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

系统发育推断方法是我们理解所有生命单位之间进化关系的基础，从基因和基因拷贝到个体、种群和物种。由于大多数基因组是由从不同祖先遗传而来的重组片段拼接而成的，因此基因组不同区域的家谱关系各不相同。这种变异一直是过去二十年来系统发育系统学领域发展的新方法的主要焦点。然而，越来越多的染色体尺度基因组序列数据的可用性为现有方法带来了许多新的机遇和挑战，这些方法传统上专注于少数统计上独立的基因区域的家谱模式，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本地谱系尺度上连接系统发育推断的新方法的发展。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，从而有助于我们对生物多样性和产生多样性的生态机制的理解。这项工作还为学生提供了培训机会，并开发了一些用于教授进化基因组学的教学软件工具，这些工具将构成新的在线交互式教科书的基础。总之，这些工具将促进系统发育和更广泛的数据科学社区之间的互动。该项目支持了许多研究和教育方法，以连接全基因组和本地谱系尺度的系统发育推断。这包括以下几种方法的结合：(1)改进非连锁snp的系统发育网络估计，而不是容易出错的基因树；(2)利用一种新的贝叶斯机器学习方法，结合来自重组图和参数化物种树或网络的先验信息，改进物种水平系统发育的局部谱系推断；(3)在高度多样化的马先蒿植物分支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用于研究马先蒿的选择和遗传渗入；(4)在生物多样性热点地区扩大马先蒿数十种种群水平的基因组采样，包括历史样本，探讨影响种内和种间遗传多样性和特有性的时空因素；(5)开发基于交互式编码练习的免费在线教科书，通过现代数据科学技术在Python中教授系统发育方法；(6)开发一门新课程，让学生参与研究和编写教材内容的过程。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.