Cardiology. 2026 Mar 18:1-24. doi: 10.1159/000551373. Online ahead of print.
ABSTRACT
BACKGROUND: Heart failure (HF) and various arrhythmias frequently co-occur in clinical practice, suggesting shared pathophysiological mechanisms. However, the extent and nature of their common genetic architecture remains incompletely understood. This study aimed to systematically investigate the genetic correlations and shared causal loci between HF-related traits and multiple arrhythmia phenotypes.
METHODS: We utilized GWAS summary statistics from European cohorts to analyze HF-related traits and ten common arrhythmias. Global genetic correlations were assessed using LDSC and HDL. Local genetic correlations were further investigated using LAVA, HESS, and SUPERGNOVA to identify regional overlaps. Pleiotropic loci were identified using PLACO, with Bayesian colocalization analysis (stringent threshold PP.H4 ≥ 0.75) to assess shared causality. Bidirectional Mendelian randomization (MR) was conducted to explore causal relationships, utilizing a discovery threshold (P < 5×10⁻⁶) and a validation threshold (P < 5×10⁻⁸) with independent FinnGen data.
RESULTS: Significant genome-wide genetic correlations were identified between HF and seven arrhythmia traits, with the strongest association for atrial fibrillation (LDSC rg = 0.42, P = 5.1×10⁻¹⁸; HDL rg = 0.63, P = 5.9×10⁻³⁷). Local genetic correlation analyses identified multiple genomic regions of significant overlap, particularly converging on a major hotspot at the 4q25/PITX2/ENPEP locus across all three methods. Pleiotropic analysis identified several high-confidence shared loci, including regions harboring BAG3 (PP.H4 = 0.990) and ZFHX3 (PP.H4 = 0.938). Bidirectional MR revealed significant causal effects of AF on HF development (IVW OR = 1.22, P = 4.83×10⁻¹⁸) and HF on reduced heart rate variability (P = 1.86×10⁻⁴), both validated in independent cohorts.
CONCLUSIONS: Our findings demonstrate substantial and complex shared genetic architecture between HF and multiple arrhythmia phenotypes. These insights identify specific pleiotropic genes, regional correlation hotspots, and causal pathways, potentially informing future precision medicine approaches for cardiovascular disease prevention and treatment.
PMID:41849624 | DOI:10.1159/000551373
