Abstract:
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Results from Genome-Wide Association Studies (GWAS) have shown that complex diseases are often affected by many genetic variants with small or moderate effects. Identification of these risk variants remains a very challenging problem. Hence, there is a need to develop more powerful statistical methods to leverage available information to improve upon traditional approaches that focus on a single GWAS dataset. Our study was motivated by the accumulating evidence suggesting that different complex diseases share common risk bases, i.e., pleiotropy. In this presentation, I will discuss our novel statistical approach, graph-GPA, to increase statistical power to identify risk variants through joint analysis of multiple GWAS data sets using a graphical modeling approach. Moreover, graph-GPA provides a parsimonious representation of genetic relationship among phenotypes, which is especially powerful when an increasing number of phenotypes are jointly studied. I will discuss the power of graph-GPA with the simulation studies and its application to real GWAS datasets.
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