CAREER: Linking Phylogenetic Inference at Genome-wide and Local Genealogical Scales

职业：将全基因组和局部谱系尺度的系统发育推断联系起来

• 批准号: 2046813
• 负责人: Deren Eaton
• 金额: $ 95.69万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-15 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046813&HistoricalAwards=false
• 关键词: CAREER; Linking; Phylogenetic; Inference; Genome

Phylogenetic inference methods are fundamental to our understanding of the evolutionary relationships among all units of life, from genes and gene copies, to individuals, populations, and species. Because most genomes are composed of a mosaic of recombined segments inherited from different ancestors, genealogical relationships vary across different regions of the genome. This variation has been the primary focus of new methods developed over the last two decades in the field of phylogenetic systematics. However, the growing availability of chromosome-scale genome sequence data presents many new opportunities and challenges for existing methods, which have traditionally focused on genealogical patterns among few statistically independent gene regions, as opposed to many contiguous and non-independent regions. This project involves the development of new methods for linking phylogenetic inference at genome-wide and local genealogical scales. These new methods will bring increased power to disentangle phylogenetic relationships among rapidly radiating clades, thus contributing to our understanding of biodiversity and the ecological mechanisms that generate diversity. This work also supports training opportunities for students, as well as the development of several didactic software tools for teaching evolutionary genomics, which will form the basis for a new online and interactive textbook. Together, these tools will promote increased interactions among the phylogenetics and broader data science communities.This project supports a number of research and educational approaches for linking phylogenetic inference at genome-wide and local genealogical scales. This includes a combination of approaches (1) to improve phylogenetic network estimation from unlinked SNPs, as opposed to error-prone gene trees; (2) to improve local genealogical inference in species-level phylogenies using a novel Bayesian machine-learning approach that incorporates prior information from recombination maps and a parameterized species tree or network; (3) to develop new genomic resources in the highly diverse plant clade Pedicularis to enable the application of genome-wide phylogenetic inference methods to investigate selection and introgression; (4) to expand population-level genomic sampling across dozens of species of Pedicularis in a biodiversity hotspot, including historical samples, to investigate spatial and temporal factors affecting genetic diversity and endemism within and across species; (5) to develop a free on-line textbook based on interactive coding exercises to teach phylogenetic methods through modern data science techniques in Python; and (6) to develop a new course where students will be involved in the process of researching and generating content for this textbook.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

系统发育推理方法是我们理解所有生命单位（从基因和基因拷贝到个体、种群和物种）之间进化关系的基础。因为大多数基因组是由从不同祖先继承的重组片段组成的，所以基因组的不同区域之间的系谱关系各不相同。这种变化一直是在过去的二十年里，在系统发育系统学领域发展的新方法的主要焦点。然而，越来越多的染色体规模的基因组序列数据的可用性提出了许多新的机遇和挑战，现有的方法，传统上集中在几个统计独立的基因区域之间的系谱模式，而不是许多连续和非独立的区域。该项目涉及在全基因组和地方系谱尺度上建立系统发育推断的新方法。这些新方法将为解开快速辐射分支之间的系统发育关系带来更大的力量，从而有助于我们理解生物多样性和产生多样性的生态机制。这项工作还支持为学生提供培训机会，以及开发几种用于教授进化基因组学的教学软件工具，这将成为新的在线互动教科书的基础。总之，这些工具将促进遗传学和更广泛的数据科学社区之间的互动增加。该项目支持一些研究和教育方法，用于在全基因组和局部谱系尺度上连接系统发育推断。这包括以下方法的组合：（1）与易错基因树相反，改善来自未链接SNP的系统发育网络估计;（2）使用新的贝叶斯机器学习方法改善物种水平系统发育中的局部谱系推断，该方法结合了来自重组图和参数化物种树或网络的先验信息;（3）在高度多样性的植物分支马先蒿中开发新的基因组资源，使全基因组系统发育推断方法能够应用于研究选择和渐渗;（4）在生物多样性热点地区的几十种马先蒿属植物中扩大种群水平的基因组采样，包括历史样本，调查影响物种内和物种间遗传多样性和特有性的空间和时间因素;（5）开发基于交互式编码练习的免费在线教科书，通过Python中的现代数据科学技术教授系统发育方法;以及（6）开发一门新课程，学生将参与研究和生成本教科书的内容。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。