Abstract:
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Genome-Wide Association Studies (GWAS) in the past 10 years have led to the identifications of thousands of genetic variants associated with hundreds of human diseases. Although successful, the results from GWAS suggest that most complex traits (e.g. cancer, schizophrenia, diabetes) are likely affected by many hundreds or even thousands of genetic variants in the human genome, so the majority of disease associated variants with relatively small effects have not been identified yet. To improve statistical power, various approaches have been proposed to jointly analyze GWAS data across multiple phenotypes in combination of rich functional annotations for the human genome to identify additional disease associated variants. In this presentation, we will first introduce different types of data and their informativeness on implicating disease associated variants, and then discuss statistical methods that can help better delineate the genetic architecture underlying various complex diseases. This is joint work with Dongjun Chung, Can Yang, Cong Li, Qian Wang, and Joel Gelernter.
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