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

根据一组生物的DNA序列中包含的信息来估计其进化历史是进化生物学中一个基本的重要问题。基因组测序项目产生的丰富的DNA序列数据导致了在估计这些系统发育关系方面的重要计算挑战。这些挑战之一是根据从整个基因组中采样的几个不同基因的DNA序列信息来估计一组物种的进化史。这项研究的重点是开发计算高效的方法来估计进化历史，当考虑的物种数量非常大时(即数百到数千)。这是通过一次考虑三个物种的集合，并使用三个一组的估计进化历史的性质来推断整体进化历史来实现的。该方法的性质和性能将在理论上以及使用模拟和经验数据集进行评估。这项工作有许多实际应用，例如研究人类种群之间的进化关系。尽管在过去10年中可用于推断系统发育物种树的基因组数据量迅速增加，但很少有人开发出有效地估计由数百或数千个物种组成的数据集的物种树的方法。提出了一种快速逼近最大似然估计(MLE)的方法，该方法保持了一致性和渐近效率等理想的统计特性。初步工作的结果表明，该方法将显著快于现有的似然和贝叶斯方法，同时也具有很高的精度。该方法可以应用于一系列数据类型，包括在沿着系统发育的布朗运动模型下产生的等位基因频率数据和由聚合体模型产生的单核苷酸多态(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，他将为该方法的发展和实施作出贡献。

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.