Genome-wide association studies in asthma: what they really told us about pathogenesis

Wjst, Matthias; Sargurupremraj, Muralidharan; Arnold, Matthias

Current Opinion in Allergy & Clinical Immunology:
doi: 10.1097/ACI.0b013e32835c1674
MECHANISMS OF ALLERGY AND ADULT ASTHMA: Edited by Stephen T. Holgate and J. Andrew Grant

Purpose of review: Over the past years, several consortia have provided a data deluge from large-scale, genome-wide association studies (GWASs) for numerous asthma and allergy related traits. Dozens of reviews have already summarized the main results, although a coherent picture is still missing, referred to as ‘missing’ or ‘unexplained’ heritability.

Recent findings: We identify the factors responsible for the unexplained heritability including imprecise phenotyping, biased single-nucleotide polymorphism selection (preferentially gene-based and high allele frequency with poor linkage disequilibrium tagging capacity), heterogeneity and insufficient significance ranking test statistics. In spite of these problems, three major outcomes can already be identified. First, rare variants give the highest risk estimates but are limited to small subgroups indicating a complex origin of asthma that may involve hundreds of variants that are either population, family or individual specific. Second, only a few common variants are shared amongst all asthmatics where the IL33/ST2 pathway turns out to be the most relevant factor. Third, transcription factor binding sites are enriched amongst the top association results pointing towards disturbed regulatory network function in asthma.

Summary: The next wave of asthma genetic studies will use full-genome sequencing and overcome most GWAS-associated problems. It will be the last step of a century-long search for asthma genes, satisfying scientific curiosity and, hopefully, also providing data applicable in translational medicine.

Author Information

aComprehensive Pneumology Center (CPC), Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg

bInstitute of Medical Statistics and Epidemiology, Technische Universitaet Muenchen, Munich

cInstitute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany

Correspondence to Matthias Wjst, Comprehensive Pneumology Center (CPC), Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Ingolstaedter Landstr. 1, Neuherberg, Germany. E-mail:

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Probably, never before in the history of medical research, a research method has been applied as broad to nearly all diseases, ranging from heart, lung, orthopaedic to psychiatric diseases as the genome-wide association study (GWAS). The asthma and allergy community had been on the forefront with at least 30 out of 1500 GWAS so far. Exact numbers are difficult to report as single-centre studies contributed to different consortia with overlapping traits and samples.

During the reporting period of 2011, asthma GWAS were still being published. Examples of original research papers include new asthma risk loci from Australia [1], Japan [2,3] England [4], Korea [5] and Russia [6]. Furthermore, a negative report on an assumed gene–environment interaction [7] and refined asthma/HLA associations has been published [8]. Innovative approaches include the screening of epigenetic changes in B lymphocytes, where CYP26A1, an enzyme regulating the cellular level of retinoic acid, was found to be associated with house dust mite allergic asthma [9]. Another important pharmacogenetic study showed an association of the GLCCI1 gene and response to glucocorticoid therapy in asthmatics [10] along with a GWAS of serum vitamin D levels in asthmatics [11].

The number of reviews summarizing the GWAS results [12–21,22▪,23–25,26▪,27,28] even exceeded the number of original research papers. It may therefore be worthwhile developing here a perspective that goes beyond listing gene names.

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The use of commercial chip systems for GWAS has been under intense debate [29]. Past reviews describe this as ‘missing’ heritability [30▪▪], while ‘unexplained’ heritability maybe a better description of this phenomenon. Although major genetic journals ignore the unexplained heritability (‘We welcome the reassuring message from the modellers that the sky is not falling, heritability is not missing and the models currently being used provide quantitative guidance’ [31]), there is a widespread concern that the bulk of GWAS still have not provided any useful functional disease explanation [30▪▪].

