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Activity Number: 134
Type: Contributed
Date/Time: Monday, August 1, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #320124 View Presentation
Title: A Graphical Model to Prioritizing GWAS Results by Integrating Pleiotropy
Author(s): Dongjun Chung* and Hang J. Kim and Hongyu Zhao
Companies: Medical University of South Carolina and University of Cincinnati and Yale University
Keywords: GWAS ; data integration ; statistical genetics ; graphical model ; Bayesian modeling
Abstract:

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.


Authors who are presenting talks have a * after their name.

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