Multi-omics and comprehensive multi-trait analysis identify shared genetic etiology of heart failure and related cancers

Heart failure (HF) and cancer exhibit a significant bidirectional risk synergy, with their comorbidity forming a vicious cycle. Although the mechanisms of their pathological interactions have attracted considerable attention, their shared genetic determinants remain unclear. Therefore, this study integrates multi-omics data and employs multi-trait analytical approaches to investigate the genetic correlations and shared genetic mechanisms between HF and multiple cancer types. Based on genome-wide association study (GWAS) data, we employed linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL) to assess the genetic correlations between HF and 11 types of cancer. We applied multi-trait analysis of GWAS (MTAG) to increase statistical power and detect potential pleiotropic loci. Through integrative approaches including functional mapping and annotation of genetic associations (FUMA), pleiotropic analysis under composite null hypothesis (PLACO), multi-marker analysis of genomic annotation (MAGMA), and polygenic priority score (PoPS), we mapped risk loci and prioritized functional genes. By integrating expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) data, we conducted risk gene prioritization and protein target identification through summary data-based Mendelian randomization (SMR) and Biomarker expression Level Imputation using Summary-level Statistics (BLISS). Functional annotation and tissue-specificity analysis were performed using deTS. Furthermore, pan-cancer analysis validated the whole-transcriptome expression of the identified risk genes. Our analysis identified 12 genetic variants closely associated with heart failure-cancer comorbidity. Among these, rs62106258 had a combined annotation-dependent depletion (CADD) score > 12.37, supporting its prioritization as a candidate variant. We not only validated known risk loci (such as rs11852372), but also discovered novel loci including rs17657502, rs10145755, and rs3096301. Gene-level analyses identified 7 unique pleiotropic candidate genes, with ERBB3 established as a tier 1 candidate gene and further validated through pan-cancer analysis. Enrichment analysis demonstrated significant clustering of these genes in cancer-related phenotypes and tissues, primarily involved in lipid metabolism pathways. Additionally, we identified 242 plasma proteins (206 unique) associated with heart failure-cancer comorbidity. This study demonstrates genetic correlations and shared genetic architecture between HF and cancers in European-ancestry individuals, providing critical insights into their comorbidity mechanisms and potential targets for synergistic therapies.