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A Multiomics Approach to Identify Genes Associated with Childhood Asthma Risk and Morbidity

Forno, Erick, Wang, Ting, Yan, Qi, Brehm, John, Acosta-Perez, Edna, Colon-Semidey, Angel, Alvarez, Maria, Boutaoui, Nadia, Cloutier, Michelle M., Alcorn, John F., Canino, Glorisa, Chen, Wei, Celedón, Juan C.·2017·American Journal of Respiratory Cell and Molecular Biology
DOI 10.1165/rcmb.2017-0002oc
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Abstract

Childhood asthma is a complex disease. In this study, we aim to identify genes associated with childhood asthma through a multiomics “vertical” approach that integrates multiple analytical steps using linear and logistic regression models. In a case–control study of childhood asthma in Puerto Ricans (n = 1,127), we used adjusted linear or logistic regression models to evaluate associations between several analytical steps of omics data, including genome-wide (GW) genotype data, GW methylation, GW expression profiling, cytokine levels, asthma-intermediate phenotypes, and asthma status. At each point, only the top genes/single-nucleotide polymorphisms/probes/cytokines were carried forward for subsequent analysis. In step 1, asthma modified the gene expression–protein level association for 1,645 genes; pathway analysis showed an enrichment of these genes in the cytokine signaling system (n = 269 genes). In steps 2–3, expression levels of 40 genes were associated with intermediate phenotypes (asthma onset age, forced expiratory volume in 1 second, exacerbations, eosinophil counts, and skin test reactivity); of those, methylation of seven genes was also associated with asthma. Of these seven candidate genes, IL5RA was also significant in analytical steps 4–8. We then measured plasma IL-5 receptor α levels, which were associated with asthma age of onset and moderate–severe exacerbations. In addition, in silico database analysis showed that several of our identified IL5RA single-nucleotide polymorphisms are associated with transcription factors related to asthma and atopy. This approach integrates several analytical steps and is able to identify biologically relevant asthma-related genes, such as IL5RA. It differs from other methods that rely on complex statistical models with various assumptions.

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Metadata

Authors
Forno, E., Wang, T., Yan, Q., Brehm, J., Acosta-Perez, E., Colon-Semidey, A., Alvarez, M., Boutaoui, N., Cloutier, M. M., Alcorn, J. F., Canino, G., Chen, W., & Celedón, J. C.
Affiliations
  • Division of Pediatric Pulmonary Medicine, Allergy, and Immunology, Children’s Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, Pennsylvania
  • Behavioral Sciences Research Institute and
  • Department of Pediatrics, University of Puerto Rico, San Juan, Puerto Rico; and
  • Department of Pediatrics, University of Connecticut Health Center, Connecticut Children’s Medical Center, Farmington, Connecticut
ORCID
Forno, Erick
Venue
American Journal of Respiratory Cell and Molecular Biology
Publisher
Oxford University Press (OUP)
Published
2017-10-01
Volume
57
Issue
4
Pages
439-447
ISSN
  • 1044-1549
  • 1535-4989
DOI
10.1165/rcmb.2017-0002oc
Cited by
31
References
7
Language
en
Licence
academic.oup.com/pages/standard-publication-reuse-rights (vor)

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