Genetics of asthma

INTRODUCTION — Asthma is a condition that likely results from complex interactions between multiple genetic and environmental influences. Studies of twins and of families of asthmatic individuals (predating current genomics technology) demonstrate a range of heritability of asthma from 25 to 80 percent [1]. There are clearly components of the asthma phenotype that appear strongly heritable, although these inherited components do not follow the simple Mendelian pattern that is seen in monogenic (or single gene) disorders such as cystic fibrosis. (See "Basic genetics concepts: DNA regulation and gene expression", section on 'Modes of inheritance'.)

Genetic testing in patients with asthma should be used only to exclude monogenic obstructive lung diseases that can be misdiagnosed as asthma, such as cystic fibrosis and primary ciliary dyskinesia in patients with recurrent bronchitis or chronic productive cough, and alpha-1 antitrypsin deficiency in patients with adult onset asthma and irreversible airflow limitation. In contrast, testing for asthma susceptibility variants is not helpful clinically and is not recommended. Nonetheless, studies of asthma genetics are helping to improve our understanding of the pathogenesis of asthma and identify new therapies.

The genetics of asthma will be reviewed here. The principles of complex trait genetics and the epidemiology and risk factors for asthma are discussed separately. (See "Principles of complex trait genetics" and "Epidemiology of asthma" and "Risk factors for asthma".)

CHALLENGES IN STUDYING ASTHMA GENETICS — Human and animal data indicate that the syndrome of asthma is likely transmitted by multiple genes. In different individuals, different genes may lead to the same phenotype (locus heterogeneity), and multiple genes can act together in one individual (oligogenic or polygenic inheritance) culminating in expression of the asthma phenotype [2].

Some genes may influence the development of asthma, while others modify asthma severity or the patient's response to therapy. Multiple variants in genes associated with asthma are strongly associated with autoimmune disease and also overlap with inflammatory diseases including cardiovascular diseases and cancer (called pleiotropy) [3-5]. Finally, interactions between genetic factors and environmental influences provide another layer of complexity.

Exploration of the genetics of asthma is hampered by the absence of a "gold standard" diagnostic test for asthma, few if any agreed upon biomarkers, and inconsistent application of a clinical diagnosis. To circumvent these issues, investigators have studied the distribution of asthma-related traits, including bronchial hyperresponsiveness and measures of atopy (eg, total serum IgE levels, skin test reactivity), in addition to the presence or absence of an asthma diagnosis [6]. (See "Asthma in adolescents and adults: Evaluation and diagnosis".)

GENETIC TECHNIQUES — Four main strategies are being employed to identify the genetic factors that predispose to the development of asthma:

●Case-control or family-based candidate or genomewide association studies

●Next-generation sequencing (targeted, whole genome) studies

●Multi-omics studies

●Animal models of asthma traits

Association studies — Association studies determine the relationship between certain disease characteristics and the presence of specific forms (referred to as alleles) of a particular DNA sequence [7]. With the publication of initial efforts in sequencing the human genome [8,9], the opportunity to genotype markers directly in genes of interest was greatly expanded, facilitating genetic association studies.  

Evolution of genomewide association studies (GWAS) for asthma — Early association studies focused on candidate genes, some of which (eg, chitinase 3-like 1 [CHI3L1], human leukocyte antigen [HLA-D]) had been identified by prior genomewide linkage studies [6,10-13]. With the development of publicly available reference databases summarizing genetic variation of human populations around the globe (eg, HapMap Project [14] and Thousand Genomes Project [15]), millions of polymorphisms (ie, substitutions, deletions, insertions) were identified in the approximately 20,000 to 25,000 genes across the 3 billion chemical base pairs that make up the human DNA [16,17]. This information was leveraged to develop GWAS that use microarrays of single nucleotide polymorphism (SNP) chips to test for associations between a specific disease or trait and polymorphisms across the entire genome, without having to genotype the entire genome. (See "Genetic association and GWAS studies: Principles and applications".)

●Linkage disequilibrium – GWAS arrays are designed on the principle of linkage disequilibrium, where two or more markers on a chromosome are physically joined through generations of a family (figure 1). SNPs that capture variation at nearby sites in the genome can be selected to represent those (untyped) SNPs, referred to as tag SNPs, reducing the number of SNPs that need to be genotyped from 3 billion to millions or less. (See "Genetics: Glossary of terms" and "Principles of complex trait genetics".)

●Allelic association studies – Allelic association studies can be performed by genotyping tag SNPs in unrelated individuals (cases versus controls) or in families of affected individuals (in which non-transmitted alleles are compared to transmitted alleles). Allelic association studies using GWAS have provided additional evidence supporting the role of several candidate gene regions in asthma that were identified from previous linkage and candidate gene studies. Importantly, GWAS analyses have identified novel genes and pathways that otherwise might not have been discovered.

