Background: Inhaled corticosteroid (ICS) response among patients with asthma is influenced by genetics, but biologically actionable insights based on associations have not been found. Various glucocorticoid response omics datasets are available to interrogate their biological effects.
Objective: We sought to identify functionally relevant ICS response genetic associations by integrating complementary multiomics datasets.
Methods: Variants with p-values<10-4 from a previous ICS response genome-wide association study were re-ranked based on integrative scores determined from: i) glucocorticoid receptor (GR)- and ii) RNA polymerase II (RNAP II)-binding regions inferred from ChIP-Seq data for three airway cell types, iii) glucocorticoid response element (GRE) motifs, iv) differentially expressed genes in response to glucocorticoid exposure according to 20 transcriptomic datasets, and v) expression quantitative trait loci (eQTLs) from GTEx. Candidate variants were tested for association with ICS response and asthma in six independent studies.
Results: Four variants had significant (q-value<0.05) multiomics integrative scores. These variants were in a locus consisting of 52 variants in high LD (r2≥0.8) near GR-binding sites by the gene BIRC3. Variants were also BIRC3 eQTLs in lung, and two were within/near putative GRE motifs. BIRC3 had increased RNAP II occupancy and gene expression with glucocorticoid exposure in two ChIP-Seq and 13 transcriptomic datasets. Some BIRC3 variants in the 52-variant locus were associated (p-value<0.05) with ICS response in three independent studies and others with asthma in one study.
Conclusion: BIRC3 should be prioritized for further functional studies of ICS response.
Clinical implication: Genetic variation near BIRC3 may influence ICS response in people with asthma.
Keywords: Asthma; ChIP-Seq; genome-wide association study; glucocorticoid receptor; glucocorticoid response; inhaled corticosteroids; integrative analysis; multiomics; transcriptomics.