Asthma Genomics and Pharmacogenomics

Introduction

Asthma is a complex disease in which both genetic and environmental factors contribute to disease susceptibility and pathogenesis. The variability in how individuals respond to commonly used asthma medications suggests that genetic variation may influence treatment outcomes. Unbiased, genome-wide, hypothesis-free study designs offer a path toward understanding underlying biology, predicting risk, and improving therapeutic approaches. This review highlights recent work exploring the relationship between genetic variation or gene expression regulation across the genome and asthma or asthma treatment outcomes. It includes genome-wide association studies (GWAS), transcriptome-wide association studies (TWAS), and epigenome-wide association studies (EWAS) published from December 2017 to December 2019.

Recent Discoveries of Asthma Risk Loci from GWAS

Genetic risk for developing asthma arises from the cumulative effects of numerous gene regions or loci that may interact with environmental exposures. Early genetic studies utilized family-based designs and targeted biologic candidate genes after mapping the human genome. In the past decade, GWAS have provided significant insights into asthma genetics. During the review period, eleven GWAS of asthma phenotypes identified a total of 64 novel loci.

The Trans-National Asthma Genetic Consortium (TAGC) conducted a large meta-analysis of GWAS across 65 asthma studies, more than doubling the size of previous asthma GWAS with over 23,000 asthmatics and 118,000 non-asthmatic controls. TAGC identified five new loci, though the majority of the participants were of European ancestry (89.7%). Two additional studies focused on non-European populations: the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) conducted a large GWAS in African ancestry populations and identified two novel loci while replicating four TAGC loci. Gignoux et al. used admixture mapping in Latinos, identifying a locus on chromosome 18q21 near the SMAD2 gene involved in TGF-beta signaling.

Three GWAS focused on asthma subphenotypes. Shrine et al. conducted a GWAS of moderate-to-severe asthma, identifying three novel loci, including MUC5AC, which affects mucin production. Smaller GWAS of asthma remission and diisocyanate-induced asthma also identified new loci, though findings require independent confirmation.

Several large-scale studies leveraged genetic databases and health records to compare childhood and adult-onset asthma. GERA conducted a multi-ethnic GWAS in older adults, identifying four novel loci. Two studies using UK Biobank data identified 39 new loci and reported stronger genetic associations and heritability for childhood-onset asthma, underscoring a greater genetic contribution in childhood forms of the disease. Variants in the FLG gene and genes affecting epithelial barrier function emerged as significant for childhood-onset asthma.

Asthma Regulatory Variation from Transcriptome and Epigenome-Wide Studies

Most GWAS-identified variants lie in non-coding regions, suggesting regulatory roles in gene expression. TWAS and EWAS evaluate gene expression regulation at the genome-wide level, accounting for both genetic and environmental influences.

TWAS use RNA sequencing or gene expression assays, while EWAS assess methylation status at CpG sites. These methods help interpret GWAS results and explore gene regulation in airway tissues. Limited access to disease-relevant tissues has posed challenges, though some studies utilized lung tissue, bronchoalveolar lavage cells, and sputum samples, identifying genes involved in viral and bacterial recognition, tissue remodeling, inflammation, and known asthma susceptibility loci.

TWAS revealed signatures of medication use and potential therapeutic targets for severe asthma. Hekking et al. conducted a gene set variation analysis across various sample types and found downregulated genes during fluticasone treatment and enrichment of mast cells and group 3 innate lymphoid cells in adult-onset severe asthma. A pediatric study identified distinct interferon-gamma-related molecular phenotypes of asthma exacerbations.

Transcriptomic studies also explored responses to environmental stimuli. For example, nanoparticle aerosol exposure induced gene expression changes related to mucociliary clearance. TGF-beta-mediated fibroblast-to-myofibroblast differentiation was linked to airway remodeling. Other studies hypothesized mechanisms for epithelial barrier dysfunction, non-coding RNA overexpression, sex-specific gene regulation, and cell-specific gene expression using single-cell sequencing.

EWAS have been primarily conducted in children, using tissues like blood, saliva, lung, and nasal epithelium. Nasal epithelium is especially useful due to its correlation with bronchial tissue and stronger effect sizes compared to blood-based EWAS. Three key EWAS published during the review period included studies in peripheral blood and nasal epithelium, revealing novel methylation sites in genes linked to microRNA production, epithelial barrier function, mast cell regulation, and immune signaling. Some genes were also targets of approved drugs, highlighting therapeutic relevance.

Pharmacogenomic Studies Identify Novel Loci for Therapeutic Responsiveness

Pharmacogenetic studies associate genetic variants with treatment outcomes, such as lung function, exacerbations, and symptom control. Traditionally, these studies focused on candidate genes for corticosteroids, beta agonists, and leukotriene modifiers. Recent pharmacogenomic studies, including GWAS, EWAS, and TWAS, have expanded the search to genome-wide analyses.

Three GWAS examined corticosteroid response. Hawcutt et al. identified variants in PDGFD associated with adrenal suppression. Levin et al. reported a variant influencing corticosteroid response in African Americans, with replication in other cohorts. Hernandez-Pacheco et al. identified a locus between APOBEC3B and APOBEC3C affecting exacerbations in children.

EWAS from the Childhood Asthma Management Program (CAMP) found methylation changes in genes like BOLA2, IL12B, and CORT associated with corticosteroid response and exacerbation risk. Kan et al. used transcriptomic analysis in airway smooth muscle cells, identifying altered pathways in fatal asthma but found no differential response to corticosteroids, suggesting the need for larger studies.

Two GWAS investigated bronchodilator response to albuterol. Mak et al. used whole-genome sequencing in admixed populations, identifying common and rare variants near DNAH5, ADAMTS3, and COX18. However, replication was not achieved. Spear et al. identified SNPs in PRKG1, a gene involved in nitric oxide signaling, though again, findings were not replicated.

The combination of inhaled corticosteroids (ICS) and long-acting beta agonists (LABA) is common for moderate-to-severe asthma. Rider et al. explored transcriptomic responses to ICS and LABA in epithelial cells, showing LABA enhanced glucocorticoid-induced gene expression without altering receptor binding. This highlights gene-specific synergy in combination therapy.

One study examined leukotriene modifier response using zileuton-treated lymphoblastoid cell lines. GWAS, protein, and expression QTL analyses identified enrichment in the PI3K signaling pathway, confirmed by functional studies.

Conclusion

Recent large-scale GWAS, aided by biobank resources, have greatly expanded our understanding of genetic variants influencing asthma risk and treatment outcomes. However, these studies are predominantly based on European ancestry cohorts. Expanding multi-ethnic studies will help address disparities and uncover additional ancestry-specific variants.

While biobank-derived phenotypes may lack precision compared to traditional cohorts, the power of large sample sizes supports both replication and discovery of novel loci. Still, much of asthma’s heritability remains unexplained, likely involving gene-environment interactions, non-coding regulatory variation, and rare variants.

Advancing multi-omic approaches, particularly in racially diverse cohorts and clinical trials with detailed phenotyping, will be essential for further dissecting asthma’s genomic landscape. The integration of GWAS, TWAS, and EWAS through systems biology offers a promising avenue for future discoveries in asthma research.