●Meta-analyses of GWAS – Since the first asthma GWAS published in 2007, at least two dozen additional GWAS have been performed [18-27]. Following individual GWAS, researchers formed consortia and performed "meta-analyses," a method of pooling GWAS results to improve the power to detect true associations and test the generalizability of findings across study populations [28]. One of the largest GWAS meta-analysis for asthma confirms prior discoveries, identifies novel associations, and suggests that variants discovered so far only explain part of the genetic risk for asthma [5], and an even larger meta-analysis identified variants uniquely associated with childhood onset asthma versus adult onset asthma [29].

ORMDL3/GSDMB — The first asthma GWAS in a European sample showed a reproducible association between childhood onset asthma and markers near the ORMDL3/GSDMB genes, which code for ORM1-lie protein 3 and gasdermin-like protein, on chromosome 17q21 [30]. The same group of investigators expanded the sample from several thousands to >26,000 European participants with or without childhood-onset asthma, comprising the GABRIEL Consortium, identified five additional genes, and considerably strengthened the association at the 17q21 locus (eg, from P<10(-12) to P=6x10(-23)) [23].  

The EVE Consortium in the United States performed a meta-analysis of >13,000 European, African, and Hispanic American asthmatics and controls that replicated association with the ORMDL3-GSDMB region on 17q21, which also contains the genes for Ikaros family zinc finger protein 3 (IKZF3) and zona pellucida-binding protein 2 (ZPBP2) [31].

The ORMDL3/GSDMB locus in chromosome 17q12–q21 was one of the strongest associations for asthma in the EVE Consortium [31] and is regarded as one of the most consistent asthma association loci to date [32]. An important observation in the EVE Consortium is that the SNPs significantly associated in the discovery population were not associated among the EVE subjects of African ancestry and African Americans in several independent studies [33-35], which supported the argument that European-associated (and European-selected) SNPs do not adequately tag common causal variants relevant for non-European individuals (see 'Genetic diversity' below). More recently, the largest GWAS to date, which focused exclusively on multiple populations of varying degrees of African ancestry, showed evidence of ancestry heterogeneity, demonstrating that the strength of genetic association (measured by effect size) increased as the average proportion of European ancestry increased; conversely, the higher the average proportion of African ancestry, the smaller the effect size [36].

IL33, ST2, PYHIN1 — The EVE Consortium also replicated association of the interleukin-33 (IL33) and ST2 (IL1R1) genes with asthma, in addition to identifying a novel susceptibility locus at the PYHIN1 (interferon-inducible protein X) gene, which is uniquely associated with individuals of African descent [31].

In an independent GWAS on eosinophil counts in >9000 Icelanders, DeCode Genetics observed that IL33 and IL1RL1 SNPs, which were noted in the EVE Consortium, were strongly associated with atopic asthma, but not non-atopic asthma [37]. IL33 is a member of the IL-1 family that induces Th2 cytokines, and the IL-33 receptor ST2 (known as interleukin 1 receptor-like 1 or IL1RL1) exists as a membrane and circulating soluble protein. IL-33, initially described for its role in expulsion of extracellular parasites from the gut [38], is produced by mast cells following IgE-mediated activation [39].

Greater IL33 expression has been observed in airway smooth muscle cells [40] and airway epithelium of patients with asthma compared with healthy individuals [41]. The DeCode Genetics team has identified a rare IL33 loss-of-function variant associated with lower eosinophil counts, less production of the IL33 protein, reduced binding to the ST2 receptor, and a reduced risk of asthma in Europeans [42].

IL-13 — IL-13 promotes IgE production by B cells, generation of eosinophil chemoattractants, and contractility of airway smooth muscle cells. Based on the identification of a susceptibility locus at IL13-RAD50-IL4 [5], the association of IL-13 with asthma symptom severity [43], and the known effects of IL-13, anti-IL-13 monoclonal antibodies (ie, lebrikizumab and tralokinumab) were developed to treat asthma. Despite evidence in favor of a role for IL-13 antibodies, clinical trials have not documented a benefit. (See "Investigational agents for asthma", section on 'Anti-IL-13 antibodies'.)

Thymic stromal lymphopoietin — A major finding in the EVE Consortium meta-analysis is confirmation of the association with the susceptibility locus for thymic stromal lymphopoietin (TSLP), which is an epithelial-cell-derived cytokine that is important in initiating allergic inflammation [31]. A single TSLP SNP (rs1837253) identified as protective against risk for allergy, asthma, and airway hyperresponsiveness, but not in linkage disequilibrium with other SNPs in the TSLP locus, influences TSLP secretion from primary nasal epithelial cells [44].

The GWAS identification of a susceptibility role for TSLP has led to an important clinical application with the development of a novel therapeutic agent, a human anti-TSLP monoclonal immunoglobulin G2-lambda antibody (AMG 157 or tezepelumab), which binds human TSLP and prevents receptor interaction. In clinical studies, tezepelumab reduces allergen-induced bronchoconstriction and exacerbation rates and improves lung function, as described separately. (See "Treatment of severe asthma in adolescents and adults", section on 'Anti-thymic stromal lymphopoietin (tezepelumab)' and "Treatment of severe asthma in adolescents and adults", section on 'Persistently uncontrolled asthma'.)

