From Genomics to Precision Cardiology: A Comprehensive Review of Clinical Applications and Challenges in Cardiovascular Diseases

This research direction explores the integration of multi-scale genomic data, clinical parameters, and artificial intelligence to advance precision cardiology. Cardiovascular diseases (CVD) present complex etiological profiles governed by both rare, high-impact genetic variants and cumulative polygenic susceptibilities. This project provides implementation support for evaluating computational frameworks that combine whole-genome/whole-exome sequencing data, multigene panels, and polygenic risk scores (PRS) to enhance personalized risk stratification. By leveraging machine learning models, the proposed methodology aims to integrate heterogeneous datasets—including genomic, clinical, imaging, and molecular biomarkers—to predict disease susceptibility and therapeutic responses. Special emphasis is placed on addressing the challenges of data dimensionality, clinical utility, and the integration of emerging RNA-based therapeutics and gene-editing

data. The framework serves as a structured guide for developing robust bioinformatics pipelines that transition genomic findings into actionable clinical decision-support systems. Through systematic simulation and evaluation of risk-prediction algorithms, this work assists researchers in identifying critical bottlenecks in genomic cardiology and establishing standardized protocols for multi-omics data fusion in cardiovascular medicine.

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