Shared Genetics of Hypertension and Preeclampsia Converges on Immune Regulation

Abstract

BACKGROUND: Hypertension and preeclampsia are clinically distinct, yet biologically related conditions characterized by vascular dysfunction and elevated cardiovascular risk. Although genome-wide association studies have identified loci associated with blood pressure traits and preeclampsia, the functional mechanisms linking shared variants to gene regulation and clinical phenotypes remain unclear. METHODS: We integrated genome-wide association study summary statistics for hypertension, systolic blood pressure, diastolic blood pressure, and preeclampsia to identify shared variants ( P ≤1×10 −4 ). Cis -expression quantitative trait loci analyses were performed in whole blood using RNA sequencing data from 180 Black women. Significant associations (false discovery rate ≤0.05) were evaluated for replication across vascular, metabolic, and endocrine tissues in the Genotype-Tissue Expression project. Associations between gene expression and blood pressure traits were also assessed. RESULTS: We identified 4792 shared genome-wide association study variants, of which 4663 were tested in expression quantitative trait loci analyses, yielding 1837 significant variant-gene associations across 78 genes. Replication in the Genotype-Tissue Expression project confirmed 645 associations involving 24 genes, many showing cross-tissue regulatory effects. Three genes ( C4B , HLA-C , and HLA-DQB1 ) demonstrated convergent evidence across genome-wide association study, gene regulation, and expression-trait analyses. C4B expression was positively associated with hypertension and systolic blood pressure, while HLA-C showed negative associations with hypertension, systolic blood pressure, and diastolic blood pressure. HLA-DQB1 expression was specifically associated with diastolic blood pressure, suggesting trait-specific effects. CONCLUSIONS: These findings highlight immune-related pathways as key mediators linking hypertension and preeclampsia. Integrating genetic, transcriptomic, and phenotypic data provides a framework for identifying functionally relevant loci and advancing mechanistic insights into cardiometabolic and pregnancy-related disorders.