Models and Methods for Population Genomics

描述（由申请人提供）：获得个体全基因组基因型，甚至全基因组测序的成本和能力的显著提高，提供了以前所未有的分辨率理解人类遗传变异及其与复杂性状变异的关系的机会。在此期间，人们发现人类种群遗传结构更多地是一种连续的现象，而不是表现为一组定义良好的离散亚种群。然而，经典的群体遗传理论和方法在很大程度上是基于非重叠、离散结构群体的假设。例如，Wright的f统计和关联检验主要是在K个不同种群的背景下研究的。最近提出了许多用于分析具有密集基因分型的结构群体的创新算法。然而，还没有为这种情况建立一个坚实的统计基础。我们建议以一种连贯的理论框架和一套统计方法将出现的方式来解决一些开放的问题。我们将基于这些方法对现有数据集进行广泛的数据分析，包括人类基因组多样性计划、1000基因组计划和涉及结构化种群的GWAS研究。

DESCRIPTION (provided by applicant): Significant improvements in the cost and ability to obtain genome-wide genotypes, even entire genome sequencing, of individuals provides an opportunity to understand genetic variation among humans and its relationship to complex trait variation at an unprecedented level of resolution. During this time, it has been discovered that human population genetic structure is more of a continuous phenomenon rather than being manifested as a set of well-defined discrete subpopulations. However, classical population genetic theory and methods for population structure are largely based on the assumption of non-overlapping, discretely structured populations. For example, Wright's F-statistics and association testing have mostly been studied in the context of K distinct populations. A number of innovative algorithms for analyzing structured populations with dense genotyping have recently been proposed. However, there has yet to be a firm statistical foundation developed for this setting. We propose to tackle a number of open problems in such a way that a coherent theoretical framework and set of statistical methodologies will emerge. We will perform extensive data analyses based on these methods on existing data sets, including the Human Genome Diversity Project, the 1000 Genomes Project, and GWAS studies involving structured populations.