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. 2021 Feb;32(2):295-304.
doi: 10.1111/pai.13385. Epub 2020 Oct 15.

Asthma in farm children is more determined by genetic polymorphisms and in non-farm children by environmental factors

Norbert Krautenbacher  1   2 , Michael Kabesch  3   4   5 , Elisabeth Horak  6 , Charlotte Braun-Fahrländer  7   8 , Jon Genuneit  9   10 , Andrzej Boznanski  11 , Erika von Mutius  5   12   13 , Fabian Theis  1   2 , Christiane Fuchs  1   2   14 , Markus J Ege  5   12 ; GABRIELA, PASTURE study groups
Affiliations

Asthma in farm children is more determined by genetic polymorphisms and in non-farm children by environmental factors

Norbert Krautenbacher et al. Pediatr Allergy Immunol. 2021 Feb.

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.

Keywords: childhood asthma; environment; farming; genome-wide association studies; machine learning; penalized regression; random forest; risk prediction; single-nucleotide polymorphisms; statistical learning.

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References

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