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

该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，病原体进化以及其他对科学和人类福祉至关重要的主题。它所解决的问题类型的例子包括：人类何时从非洲迁移出来？北极熊在上一次全球变暖事件中表现如何？高个子会带来进化优势吗？COVID-19大流行的现状如何，何时结束？虽然明确回答这些问题具有挑战性，但进化以遗传变异的形式提供了有关它们的线索。这些线索可以使用数学模型来解码，以分析当前人群的DNA样本。近年来，可用的遗传数据量急剧增加，因此需要更快和更准确的分析方法来充分利用这些丰富的新信息来源。本项目将发展这些方法。此外，该项目还将促进创建新的课程材料，旨在教育学生了解数量遗传学和计算生物学。该项目将开发用于系统发育和群体遗传推断的新的可扩展方法，特别侧重于分析病原体遗传数据。虽然这些领域已经相当成熟，但历史上许多设计用于分析遗传数据的方法基于人类生物学和数据可用性进行建模假设。这种假设使研究生物学与人类非常不同的物种的遗传学的努力复杂化，即使这些物种对人类健康有重要影响。该项目通过以下方式解决这些缺点：创建新的系统发育推断方法，该方法可以适应数据中存在的系统发育信号的数量;更快的基于似然性的系统发育网络推断方法，该方法允许水平基因转移或其他网状事件;从大流行数据快速推断流行病学参数的变分方法;和新的应用结合隐马尔可夫模型，这是更快，有更少的偏见比现有的方法。这一奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

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.