Developing novel statistics and coalescent approaches for the improved study of virus evolution

病毒群体的许多特征使它们成为群体遗传学研究的理想对象，包括 非常高的突变率，高水平的核苷酸多样性，特别大的人口普查人口规模， 和频繁的正向选择。然而，这些属性也意味着必须特别注意 群体遗传推断。例如，高度偏斜的子代分布，频繁和严重 与感染和区隔相关的种群瓶颈事件，以及强选择 影响遗传变异的分布，但一般不考虑。因此，改进了 对病毒种群的推断必然不仅需要理论发展，而且还需要 将这一发展的理论应用于统计推理工具，能够分析数千个 病毒基因组以一种计算高效的方式。在这里，我提出了以下必要的发展(目标 1-2)，并提供对我们具有唯一访问权限的两个异常深入的数据集的应用程序 通过我们的财团关系(目标3)。总而言之，这项提议不仅代表着 在这些极端参数空间中推动我们对种群遗传学的理解，但也将 提供有价值的临床见解，有望改进未来的患者治疗策略。

Many features of virus populations make them ideal candidates for population genetic study, including a very high rate of mutation, high levels of nucleotide diversity, exceptionally large census population sizes, and frequent positive selection. However, these attributes also mean that special care must be taken in population genetic inference. For example, highly skewed progeny distributions, frequent and severe population bottleneck events associated with infection and compartmentalization, and strong selection all affect the distribution of genetic variation but are generally not taken into account. Thus, improved inference of viral populations will necessarily require not only theoretical development, but also the implementation of this developed theory into statistical inference tools capable of analyzing thousands of viral genomes in a computationally efficient manner. Here, I propose these necessary developments (Aims 1-2), as well as present an application to two exceptionally deep datasets to which we have unique access via our consortium affiliations (Aim 3). In total, this proposal represents not only a significant step in forwarding our understanding of population genetics in these extreme parameter spaces, but will also provide valuable clinical insights that are expected to improve future patient treatment strategies.