分布的概念扩展到了多个维度， 专注于种群中突变效应的差异。 PI提议将这种方法应用于 众多系统，以阐明遗传和环境环境在造成差异的相对作用 效果。该小组还将扩展这种方法，以考虑随着时间的推移效应的差异。 PI开发并维护软件DADI，这是最受欢迎的人口的方法之一 基因组模型到数据。 PI将继续支持和增强DADI，同时开发完件 推理方法。这些将包括基于基因座对的新扩散方法和之间的联系 他们和一种新颖的深度学习方法来推断效果的分布。 PI帮助建立了流行联盟，该联盟旨在扩大人口的严格和透明度 科学界的提名模型。 PI的小组将继续在财团中活跃，特别是 领导一项新计划，以通过公开竞争来促进对种群基因组方法的严格测试。 拟议的研究计划在概念和方法上都是创新的。一个新颖的概念 多维效应效应的多维分布有许多应用，该组将开发新方法 - 多种人群基因组学推断的神学。拟议研究的预期结果是新的 洞悉突变效果的生态和生物学，新的人群基因组推断工具以及 盲目评估此类工具的框架。这些结果有望产生重要的积极影响 在人群基因组学的领域。这些方法将被广泛适用且支持良好，并推论 将进食推断进化过去并预测进化未来的方法。