Multiomics Mendelian Randomization of GWAS, eQTL, and pQTL Combined With Drug Analysis to Predict Drug Targets in...

Despite significant advances in the management of myocardial infarction (MI), therapeutic options targeting upstream pathogenic mechanisms remain scarce. This study introduces a novel multiomics-to-drug discovery framework to identify and validate causal therapeutic targets for MI. We conducted a systematic two-sample Mendelian randomization (MR) analysis integrating expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) data from the IEU OpenGWAS database, with replication in the UK Biobank cohort. Causal inference was rigorously validated using HEIDI heterogeneity tests, Bayesian colocalization, bidirectional MR, and multivariate MR (MVMR) to account for potential confounders. Downstream applications were explored via protein-protein interaction (PPI) network analysis, phenome-wide association studies (PheWAS), and molecular docking simulations. Initial screening identified four candidate genes (BMP1, APOB, FABP2, and ALDH2) associated with MI risk in both discovery and replication cohorts. However, only BMP1 demonstrated consistent causal effects at both transcriptional and proteomic levels, passing all sensitivity analyses with no evidence of horizontal pleiotropy in PheWAS. Colocalization and bidirectional MR further confirmed BMP1 as a robust, independent causal driver of MI. Molecular docking revealed that UK-383367, a selective BMP1 inhibitor, exhibits high binding affinity to the BMP1 active site. While BMP1 is traditionally associated with extracellular matrix remodeling, this study provides the first genetic evidence establishing it as an independent causal risk factor for MI, distinct from conventional traits such as hypertension. By bridging causal genetic inference with structure-based drug prediction, we propose BMP1 inhibition, specifically via agents like UK-383367, as a promising therapeutic strategy to mitigate MI-related pathological remodeling.