Cardiovascular Genetic Epidemiology in the Genome-Wide Era: From Association Discovery to Mechanistic Dissection and Clinical Translation.

Despite major advances in prevention and treatment, cardiovascular disease remains a leading cause of death and disability worldwide. Cardiovascular genetic epidemiology has moved from family-based and candidate-gene studies to a genome-wide discipline supported by genome-wide association studies (GWAS), population-scale biobanks, whole-genome sequencing, and multi-omics resources. This narrative review examines how the field is shifting from locus discovery toward a continuous but still incomplete evidence chain that links variants to genes, cellular context, causal pathways, functional validation, and clinical use. We emphasize a central tension: cardiovascular genetics has been exceptionally successful at discovering associations, but robust mechanistic resolution and implementation-ready clinical translation remain uneven across diseases, populations, and use cases. We synthesize progress in coronary artery disease, blood pressure traits, atrial fibrillation, stroke, heart failure, inherited cardiovascular disorders, and intermediate phenotypes, and we distinguish established applications from promising but still emerging approaches such as polygenic risk scores, spatial omics, EHR-linked implementation, and genetically informed target prioritization. We also highlight persistent bottlenecks, including ancestry imbalance, noncoding locus interpretation, limited functional validation, imperfect polygenic score portability, data-governance constraints, and the need for privacy-preserving analytical frameworks. Overall, the next phase of cardiovascular genetic epidemiology will depend less on the mere accumulation of loci and more on rigorous evidence triangulation, context-specific functional testing, ancestry equity, and feasible clinical implementation pathways.