Abstract 056: Integrative Genomic Analysis Unravels Novel Pathways And Key Regulatory Networks In Blood Pressure

Blood pressure (BP) is a highly heritable trait and hypertension is a major risk factor for cardiovascular diseases. Recent genome-wide association studies (GWAS) have implicated a number of susceptibility loci for systolic (SBP) and diastolic (DBP) blood pressure. However, these loci together only explain 1% of the BP variability and the underlying mechanisms remain elusive. In this study, we utilized an integrative genomics approach that leveraged multiple genetic and genomic datasets including 1) GWAS from CHARGE (The Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium for DBP and SBP, 2) expression quantitative trait loci (eQTLs) from genetics of gene expression studies of human tissues related to hypertension (such as peripheral blood, whole blood, human aortic endothelial cells, and adipose tissue), 3) knowledge-driven biological pathways, and 4) data-driven regulatory gene networks. The integration of these diverse data sources enabled tissue-specific investigations on whether the genetic variants associated with BP concentrated on specific parts of gene regulatory networks, termed as “subnetworks”, and whether novel key regulators in the subnetworks could be identified based on data-driven network topology. We identified 10 and 8 subnetworks for DBP and SBP respectively. Among these, subnetworks associated with ion homeostasis, ALK in cardiac myocytes, B cell receptor signaling, and Regulation of Insulin Secretion, were shared between DBP and SBP. More interestingly, we detected tissue-specific pathways, for example, L1CAM interactions in aortic endothelial cells and Leukocyte transendothelial migration in adipose. Among the trait-specific subnetworks, those involved in megakaryocyte development and cytoskeleton remodeling were found to be SBP-specific while GAB1 signalosome subnetwork was DBP-specific. Finally, by utilizing the gene-gene relationships revealed by the network architecture, we detected key regulator genes, both known (e.g. COL1A1, KL, and OSR1) and novel (e.g. EFEMP1, and CRABP2), in these blood pressure subnetworks. Our results shed lights on the complex mechanisms underlying blood pressure and highlight potential novel targets for hypertension and cardiovascular diseases.