Deep learning and statistical methods identify novel asthma risk variants in Europeans

Asthma is a common heritable respiratory disorder with a complex genetic basis. Although large-scale genome-wide association studies have identified many risk loci, the full spectrum of its polygenic architecture remains to be defined.

We refined the genetic landscape of asthma in individuals of European ancestry and improve polygenic risk prediction through statistical and deep learning-based methods.

We conducted the largest genome-wide association study meta-analysis of asthma in individuals of European ancestry, combining data from the Global Biobank Meta-analysis Initiative (121,940 cases, 1,254,131 controls) and the Million Veteran Program (36,823 cases, 398,278 controls). To enhance discovery, we applied pleiotropy-informed multitrait analysis and conditional false discovery rate approaches, each incorporating eosinophil counts as a secondary trait. In parallel, we used a Transformer-based deep learning framework to further prioritize variants and improve polygenic risk prediction.

The meta-analysis identified 69 independent genome-wide significant loci (P < 5 × 10^-8) not previously reported in asthma. Multitrait analysis of genome-wide association studies, conditional false discovery rate, and deep learning approaches uncovered additional candidate loci. Functional annotation and expression quantitative trait locus mapping implicated novel genes in immune regulation, airway remodeling, and metabolic processes. Polygenic risk score models derived from deep learning-prioritized variants outperformed those based on conventional genome-wide association study and standard statistical approaches.

Our study yields a comprehensive map of asthma-associated loci in European ancestry populations, improves genetic risk prediction, and informs future mechanistic studies.