Identification of the molecular components of enhancer-mediated gene expression variation in multiple tissues regulating blood pressure

Inter-individual variation in blood pressure (BP) arises in part from sequence variants within numerous enhancers modulating expression of an unknown number of causal genes. We propose that these genes are active in tissues relevant to BP physiology and can be identified from tissue epigenomic data and genotypes of BP-phenotyped individuals. We used the ENCODE projects H3K27ac and ATAC-seq data from the heart, adrenal gland, kidney, and artery to comprehensively identify all cis regulatory elements (CREs) in these tissues to estimate the impact of all common human single nucleotide variants (SNVs) in CREs on gene expression, using machine learning methods. To identify specific genes, we integrated these results through a gene-wise association test against BP. We conducted analyses in two separate large-scale cohorts: 77,822 individuals from the Genetic Epidemiology Research on Adult Health and Aging (GERA) of Kaiser Permanente North California and 315,270 individuals from the UK Biobank (UKB). We identified 309, 259, 331 and 367 genes (FDR<0.05) for diastolic BP (DBP), and 191, 184, 204, and 204 genes for systolic BP (SBP), in the artery, kidney, heart, and adrenal gland, respectively, in GERA; 50-70% of these genes were replicated in the UKB and is significantly higher than the 12-15% expected by chance (P <10-4). These results enabled the prediction of tissue expression of these 988-2,875 putative BP-genes in individual participants of both cohorts to construct an expression polygenic score (exPGS). This score explained [~]27% of the reported SNV heritability (h2, 21%), substantially higher than that expected from prior studies. Additionally, we utilized these methods to provide dual-modality supporting evidence, CRE and expression-based, for the causality of genes previously detected by GWAS.