ABI Innovation: Coalescence-based Inference of Adaptation

ABI 创新：基于合并的适应推理

• 批准号: 1564822
• 负责人: Peter Beerli
• 金额: $ 75.77万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-15 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1564822&HistoricalAwards=false
• 关键词: ABI; Innovation; Coalescence; based; Inference

This project will develop a mathematical framework for investigating genomic sequences that respond differently to local environmental stresses. This framework will compare mutation patterns from samples taken at different locations to estimate possible genealogies of these samples; these genealogies will be used to estimate the parameters of a number of population genetic models. These parameters will be able to tell us about potential selection differences among geographical locations. This new mathematically rigorous framework has the promise to supersede current ad hoc and inadequate summary statistics. Potential applications of this framework include improving interventions for diseases (e.g. individualized responses for HIV patients), and improving our understanding of which gene regions are responsible for long-term survival in harsh environments. The framework will be publicly available in standalone computer software that can be run on small computers or large computing clusters. This research draws from multiple science and technology disciplines (biology, computational science, and statistics) and thus will provide a great basis on which to mentor undergraduate, graduate, and postdoctoral students and foster their interest towards a field that desperately needs more training opportunities. The building blocks for this framework are rooted in coalescence theory, a branch of theoretical population genetics discussing the shapes of genealogies of individuals, and Bayesian statistics evaluating different scenarios and integrating over possible solutions using Markov chain Monte Carlo technology. The data will be genomic sequences which are known to contain technical errors; to successfully differentiate among gene regions that are under selection for particular environments, these errors must be taken into account, but currently are not. Additionally, samples from different geographical locations (for example different patients, different islands, or different habitats) can be grouped in different ways, which requires that the framework be capable of delivering optimality criteria that help to order different scenarios. Progress and the final work will be documented on the websites http://popgen.sc.fsu.edu and http://peterbeerli.com.

该项目将开发一个数学框架，用于研究对当地环境压力有不同反应的基因组序列。该框架将比较在不同地点采集的样本的突变模式，以估计这些样本可能的谱系;这些谱系将用于估计许多群体遗传模型的参数。这些参数将能够告诉我们地理位置之间的潜在选择差异。这种新的数学上严格的框架有望取代当前临时且不充分的汇总统计数据。这一框架的潜在应用包括改善疾病干预措施（例如艾滋病毒患者的个体化反应），以及提高我们对哪些基因区域负责在恶劣环境中长期生存的理解。该框架将以独立计算机软件的形式公开提供，这些软件可以在小型计算机或大型计算集群上运行。这项研究来自多个科学和技术学科（生物学，计算科学和统计学），因此将为指导本科生，研究生和博士后学生提供一个很好的基础，并培养他们对迫切需要更多培训机会的领域的兴趣。这个框架的构建块植根于聚结理论，一个分支的理论群体遗传学讨论的形状的家谱的个人，贝叶斯统计评估不同的情况下，并整合可能的解决方案，使用马尔可夫链蒙特卡罗技术。数据将是已知包含技术错误的基因组序列;为了成功区分特定环境下选择的基因区域，必须考虑这些错误，但目前没有。此外，来自不同地理位置（例如不同患者、不同岛屿或不同栖息地）的样本可以以不同的方式进行分组，这要求框架能够提供有助于排序不同场景的最优标准。进度和最终工作将记录在网站http://popgen.sc.fsu.edu和http://peterbeerli.com上。

项目成果

Fractional coalescent

分级聚结剂

• DOI: 10.1073/pnas.1810239116
• 发表时间: 2019
• 期刊: Proceedings of the National Academy of Sciences
• 作者: Mashayekhi, Somayeh;Beerli, Peter
• 通讯作者: Beerli, Peter