The basis of the GWAS strategy – picking up frequent point mutations by a company, managing to get a miniaturized assay on a chip and letting researchers find out in large cross-sectional studies what are the most significant disease association – has been more fuelled by commercial interests than by a rational strategy. At least, this has been the conclusion of the most cited papers in the GWAS period [30▪▪,32]. Although the single-nucleotide polymorphism (SNP) selection by the chip manufacturers was mainly gene-based, 88% of all associated SNPs are now in intronic or intergenic regions [33].

Imprecise phenotyping and phenotypic heterogeneity may have additionally reduced effect estimates, whilst the main problem seems to be the biased selection of SNP markers. To get a countable outcome, SNPs have been selected for being common in the Hapmap samples. The shortcomings of this approach have been discussed on multiple blog posts as well in many scientific papers [30▪▪,34–36].

Structural variation (repeats, duplications, deletions and inversions) were only partially included in GWAS [37] or could be analysed only indirectly. The genomic coverage of SNPs varied along the chromosomes with insufficient coverage of sex chromosomes [38]. Multiple assays also probed sequences that included unanticipated SNPs [39] leading to distorted allele frequencies. Unfortunately, also the linkage disequilibrium tagging characteristics of common variants did not work in many genomic regions as the linkage disequilibrium in the human genome is much weaker than anticipated from the Hapmap samples [39]. The major analytical approach was also rather primitive – ranking only the most significant differences between cases and controls, whilst ignoring the complex genetic interaction.

The conclusions therefore may not be unexpected that ‘many questions remain, … as most identified genetic variants contribute only nominally to overall disease risk, genetic disease mechanisms remain uncertain, and disease-associated variants are not consistent across studies’ [25]. It looks naïve to expect GWAS to be ‘hypothesis-free’ as assumed by some researchers, as there are numerous a priori hypotheses by population sampling and phenotyping strategy, SNP selection, genotyping platform and analysis strategy [36].

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Asthma genetics research can be divided into a period of early family studies starting with the heredity studies of Cooke 1914. It followed the period of genetic linkage studies since 1986 (summarized in [40▪▪]) that identified a couple of asthma genes by positional cloning, whilst the current, third period since 2007 was characterized by large GWAS (summarized by [30▪▪]).

Of all the genes published during the linkage period, a ‘top 100’ has been nominated with a small group of genes even standing out: ‘The elite group of genes that have been associated with an asthma or atopy phenotype [… were …] IL4, IL13, ADRB2, TNF, HLA-DRB1, FCER1B and IL4RA, as well as the CD14, HLA-DQB1 and ADAM33 genes’ [41▪]. It may be interesting to note that with a few exceptions most of these were never replicated in the four major asthma GWAS (Decode [42▪▪], Gabriel [43▪▪], EVE [44▪▪] and APCAT [45▪▪]). This may be because of an insufficient coverage of SNP marker at the regions of interest or – equally likely – replication is not possible as most disease-causing variants have been confined to single families and individuals.

Even the results between the major meta-analysis asthma GWAS do not fully match. Although the Gabriel study, for example, included more than 10 000 asthmatics from 23 studies (and should therefore be immune against bias introduced by regional population characteristics), the most significant association with a SNP in HLA-DQ (7.0 × 10−14) was not replicated in the ethnically diverse EVE study. Another variant at 17q21 (promoted as ‘ORMDL3’ association [46]) seems to be associated with childhood onset asthma only. Although it may be formally correct to assume an underpowered sample size in replication studies, this argument does not make so much sense from a biological viewpoint as already thousands of asthmatics have been included. It seems that there is simply no HLA genotype that is shared by all asthmatics.

A different situation comes with SNPs in IL33 and IL1RL1/ST2 that were associated in all EVE subgroups, and also in the three other meta-analyses as the second and third most prominent SNP in GABRIEL (reporting at least SNP in IL18R1 in high linkage disequilibrium with IL1RL1 SNPs), the first and third in Decode and the first and second most significant association in APCAT.

