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: 9894383
• 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 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9894383
• 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.

病毒群体的许多特征使它们成为群体遗传学研究的理想候选者，包括a