The population genetics of disease risk and other quantitative traits

 描述(申请人提供)：许多感兴趣的表型，包括对许多常见疾病的易感性，都是“定量的”，因为该性状的可遗传变异在很大程度上是由于在种群中分离的许多小效应的遗传变异。一个多世纪以来，进化生物学一直在探索数量遗传变异的原因。这一追求最近也走到了人类遗传学研究的前沿，推动了绘制疾病风险中可遗传基因变异的变异图谱。自2007年以来，人类全基因组关联研究(GWAS)已经识别出数千个与数百个数量性状可重复相关的变异，包括对各种疾病的易感性。这些研究揭示了它们的遗传结构特征(即相关变异的数量、效应大小和频率)和可遗传变异所解释的比例(即“缺失遗传性”问题)之间的有趣差异。然而，解释这些发现一直很困难，因为缺乏进化过程如何产生遗传结构的模型。同样，最近在人类、果蝇和其他物种中进行的种群遗传学研究表明，许多(如果不是大多数)适应可能涉及多基因反应。然而，由于缺乏将数量性状的定向选择与其潜在的遗传结构联系起来的模型，我们对多基因适应的理解受到阻碍。我们建议将进化生物学的方法与人类遗传学的发现结合起来，以了解形成人类数量遗传变异和多基因适应的进化过程。在目标1中，我们将模拟群体遗传参数，特别是稳定选择和多效性，如何塑造数量性状的遗传结构。这将提供一个急需的框架，用于解释特征之间的架构差异和缺失的遗传性。在目标2中，我们将开发一种似然方法来从Gwas数据中推断特征遗传结构背后的进化参数，并将其应用于一系列(至少12个)人类特征。从这些推论中，我们将了解结构如何在不同的特征之间变化，例如，在复杂的疾病和拟人化的特征之间，以及更一般地关于维持数量遗传变异的力量。我们还将使用这些推论来指导未来地图绘制策略的设计。在目标3中，我们将模拟遗传结构和多效性如何塑造对数量性状上新的选择压力的反应。我们将在多态数据中表征多基因适应的特征(特别是在种群分化和多样性水平方面)，并评估结合单个基因座上的弱信号来检测数量性状选择的方法的能力。这项工作将有助于确定人类以及其他物种的多基因适应。因此，拟议的研究应该填补我们对数量性状自然选择理解的根本空白，并为人类和进化遗传学的进一步研究提供一套重要的模型和工具。

 DESCRIPTION (provided by applicant): Many phenotypes of interest, including the susceptibility to many common diseases, are "quantitative", in that the heritable variation in the trait is largely due to many genetic variants of small effects segregating in the population. The causes of quantitative genetic variation have been pursued in evolutionary biology for over a century. This pursuit has recently come to the forefront of research in human genetics as well, with the push to map the variants that underlie heritable genetic variation in disease risk. Since 2007, genome-wide association studies (GWAS) in humans have led to the identification of thousands of variants reproducibly associated with hundreds of quantitative traits, including susceptibility to a wide variety of diseases. These studies reveal intriguing differences among traits in their genetic architecture (i.e., the number of associated variants, their effect sizes ad frequencies) and in the fraction of the heritable variation explained (i.e., the "missing heritabilty" problem). Interpreting these findings has been difficult, however, because of the lack of models for how evolutionary processes give rise to genetic architecture. Similarly, recent population genetic studies in humans, Drosophila and other species indicate that many, if not most, adaptations may involve a polygenic response. Yet our understanding of polygenic adaptation is stymied by the lack of models that relate directional selection on quantitative traits to their underlying genetic architecture. We propose to marry approaches from evolutionary biology and findings in human genetics in order to learn about the evolutionary processes that shape quantitative genetic variation and polygenic adaptation in humans. In Aim 1, we will model how population genetic parameters, notably of stabilizing selection and pleiotropy, shape the genetic architecture of quantitative traits. This will provide a much-needed framework for interpreting differences in architecture and missing heritability among traits. In Aim 2, we will develop a likelihood method to infer the evolutionary parameters underlying the genetic architecture of traits from GWAS data, and apply it to range of (at least 12) human traits. From these inferences, we will learn how architecture varies among traits, e.g., between complex diseases and anthropomorphic traits, and more generally about the forces that maintain quantitative genetic variation. We will also use these inferences to guide the design of future mapping strategies. In Aim 3, we will model how genetic architecture and pleiotropy shape the response to novel selection pressures on quantitative traits. We will characterize the signatures of polygenic adaptation in polymorphism data (notably in terms of population differentiation and diversity levels) and assess the power of methods that combine the weak signals across individual loci to detect selection on quantitative traits. This work will help to identify polygenc adaptation in humans, as well as in other species. The proposed research should thus fill fundamental gaps in our understanding of natural selection on quantitative traits, and provide an important set of models and tools for further studies in both human and evolutionary genetics.