Joint analyses of multiple genetic variants and multiple trait phenotypes

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The long-term objective of this research program is to develop statistical methods and computational tools to address issues arising from genetic studies of complex human traits.The recent high-throughput technologies provide efficient means to collect vast amount of data with low cost. However, a major hurdle to gene discoveries is the development of powerful and robust statistical methods that take full advantage of the breadth and depth offered by such data.The proposed research focus on developing novel multivariate methods for joint analyses of multiple genetic variants and multiple trait phenotypes that are robust to model misspecifications. We evaluate methods for analyzing a group of rare (or common) genetic variants under a unified framework and provide statistical insights to answer some of the recurring questions and clarify some of the confusions that exist in the communities of human genetics and statistical genetics. This in turn guides the development of robust association test statistics that deal with, population stratification, measurement errors and non-random sampling design. We also discuss the conceptual and statistical challenges in joint analyzing multiple correlated phenotypes in the context of genetic pleiotropy studies, and provide some viable solutions to the problem. The proposed methodology will provide emerging genetic studies with various robust and powerful multivariate statistics, advancing our interpretation and understanding of next generation genetic data.