GWAS of asthma-associated traits — GWAS of asthma-associated traits, such as lung function, have been performed. For example, a meta-analysis on percent predicted forced expiratory volume in one second (ppFEV₁) values as a measure of lung function in asthma was performed on GWAS data from four European American asthma populations ([i] Severe Asthma Research Program [SARP]; [ii] The Epidemiology and Natural History of Asthma: Outcomes and Treatment Regimens [TENOR] multicenter study; [iii] a subset of the Collaborative Studies on the Genetics of Asthma [CSGA]; and [iv] two subsets from the Asthma Clinical Research Network [ACRN] clinical trials [the Tiotropium Bromide as an Alternative to Increased Inhaled Corticosteroid in Patients Inadequately Controlled on a Lower Dose of Inhaled Corticosteroids (TALC) and Best Adjustment Strategy for Asthma in Long Term (BASALT) trials]). Four of the 32 loci associated with ppFEV₁ values were Th1 pathway genes (IL12A, IL12RB1, STAT4, and IRF2), and together they explained 2.9 to 7.8 percent of the variance in this lung function outcome [45].

GWAS for the identification of shared and distinct risk genes for asthma and other allergic traits — An increasingly consistent message from GWAS is that a large number of asthma loci overlap with loci associated with allergy-related phenotypes and immunologically-related diseases such as autoimmune or infection-related diseases [5,46,47]. This phenomenon of variants in one gene affecting more than one disease or phenotype is called "pleiotropy" [48]. Disentangling the genetic basis for eczema, hay fever, and asthma has been difficult because these traits often present sequentially in the life of the patient (ie, the atopic march), occur together, and the development of one allergic phenotype can predict the development of another [49]. Large meta-analyses have been performed to determine to what extent genetic risk factors are shared between asthma and other allergic phenotypes including hay fever, eczema and food allergy, and which ones are distinct. A GWAS of allergic disease (asthma, allergic rhinitis or eczema) in approximately 360,000 individuals identified 136 independent variants [47], and a GWAS on >200,000 individuals found 41 independent risk loci associated with allergic rhinitis [50].  

Success and limitations of GWAS — The Trans-National Asthma Genetic Consortium (TAGC) published a worldwide asthma GWAS meta-analysis that included >142,000 individuals from 75 independent GWAS comprised of diverse racial/ethnic groups, a doubling of the largest GWAS previously performed [5]. TAGC confirmed nine known loci (IL1RL1, TSLP, IL13-RAD50-IL4, HLA-DRB1/HLA-DQA1, IL33, LRRC32, RORA, SMAD3-SMAD6-AAGAB, 17q12-q21), identified two new associations at known asthma loci, and identified five new loci.

Despite the success of asthma GWAS, several factors appear to have limited discovery, such as clinical heterogeneity of the asthma phenotype, age of onset of disease, and environmental exposures. For example, the chromosome 17q12-q21 locus is one of the strongest and most replicated associations for asthma to date, but an effect size (odds ratio) of only 1.16 was observed in the TAGC GWAS (see 'IL-13' above). However, when studies have limited analyses to more homogeneous cases and account for age of onset of disease and exposure, the effect size has increased [51]. Several studies have focused on genetic architectures of asthma specific to age of onset, demonstrating the power of focusing on specific subtypes of asthma [29,52].  

Honing in on the causal variant(s) in asthma risk loci has been challenging. While initial studies implicated the ORMDL3 gene, this locus is within a 300-kb region that includes a dozen genes of wide-ranging functions, and three of these genes, gasdermin B (GSDMB), IKAROS family zinc finger 3 (Aiolos [IKZF3]), and zona pellucida binding protein 2 (ZPBP2), are in tight linkage disequilibrium with ORMDL3, which has complicated identification of the true causal variant(s) driving association in this region [53,54]. (See 'ORMDL3/GSDMB' above.)

In an "expression Quantitative Trait Locus" (eQTL) study (see 'Multi-omics studies' below), SNPs associated with asthma in a subset of the GABRIEL sample were consistently and strongly associated (P<10^-22) with transcript levels of ORMDL3 [30], and a separate study identified polymorphisms in a putative promoter region of ORMDL3 which altered transcriptional regulation of the gene and correlated with changes in Th2 cytokine levels [55], shedding light on its potential role in asthma. (See "The adaptive cellular immune response: T cells and cytokines", section on 'Th2'.)

Additional studies implicated genes encoding GSDMA and PGAP3, also in this locus, as independent asthma loci [5,56,57]. Interestingly, it has long been observed that the "core" 17q locus is associated with childhood asthma, but in the most extensive review of the role of the 17q12-q21 locus in asthma, it was suggested that the associations at the proximal and distal loci are not specific to early-onset asthma, but are mediated by ORMDL3, PGAP3 and GSDMA expression [32]. (See 'ORMDL3/GSDMB' above.)

