Identifying complex modes of adaptation from population-genomic data

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， 自然选择留下的独特足迹然而，一些非适应性的力量可以掩盖这些信号， 这使得开发能够解释影响遗传学的多种因素的统计方法变得非常重要。 变化量我在这一领域的研究集中在统计方法的设计和应用， 鉴定经历平衡选择的区域，其维持群体中等位基因的频率，和 积极选择，增加了群体中有益等位基因的频率。具体来说，我们贡献了 在这一领域取得的一些进展，包括开发第一个基于模型的方法来检测平衡 选择，第一个确定积极选择的可能性方法，同时考虑混杂因素， 负选择的影响，第一似然法检测自适应基因渗入内的单一 人口，和计算效率的统计量，为确定信号的祖先积极选择。 我们将这些方法和其他方法应用于人类基因组数据，发现了新的候选人， 高原适应性在美洲原住民和适应欧洲传播的病原体，以及 用于通过分离失真来平衡选择。在接下来的五年里，我建议开发新的统计数据， 利用不同进化力量如何塑造空间分布的信息的方法 适应性位点周围的遗传多样性，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，以回答有关 适应在古代和近代进化史中的作用。特别是，我们未来的研究将细分 几个相互关联的目标：设计统计技术，以确定积极的选择混合 群体，并使用这些技术来确定基因组区域进行积极的选择混合 人口;发展方法来识别经历复杂的古代平衡的地区 选择，并将这些方法应用于多个灵长类物种，以调查古代的流行情况。 平衡此谱系中的选择;构建统计数据，以揭示集成数据的自适应足迹 从古代和现代的样本，并使用这些统计数据来了解过去的适应历史，在欧洲 人类群体;建立新的功能数据分析程序，对选择模式进行分类 在整个基因组中发挥作用，并使用这些程序来更好地了解硬扫描的相对作用， 软扫描，适应性渐渗，以及人类进化史上的近代和古代平衡选择。 这些研究的优势是双重的，因为它们都将产生强大的新方法来识别 从基因组数据的不同模式的适应的签名，以及阐明进化的力量， 适应性表型的获得，如与疾病抗性和病原体防御有关的表型。

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.