Inferring Species Relationships under the Network Multispecies Coalescent Model: Theory and Practical Methods

由于生物体的基因组是其进化史的产物，不同生物体之间的关系可以从它们被测序的基因组之间的相似性和差异性中找到证据。然而，许多生物过程以复杂的方式塑造了基因组，因此从基因组数据中提取关系远非一帆风顺。谱系之间的杂交和其他形式的基因转移构成了一个特别的挑战，因为进化树对有机体关系的简单描述是不够的，而网络状的网络更准确地描述了它们的历史。目前在这种情况下分析基因组的计算方法效率低下，只能处理较小的数据集，即使在已经收集的数据中也没有检查潜在信息的丰富。在这个项目中，将开发新的计算和统计方法来推断类似网络的关系，并在实用和高效的软件工具中实施。基于进化基因组的详细数学模型，并实施便于科学家使用，这些新方法将产生合理的统计结论。它们将适用于广泛的生物学研究，如了解生命网络、农业重要作物、保护生物学和与生物医学相关的病原体。进一步的项目工作包括对研究生进行跨学科的数学和生物培训，以及通过夏季研究学院向高中生推广。尽管生物体之间的网络关系导致了由不同树代表的个体基因座的进化史，但这种网状结构的信号与不完全谱系排序的信号相混淆，不完全谱系排序是一种种群遗传过程，即使物种级别的关系是树状的，也会产生不同的基因树。这些过程将通过使用数学网络多物种合并模型进行联合研究。由于它与标准似然和贝叶斯统计方法一起用于数据分析带来了过多的计算需求，该项目将开发更快、更可行的方法。一个困难来自潜在网络空间的大小，这比已经很大的树木空间要大得多。然而，通过利用新的基于距离的方法，该项目开发了绕过网络空间缓慢探索的方法，同时仍然获得了统计上一致的网络估计。特别是，将使用基于四元组和有根三元组的网络距离来利用小网络单元的推断，通过适当地解释分裂图来产生大的网络估计。子项目包括从串联的基因组序列中开发基于合并的网络推理，从而避免对单个基因树的推断；将理论和实践扩展到级别1网络之外；以及更好地解决模型产生的四重或有根三元组统计之间的协方差。该项目由DMS的数学生物学计划和生命科学风险基金以及已建立的刺激竞争研究计划(EPSCoR)共同资助。该奖项反映了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。

Because the genome of an organism is a product of its evolutionary history, evidence of the relationships between different organisms can be found in the similarities and differences between their sequenced genomes. However, many biological processes have shaped the genomes in complicated ways, so extracting relationships from genomic data is far from straightforward. Hybridization and other forms of gene transfer between lineages pose a particular challenge, as a simple depiction of organism relationships by an evolutionary tree becomes inadequate, with a web-like network more accurately describing their history. Current computational methods for analyzing genomes in this setting are inefficient, are only able to process small datasets, and leave unexamined the wealth of potential information even in already collected data. In this project, new computational and statistical methods to infer network-like relationships will be developed and implemented in practical and efficient software tools. Based on detailed mathematical models of evolving genomes, and implemented for easy use by scientists, these new methodologies will yield sound statistical conclusions. They will be applicable to a broad range of biological studies, such as for understanding the network of life, agriculturally important crops, conservation biology, and biomedically relevant pathogens. Further project work includes interdisciplinary training of graduate students in mathematics and biology and outreach to high school students through a summer research academy.While network relationships between organisms lead to the evolutionary history of individual genetic loci being represented by different trees, this signal of reticulation is confounded with that of incomplete lineage sorting, a population genetic process that produces differing gene trees even when the species-level relationships are tree-like. These processes will be investigated jointly, through use of the mathematical network multispecies coalescent model. Since its use with standard likelihood and Bayesian statistical approaches to data analysis pose excessive computational demands, this project will develop faster, more feasible approaches. One difficulty arises from the size of the space of potential networks, which is vastly larger than the already large space of trees. By utilizing new distance-based approaches, however, this project develops methods circumventing slow explorations of network space, while still obtaining statistically-consistent network estimates. In particular, quartet and rooted-triple based network distances will be used to leverage the inference of small network units to yield large network estimates through appropriate interpretation of a splits graph. Subprojects include development of coalescent-based network inference from concatenated genomic sequences, thereby avoiding inference of individual gene trees; extension of theory and practice beyond the class of level-1 networks; and better addressing covariances between quartet or rooted-triple statistics arising from the model.This project is jointly funded by the Mathematical Biology program and Life Science Venture funds in DMS, and the Established Program to Stimulate Competitive Research (EPSCoR).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.