One of the most significant findings in the TAGC meta-analysis is confirmation of an observation from other GWAS of complex traits: most significantly associated SNPs are located in noncoding regions of the genome [5]. In TAGC, all top SNPs in the 18 loci were located in non-coding sequences except for a missense variant in IL13. Rather than being located in coding regions of the gene(s), the top TAGC associations were enriched in enhancer marks, especially in immune cells, and are therefore most likely to be involved in gene regulation. Enhancers play a role in driving cell-type-specific gene expression, and can activate transcription of their target genes at great distances.  

The UK Biobank Study includes approximately 500,000 phenotyped and genotyped individuals from the UK for which it is possible to leverage existing data to test for replication of prior findings. Relying on the self-reported status of asthma, interrogation of this resource determined that at least 28 of 31 published SNPs associated with asthma in European individuals are associated with asthma in the UK Biobank sample. However, these 31 SNPs only explain 2.5 percent of the risk of disease [58]. The same group has postulated that, by combining replication from the UK Biobank Study with additional risk variants from other asthma GWAS, the following genes are potentially the strongest candidates for asthma: IL1RL1 (ST2), HLA-DQA1, HLA-DQB1, TLR1, IL6R, ZPBP2, GSDMA, and GSDMB.

Next generation sequencing — Next generation sequencing (NGS), also referred to as massively parallel or deep sequencing has significantly advanced genomic research [59,60]. With NGS, nearly all of the 3 billion bases of the human genome are sequenced multiple times and much faster – as much as 50,000-fold faster – than conventional sequencing platforms (eg, Sanger sequencing). NGS can be used to sequence the entire genome, or specific parts of the genome, such as the exome (coding genes) [61] or a targeted gene(s) [62]. (See "Next-generation DNA sequencing (NGS): Principles and clinical applications".)

Analyzing genetic variation outside of protein-coding regions and variants beyond SNPs is a major step forward in dissecting common, complex diseases, but is a daunting analytical task. Some notable examples of success in identifying underlying genetic mechanisms for asthma using NGS technology are described in the following sections.

Exome sequencing — Exome sequencing, while useful when applied to Mendelian traits in which most causal alleles disrupt protein-coding sequences, has proved of limited value in complex traits. Exceptions include those diseases for which highly penetrant variants (ie, copy number variants) play a key role in disease risk (ie, autism) and in families where multiple individuals are affected by a complex disease [63].

Using exome technology, investigators have identified a functional asthma variant in the GSDMB gene [64]. However, in a comprehensive exome study of asthma, the EVE Consortium investigated the role of rare (<1 percent frequency) and low-frequency (1 to 5 percent) variants using Illumina's HumanExome BeadChip array and identified several ethnic-specific associations, but concluded that rare coding variation is not likely to explain a significant proportion of asthma heritability [65].      

Targeted deep resequencing — The development of NGS technology allowed for targeted deep resequencing of candidate gene loci, typically in coding exons and flanking noncoding regions, to identify rare variants potentially contributing to disease. The EVE Consortium selected nine candidate asthma-associated genes and a subset of European American, African American, and Hispanic asthma cases and controls and resequenced the coding exons and flanking noncoding regions [66]. Although the team expected to discover associations between rare coding variants and risk of asthma, the majority of significant associations were for rare noncoding variants flanking the coding exons, and multiple rare variants at single loci contributed to disease.

By refining the phenotype to asthma following severe respiratory syncytial virus (RSV) bronchiolitis in infancy, four novel, potentially functional, nonsynonymous (coding) variants were identified by targeted sequencing in the following genes: ADRB2 (beta-2 adrenergic receptor), FLG (filaggrin), NCAM1 (neural cell adhesion molecule 1), and NOS1 (nitric oxide synthase 1) [67].

Whole genome sequencing — Because the vast majority of asthma risk variants identified by GWAS lie outside of coding regions, the optimal tool is whole genome sequencing (WGS) in large numbers of well-characterized cases and controls. WGS allows for an unbiased analysis of all types of variation (SNPs, structural variants [SVs] including duplications or deletions of bases, inversions and translocations), variants of common to rare frequencies and coding and non-coding. Challenges include the complexities of "calling" SVs (identifying variants from sequence data), the high costs associated with computation of vast amounts of data (in terabytes and even petabytes), and statistical methodologies.

In one of the first examples of applying WGS technology to asthma, multiple copy number variants (CNVs), SVs, and rare coding variants were identified with limited validation in a founder population [68]. More recently, the Consortium of Asthma in African Populations of the Americas (CAAPA) [69,70] and consortia who are part of the NHLBI-supported Trans-Omics for Precision Medicine (TOPMed) program have begun leveraging WGS technology to identify novel associations with asthma, and results from these programs are forthcoming [71]. With the availability of large research consortia, discovery of novel asthma genes using WGS is now underway (ie, IL33 [42], CRISPLD2 [72]).