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Which of the numerous pitfalls affecting GWAS had the main impact is difficult to assess. The bias in selecting common SNPs at least has been severe as low-frequency (1–5%) and rare variant (<1% minor allele frequency) [47] is much more abundant in the human genome [48] than previously anticipated with every 17th base being exchanged [49].

Our own reanalysis of the supplemental data file deposited at the of the Gabriel study provides further insight as it includes also those SNPs with a minor allele frequency less than 1% that had been removed from the analysis. Such an exclusion of rare variants may have been a necessary quality control measure in the early days of genotyping but is now considered as being inadequate as the error rate of chip genotyping system is reported to be less than 0.01%. Upon reanalysis (Fig. 1), rare variants are more than seven-fold enriched in the group of highly significant SNPs, making up 45% of the 109 SNPs with P < 7.2 × 10–8. Rare variants are distributed amongst more than 50 unique genes with odds ratios up to 50. Calling of these rare variants certainly needs to be verified, statistics adjusted for rare events and residual stratification excluded before making any further conclusions. Most of these high-risk alleles are not even seen in the 1000 genomes CEU panel and are contributed by just one population. As outlined above, the analysis of rare variants is not an easy task [50,51] as calling of these variants is more difficult than with common variants. In addition, correct phasing of variants is necessary [52].

The importance of rare variants is further confirmed by the EVE consortium [53], in which the coding exons and flanking regions of nine asthma candidate-genes were sequenced showing an excess of P-values of rare variants in four genes including IL12RB1 amongst African-Americans. Rare variants in IL12RB1 were also associated with asthma amongst European-Americans, although by a different set of rare variants.

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Unfortunately, the long-known HLA association with asthma [54] could not be turned into any functional readout so far. The MHC II class proteins present peptides broken down to about 10 amino acid length, stimulating the expansion of T cells. According to their commitment, the MHC locus is one of the most genetically variable regions in the human genome, whilst a detailed knowledge of the HLA antigenic recognition sequence should allow some predictions about the antigen target. Indeed, a haplotype analysis of rheumatoid arthritis GWAS data identified recently three amino acid positions (11, 71 and 74) in HLA-DRβ1 with two other single-amino-acid polymorphisms that could almost completely explain the MHC association [55]. Such an analysis is still missing from the asthma GWAS.

As described above, IL33 is the most interesting finding from the GWAS. IL33 has been described only in 2005 and together with IL1, IL18, TGFβ and TSLP a major action site may be seen at the epithelial innate-immunity response to microbial antigens in the gut [56]. Dendritic cells induce the development of Treg cells through a TGFβ and retinoic-acid-dependent process. IL18 and IL33 are expressed also in other epithelial tissues [57], but when released extracellularly, IL18 is more associated with a prototypic Th1 response than IL33 that favours a Th2-type reaction [57]. IL33 binds with high affinity to IL33R/ST2 and involves MyD88 and TRAF6-dependent pathways. IL33 is regarded as a danger signal of damaged tissue regulating not only macrophage response to endotoxin [58], but also nematode infection [59]. IL33 promotes eosinophil survival, adhesion, superoxide production and degranulation. A recent review [57] concludes that IL33 action is similar to IL5 signalling but acting more on a local level. IL33 is synergistic with TSLP, IgE and SCF for mast cell survival, as well as IgE receptor signalling leading to the release of histamine [60]. Which functional variants in IL33 and its receptor are responsible for the observed association is unclear, with the IL33/ST2 association being primarily restricted to allergic asthma [42▪▪]. Experimentally induced allergic asthma confirms the dendritic-cell-mediated mechanism of IL33 [61].

Using the supplemental data file deposited at of the Gabriel study 2010 [43▪▪], we calculated the association results for childhood-onset disease under a random effects model. We took all genome-wide significant markers (P < 10−7) and extended the SNPs via linkage disequilibrium (r2 ≥ 0.9) based SNPs of the 1000 genomes project CEU panel [48]. This marker set was annotated using the variant effect predictor tool and all SNPs located in regulatory regions were extracted [62]. To infer functionality, we used these SNPs as input for intersection with eQTL information from two large-scale studies [63–65] accessible through GENEVAR. This resulted in 36 genes which are significantly (P < 0.01) associated with allele-specific differential expression in at least one of three tissues.

