Abstract
BACKGROUND: Inter-individual variation in blood pressure (BP) arises in part from sequence variants within enhancers modulating the expression of causal genes. We propose that these genes, active in tissues relevant to BP physiology, can be identified from tissue-level epigenomic data and genotypes of BP-phenotyped individuals. METHODS: We used chromatin accessibility data from the heart, adrenal, kidney, and artery to identify cis-regulatory elements (CREs) in these tissues and estimate the impact of common human single-nucleotide variants within these CREs on gene expression, using machine learning methods. To identify causal genes, we performed a gene-wise association test. We conducted analyses in 2 separate large-scale cohorts: 77 822 individuals from the Genetic Epidemiology Research on Adult Health and Aging and 315 270 individuals from the UK Biobank. RESULTS: We identified 309, 259, 331, and 367 genes (false discovery rate <0.05) for diastolic BP and 191, 184, 204, and 204 genes for systolic BP in the artery, kidney, heart, and adrenal, respectively, in Genetic Epidemiology Research on Adult Health and Aging; 50% to 70% of these genes were replicated in the UK Biobank, significantly higher than the 12% to 15% expected by chance (P<0.0001). These results enabled tissue expression prediction of these 988 to 2875 putative BP genes in individuals of both cohorts to construct an expression polygenic score. This score explained ≈27% of the reported single-nucleotide variant heritability, substantially higher than expected from prior studies. CONCLUSIONS: Our work demonstrates the power of tissue-restricted comprehensive CRE analysis, followed by CRE-based expression prediction, for understanding BP regulation in relevant tissues and provides dual-modality supporting evidence, CRE and expression, for the causality genes.