Next-generation genotyping arrays — A major application of NGS has been improved imputation panels and the design of next-generation genotyping arrays [69,73]. GWAS is a powerful tool for potentially identifying associations between a trait and variants in the genome; however, GWAS arrays are designed to capture relatively common variation SNPs (with a minor-allele frequency >0.05) and the majority of commercially available GWAS arrays were designed from European populations (therefore missing much of the common variation in non-European populations). Even with the addition of imputation to fill in the gaps, it is not possible to account for low-frequency (minor-allele frequency between 0.005 and 0.05) or rare variants (<0.005), although this category of variation accounts for the vast majority of human variation [15]. Sequence-based reference datasets cataloging population variation (Thousand Genomes Project [15], CAAPA [69,70], the Michigan Imputation Server) have facilitated genotype imputation [74].  

Multi-omics studies — GWAS have been successful in identifying genetic variants associated with asthma susceptibility, as described above, but the vast proportion of asthma GWAS associations involve intergenic or intronic variants that could potentially affect gene expression and whose direct role is not understood. A molecular paradigm, which takes into account that DNA is transcribed into RNA, RNA is translated to proteins subsequently controlling metabolism, and DNA methylation controls this process at the transcriptional level, has a greater potential for understanding the true underlying mechanisms of asthma than a single "omics" approach. "Omics" refers to a global assessment of a set of molecules, and omics research has been driven by the revolution of technological advances that include array and NGS technology and methodological approaches that support the interrogation of "big data" and the modeling of biologic networks [75].

Other omics technologies, such as transcriptomics (gene expression), proteomics and metabolomics (measures of protein and metabolites, respectively), and epigenomics (measuring the collection of epigenetic marks throughout the genome) have been incorporated into asthma genetics research, with increasing success [76]. Examples of the application of each of these omics approaches in asthma are described below:

Epigenetics — Epigenetics (chemical modifications of DNA that switch parts of the genome on and off) is thought to be one of the mechanisms by which the environment interacts with the genome to cause changes in gene expression (ie, heritable changes in gene expression due to non-coding changes to the DNA) [77-79]. As an example, DNA methylation, a reversible covalent modification of DNA in which a methyl group is transferred from S-adenosylmethionine to cytosine residues at cytosine-guanine (CG) dinucleotides by DNA-methyltransferases, is associated with reduced gene expression [80]. (See "Genetics: Glossary of terms" and "Principles of epigenetics".)  

Several observations suggest epigenetics may be involved in pathogenesis of asthma [81].

●First, the concordance rate in monozygotic twins of only approximately 50 percent argues for non-genetic factors [82,83].

●Second, complex gene-environment interactions are involved in asthma, which could reflect epigenetic genome modifications. Interactions have been observed between maternal smoking during pregnancy and markers, such as interleukin-1 receptor antagonist (IL-1RN) with a significant increase in risk of asthma in offspring [84]. In utero exposure to rural environments (ie, endotoxin exposure) seems to have protective effects against the development of asthma [85,86]. Neonatal immune cells were found to harbor nearly 600 differentially methylated regions that could distinguish children who would go on to develop asthma by the age of nine years, and the same study found that methylation of the SMAD3 gene was significantly increased in asthmatic children of asthmatic mothers and associated with childhood risk of asthma.

●Third, sex-specific differences exist, where the prevalence of asthma is higher in boys during prepuberty, but highest among girls after puberty [87]. Also, a parent-of-origin effect exists in susceptibility to asthma and elevated total serum IgE [88] with a clear difference in risk to offspring of asthmatic mothers compared with asthmatic fathers, raising the possibility that imprinted genes may be involved [89-93].

●A number of genetic studies of asthma have reported linkage and association in clusters of related genes [48], which suggests a higher order, epigenetic level of gene regulation.

●Multiple studies have identified candidate genes and asthma phenotypes using peripheral blood [94-96], buccal [97], and, more recently, upper airway sampling of nasal cells as a proxy for the lower airway [98,99]. A study combining GWAS data with methylation and transcriptome data from whole blood and isolated CD4+ T cell samples from asthmatics and non-asthmatics showed that local CpG methylation mediates some of the functional effects of cis-acting asthma risk variants on ORMDL3 and GSDMB gene expression [100].

Expression quantitative trait (eQTL) mapping — DNA is transcribed into messenger RNA (mRNA) which plays a role in making proteins. Transcriptomics refers to the study of the collection of all these gene readouts, or transcripts, present in a cell. Gene expression is heritable [101,102]. Expression quantitative trait loci (eQTL) mapping is a process connecting RNA expression levels to genetic variation, and its overlay with GWAS is a powerful tool for identifying networks of genes involved in disease [103-105].

Several studies have integrated findings from asthma GWAS with cataloged genome-wide gene expression data, such as the GTEx public resource [106-115]. Many of the asthma eQTL studies to date have focused on modestly relevant target tissue such as immortalized lymphoblastoid cell lines (LCLs) [116-118] and peripheral blood mononuclear cells (PBMCs) [55,119,120].

