Exploration of candidate genes associated with rare SNVs in pulmonary stenosis using whole-exome sequencing and machine learning.

Background: Pulmonary stenosis (PS) is a common form of congenital heart disease (CHD) that impairs cardiopulmonary function and can be life-threatening in severe cases. As a complex polygenic disorder, the genetic basis of PS remains incompletely understood. Methods: Rare pathogenic single nucleotide variants (SNVs) were identified from whole-exome sequencing (WES) data of 185 sporadic PS patients and 100 healthy controls using multiple pathogenicity-filtering strategies. Gene-level burden test was performed, with complementary analysis using sequence kernel association test-optimal (SKAT-O). Three machine learning algorithms-least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (XGBoost)-were applied to prioritize candidate genes. The overlap between machine learning-based selections and burden test results was systematically evaluated. Final candidate genes were further prioritized through protein-protein interaction (PPI) network analysis, and their expression in human pulmonary artery endothelial cells (HPAECs) was assessed by reverse transcription quantitative polymerase chain reaction (RT-qPCR). Results: Comparative analyses showed that different machine learning algorithms exhibited distinct feature selection patterns, with RF demonstrating the highest concordance with burden test results. A total of 17 candidate genes were prioritized ( Conclusion: These findings indicate that machine learning can complement conventional gene-based analyses of WES data. This study provides a set of candidate genes associated with PS and offers a basis for further investigation of its genetic architecture.