Scalable Model-Based Reconstruction of Network Evolution

来自许多种群和许多物种的个体的全基因组的可获得性提供了关于过去进化史的丰富信息。通过比较不同个体的基因，人们可以检测哪些个体关系最密切，并重建种群分裂和物种形成的历史，如系统发育树中所示。然而，挑战的出现是因为每个物种内的个体之间的谱系差异，无论是当前的还是祖先的。这个项目的重点是检测物种的融合：当物种杂交时，或者当一个物种的个体迁移到另一个物种时，或者当菌株重组时。然后，用网络来描述一组物种的历史是最好的，其中一棵主干树代表物种形成，额外的分支描述基因从一个种群流向另一个种群。目前估计系统发育网络的方法不能分析超过几十个物种的数据集。基于新的理论基础，PI将开发统计方法和软件，这些方法和软件将扩展到数百个物种和数千个遗传位点。这些新方法对促进细菌和病毒进化方面的知识也特别有价值，因为在细菌和病毒进化中，重组是很普遍的。该项目将支持研究生和本科生，通过参与对数据科学网络感兴趣的更大的校园研究人员社区，他们将获得超越传统学科界限的培训。通过对系统发育网络上的联合过程进行数学分析，PI将确定这些网络的最大子结构，这些结构可以从各种数据类型(如基因树)或使用每个种群的一个或多个个体的遗传距离来确定。还将开发理论来确定准确重建系统发育网络所需的数据量。这些理论发现将指导开发新的统计方法和软件，以根据数据估计系统发育网络，重点是使用遗传距离来设计可以处理数百个物种的快速算法。这些快速重建方法将允许部署交叉验证方法，以从数据中了解网络的适当复杂性，即适当数量的基因流事件。这项拟议的研究将促进对许多怀疑基因流动和重组的群体的进化史的了解，例如哺乳动物或陆地植物的早期辐射，以及疱疹病毒家族的进化史。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The availability of full genomes across individuals from many populations and many species offers rich information about past evolutionary history. By comparing the genes of different individuals, one can detect which individuals are most closely related and reconstruct the history of population splits and speciation, as visualized in a phylogenetic tree. Challenges arise however because of genealogical differences between individuals within each species, current or ancestral. This project focuses on the detection of species convergences: when species hybridize, or when individuals from one species migrate to another, or when strains recombine. The history of a group of species is then best described by a network, where a backbone tree represents speciation and extra branches describe gene flow from one population into another. Current methods to estimate phylogenetic networks cannot analyze data sets with more than a few dozen species. Based on novel theoretical foundations, the PIs will develop statistical methods and software that will scale to hundreds of species and thousands of genetic loci. These new methods will also be particularly valuable to advance knowledge in bacterial and virus evolution, where recombination is prevalent. The project will support graduate and undergraduate students, who will gain training beyond traditional disciplinary boundaries with involvement in the larger community of campus researchers interested in networks in data science.Through the mathematical analysis of coalescent processes on phylogenetic networks, the PIs will determine the maximal substructures of these networks that can be theoretically identified from various data types, such as from gene trees, or genetic distances between pairs of individuals, using one or more individuals per populations. Theory will also be developed to determine the amount of data necessary to reconstruct the phylogenetic network with accuracy. These theoretical findings will guide the development of new statistical methods and software to estimate phylogenetic networks from data, with a focus on the use of genetic distances to devise fast algorithms that can handle hundreds of species. These fast reconstruction methods will allow the deployment of a cross-validation method to learn from data the appropriate complexity of the network, that is, the appropriate number of gene flow events. The proposed research will advance knowledge of the evolutionary history in many groups where gene flow and recombination is suspected, such as the early radiation of mammals or land plants, and the evolutionary history of the herpes virus family.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.