Multiomics analysis identifies BIRC3 as a novel glucocorticoid response-associated gene

Abstract

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 data sets are available to interrogate their biological effects.

Objective: We sought to identify functionally relevant ICS-response genetic associations by integrating complementary multiomics data sets.

Methods: Variants with P values less than 10^-4 from a previous ICS-response genome-wide association study were reranked on the basis of integrative scores determined from (1) glucocorticoid receptor- and (2) RNA polymerase II-binding regions inferred from ChIP-Seq data for 3 airway cell types, (3) glucocorticoid response element motifs, (4) differentially expressed genes in response to glucocorticoid exposure according to 20 transcriptomic data sets, and (5) expression quantitative trait loci from GTEx. Candidate variants were tested for association with ICS response and asthma in 6 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 linkage disequilibrium (r² ≥ 0.8) near glucocorticoid receptor-binding sites by the gene BIRC3. Variants were also BIRC3 expression quantitative trait loci in lung, and 2 were within/near putative glucocorticoid response element motifs. BIRC3 had increased RNA polymerase II occupancy and gene expression, with glucocorticoid exposure in 2 ChIP-Seq and 13 transcriptomic data sets. Some BIRC3 variants in the 52-variant locus were associated (P < .05) with ICS response in 3 independent studies and others with asthma in 1 study.

Conclusions: BIRC3 should be prioritized for further functional studies of ICS response.

Keywords: Asthma; ChIP-Seq; genome-wide association study; glucocorticoid receptor; glucocorticoid response; inhaled corticosteroids; integrative analysis; multiomics; transcriptomics.