Integration of Encode Data to Understand the Regulatory Mechanism of Disease-Associated Variants Identified in GWAS

• Activity Number: 649
• Type: Contributed
• Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
• Sponsor: ENAR
• Author(s): Dongjun Chung*+ and Hongyu Zhao
• Companies: Yale University and Yale University
• Keywords: GWAS ; epigenetics ; data integration ; statistical genomics ; computational biology

Abstract

Genome-wide association studies (GWAS) have been successfully identified single nucleotide polymorphisms (SNPs) associated with various diseases. However, it is still challenging to understand the biological mechanism linking associated variants to diseases because the vast majority of associated SNPs identified in GWAS are located in non-coding regions and associated SNPs are also often in perfect linkage disequilibrium with SNPs several hundred kilobases away. Recently, large collections of epigenetic information gathered by the ENCODE project provide a new opportunity to study aberrations and mutations in non-coding regions and understand regulatory mechanism that genetic variants are related to various diseases. In this presentation, we will examine the informativeness of ENCODE data on the analysis of GWAS data and propose a statistical framework to integrate these data to facilitate identifying functional SNPs associated with diseases and understanding their regulatory mechanism.