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历史的产物，不同生物体之间关系的证据可以在它们测序的基因组之间的相似性和差异性中找到。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简单。血统之间的杂交和其他形式的基因转移构成了一个特殊的挑战，因为进化树对生物关系的简单描述已经不够了，一个网状网络更准确地描述了它们的历史。在这种情况下，目前用于分析基因组的计算方法效率低下，只能处理小数据集，并且即使在已经收集的数据中也无法检查潜在信息的丰富性。在这个项目中，新的计算和统计方法来推断网络状的关系将开发和实施的实用和有效的软件工具。这些新方法基于进化基因组的详细数学模型，并为科学家提供方便，将得出合理的统计结论。它们将适用于广泛的生物学研究，如了解生命网络，农业上重要的作物，保护生物学和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数学和生物学的跨学科培训，并通过暑期研究学院向高中生推广。虽然生物体之间的网络关系导致个体遗传位点的进化历史由不同的树表示，但这种网状信号与不完整的谱系排序相混淆，一种群体遗传过程，即使物种水平的关系是树状的，也会产生不同的基因树。这些过程将联合调查，通过使用的数学网络多物种聚结模型。由于它与标准似然和贝叶斯统计方法一起用于数据分析，造成了过多的计算需求，因此该项目将开发更快，更可行的方法。一个困难来自潜在网络空间的大小，它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，该项目开发了规避网络空间缓慢探索的方法，同时仍然获得了理论上一致的网络估计。特别是，基于四元组和有根三元组的网络距离将用于利用小网络单元的推断，通过对分裂图的适当解释来产生大网络估计。 子项目包括从连接的基因组序列开发基于聚结的网络推理，从而避免对单个基因树的推理;将理论和实践扩展到1级网络之外;以及更好地解决模型产生的四重或有根三重统计量之间的协方差。该项目由DMS的数学生物学计划和生命科学风险基金联合资助，该奖项反映了NSF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。

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.