Asthma in farm children is more determined by genetic polymorphisms and in non-farm children by environmental factors.
childhood asthma
environment
farming
genome-wide association studies
machine learning
penalized regression
random forest
risk prediction
single-nucleotide polymorphisms
statistical learning
Journal
Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology
ISSN: 1399-3038
Titre abrégé: Pediatr Allergy Immunol
Pays: England
ID NLM: 9106718
Informations de publication
Date de publication:
02 2021
02 2021
Historique:
received:
27
05
2020
revised:
22
09
2020
accepted:
23
09
2020
pubmed:
1
10
2020
medline:
19
8
2021
entrez:
30
9
2020
Statut:
ppublish
Résumé
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. 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. 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]). 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.
Sections du résumé
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.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
295-304Informations de copyright
© 2020 The Authors. Pediatric Allergy and Immunology published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.
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