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

病毒群体的许多特征使其成为群体遗传学研究的理想候选者，包括 非常高的突变率，高水平的核苷酸多样性，特别大的人口普查规模， 以及频繁的正选择。然而，这些属性也意味着必须特别注意 群体遗传推断例如，高度偏斜的后代分布，频繁和严重的 与感染和区室化相关的种群瓶颈事件以及强选择都 影响遗传变异的分布，但一般不考虑。因此改进的 病毒种群的推断不仅需要理论上的发展， 将这一理论应用到统计推断工具中，能够分析数千个 病毒基因组的计算效率。在此，我提出这些必要的发展（目标 1-2），并提出了一个应用程序，以两个非常深的数据集，我们有独特的访问 通过我们的联盟关系（目标3）。总的来说，这一建议不仅是一个重要的步骤， 在这些极端参数空间中推进我们对群体遗传学的理解，但也将 提供有价值的临床见解，有望改善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.