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.
FörderungenGerman Center for Lung Research Federal Ministry of Education and Research German Research Foundation European Commission European Commission as part of GABRIEL (a multidisciplinary study to identify the genetic and environmental causes of asthma in the European Community)