Integrated Method Leveraging Across Omics Data Sets for Uncovering Functional Mechanisms in the Post-GWAS Era

• Activity Number: 683
• Type: Contributed
• Date/Time: Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Yian Chen*+
• Companies: Moffitt Cancer Center & Research Institute
• Keywords: GWAS ; SNP ; TCGA ; omics ; integration

Abstract

Genome-wide association studies (GWAS) have discovered more than 200 susceptibility loci for cancer. However, characterizing their functional mechanisms is still one of the greatest challenges in the post-GWAs era. We have developed an integrated method for identifying functional mechanisms in the post-GWAS studies leveraging across omics datasets including germline single nucleotide polymornphism (SNP), microarray gene expression, miRNA, copy number variation (CNV), and methylation data using multivariable generalized linear models. The data used in this study included tgenome-wide association studies from North America, the United Kingdom, and Poland (3,995 EOC cases and 3,277 controls), the Cancer Genome Atlas (TCGA) data (N = 462 serous cases) and additional 9,854 EOC cases and 17,633 controls genotyped through an international effort known as the Collaborative Oncological Gene-Environment study (COGS). We applied our proposed method to investigate the potential functional mechanisms of risk loci in lncRNAs for ovarian cancer and identify plausible mechanisms that merit further investigation.