The population genetics of disease risk and other quantitative traits

 描述（由申请人提供）：许多感兴趣的表型，包括对许多常见疾病的易感性，是“定量的”，因为性状的可遗传变异主要是由于群体中分离的许多小影响的遗传变异。数量遗传变异的原因已经在进化生物学中研究了世纪。这种追求最近也进入了人类遗传学研究的前沿，推动绘制疾病风险中可遗传变异的变异。自2007年以来，人类全基因组关联研究（GWAS）已经鉴定出数千种变异，这些变异与数百种数量性状（包括对多种疾病的易感性）可重复地相关。这些研究揭示了性状在其遗传结构中的有趣差异（即，相关变异的数量、它们的效应大小和频率）和所解释的可遗传变异的分数（即，（即“缺失的遗传性”问题）。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，最近对人类、果蝇和其他物种的群体遗传学研究表明，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，以了解人类形成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来解释结构差异和特征之间的遗传性缺失。在目标2中，我们将开发一种似然方法来推断GWAS数据中特征遗传结构的进化参数，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特征之间变化，例如，复杂疾病和拟人特征之间的关系，以及更普遍的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映射策略的设计。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的新选择压力的反应。我们将描述多态性数据中多基因适应的特征（特别是在群体分化和多样性水平方面），并评估联合收割机将单个基因座上的弱信号结合起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，拟议中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.