Computational and statistical models for human genetics

描述(申请人提供)：在过去的五年里，遗传关联研究以前所未有的详细程度评估了常见的SNP变异对复杂性状的贡献。这些全基因组研究不仅依赖于基因分型技术的进步，而且依赖于改进的研究设计和统计和计算方法的进步--从开发具有成本效益的两阶段设计，到控制种群结构的新战略，再到用于基因归算和交叉研究荟萃分析的方法和软件。在接下来的五年里，基因分型和测序技术有望再次取得重大进展。有效地利用这些技术来加深我们对复杂性状的理解，将需要在设计和分析遗传研究的方法方面不断进步。在这项应用中，我们建立在我们为人类基因研究开发实用有用的分析方法、计算工具和研究设计的记录之上。我们着手开发计算和统计方法，使人类复杂特征的研究能够有效地利用这些新技术。具体地说，我们将开发新的方法和计算工具，用于基因定位和短读取序列数据的解释，评估基于序列和基因分型的复杂性状研究的设计策略，并开发统计方法，促进在遗传关联研究中对可能的功能变异进行优先排序。好了！

DESCRIPTION (provided by applicant): In the past five years, genetic association studies have evaluated the contribution of common SNP variation to complex traits at an unprecedented level of detail. These genome wide studies relied not only on advances in genotyping technologies but also on improved study designs and advances in statistical and computational methods - ranging from the development of cost-effective two stage designs, to new strategies to control for population structure, to methods and software for genotype imputation and for cross study meta-analyses. In the next five years, great advances are again expected in genotyping and sequencing technologies. Effectively using these technologies to further our understanding of complex traits will require continued advances in methods for the design and analysis of genetic studies. In this application, we build on our record of developing practical useful analytical methods, computational tools, and study designs for human genetic studies. We set out to develop computational and statistical methods that will enable studies of complex traits in humans to effectively exploit these new technologies. Specifically, we will develop new methods and computational tools for genotype imputation and for the interpretation of short read sequence data, evaluate sequence and genotyping based design strategies for complex trait studies, and develop statistical methods that facilitate the prioritization of likely functional variants in genetic association studies. !