Abstract 17892: Network-Driven Integrative Genomics Analysis of the Cardiogram Gwas Reveals Key Drivers and Subnetworks of Coronary Artery Disease

Abstract

Objective: The molecular mechanisms underlying most CAD susceptibility loci remain unclear and a large proportion of the heritability of CAD remains unexplained. We hypothesize that genetic variation with both strong and subtle effects drives gene subnetworks that affect risk of CAD. Methods: We surveyed CAD related molecular interactions by integrating CARDIoGRAM GWAS associations, expression SNPs (eSNPs), and gene networks constructed from 6 tissues of orthogonal mouse and human studies. We first mapped eSNPs to 12 established CAD gene sets or expression signatures from the literature (positive controls) and 2652 coexpression network modules to derive corresponding eSNP sets. We assessed the degree of enrichment for low p value associations with CAD within each eSNP set using Fisher's exact and Kolmogorov-Smirnov (KS) tests. We screened coexpression modules in the Wellcome Trust case control cohort and took 18 forward for testing in all of CARDIoGRAM. Enriched eSNP sets were in turn integrated with tissue-specific Bayesian networks and a protein-protein interaction (PPI) network to identify central network nodes driving these CAD-related gene sets (key drivers or KDs). Top KDs were then used as seeds to derive subnetworks linking KDs. Results: We found 11 of 12 positive control gene sets and 12 of 18 coexpression network modules to be significantly enriched for eSNPs with low p values (Bonferroni-corrected p<0.05 for both tests). We identified both tissue-specific KDs and KDs common to multiple tissues and found them to be enriched for CAD-related biological processes such as circulation, inflammatory response, and coagulation. The top KDs across tissues are IL1RN, EGR2, NCF2, and LDLR, and the tissue-specific KDs include LPL and APOE from liver, ALOX5AP and ACE from kidney, F7 and ANAX2 from adipose, BACH1 and FER1L3 from blood, and CD36 and PPARG from the PPI network. We found KDs to be highly connected in the networks. We derived representative subnetworks of the top KDs (21 known and 27 novel). Conclusions: Our network-driven integrative analysis not only identified known and novel CAD risk genes but also defined a network structure that sheds light on the molecular interactions of CAD risk genes within, between and across tissues.