Models and Methods for Population Genomics

项目摘要 标题： 群体基因组学的模型和方法 摘要： 了解全基因组的遗传变异及其在人类健康相关复杂性状中的作用是 现代生物医学研究的最重要目标。继续存在对新的 可应用于这些研究的统计模型和方法，特别是在研究设计变得更多的情况下 雄心勃勃，样本量增加。这笔赠款的首要目标是发展统计理论、方法、 以及有助于理解涉及全基因组基因分型的群体基因组学研究的软件， 测量的性状范围、非常大的样本量、结构化的总体和不同的研究设计。 现代种群基因组研究最具挑战性的方面之一是存在一个复杂的 我们观察到的当今基因变异背后的进化史。个人是 具有不同程度关联的结构化人群，不遵循作为基础的简单假设 经典的群体遗传学理论。需要对任意形式的结构和 关联性，从而可以准确地表征人类群体中的遗传变异，这反过来又允许 以准确理解复杂性状的遗传基础。我们的第一个重点是灵活性、广泛性 适应这种任意人口结构和关联性的适用模型，导致原则上 做出准确推断的统计方法。然后，我们展示了我们的方法如何提高识别 遗传关联，估计特征的全基因组遗传力，并有助于理解如何 可以稳健地构建预测性多基因风险分数。 具体目标包括：(1)引入一个参数框架，用于估计亲属关系和FST，从而在 具有随机等位基因频率的结构同源模型；(2)推进模型 以及用于量化全基因组遗传性、测试关联性和建立多基因风险分数的方法 纳入我们新的亲属关系和FST估计框架；(3)开发和分发软件；以及 (4)分析重要的数据集，以发现新的生物学并验证我们的方法和软件。

Project Summary Title: Models and Methods for Population Genomics Abstract: Understanding genome-wide genetic variation and its role in health-related complex traits in humans is one of the most important goals of modern biomedical research. There continues to be a substantial need for new statistical models and methods that can be applied in these studies, particularly as study designs become more ambitious and sample sizes increase. The overarching goal of this grant is to develop statistical theory, methods, and software useful in understanding population genomics studies that involve genome-wide genotyping, a wide range of measured traits, very large sample sizes, structured populations, and varying study designs. One of the most challenges aspects of modern population genomics studies is that there is a complex evolutionary history underlying the present-day genetic variation that we observe. Individuals are members of structured populations with varying levels of relatedness that do not follow the simple assumptions that underlie classical population genetics theory. There is a need to model and estimate arbitrary forms of structure and relatedness so that genetic variation in human populations can be accurately characterized, which in turn allows for an accurate understanding of the genetic basis of complex traits. Our first focus is on flexible, broadly applicable models that adapt to this arbitrary population structure and relatedness, resulting in principled statistical methods that make accurate inferences. We then show how our methods improve the ability to identify genetic associations, estimate genome-wide heritability of traits, and contribute to an understanding of how predictive polygenic risk scores can be robustly constructed. The specific aims involve (1) introducing a parametric framework for estimating kinship and FST, thereby bridging identity-by-descent models with random allele frequency coancestry models of structure; (2) advancing models and methods for quantifying genome-wide heritability, testing for associations, and building polygenic risk scores by incorporating our new estimation framework of kinship and FST; (3) developing and distributing software; and (4) analyzing important data sets to discover new biology and validate our methods and software.