Population Genetic Consequences of Recent Explosive Population Growth in Humans

最近人类人口爆炸性增长的人口遗传后果

基本信息

批准号：
8613540
负责人：
ANDREW G CLARK
金额：
$ 58.89万
依托单位：
CORNELL UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-05-10 至 2018-04-30
项目状态：
已结题

项目摘要

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.

在过去的400代中，人类全球人口已扩大了1000倍以上，导致相对于遗传变异而言，这是完全超出平衡的状态。最近的增长产生了罕见差异过多，这对于寻找基于复合物的基因具有重要的后果疾病风险。我们的总体目标是开发和测试人口遗传分析的方法以了解人口迅速扩展在遗传变异模式中的作用。在目标1中，我们将发展理论方法了解爆炸性生长如何以及为什么影响遗传模式变化。我们还将得出使用如此大的样本的分析含义违反中性结合的假设。我们已经展示了大型样品可以导致多个合并，因此快速生长和大型样本量扭曲了A的基因家谱的拓扑结构样本使标准合并理论无效。我们将用生成的新方法替换在具有快速生长和大型样品的模型下，适当的样品位点频谱。给定大量的数据集，我们想推断人口遗传参数和此类估计值通常需要适当的模型，该模型将人口规模和突变率与变异水平有关。在目标2我们将开发新颖的统计和计算推理方法来适应增长种群并将其应用于大规模数据。我们将使用在适当的人口统计模型下生成的仿真数据。这个目标将产生新颖的软件为社区提供广泛实用性的包装。在AIM 3中，我们将了解自然选择如何迅速选择人口不断增长会影响人口遗传变异和复杂性状的结构。这目标将通过广泛的及时模拟实现。除其他外，结果会证明在美国的条件下，快速生长会膨胀个体突变负荷。通过发展理解这种快速增长影响人类遗传变异的方式，我们预计这些结果将为预期的疾病风险遗传结构提供更准确的了解，这又将指南改进关联测试的方法。