Principled phylogenomic analysis without gene tree estimation

该项目旨在改进从基因组数据集中估计树种的方法。这一估计具有挑战性，因为不同的基因组区域在使它们的进化史(即基因树)不一致的过程中进化。现代系统发育分析中普遍存在的基因树估计错误加剧了这一问题。为了应对这一挑战，该项目的主要目标是设计创新的数学、统计和计算技术来分析系统基因组数据集，而不依赖于基因树估计。这种方法在存在混淆过程的情况下将产生更可靠的树种估计。物种树提供了一个进化和比较的背景，在这个背景下可以解决许多生物学问题。它们在理解基因进化、估计分化日期、检测适应、研究性状进化等方面发挥着至关重要的作用。所开发的方法将提高基于物种树的生物发现的精度，促进利用系统发育的研究。该项目包括对研究生的跨学科研究培训，以及通过地方倡议招募的本科生的参与。将为现有的研究生课程编写基于拟议研究的新课程材料，并通过PI的网站提供。该项目将利用与NSF资助的跨学科研究所的联系。众所周知，由于不完全谱系排序、基因复制和丢失以及横向基因转移等过程，基因组的不同区域可以在不同的基因树下进化，这使得物种树的估计变得复杂。许多方法首先估计基因树，然后结合这些信息来估计物种树，众所周知，在假设真实的基因树是已知的情况下，这些方法有很好的理论保证。这一假设在实践中并未得到满足。从理论上解释基因树估计误差已被证明是具有挑战性的，而且几乎没有可用的结果。这项拟议的研究将建立在PI之前对在这种系统基因组学背景下产生的随机过程的严格研究的基础上，为分析多基因座、多地点数据集和估计没有基因树的物种树奠定急需的理论基础，包括开发新的估计器，推导不可能结果和匹配的有限样本界，以及调查基因座内重组的影响。该项目还将有助于开发统计上严格的、可伸缩的算法。这项跨学科的研究将涉及应用概率、统计理论、图形算法和进化生物学的紧密结合。这项建议由数学科学部的数学生物学和统计项目联合资助。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。

This project aims to improve the estimation of species trees from genomic datasets. This estimation is challenging because different genomic regions evolve under processes that make their evolutionary histories (i.e., gene trees) discordant. This issue is exacerbated by widespread gene tree estimation errors in modern phylogenomic analyses. To address this challenge, this project's primary objective is to devise innovative mathematical, statistical, and computational techniques to analyze phylogenomic datasets without relying on gene tree estimation. This approach will produce more reliable species tree estimates in the presence of confounding processes. Species trees provide an evolutionary and comparative context in which many biological questions can be addressed. They play a vital role in understanding gene evolution, estimating divergence dates, detecting adaptation, studying trait evolution, etc. The developed methods will enhance the precision of biological discoveries based on species trees, advancing research that utilizes phylogenies. The project includes interdisciplinary research training for graduate students as well as the involvement of undergraduate students recruited through local initiatives. New course materials based on the proposed research will be developed for existing graduate courses and be made available through the PI’s website. The project will leverage connections to NSF-funded interdisciplinary institutes.It is well established that different regions of a genome can evolve under different gene trees, due to processes such as incomplete lineage sorting, gene duplication and loss, and lateral gene transfer, complicating the estimation of species trees. Many methods that first estimate gene trees and then combine this information to estimate a species tree are known to have good theoretical guarantees, under the assumption that the true gene trees are known. That assumption is not satisfied in practice. Accounting theoretically for gene tree estimation error has proved challenging and few results are available. Building on prior work by the PI on the rigorous study of stochastic processes arising in this phylogenomic context, the proposed research will establish much-needed theoretical foundations for the analysis of multi-locus, multi-site datasets and the estimation of species trees without gene trees, including the development of novel estimators, the derivation of impossibility results and matching finite sample bounds, and the investigation of the effect of intra-locus recombination. This project will also enable the development of statistically rigorous, scalable algorithms. This interdisciplinary research will involve a close integration of applied probability, statistical theory, graph algorithms, and evolutionary biology.This proposal is jointly funded by the Mathematical Biology and Statistics Programs at the Division of Mathematical Sciences.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.