Computational and statistical models for human genetics

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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.