Identifying complex modes of adaptation from population-genomic data

从群体基因组数据中识别复杂的适应模式

• 批准号: 9975871
• 负责人: Michael DeGiorgio
• 金额: $ 34.18万
• 依托单位: FLORIDA ATLANTIC UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9975871
• 关键词: Accounting; Affect; Alleles; Altitude; Area; Complex; DNA sequencing; Data; Data Analyses; Disease; Disease Resistance; Environment; Equilibrium; Ethiopian; European; Frequencies; Gene Frequency; Genetic Variation; Genome; Genomic Segment; Genomics; Goals; Human; Human Genome; Left; Methods; Modeling; Modernization; Native Americans; Natural Selections; Phenotype; Population; Prevalence; Primates; Procedures; Recording of previous events; Research; Research Personnel; Role; Sampling; Shapes; Signal Transduction; Site; Spatial Distribution; Statistical Methods; Techniques; base; cost; design; fighting; genome sequencing; genomic data; novel; novel strategies; pathogen; segregation distortion; statistics; whole genome

Project Summary Low-cost DNA sequencing has provided researchers with abundant genomic data in which to search for the unique footprints left by natural selection. However, a number of non-adaptive forces can obscure these signals, making it important to develop statistical methods that can account for multiple factors that influence genetic variation. My research in this area has focused on the design and application of statistical approaches for identifying regions undergoing balancing selection, which maintains the frequency of alleles in a population, and positive selection, which increases the frequency of beneficial alleles in a population. Specifically, we contributed to a number of advances in this area, including developing the first model-based methods for detecting balancing selection, the first likelihood approach for identifying positive selection while accounting for the confounding effects of negative selection, the first likelihood method for detecting adaptive introgression within a single population, and a computationally-efficient statistic tailored for identifying signals of ancestral positive selection. Our applications of these and other methods to human genomic data have uncovered novel candidates for high- altitude adaptation in Ethiopians and adaptation to European-borne pathogens in Native Americans, as well as for balancing selection via segregation distortion. During the next five years, I propose to develop novel statistical methods that leverage information about how different evolutionary forces shape the spatial distribution of genetic diversity around adaptive sites to identify genomic targets affected by complex modes of natural selection. These methods will be applied to whole-genome sequencing data from primates to answer questions about the role of adaptation in ancient and recent evolutionary history. In particular, our future research will be subdivided into several interrelated goals: designing statistical techniques for identifying positive selection in admixed populations, and using these techniques to identify genomic regions undergoing positive selection in admixed human populations; developing methods for identifying regions that underwent complex ancient balancing selection, and applying these methods to multiple primate species to investigate the prevalence of ancient balancing selection in this lineage; constructing statistics for uncovering adaptive footprints that integrate data from ancient and modern samples, and using these statistics to understand past adaptive history in European human populations; and building novel functional data analysis procedures for classifying modes of selection acting across the genome, and using these procedures to better understand the relative roles of hard sweeps, soft sweeps, adaptive introgression, and recent and ancient balancing selection in human evolutionary history. Advantages of these studies are two-fold, in that they will both yield powerful new approaches for identifying signatures of diverse modes of adaptation from genomic data, as well as elucidate evolutionary forces underlying the acquisition of adaptive phenotypes, such as those involved in disease resistance and pathogen defense.

项目摘要 低成本的DNA测序为研究人员提供了丰富的基因组数据，可以在其中搜索 自然选择留下了独特的足迹。然而，许多非自适应的力量可能会模糊这些信号， 因此，重要的是开发能够解释影响遗传的多种因素的统计方法 变种。我在这方面的研究主要集中在统计方法的设计和应用上 识别正在进行平衡选择的区域，这保持了种群中等位基因的频率，以及 正向选择，增加群体中有益等位基因的频率。具体地说，我们为 在这一领域取得了许多进展，包括开发了第一个基于模型的检测平衡的方法 选择--识别正向选择并解释混淆的第一种似然方法 负选择的效果--检测单株自适应渗入的第一似然方法 以及为识别祖先正向选择信号而量身定做的计算效率统计。 我们将这些方法和其他方法应用到人类基因组数据中，发现了新的高度依赖的候选方法。 埃塞俄比亚人的海拔适应和美洲土著人对欧洲传播的病原体的适应 用于通过分离扭曲来平衡选择。在接下来的五年里，我建议开发新的统计方法 利用有关不同进化力量如何塑造人类空间分布的信息的方法 适应部位周围的遗传多样性，以确定受复杂自然选择模式影响的基因组目标。 这些方法将被应用于灵长类动物的全基因组测序数据，以回答关于 适应在古今进化史中的作用。特别是，我们未来的研究将被细分 到几个相互关联的目标：设计统计技术，以识别混合的积极选择 群体，并使用这些技术来鉴定正在进行正选择的混合基因组区域 人类人口；开发方法来识别经历了复杂的古代平衡的地区 选择，并将这些方法应用于多种灵长类物种，以调查古代 平衡此谱系中的选择；构建统计数据，以揭示整合数据的适应性足迹 从古代和现代的样本中，并使用这些统计数据来了解欧洲过去的适应历史 人类人口；建立新的功能数据分析程序，用于对选择模式进行分类 在整个基因组中发挥作用，并使用这些程序更好地理解硬扫描的相对作用， 人类进化史上的软扫描、自适应渗透和现代与古代的平衡选择。 这些研究的优点是双重的，因为它们都将产生识别 来自基因组数据的不同适应模式的特征，以及阐明潜在的进化力量 获得适应性表型，如与抗病和病原防御有关的表型。