Population Genetics Methods for Understanding Complex Trait Evolution

摘要 了解复杂表型的遗传基础是医学和医学中的一个关键问题 进化遗传学。随着丰富的基因组和表型数据通过生物库水平加速涌入 资源和揭示更大规模的遗传信息，现代种群遗传方法将 研究进化过程对复杂性状分布的影响至关重要。 我在这方面的研究主要集中在描述近代人口历史对 有害变异在个体中的分布及其统计方法的设计和应用 群体正向选择的推论。具体地说，我的工作表明，某些人群 过程在基因组中产生了有害纯合子的集中，而 集中度取决于最近的人口历史。这项工作揭示了一个重要的机制，即 种群历史可以影响复杂性状的基因组学。我还在方法论方面做出了贡献 包括设计小说在内的正选择基因组区域识别的研究进展 单倍型汇总统计量，解释基因组中空间自相关性的新似然统计量， 以及执行这项工作的高效软件，这项工作已被引用数百次。我用了这些 和其他方法处理人类和非人类基因组数据，揭示适应 不同人群病原体暴露与高原适应的多基因特性 恒河猴。 在接下来的五年和以后，我的研究将集中在识别进化力量如何 塑造现代人的表型景观和发展我们对人类知识的利用 历史来学习复杂性状的遗传基础。为此，我将开发新的方法来推断 在基因组中的自然选择，我将开发理论模型，将进化史与 复杂性状通过其对非中性遗传变异分布的影响，我将开发新的 利用进化信息来识别与特征相关的基因组变异的统计方法。 这项工作将结合不同的显性模型、遗传结构模型和基因重叠模型。 两个或两个以上的特征。我将把这些模型和方法应用于人类全基因组测序数据集 与来自具有不同历史的不同人群的生物医学表型数据配对，使用 生物库资源，如精准医学跨基因组计划和西班牙裔社区 健康研究/拉丁裔研究。我在这些领域的工作将理清各种 导致包括疾病风险在内的复杂特征差异的进化过程 并将扩大我们对这些特征在未被研究的群体中的遗传基础的理解。

Abstract Understanding the genetic basis of complex phenotypes is a critical problem in medical and evolutionary genetics. As the influx of rich genomic and phenotypic data accelerates via biobank-level resources and reveals even greater fine scale genetic information, modern population genetic methods will be critical to examine the influence of evolutionary processes on the distribution of complex traits. My research in this area has focused on characterizing the effects of recent population history on the distribution of deleterious variation in individuals and on the design and application of statistical methods for the inference of positive selection in populations. Specifically, my work demonstrates that certain population processes create concentrations of deleterious homozygotes in genomes and that the strength of concentration depends on recent population history. This work reveals an important mechanism by which population history can influence the genomics of complex traits. I have also contributed to methodological advances for the identification of genomic regions undergoing positive selection, including designing novel haplotype summary statistics, novel likelihood statistics that account for spatial autocorrelation in genomes, and efficient software implementing this work which has been cited hundreds of times. I have applied these and other methods to human and non-human genomic data, uncovering the genomic basis of adaptation to pathogen exposure in different human populations and the polygenic nature of adaptation to high altitude in rhesus macaques. During the next five years, and beyond, my research will focus on identifying how evolutionary forces shape the phenotypic landscape of modern humans and on developing uses for our knowledge of human history to learn the genetic basis of complex traits. To this end, I will develop novel methods for the inference of natural selection in genomes, I will develop theoretical models that connect evolutionary history to variation of complex traits via its effect on the distribution of non-neutral genetic variation, and I will develop novel statistical methods that leverage evolutionary information to identify genomic variation associated with traits. This work will incorporate varying models of dominance, of genetic architecture, and of genetic overlap among two or more traits. I will apply these models and methods to human whole-genome sequencing data sets paired with biomedical phenotype data from diverse human populations with their own distinct histories, using biobank resources such as the Trans-Omics for Precision Medicine program and the Hispanic Community Health Study / Study of Latinos. My work in these areas will disentangle the relative influences of various evolutionary processes that contribute to differences in complex traits, including disease risk, among populations and will expand our understanding of the genetic basis of these traits in understudied populations.