Our primary goals in developing a program for the analysis of sequence data from the 1000 Genomes Project (TGF) are to address fundamental issues in human biology, including how best to identify rare variants affecting complex human phenotypes, what proportion of the heritability for complex traits is attributable to rare vs. common variants, and how to predict which genes are most likely to harbor rare variants associated with complex traits.. To achieve these goals we have assembled a multi-disciplinary team with access to a variety of unique resources.
Our specific aims are: 1) We will develop and apply a variety of approaches for characterizing rare variants that affect complex human phenotypes. While this aim will focus on development of methods to relate rare variants to complex traits, our studies may also allow us to identify novel functional elements. 2) We will use results of comprehensive association studies in cytotoxicity phenotypes with common and rare variants throughout the human genome to determine the proportion of the total phenotypic variance in cytotoxicity phenotypes is attributable to rare vs. common variants. 3) We will attempt to develop predictive models for identifying genes likely to have rare variants affecting complex human phenotypes. Although the primary focus of the proposed research is on cytotoxicity phenotypes because we believe that these phenotypes are an outstanding model for general complex human phenotypes, we will make extensive use of our expression and miRNA data and results from these same samples to enhance our studies. Collectively, the datasets that we utilize in our studies and the tools that we develop to achieve our goals will provide novel and valuable resources to the 1000 Genomes Project and the larger scientific community.
The sequence data available in the 1000 Genomes Project and the large number of cytotoxicity phenotypes we have assayed in the same samples provides us with unique opportunities to develop methods for relating rare variants to complex traits, to determine the proportion of phenotypic variance attributable to rare vs. common variants, and to develop predictive models for genes with rare variant associations.
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