Krautenbacher, Norbert and Kabesch, Michael and Horak, Elisabeth and Braun-Fahrlaender, Charlotte and Genuneit, Jon and Boznanski, Andrzej and von Mutius, Erika and Theis, Fabian and Fuchs, Christiane and Ege, Markus J. (2021) Asthma in farm children is more determined by genetic polymorphisms and in non-farm children by environmental factors. PEDIATRIC ALLERGY AND IMMUNOLOGY, 32 (2). pp. 295-304. ISSN 0905-6157, 1399-3038
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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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | GENOME-WIDE ASSOCIATION; CHILDHOOD ASTHMA; COMPLEX TRAITS; HAY-FEVER; PREDICTION; RISK; childhood asthma; environment; farming; genome-wide association studies; machine learning; penalized regression; random forest; risk prediction; single-nucleotide polymorphisms; statistical learning |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Kinder- und Jugendmedizin |
| Depositing User: | Petra Gürster |
| Date Deposited: | 20 Apr 2021 12:09 |
| Last Modified: | 20 Apr 2021 12:09 |
| URI: | https://pred.uni-regensburg.de/id/eprint/43558 |
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