An AI-Integrated Framework for Precision Genomics in Coronary Artery Disease Using Whole Exome and Phenotypic Data

Abstract Coronary artery disease (CAD) is a multifactorial condition influenced by genetic, phenotypic, and environmental factors. Traditional risk prediction models fall short in capturing the polygenic complexity of CAD, particularly in underrepresented populations. This study presents SIGMA (Scoring Importance of Genes specific to disease using Machine learning Algorithms), a novel AI-powered framework that enhances CAD risk prediction by integrating genomic and phenotypic data. Our approach leverages GEMS (GeneConnectRx Evidence Metrics), an LLM-driven system to score 1772 CAD-associated genes, and CASCADE (Comprehensive Assessment of Sequence and Clinical Annotation Data Evaluation), a tiered variant scoring pipeline. Using whole exome sequencing (WES) data from 1,243 individuals (628 controls, 615 CAD cases), the model integrates age and gender as key non-modifiable phenotypes. Results show significant improvements in sensitivity (from 0.41 to 0.79), specificity (0.70 to 0.72), and AUC (0.59 to 0.81) when phenotype data are incorporated. Our findings highlight the potential of AI-integrated genomics for population-specific CAD risk stratification.