Using whole genomes to study demography and mapping power of a population isolate

使用全基因组研究人口统计学和群体隔离的绘图能力

• 批准号: 8527468
• 负责人: Charleston Chiang
• 金额: $ 5.22万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8527468
• 关键词: Accounting; Architecture; Behavior; Biological Assay; Biology; Chromosome Mapping; Complement; Complex; DNA Resequencing; Data Set; Demography; Disease; Evolution; Founder Generation; Frequencies; Future; Genes; Genetic; Genetic Population Study; Genetic Processes; Genetic Risk; Genetic Variation; Genome; Genomics; Hereditary Disease; Human; Human Genome; Individual; Light; Maps; Measures; Methods; Modeling; Mutation; Pattern; Population; Population Genetics; Population Heterogeneity; Positioning Attribute; Property; Recording of previous events; Research Design; Sampling; Sardinia; Scanning; Shapes; Simulate; Testing; Time; Variant; base; design; disorder risk; efficacy testing; expectation; genetic association; genetic pedigree; genetic variant; genome sequencing; genome wide association study; human disease; improved; insight; novel; novel strategies; public health relevance; success; trait

DESCRIPTION (provided by applicant): Population genetics aims to understand the patterns of genetic variation, the forces that shaped our genome, and the evolution of our genome through time. The increasing ease to sequence the entire genome of a large number of individuals will answer some of these fundamental questions. Insights gained from population genetics will in turn fuel the design and execution of genetic mapping studies. Due to their special demographic histories, a single resequencing study can capture the greatest amount of genetic variation in isolated populations such as the Finns or the Sardinians. Thus, these populations are likely to be the most informative for population genetic questions in the first wave of large-scale whole-genome resequencing efforts. Moreover, because the patterns of rare variants are tightly related to the demographic history of the population, understanding the demography is essential for studying the genetic contribution of rare variants to complex traits and diseases. This is particularly pertinent in light of the limited success of genome-wide association studies to explain the genetic contribution attributable to common variants. Moreover, demography is not the only population genetic process that influences the patterns of genetic variation. The mutation rate, the rate at which new variants are introduced to the human population, is another important force that shapes the human genome. Knowing the mutation rate specific to a population and/or to a local genomic region will enable accurate modeling of the null expectation of rare variant distributions and proper testing of the association of a genomic locus to a disease or trait. Furthermore, an accurate estimate of the mutation rate would more broadly impact genetics and evolutionary biology, such as inferring the divergence time between species. However, current approaches for estimating mutation rates are either based on limited pedigrees or dependent on an accurate evolutionary model. In the present proposal we aim to develop approaches that will investigate the population genetic, demographic, and disease genetic properties of a population. Specifically, we will develop a novel approach to estimate mutation rates that complements current approaches and is independent of the population demography. Secondly, we will extend an existing method of demographic history inferences to scenarios involving interactions between multiple populations. Finally, we will test the efficacy of genetic mapping studies specifically designed to take advantage of the unique demography of isolated populations. In each case, we will first develop and test our methods on simulated datasets, and then apply them to the dataset of Sardinians, one of the largest existing whole-genome resequencing datasets of an isolated population. Notably, our framework will not only help us understand the history of Sardinia, but can also be extended to other outbred and potentially diverse populations as these datasets become available.

描述(申请人提供)：种群遗传学的目标是了解遗传变异的模式，塑造我们基因组的力量，以及我们基因组随时间的进化。对大量个体的整个基因组进行测序变得越来越容易，这将回答这些基本问题中的一些。从群体遗传学中获得的洞察力将反过来推动基因图谱研究的设计和执行。由于它们特殊的人口学历史，单一的重新测序研究可以捕捉到孤立种群中最大数量的遗传变异，如芬兰人或撒丁岛人。因此，在第一波大规模全基因组重测序努力中，这些群体很可能是群体遗传学问题中最有信息量的群体。此外，由于稀有变异的模式与种群的人口学历史密切相关，了解人口统计学对于研究稀有变异对复杂特征和疾病的遗传贡献至关重要。鉴于全基因组关联研究在解释常见变异的遗传贡献方面取得的成功有限，这一点尤其相关。此外，人口统计学并不是影响遗传变异模式的唯一种群遗传过程。突变率，即人类种群中引入新变种的速度，是塑造人类基因组的另一股重要力量。知道特定于群体和/或局部基因组区域的突变率将使得能够对罕见变异分布的零预期进行准确的建模，并能够正确地测试基因组座位与疾病或性状的关联。此外，对突变率的准确估计将对遗传学和进化生物学产生更广泛的影响，例如推断物种之间的差异时间。然而，目前估计突变率的方法要么基于有限的家系，要么依赖于准确的进化模型。在目前的提案中，我们的目标是开发一种方法来研究一个群体的群体遗传、人口统计学和疾病遗传属性。具体地说，我们将开发一种新的方法来估计突变率，该方法补充了现有的方法，并独立于人口统计学。其次，我们将把现有的人口历史推断方法扩展到涉及多个人口之间相互作用的场景。最后，我们将测试专门为以下目的设计的遗传图谱研究的有效性 利用与世隔绝的人口的独特人口结构。在每种情况下，我们都将首先在模拟数据集上开发和测试我们的方法，然后将它们应用到撒丁岛人的数据集，这是孤立种群中现有的最大的全基因组重测序数据集之一。值得注意的是，我们的框架不仅将帮助我们了解撒丁岛的历史，而且随着这些数据集的出现，还可以扩展到其他近亲繁殖和潜在的多样化种群。