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

基本信息

  • 批准号:
    10623079
  • 负责人:
  • 金额:
    $ 15.16万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 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

项目摘要

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 日益丰富的人口基因组数据产生了对推理方法的迫切需求,这种方法可以 揭示进化史。PI的长期目标是了解自然选择是如何塑造进化的 以及构成生命的分子网络的功能。为了实现这一目标,PI的小组开发并应用了 从种群基因组数据推断进化历史的方法。此应用程序的目标是 了解上下文如何影响突变fi度效果,开发改进的推理方法,并支持 人口基因组学研究社区。理由是,这项研究计划既将揭示新的 对进化的洞察,并增强同事揭示互补洞察力的能力。 PI的研究小组将fi度效应的分布概念扩展到多个维度, 关注不同群体间突变fi度效应的差异。国际和平研究所建议将这一方法应用于 众多系统,以阐明遗传和环境背景在造成差异方面的相对作用 fiTness效果。该小组还将扩展这一方法,以考虑随着时间的推移fi明度影响的差异。 PI开发和维护软件DADI,这是用于fi设置种群的最流行的方法之一 从基因组模型到数据。国际和平研究所将继续支持和加强DADI,同时发展补充 推理方法。这些将包括基于基因座对的新扩散方法以及它们之间的联系 他们和一种新的深度学习方法来推断fi度效应的分布。 PI帮助建立了PopSim财团,旨在扩大人口年龄的严密性和透明度- Sciencefic社区的经济学模型。PI的小组将继续活跃在财团中,特别是 领导一项新的倡议,通过公开竞争促进种群基因组方法的严格测试。 拟议的研究方案在概念和方法上都是创新的。一种新的概念 fi度效应的多维分布有许多应用,该小组将开发新的方法-- 几个群体基因组学推论。拟议研究的预期结果是新的。 对突变fi强度效应的生态学和生物学的见解,新的群体基因组推断工具,以及 对这类工具进行盲目评估的框架。预计这些结果将产生重要的积极影响。 以fi为主导的群体基因组学。这些方法将具有广泛的适用性和良好的支持性,其推论 将用于推断进化的过去和预测进化的未来的方法。

项目成果

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Ryan Gutenkunst其他文献

Ryan Gutenkunst的其他文献

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{{ truncateString('Ryan Gutenkunst', 18)}}的其他基金

Joint inferences of natural selection between sites and populations
地点和种群之间自然选择的联合推论
  • 批准号:
    10560525
  • 财政年份:
    2019
  • 资助金额:
    $ 15.16万
  • 项目类别:
Joint inferences of natural selection between sites and populations
地点和种群之间自然选择的联合推论
  • 批准号:
    10331017
  • 财政年份:
    2019
  • 资助金额:
    $ 15.16万
  • 项目类别:
Joint inferences of natural selection between sites and populations
地点和种群之间自然选择的联合推论
  • 批准号:
    10092189
  • 财政年份:
    2019
  • 资助金额:
    $ 15.16万
  • 项目类别:
Joint inferences of natural selection between sites and populations
地点和种群之间自然选择的联合推论
  • 批准号:
    10166182
  • 财政年份:
    2019
  • 资助金额:
    $ 15.16万
  • 项目类别:

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