Informing Genome-Wide Association Studies by Incorporating Gene Expression and Network Data

• Activity Number: 658
• Type: Invited
• Date/Time: Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
• Sponsor: ENAR
• Author(s): Li Hsu*+
• Companies: Fred Hutchinson Cancer Research Center
• Keywords: gwas ; eQTL ; network ; hierarchical modeling

Abstract

Genome-wide association studies (GWAS) have demonstrated considerable success in identifying common genetic variants associated with many complex quantitative traits and diseases. However, it is important to note that the number of identified variants is still modest. This is because current GWAS mainly uses a brute-force approach of testing association for all genetic variants across the entire genome, which, due to the large number of tested variants, lead to very stringent genome-wide significance cut-offs and a lack of power to detect variants with smaller effect or less frequent variants. Limited work has been conducted so far to incorporate other types of data such as gene expression that could provide functional predictions to prioritize the tested genetic variants. In this talk, I will present a hierarchical model-based approach to incorporate the functional data and the network information in the association analysis. We derive score statistics and show by simulation that the proposed test maintains type I error and gains power compared with the existing methods. An application to colorectal cancer will be presented.