Scalable Population Genetic and Phylogenetic Inference Using Large Samples of Microbial Data

这个项目将使我们能够更有效地利用大量的DNA和其他基因组数据来研究人类历史、自然选择、病原体进化和其他对科学和人类福祉重要的主题。它涉及的问题类型包括：人类何时迁出非洲？北极熊在上一次全球变暖事件中表现如何？个子高会带来进化优势吗？新冠肺炎大流行目前处于什么状态，什么时候结束？尽管明确地回答这些问题具有挑战性，但进化论以遗传变异的形式提供了关于这些问题的线索。这些线索可以用数学模型来分析当前种群的DNA样本。近年来，现有的遗传数据数量急剧增加，因此需要更快、更准确的分析方法来充分利用这些丰富的新信息来源。该项目将开发这些方法。此外，它还将促进创建新的课程材料，旨在教育学生有关数量遗传学和计算生物学的知识。该项目将开发新的和可扩展的方法，用于系统发育和种群遗传推断，特别侧重于分析病原体遗传数据。尽管这些领域已经相当成熟，但从历史上看，许多用于分析基因数据的方法都是基于人类生物学和数据可用性进行建模假设的。这些假设使研究生物与人类截然不同的物种的遗传学的努力复杂化，尽管这些物种可能对人类健康产生重要影响。该项目通过以下方式解决这些不足：创建能够适应数据中存在的系统发育信号量的新的系统发育推断方法；允许水平基因转移或其他网状事件的更快的基于可能性的系统发育网络推断方法；从大流行数据快速推断流行病学参数的变化方法；以及比现有方法更快且偏差更小的合并隐马尔可夫模型的新应用。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 will enable us to more effectively use large amounts of DNA and other genomic data to study human history, natural selection, pathogen evolution, and other topics that are important to science and human well-being. Examples of the types of questions it addresses include: When did humans migrate out of Africa? How did polar bears fare during the last global warming event? Does being tall confer evolutionary advantages? What is the current status of the COVID-19 pandemic, and when will it end? Although definitively answering these questions is challenging, evolution furnishes clues about them in the form of genetic variation. These clues can be decoded using mathematical models to analyze DNA samples from current populations. The amount of available genetic data has increased dramatically in recent years, and consequently faster and more accurate analytical methods are needed to fully utilize these rich new sources of information. This project will develop those methods. In addition, it will facilitate the creation of new curriculum materials designed to educate students about quantitative genetics and computational biology.This project will develop new and scalable methods for phylogenetic and population genetic inference, with a particular focus on analyzing pathogen genetic data. Although these areas are already quite mature, historically many methods designed to analyze genetic data made modeling assumptions based on human biology and data availability. Such assumptions complicate efforts to study the genetics of species whose biology is very different from humans, even though these species can have important impacts on human health. This project addresses these shortcomings through: the creation of novel methods for phylogenetic inference which can adapt to the amount of phylogenetic signal present in the data; faster likelihood-based phylogenetic network inference methods which allow for horizontal gene transfer or other reticulate events; variational methods for rapidly inferring epidemiological parameters from pandemic data; and new applications of coalescent hidden Markov models which are faster and have less bias than existing methods.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.