●In one of the first asthma eQTL studies, SNPs associated with asthma in a subset of the GABRIEL sample were consistently and strongly associated (P<10^-22) with transcript levels of ORMDL3 [30].

●A separate eQTL analysis used lung samples from transplant patients to identify variants affecting gene expression in human lung tissue, then integrated their lung eQTLs with GWAS data from GABRIEL to determine one of their strongest eQTLs was a SNP in the chromosome 17q21 region [107]. Resting peripheral blood mononuclear cells (PBMCs) and upper airway epithelial cells from children participating in the Children’s Respiratory and Environmental Workgroup (CREW) were used to perform an eQTL focused on SNPs associated with childhood-onset asthma in the 17q12-21 genes; SNPs regulating GSDMB expression in airway epithelial cells appeared to contribute to childhood-onset asthma, but that SNPs regulating expression of 17q12-21 genes in blood did not [121].  

There appears to be a strong correlation between the transcriptomes of the nasal airway epithelium and the bronchial epithelium, and some of the top published asthma GWAS genes are differentially expressed in the nasal airway epithelium between asthmatics and nonasthmatics [122].

A major advance in the field of transcriptomics and application of eQTL to asthma has been the development of RNA sequencing, called "RNA-Seq," which uses NGS technology to determine both the presence and quantity of RNA.

Metabolomics and proteomics — Metabolomics and proteomics have been used to explore various aspects of asthma including phenotyping [123,124], pathogenesis [125-127], and altered metabolism [125,128].

●The inflammatory marker, leukotriene E4 (LTE4), may predict asthma exacerbations and responses to treatment, and distinguish between severe and mild asthma [129,130], although this biomarker does not have an established role in asthma management.

●The relationship between genetics and metabolism has been delineated through several metabolic diseases and population studies. Some links have been suggested; for example, regulation of sphingolipids has been correlated with the ORMDL3 gene [131], and some groups are beginning to implicate sphingolipid metabolism in the pathogenesis of asthma [127].

●Comprehensive proteomics studies of asthma have not been conducted to the same extent as metabolomics, but successful application of this platform includes the observation that S100A8 and S100A9, albumin, cytokeratins, actin, complement cascades hemoglobin, and α2-macroglobulin are all protein biomarkers associated with asthma identified in multiple studies and/or multiple biofluids [132], and plasma levels of erythropoietin (EPO) and soluble GP130 is associated with childhood asthma [133].

Animal models — Different animal species have been used to study asthma including Drosophila fruit flies, rats, guinea pigs, dogs, and swine, but the most common model is the mouse [134]. Certain strains of mice either naturally manifest or can be manipulated to express certain aspects of the asthma phenotype, including the allergic response and innate airway hyperresponsiveness. Linkage studies of cross-bred strains of phenotypic extremes, or broad cross-strain association studies have long been employed in an attempt to map the underlying genetic determinants of these traits. Animal models also serve as a powerful tool for understanding asthma disease mechanisms and the evaluation of both safety and efficacy of novel therapeutics.

Murine models have been especially useful for validation of genetic variants identified in human association studies, including ORMDL3 and other genes in the 17q12-q21 locus [135-140], IL33 [141,142], ST2 [142,143], IL4R [144], IL13 [145], and TSLP [146]. In an early application of integrating a mouse model for asthma (eg, airway hyperreactivity) and asthma GWAS data (eg, the EVE Consortium), the Kv channel interacting protein 4 (KCNIP4) was identified as a novel candidate gene for asthma, and was replicated in the GABRIEL consortium [147].

GENETIC DIVERSITY — Studies of asthma genetics have not adequately sampled the diverse human populations afflicted with asthma, although representation of non-European populations in meta-analyses is increasing [5,31]. One review revealed that genetic studies in White populations (ie, of northern and western European ancestry) represented 60 percent of the association studies reported from 1987 to 2005 [10]; conversely, there were only 25 studies (3 percent) of African-based populations and 41 studies of Hispanic populations. These populations are clearly under represented, despite the fact that they suffer disproportionately from asthma morbidity and mortality [97,148,149].

Certain allelic variants in allergic response candidate genes are more common in people of non-European descent. In other cases, the "wild type" allele, rather than the variant, confers risk of the trait. One example is the functional variant T allele at position -260 in the CD14 gene, which has been associated with lower total IgE levels and less severe asthma [150-156]. In contrast, a functional polymorphism leading to lack of expression of Duffy antigen/receptor for chemokines (DARC) appears to increase susceptibility to asthma and atopy among certain populations of African descent [157].

Variants associated with response to therapy vary considerably in frequency according to ancestry. The most notable example is a common coding variant (Gly16Arg) in the gene encoding the beta-2 adrenergic receptor (ADRB2) [158]. Asthma patients of African and Asian ancestry are more likely to carry homozygous (Arg¹⁶Arg), which is associated with lower peak flows during regular treatment with albuterol, compared with asthma patients who are homozygous (Gly¹⁶Gly) [159].

