Norbert Krautenbacher1,2, Michael Kabesch3,4,5, Elisabeth Horak6, Charlotte Braun-Fahrländer7,8, Jon Genuneit9,10, Andrzej Boznanski11, Erika von Mutius5,12,13, Fabian Theis1,2, Christiane Fuchs1,2,14, Markus J Ege5,12. 1. Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany. 2. Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Technische Universität München, Garching, Germany. 3. University Children's Hospital Regensburg (KUNO), Regensburg, Germany. 4. Clinic for Pediatric Pneumology and Neonatology, Hannover Medical School, Hannover, Germany. 5. The German Center for Lung Research (DZL), Germany. 6. Department of Pediatrics and Adolescents, Innsbruck Medical University, Innsbruck, Austria. 7. Swiss Tropical and Public Health Institute Basel, Basel, Switzerland. 8. University of Basel, Basel, Switzerland. 9. Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany. 10. Pediatric Epidemiology, Department of Pediatrics, Medical Faculty, Leipzig University, Leipzig, Germany. 11. Wroclaw Medical University, Wroclaw, Poland. 12. Dr von Hauner Children's Hospital, LMU Munich, Munich, Germany. 13. Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Asthma and Allergy Prevention, Neuherberg, Germany. 14. Department of Business Administration and Economics, Bielefeld University, Bielefeld, Germany.
Abstract
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.
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.
Authors: Natascha S Borchers; Elisangela Santos-Valente; Antoaneta A Toncheva; Jan Wehkamp; Andre Franke; Vincent D Gaertner; Peter Nordkild; Jon Genuneit; Benjamin A H Jensen; Michael Kabesch Journal: Front Immunol Date: 2021-02-25 Impact factor: 7.561
Authors: Alexander J Hose; Giulia Pagani; Anne M Karvonen; Pirkka V Kirjavainen; Caroline Roduit; Jon Genuneit; Elisabeth Schmaußer-Hechfellner; Martin Depner; Remo Frei; Roger Lauener; Josef Riedler; Bianca Schaub; Oliver Fuchs; Erika von Mutius; Amandine Divaret-Chauveau; Juha Pekkanen; Markus J Ege Journal: Front Immunol Date: 2021-04-27 Impact factor: 7.561