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，特别是当研究设计变得 更雄心勃勃，样本量增加。本研究的总体目标是发展统计学 用于理解涉及全基因组的群体基因组学研究的方法和软件 基因分型，许多同时测量的性状，和非常大的样本量。我们的重点是灵活 适应系统变化的建模，并对这些现代研究中遇到的数据进行鲁棒建模。 具体目标包括：（1）开发对任意种群结构免疫的关联测试， 对于性状的一般分布，许多同时发生的性状，或超大样本量;（2）引入新的 模型和亲属关系的估计和FST在广义上的设置，这将导致改善定量 复杂性状的遗传建模;（3）引入新的群体估计和检验框架 显示出比现有方法上级性能结构;（4）开发和分发软件;以及 (5)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 overall goal of the proposed research is to develop statistical methods and software useful in understanding population genomics studies that involve genome-wide genotyping, many simultaneously measured traits, and very large sample sizes. Our focus is on flexible modeling that adapts to systematic variation and robustly models data encountered in these modern studies. The specific aims involve (1) developing tests of association immune to arbitrary population structure that work for general distributions of traits, many simultaneous traits, or extreme large sample sizes; (2) introducing new models and estimates of kinship and FST in generalized settings, which will lead to improved quantitative genetic modeling of complex traits; (3) introducing new estimation and testing frameworks for population structure that show superior performance to existing approaches; (4) developing and distributing software; and (5) analyzing important data sets to discover new biology and validate our methods and software.