Replication of genome-wide association studies (GWAS) findings in non-European groups has not always confirmed associations observed on populations of European ancestry, supporting the notion that certain genes (or polymorphisms in those genes) may be unique to different ethnic groups. For example, as described above, associations in single nucleotide polymorphisms (SNPs) near the ORMDL3 gene reported in the first asthma GWAS have been widely replicated in several ethnically diverse populations. However, the SNPs significantly associated in the discovery population (European), and replicated by other groups, were not significantly associated with asthma in several independent African American populations [33,34,160], until the TAGC meta-analysis [5]. The weak and inconsistent associations at this locus among African ancestry individuals may be attributable to different early-life asthma endotypes, the breakdown of linkage disequilibrium, and difference in 17q allele frequencies in African-derived genomes [32]. In the largest GWAS focused exclusively on populations of African ancestry (Consortium on asthma among African Ancestry Populations, or CAAPA) [36], in addition to evidence for association for some previously associated asthma loci whose discovery was driven by non-African populations, association was also observed for novel loci that could be specific to asthma risk in African ancestry populations. Collectively, these studies suggest that polymorphisms may explain, at least in part, the marked disparities observed in risk for allergic asthma in certain populations.

GENE-ENVIRONMENT INTERACTIONS — Concordance rates for atopy among monozygotic twins raised together are only 50 to 60 percent, suggesting that differences in exposure to certain environmental triggers may account for some of the disparity of disease expression [82]. In a study of asthma and allergic rhinitis among 3808 pairs of Australian twins, about 40 percent of the genetic liability for both diseases was due to environmental factors [1]. Elsewhere, it has been demonstrated that genes and the environment contribute approximately equally to asthma and associated traits such as total IgE [161]. The marked increase in asthma prevalence over the past century supports the role of unknown environmental factors.

Added to the complexity is the observation that associations with alleles at candidate genes and interactions between these genes might only be observed among certain subpopulations with nearly identical environmental and genetic backgrounds. As an example, an association between the glycoprotein CD14(-260)C>T variant (chromosome locus 5q23-31) and low total IgE was noted in school children living in urban/suburban Tucson, AZ; however, the opposite association was reported in a farming community [150,162]. In other studies, the association of asthma with CD14(-260)C>T was influenced by pet exposure, the dose of endotoxin in the domestic environment, or country living [151,163,164], suggesting that the unusual role that endotoxin exposure plays in asthma may be due to a unique combination of susceptibility genes and the degree of exposure to endotoxin.

Certain alleles may have divergent effects depending on environmental exposures.

●In a study of 983 children, single nucleotide polymorphisms related to ORMDL3 and GSDMB at chromosome locus 17q21 were associated with decreased risk of asthma by age six among children exposed to farm animals, but increased risk of asthma among those with an older sibling [165]. (See 'Association studies' above.)

●Environmental tobacco exposure has been shown to affect the extent to which significant associations are observed between candidate gene variants and asthma and atopy [163,166-168]. The interaction of early life tobacco smoke exposure and certain genetic variants to increase asthma risk was demonstrated in a study of 372 families with asthma [169]. Certain single nucleotide polymorphisms (SNPs) in the 17q21 region increased the risk of early-onset asthma and this risk was further increased by early life tobacco smoke exposure. Additionally, variants in the NAT1 gene are associated with risk of asthma among children exposed to secondhand smoke [170].

●Certain SNPs in the transforming growth factor-beta (TGF-beta) gene were associated with increased airway responsiveness and increased asthma exacerbation rates in cohorts of Costa Rican and non-Hispanic White children [171]. In this study, high dust mite exposure further increased the likelihood of asthma exacerbations among patients with alleles of two SNPs (rs2241712 and rs1800471).

●In a separate study, dust mite exposure modified the effect of the SNP rs117902240 in the interleukin (IL)-2 receptor subunit beta (IL2RB) gene on forced expiratory volume in one second (FEV₁) among children with asthma; the SNP was positively associated with FEV₁ in conditions of low dust mite exposure, but negatively associated with FEV₁ in conditions of high dust mite exposure [172].

PHARMACOGENETICS — Some asthmatic patients do not respond to the most commonly prescribed therapeutics [173], and it appears likely that genetic factors influence an individual patient’s response to a specific medication. Pharmacogenetics is a tool that studies how an individual's genetic makeup determines his/her drug response with the goal to optimize drug efficacy and reduce toxicity. Multiple candidate gene studies have identified variants in genes and response to beta-2 agonists (ADRB2, corticotropin releasing hormone receptor 2, arginase 1 [174-176]), leukotriene antagonists (arachidonate 5-lipoxygenase, leukotriene A4 hydrolase, leukotriene C4 synthase, cysteinyl leukotriene receptor 2) and inhaled glucocorticoids (CRHR1, TBX21, FCER2) [176,177].

