Population Genetic Consequences of Recent Explosive Population Growth in Humans

在过去的400代人中，人类全球人口增长了1000多倍， 在遗传变异方面极度不平衡的状态。最近的增长产生了一个 大量的稀有变异，这对寻找复杂的基因有重要的影响。 疾病风险。我们的总体目标是开发和测试群体遗传分析的方法， 人口的快速增长在形成遗传变异模式中的作用。在目标1中，我们将开发 了解爆炸性增长如何以及为什么会影响遗传模式的理论方法 变化量我们还将推导出使用如此大的样本的分析含义， 违反了中性聚结剂的假设。我们已经展示了大样本如何导致多个 合并，因此快速增长和大样本量扭曲了一个基因谱系的拓扑结构， 从而使标准聚结理论失效。我们将用新的方法来替换它， 快速增长和大样本模型下的合适样点频谱。 给定大数据集，我们希望对群体遗传参数进行推断， 一般需要一个适当的模型，将种群规模和突变率与变异水平联系起来。在 目标2我们将开发新的统计和计算推理方法，以适应日益增长的 并将其应用于大规模数据。我们将彻底测试我们的推理方法， 根据适当的人口模型生成的模拟数据。这一目标将产生新的软件 为社区提供广泛实用的软件包。在目标3中，我们将学习自然选择是如何快速地 不断增长的种群影响种群遗传变异和复杂性状的结构。这 这一目标将通过广泛的前瞻性模拟来实现。除其他外，结果将告诉 美国条件下，快速增长膨胀的个人突变负荷。通过了解 如此快速的增长影响了人类的遗传变异，我们预计这些结果 将提供一个更准确的疾病风险的预期遗传结构的图片，这将反过来指导 改进关联测试的方法。

The human global population has expanded more than 1000-fold in the last 400 generations, resulting in a state that is profoundly out of equilibrium with respect to genetic variation. The recent growth produces a large excess of rare variation, which has important consequences for finding genes that underlie complex disease risk. Our overall objective is to develop and test methods of population genetic analysis to understand the role of rapid population expansion in shaping patterns of genetic variation. In Aim 1 we will develop theoretical approaches to understand how and why explosive growth impacts patterns of genetic variation. We will also derive the analytical implications of using samples that are so large as to violate assumptions of the neutral coalescent. We have shown how large samples can result in multiple mergers, and so both rapid growth and large sample sizes distort the topology of the gene genealogies of a sample so as to make standard coalescent theory invalid. We will replace this with new methods that generate the appropriate sample site frequency spectrum under models with both rapid growth and large samples. Given large data sets, we want to make inference about population genetic parameters, and such estimates generally require an appropriate model relating population size and mutation rates to levels of variation. In Aim 2 we will develop novel statistical and computational inference methods to accommodate growing populations and apply them to large-scale data. We will thoroughly test our inference methods using simulation data generated under appropriate demographic models. This aim will generate novel software packages with broad utility for the community. In Aim 3 we will learn how natural selection in a rapidly growing population impacts population genetic variation and the architecture of complex traits. This goal will be accomplished through extensive forward-in-time simulations. Among other things, results will tell us conditions under which rapid growth inflates the individual mutation load. By developing an understanding of the way that such rapid growth has impacted genetic variation in humans, we anticipate that these results will provide a more accurate picture of the expected genetic architecture of disease risk, which will in turn guide methods for improved association testing.