Analysis of conserved transcription factor binding sites around these genes returned significant (P < 0.01) enrichment of the TAL1α/E47 motif which is bound by TCF3, a binding partner of β-catenin and thus downstream effector in the Wnt signalling pathway [66]. Wnt signalling genes have been implicated in asthma pathogenesis before [67], whilst, just recently, TCF3-binding sites have been described as being enriched in asthma-associated gene sets [68]. TCF3 is also a negative regulator of CD5 expression during thymocyte development [69], whilst CD5 mitigates B cell receptor signalling so that cells can only be activated by very strong stimuli as bacterial proteins [70]. Finally, the purification of a complex associated with TCF3 identified DNA methyltransferases [71] that may be involved in some epigenetic changes implied earlier in asthma pathogenesis [19].

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The clinical relevance of GWAS results has been discussed before [72]. Although some new therapeutic targets have been identified like VCAM1 in multiple sclerosis, the number of these examples is limited.

What did we really learn so far? Asthma is a complex disease entity with a huge physiologic range from healthy airways, exaggerated bronchial hyperreactivity to lethal asthma attacks. It is unlikely that one or at least a few functional gene variants will ever explain all known pathophysiological events of innate and adaptive immune response [73]. Attempts are now underway to separate the asthma phenotype by latent class clustering of GWAS SNPs (Siroux, personal communication, 2012). Possibly, there might also be monogenic asthma cases that cannot be discriminated so far from other sporadic asthma cases. And of course, there are numerous false-positive associations, most of them unwittingly published, whilst many causative associations are still resting in the ‘long tail of the distribution’ not passing the arbitrary significance thresholds [74].

Although improved SNP panels are already available, the interest in improving on GWAS is rather modest. Some researchers already fear that the unexplained heritability is such a serious problem that it will remain intractable also by genome sequencing (see Fig. 2), as each individual variant tested alone has zero penetrance even when tested in a large sample.

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The negligible outcome of translational research [72,75] may not be disencouraging. We clearly do not need to catalogue all asthma-associated variants but find only those that explain the main pathology.

GWAS aiming at disease-causing genes should also be separated from the studies on variants associated with medication-related outcomes. A possible impact of genetic variants on β-agonist therapy (ADRB2, ARG1 and GSNOR), inhaled steroid use (CRHR1, GLCC11, TBX21, FCER2 and STIP1) or leukotriene signalling (ALOX5, LTC4S and SCCo2B1) may already be tested now in further clinical trials [26▪].

The next wave of asthma studies will probably not so much improve on SNP chips, but already use full-genome sequencing analysis [76]. For that purpose, we need to go back to family studies in which phased chromosomes can be used as reference genomes [77▪]. In addition, parental origin of genes may be a critical factor [33]. Lastly, as a pure genomic approach is at risk of being lost amongst many irrelevant genetic associations, cell-specific gene-expression pattern obtained from patients with various asthma types and stages may be equally important.

This may be the last step of a century-long search for asthma genes – satisfying scientific curiosity and hopefully also providing data applicable in translational medicine.

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Conflicts of interest

The authors declare no particular conflict of interest with the data and conclusions presented here. Matthias Wjst received funding during the past 3 years from the European Union and the Helmholtz Center Munich and lecture fees from various medical associations as well as from Novartis.

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Papers of particular interest, published within the annual period of review, have been highlighted as:

▪ of special interest

▪▪ of outstanding interest

Additional references related to this topic can also be found in the Current World Literature section in this issue (p. 124).

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asthma; GWAS; HLA; IL33; rare variants; transcription factor

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