Pathway-level epigenetic modeling illuminates the methylation architecture to asthma risk across tissues.

BACKGROUND

Asthma is a clinically heterogeneous airway disorder characterized by complex interactions between environmental exposures, immune activation, and molecular regulatory programs, whose underlying mechanisms are not fully elucidated by known genetic loci. DNA methylation serves as a mechanistic interface bridging genetic predisposition and environmental influences; however, most epigenetic studies remain confined to isolated CpG sites, lacking robust biological interpretability.

METHODS

We developed a cross-tissue, multi-cohort, and mechanistically interpretable epigenetic framework to delineate pathway-level methylation mechanisms underlying asthma. Leveraging data from 908 participants across one combined training cohort and three independent validation cohorts, we constructed a linear support vector classifier based on pathway-derived methylation scores. Additionally, SHapley Additive exPlanations (SHAP) were applied to quantify the contributions of individual pathways. To assess the statistical significance of pathway contributions, one-sample t-tests were performed for each pathway's SHAP values against zero, followed by Benjamini-Hochberg false discovery rate (FDR) correction to obtain adjusted p values.

RESULTS

The model exhibited reproducible and cross-tissue performance, achieving area under the curve (AUC) values of 0.792 (95% CI: 0.782-0.799; GSE65163) and 0.980 (95% CI: 0.974-0.990; GSE201872) in two airway epithelial cohorts, and 0.736 (95% CI: 0.690-0.771; GSE104471) in peripheral blood samples. Pathway interpretability analyses identified dominant roles of amino acid metabolism, epithelial and mesodermal developmental programs, metabolic-immune transport pathways, and neuroimmune signalling in shaping asthma-associated methylation patterns. Mediation analyses further revealed that these pathways influence asthma both directly and indirectly via eosinophil activity, epithelial proliferative dynamics, and nitric oxide-linked airway inflammation. Notably, pathways annotated by GO:0061205 and GO:0098727 exerted significant direct effects independent of immune intermediates.

CONCLUSIONS

This study describes a pathway-level methylation model designed for biological interpretability that shows associations with both clinical severity and latent molecular heterogeneity. It provides statistical evidence contributing to the understanding of epigenetic, immune, and metabolic signatures in asthma, offering a potential framework warranting further validation for precision respiratory medicine.