Using a different approach, the GWAS technique has been used to assess whether a genetic variant might explain variability in the responsiveness to inhaled glucocorticoids among patients with asthma [178-180].

●Among subjects in the Childhood Asthma Management Program, an association was found between SNPs in the gene for glucocorticoid-induced transcript 1 (GLCCI1) and response to inhaled glucocorticoids [178]. The clinical effect of the SNP was small: the increase in forced expiratory volume in one second (FEV₁) in the treated subjects who were homozygous for the mutant allele was only about one-third of that seen in similarly treated subjects who were homozygous for the wild-type allele.

●SNPs in the gene encoding for a low-affinity IgE receptor (FCER2) have been associated with response to glucocorticoids in both European American and African American asthmatics and one of these SNPs has been replicated in independent studies [179,181].

●The adrenal restrictive HSD3B1 (1245) genotype was recently found to be associated with glucocorticoid resistance among patients participating in the Severe Asthma Research Program (SARP) III cohort, and was replicated in SARP 1 & II [182].

Despite the expansion of pharmacogenetics studies of anti-inflammatory medications such as inhaled glucocorticoids and leukotriene modifiers (LTMs), there remains limited clinical applicability [180].  

ON THE HORIZON — Genomewide association studies (GWAS) findings combined with expression Quantitative Trait Locus (eQTL) information and other "omics" is beginning to be used to inform drug repurposing or development of novel therapies. An integrative QTL mapping and pathway enrichment study was used to examine data from a GWAS of zileuton response plus mRNA expression profiles plus leukotriene production data to identify genes and pathways associated with differential responsiveness to zileuton [183].

Integrative functional genomics studies including a multi-omics approach and new approaches, such as phenome-wide association studies (PheWAS) are also on the horizon [184]. PheWAS flips GWAS on its head by detecting the association of a specific variant with a range of clinical outcomes and phenotypes.  

Polygenic risk scores (PRSs) are a summation of an individual’s particular genetic variants weighted by their disease-specific effect sizes, in which these effect sizes are typically derived from an external, independent genome wide association study (GWAS). PRSs have already demonstrated success in predicting risk of disease for coronary artery disease and breast cancer [185]. Identification of genetic associations for asthma and associated traits leveraging PRS is nascent, but early studies are promising [186,187].

SUMMARY

●Asthma is a syndrome that is passed down through families in complex patterns. Some components of the asthma phenotype are clearly heritable, although the specific genes and their complex interactions remain under investigation. Genetic testing for risk of asthma is not yet clinically useful. (See 'Introduction' above.)

●The study of asthma genetics is complicated by a number of factors, such as different genes in different individuals leading to the same phenotype, multiple genes acting in one individual to produce the given phenotype, and complex interactions between environmental factors, in addition to the problematic lack of a "gold standard" diagnostic test for asthma. (See 'Challenges in studying asthma genetics' above.)

●Further complexity arises from the influence of asthma pathogenesis genes, asthma severity modifying genes, and genes that modify the response to asthma treatments. (See 'Challenges in studying asthma genetics' above.)

●The four main strategies used to identify the genetic factors that predispose to the development of asthma are case-control or family-based candidate or genomewide association studies, next-generation sequencing (targeted, whole genome) studies, multi-omics studies, and animal models of asthma traits. (See 'Genetic techniques' above.)

●Association studies determine the relationship between certain disease characteristics and the presence of specific alleles of a particular DNA marker. Using this technique, a number of genes, such as ORMDL3/GSDMB, thymic stromal lymphopoietin (TSLP), and interleukin-33 (IL33), have been identified as possibly important. (See 'Association studies' above.)

●A further observation from genomewide association studies (GWAS) of asthma is that most of the significantly associated SNPs are located in noncoding regions of the genome. (See 'IL-13' above.)

●Exome sequencing, while useful when applied to Mendelian traits in which most causal alleles disrupt protein-coding sequences, appears to be of limited value in complex traits like asthma. (See 'Exome sequencing' above.)

●Whole genome sequencing (WGS) allows for an unbiased analysis of all types of variation (SNPs, structural variants [SVs], variants of common to rare frequencies and coding and non-coding) and is a promising approach to characterizing asthma risk variants identified by GWAS that lie outside of coding regions. (See 'Whole genome sequencing' above.)

●The role of epigenetic mechanisms in the pathogenesis of asthma remains poorly understood, but several observations suggest that epigenetics is likely to be involved. (See 'Epigenetics' above.)

●There appears to be a strong correlation between the transcriptomes of the nasal airway epithelium and the bronchial epithelium, and some of the top published asthma GWAS genes are differentially expressed between asthmatics and nonasthmatics in the nasal airway epithelium. (See 'Expression quantitative trait (eQTL) mapping' above.)

●Certain groups of people, such as those of African and/or Hispanic ancestry, have been underrepresented in asthma genetics research, despite the fact that they suffer disproportionately from asthma morbidity and mortality. (See 'Genetic diversity' above.)