Machine Learning Reveals the Contribution of Rare Genetic Variants and Enhances Risk Prediction for Coronary Artery Disease in the Japanese Population.

ABSTRACT:

Background

GWASs (genome-wide association studies) have advanced our understanding of coronary artery disease (CAD) genetics and enabled the development of polygenic risk scores (PRSs) for estimating genetic risk based on common variant burden. However, GWASs have limitations in analyzing rare variants due to insufficient statistical power, thereby constraining PRS performance.

Methods

We conducted whole-genome sequencing of 1752 Japanese patients with CAD and 3019 controls. A machine learning-based analytical framework was applied to identify and interpret rare genetic variants associated with CAD pathogenesis.

Results

This approach identified 59 CAD-related genes, including known causal genes such as LDLR and those not previously captured by GWASs. A rare var

SUBMITTER: Ieki H 

PROVIDER: S-EPMC13310176 | biostudies-literature | 2026 Aug

REPOSITORIES: biostudies-literature