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

开发新的统计和合并方法以改进病毒进化研究

• 批准号: 10001558
• 负责人: Jeffrey D Jensen
• 金额: $ 18.36万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10001558
• 关键词: Affect; Biology; Caring; Censuses; Clinical; Cytomegalovirus; Data Analyses; Data Set; Demography; Development; Event; Evolution; Future; Genealogy; Genetic Population Study; Genetic Variation; Genome; Human; Infection; Influenza A virus; Methodology; Modeling; Mutation; Nucleotides; Patients; Population; Population Genetics; Population Sizes; Population Theory; Process; Recording of previous events; Reproductive Biology; Shapes; System; Time; Variant; Viral; Viral Genome; Virus; Virus Diseases; base; improved; insight; novel; offspring; statistics; theories; tool; treatment strategy

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.

病毒种群的许多特征使它们成为种群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、异常大的人口普查人口规模， 以及频繁的正向选择。然而，这些属性也意味着必须特别小心 群体遗传推断。例如，高度倾斜的后代分布、频繁且严重的 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，但通常不予考虑。由此，改进了 病毒种群的推断不仅需要理论的发展，还需要 将这一发展的理论应用到能够分析数千个数据的统计推断工具中 以计算有效的方式分析病毒基因组。在这里，我提出这些必要的发展（目标 1-2），并展示了我们拥有独特访问权限的两个特别深的数据集的应用程序 通过我们的联盟关系（目标 3）。总的来说，该提案不仅代表了 推进我们对这些极端参数空间中群体遗传学的理解，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。

项目成果

Inferring Demography and Selection in Organisms Characterized by Skewed Offspring Distributions.

推断以后代分布不均为特征的生物体的人口统计学和选择。

• DOI: 10.1534/genetics.118.301684
• 发表时间: 2019
• 期刊: Genetics
• 影响因子: 3.3
• 作者: Sackman,AndrewM;Harris,RebeccaB;Jensen,JeffreyD
• 通讯作者: Jensen,JeffreyD