Models and Methods for Population Genomics

项目摘要 职务名称： 群体基因组学的模型与方法 摘要： 了解全基因组遗传变异及其在人类健康相关复杂性状中的作用是 现代生物医学研究最重要的目标。继续大量需要新的 可以应用于这些研究的统计模型和方法，特别是随着研究设计变得越来越多， 雄心勃勃，样本量增加。该补助金的首要目标是发展统计理论，方法， 和软件有助于理解人口基因组学研究，涉及全基因组基因分型，广泛的 一系列测量的性状，非常大的样本量，结构化的人群和不同的研究设计。 现代人群基因组学研究最具挑战性的方面之一是， 我们所观察到的遗传变异背后的进化历史。个人是 具有不同相关性水平的结构化人群，这些人群不遵循 经典种群遗传学理论需要对任意形式的结构进行建模和估计， 相关性，以便可以准确地描述人类群体中的遗传变异，这反过来又允许 准确理解复杂性状的遗传基础。我们的第一个重点是灵活，广泛 适用的模型，适应这种任意的人口结构和相关性，导致原则性 作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能力， 遗传关联，估计性状的全基因组遗传力，并有助于了解 可以稳健地构建预测性多基因风险评分。 具体目标包括：（1）引入估计亲属关系和FST的参数框架，从而桥接 结构的随机等位基因频率共祖模型;（2）推进模型 以及用于量化全基因组遗传性、检验关联性和建立多基因风险评分的方法 通过结合我们新的亲属关系和FST估计框架;（3）开发和分发软件; (4)分析重要的数据集，以发现新的生物学和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.