BACKGROUND: The asthma syndrome is influenced by hereditary and environmental factors. With the example of farm exposure, we study whether genetic and environmental factors interact for asthma. METHODS: Statistical learning approaches based on penalized regression and decision trees were used to predict asthma in the GABRIELA study with 850 cases (9% farm children) and 857 controls (14% farm children). Single-nucleotide polymorphisms (SNPs) were selected from a genome-wide dataset based on a literature search or by statistical selection techniques. Prediction was assessed by receiver operating characteristics (ROC) curves and validated in the PASTURE cohort. RESULTS: Prediction by family history of asthma and atopy yielded an area under the ROC curve (AUC) of 0.62 [0.57-0.66] in the random forest machine learning approach. By adding information on demographics (sex and age) and 26 environmental exposure variables, the quality of prediction significantly improved (AUC=0.65 [0.61-0.70]). In farm children, however, environmental variables did not improve prediction quality. Rather SNPs related to IL33 and RAD50 contributed significantly to the prediction of asthma (AUC=0.70 [0.62-0.78]). CONCLUSIONS: Asthma in farm children is more likely predicted by other factors as compared to non-farm children though in both forms, family history may integrate environmental exposure, genotype, and degree of penetrance.
- Krautenbacher, N.
- Kabesch, M.
- Horak, E.
- Braun-Fahrländer, C.
- Genuneit, J.
- Boznanski, A.
- von Mutius, E.
- Theis, F.
- Fuchs, C.
- Ege, M. J.
Keywords
- Childhood asthma
- Gwas
- SNPs
- environment
- farming
- machine learning
- penalized regression
- random forest
- risk prediction
- statistical learning
