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

项目摘要 技术突破，如下一代测序，最近导致了巨大的 “生物银行”的特色是从数十万人中收集的基因组信息， 大流行导致了一个包含超过1000万个SARS-CoV-2基因组的更极端的储存库。 不幸的是，在大多数情况下，现有的推断进化模型的技术只能分析一个微小的 这些数据集中包含的信息的一部分。在我们应该能够使用大量 数据来回答越来越微妙的进化问题，缺乏足够的方法限制了我们的研究。 这给我们提供了发现的机会，并阻碍了我们应对当前大流行病的能力。 拟议的研究通过创建新的统计和计算模型来解决这个问题。 使用生物银行和流行病规模的数据集研究目标进化假说的方法。 首先，我们将开发新的流行病学推断的动态方法， 取样的病原体基因组除了更具可扩展性外，这些方法将在以前的工作中进行创新 通过更符合生物学现实，并对数据做出更少的简化假设。我们尤其 将研究多个菌株共同循环并具有不同适应性的系统，我们将使用这个模型来 提高我们对自然选择在形成这一流行病方面所起作用的理解。我们将进一步 将这种方法扩展到整合非遗传信息源，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异，并估计变异特异性有效 繁殖数。其次，我们将开发改进的方法来推断系统发育网络，并使用 他们了解重组在冠状病毒进化中所起的作用，以及它的作用 早期的研究错误地假设SARS-CoV-2的进化可以用一个 一棵树所有这些进步都将作为易于使用的开源软件来实现和发布 包装件. 总之，这项工作代表了统计遗传学几个领域的进展，包括遗传动力学， 建模，遗传流行病学，自然选择的推理和系统发育网络分析，并将 为实证研究人员提供推动下一代发现所需的现代工具， 领域的

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.