Collaborative Research: Estimation of Large Species/Population Trees Using Tree Space

根据DNA序列中所含的信息来估计一批生物的进化历史是进化生物学中一个至关重要的问题。基因组测序项目产生的丰富的DNA序列数据导致了重要的计算挑战，估计这些系统发育关系。这些挑战之一是根据从整个基因组中取样的几个不同基因的DNA序列信息来估计一组物种的进化历史。这项研究的重点是发展计算效率高的方法，用于估计进化历史时，所考虑的物种的数量是非常大的（即，数以千计）。这是通过一次考虑三个物种的集合，并使用三个群体的估计进化历史的属性来推断整体进化历史来实现的。该方法的属性和性能将在理论上以及模拟和经验数据集进行评估。这项工作有许多实际应用，如人类种群之间的进化关系的研究，虽然可用于推断系统发育物种树的基因组数据量在过去10年中迅速增加，已经开发出一些方法来有效地估计物种树的数据集组成的数百或数千个物种。提出了一种快速逼近最大似然估计（MLE）的方法，该方法保留了所需的统计特性，如一致性和渐近效率。初步工作的结果表明，这种方法将比现有的可能性和贝叶斯方法快得多，同时也非常准确。该方法可以应用于一系列数据类型，包括在布朗运动模型下沿着遗传学产生的等位基因频率数据和从结合模型产生的单核苷酸多态性（SNP）数据。将开发一个软件包来实施这一方法。该项目还将支持一名博士生，他将为该方法的制定和实施做出贡献。

The estimation of the evolutionary history of a collection of organisms based on the information contained in their DNA sequences is a problem of fundamental importance in evolutionary biology. The abundance of DNA sequence data arising from genome sequencing projects has led to important computational challenges in the estimation of these phylogenetic relationships. Among these challenges is the estimation of the evolutionary history for a group of species based on DNA sequence information from several distinct genes sampled throughout the genome. This research is focused on the development of computationally efficient methods for estimating the evolutionary history when the number of species under consideration is very large (i.e., hundreds to thousands). This is accomplished by considering collections of three species at a time, and using properties of the estimated evolutionary history for groups of three to infer the overall evolutionary history. Properties and performance of the method will be evaluated theoretically as well as with both simulated and empirical data sets. This work has numerous practical applications, such as the study of the evolutionary relationships among human populations.Though the amount of genomic data available for inferring phylogenetic species trees has increased rapidly within the last 10 years, few methods have been developed to efficiently estimate species trees for data sets consisting of hundreds or thousands of species. A fast approximation to the maximum likelihood estimate (MLE) that retains desirable statistical properties, such as consistency and asymptotic efficiency, is proposed. Results from preliminary work suggest that this approach will be significantly faster than existing likelihood and Bayesian approaches, while also being highly accurate. The method can be applied to a range of data types, including allele frequency data arising under a Brownian motion model along the phylogeny and single nucleotide polymorphism (SNP) data arising from the coalescent model. A software package will be developed to implement the methodology. The project will also support one PhD student, who will contribute to the development and implementation of the methodology.