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

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.

Keywords: asthma genetics; asthma genomics; childhood asthma; multiomics.

Figure 1.

Pathogenic and analytical steps. Pathogenic steps from genotype to disease (left) and analytical steps (right) performed in the current study. Shown are the numbers of genes, probes, or single-nucleotide polymorphisms (SNPs) included in each sequential step, as well as the generic analytical model used. All models were adjusted for pertinent covariates (not shown in the figure for simplicity).

Figure 2.

Venn diagram showing number of genes in top pathways from gene-set enrichment analysis. Reactome pathway “cytokine signaling in the immune system” (n = 269 genes) and the contained pathways were highly enriched for genes significant in analytical step 1 (modification of gene expression versus cytokine level by asthma status). The next encompassing pathway (“immune system”) was deemed too broad for further analysis.

Figure 3.

Results from confirmatory analysis. Shown is the number of SNPs associated with each of four outcomes: asthma; intermediate phenotypes; cytokine levels; and gene expression. All results are from adjusted regression models adjusted for relevant covariates and principal components. Gene expression analysis additionally adjusted for white blood cell differential cell types. A total of 148 SNPs from 40 genes (see Table E4) were associated with at least three of four outcomes. Only one SNP, rs71058675, in IL-5 receptor α (IL5RA), was associated with all four outcomes. There was also only one expression quantitative trait loci, rs1153462 (also in IL5RA), associated with asthma and intermediate phenotypes. CellAdj, adjusted for CBC cell differential; eQTL, expression quantitative trait loci; GWAS, genome-wide association study; MedPhn, asthma intermediate phenotype.