Large-scale phylodynamics under non-neutral and non-treelike models of evolution

项目摘要 诸如下一代测序等技术突破最近导致了巨大的 “生物库”以从数十万人那里收集的基因组信息为特色，正在进行 SARS-CoV-2大流行导致了一个更极端的存储库，其中包含1000多万个SARS-CoV-2基因组。 不幸的是，现有的用于推断进化模型的技术在大多数情况下只能分析微小的 这些数据集中包含的信息的一小部分。在我们应该能够使用大量的 数据来回答日益微妙的进化问题，缺乏足够的方法限制了我们的 这为我们提供了发现的机会，并阻碍了我们应对当前大流行的能力。 拟议的研究通过创建新的统计和计算来解决这一问题 使用生物库和大流行规模的数据集研究有针对性的进化假设的方法。 首先，我们将开发用于流行病学推断的新的系统动力学方法，使用数万个 病原体基因组样本。除了可扩展性更强外，这些方法还将在以前的工作基础上进行创新 通过在生物学上更加现实，对数据做出更少的简单化假设。特别是，我们 我们将研究多个菌株共同循环并具有不同适应度的系统，我们将使用这个模型来 提高我们对自然选择在形成大流行中所起作用的理解。我们将进一步 将此方法扩展到集成非遗传信息源，如病例计数数据，这将使 公共卫生研究人员将病例计数划分为不同的变量并估计特定变量的有效性 复制数。其次，我们将开发推断系统发育网络的改进方法，并使用 让他们了解重组在冠状病毒进化中所起的作用以及它所起的作用 在混淆了早期错误地假设SARS-CoV-2进化可以由一个 一棵树。所有这些改进都将作为易于使用的开源软件实施和发布 包裹。 总而言之，这项工作代表了统计遗传学的几个领域的进展，包括系统动力学 建模、遗传流行病学、自然选择的推断和系统发育网络分析，以及将 为实证研究人员提供所需的现代工具，以推动在这些领域的下一代发现 菲尔兹。

Project Summary Technological breakthroughs such as next-generation sequencing have recently led to the creation of immense “BioBanks” featuring genomic information collected from hundreds of thousands of people, and the ongoing pandemic has resulted in an even more extreme repository containing over 10 million SARS-CoV-2 genomes. Unfortunately, existing techniques for inferring evolutionary models can, in most cases, only analyze a tiny fraction of the information contained in these datasets. At a time when we should be able to use vast quantities of data to answer increasingly nuanced evolutionary questions, lack of adequate methods has limited our opportunities for discovery and hampered our ability to respond to the ongoing pandemic. The proposed research addresses this problem through the creation of novel statistical and computational methods designed to study targeted evolutionary hypotheses using BioBank- and pandemic-scale datasets. First, we will develop new phylodynamic methods for epidemiological inference using tens of thousands of sampled pathogen genomes. Apart from being more scalable, these methods will innovate over previous work by being more biologically realistic and making fewer simplifying assumptions about the data. In particular, we will study systems where multiple strains co-circulate and have differential fitness, and we will use this model to improve our understanding of the role that natural selection has played in shaping the pandemic. We will further extend this method to integrate non-genetic sources of information such as case count data, which will enable public health researchers to partition case counts into different variants and estimate variant-specific effective reproduction numbers. Second, we will develop improved methods for inferring phylogenetic networks, and use them to understand the role that recombination has played in the evolution of the coronavirus, as well as its role in confounding earlier studies that incorrectly assumed that SARS-CoV-2 evolution could be represented by a single tree. All of these advances will be implemented and released as easy to use open source software packages. In summary, this work represents advances in several areas of statistical genetics including phylodynamic modeling, genetic epidemiology, inference of natural selection and phylogenetic network analysis, and will provide empirical researchers with modern tools needed to propel the next generation of discoveries in these fields.