Principled phylogenomic analysis without gene tree estimation

本项目旨在改进基因组数据集对物种树的估计。这种估计是具有挑战性的，因为不同的基因组区域在使其进化历史（即基因树）不一致的过程下进化。现代系统基因组学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。在存在混杂过程的情况下，这种方法将产生更可靠的物种树估计。物种树提供了一个进化和比较的背景下，许多生物学问题可以解决。它们在理解基因进化、估计分化时间、检测适应性、研究性状进化等方面发挥着重要作用。开发的方法将提高基于物种树的生物学发现的精度，推进利用系统发生学的研究。该项目包括对研究生的跨学科研究培训，以及通过当地倡议招募的本科生的参与。根据拟议的研究，将为现有的研究生课程开发新的课程材料，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。由于谱系分类不完整、基因复制和丢失以及基因横向转移等过程，使物种树的估计复杂化，基因组的不同区域可以在不同的基因树下进化。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，在假设真正的基因树已知的情况下，已知有很好的理论保证。这种假设在实践中是不成立的。从理论上解释基因树估计误差具有挑战性，而且很少有结果可用。在PI之前对系统基因组背景下产生的随机过程进行严格研究的基础上，提出的研究将为多位点、多位点数据集的分析和无基因树的物种树估计建立急需的理论基础，包括开发新的估计器，推导不可能结果和匹配有限样本边界，以及研究基因座内重组的影响。该项目还将开发统计严谨、可扩展的算法。这项跨学科的研究将涉及应用概率论、统计理论、图算法和进化生物学的紧密结合。本研究由数学科学部的数学生物学和统计学项目共同资助。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。

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.