Introduction

Asthma is a complex respiratory disease characterized by chronic inflammation of the airways and frequently associated with atopy, pulmonary obstruction and bronchial hyper-responsiveness against a diversity of stimulus [1]. Its prevalence varies widely among different populations around the world (1–18%) [2]. Familiar clustering [3], twin studies [4], and genetic studies [5], [6] support an important genetic component of the disease, with an estimated heritability of 60% [7].

Before the advent of genome-wide association studies (GWAS) [8], almost a thousand candidate-gene association studies for asthma and related traits were published [9]. Considering the gene as the unit of replication and using a broad definition for asthma, Ober & Hoffjan [5] elegantly summarized the accumulated evidence for candidate-gene association studies from the literature by assessing the consistency of findings [10]. This yielded a ranking of candidate genes based on the number of positive associations between any polymorphism and any asthma trait [5]. As a result, eight non-HLA genes were put forward among the most replicated (in >10 independent studies) and, therefore, these genes were suggested as firm candidates for asthma susceptibility [5]. Four of these genes were located in the linked region for asthma on chromosome 5q: interleukin (IL) 4 (IL4), IL13, CD14 and the β2-adrenergic receptor (ADRB2); one gene was located in the linked region 6p21: the tumor necrosis factor (TNF); one was the first positionally cloned asthma gene, ADAM metallopeptidase domain 33 (ADAM33); another was the gene encoding the α chain of the IL-4 and IL-13 receptors (IL4R); and finally, the gene encoding the IgE Fc receptor beta-subunit (MS4A2, alias FCER1B). However, most of them were assessed in studies with limited sample sizes averaging ≈200 individuals per group [5], [11], and lacked of a systematical analysis in candidate-gene studies by surveying more than few variants (for example, by using tagging SNPs [tSNPs]). To date, despite the fact that more than ten GWAS of asthma have been published, none of these eight firm candidates have been replicated at genome wide significance, nor have been found in close proximity of GWAS hits, except for the IL13-IL4 region [8], [12]–[21].

Asthma is clinically recognized as an amalgam of several distinct phenotypes [22], [23], which blur the complex genetic architecture underlying the disease susceptibility. Among these phenotypes, the age-at-onset of asthma could differentiate asthmatic groups, so that genetic variants might inconsistently associate with childhood and later-onset disease [17], [24], [25]. Motivated by this evidence, here we aimed to assess the reliability of asthma associations for the eight most replicated non-HLA asthma candidate genes and to explore whether effects of risk alleles varied with the disease age at diagnosis.

Methods

Selection of tagging SNPs

Tagging SNPs (tSNPs) were selected by means of TagIT 3.03 [26], using available re-sequencing data from European samples from different sources (Table 1 and Table S2). The IL13 and IL4 genes, which lie in close proximity, were considered as a single region. Similarly, given the strong linkage disequilibrium (LD) between LTA and TNF genes [27], common variants of the LTA gene were also tagged and jointly analyzed with TNF. See Text S1 and Table S2 for further details.

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Table 1. Summary information used for the selection of tagging SNPs (tSNPs) on the candidate genes.

https://doi.org/10.1371/journal.pone.0073157.t001

Results

A total of 13 samples (1 case and 12 controls) were excluded from the analyses because of genotype quality (completion rate <90%). Out of the initial set of 82 tSNPs, 6 were found monomorphic (rs5744440, rs35684381, rs597040, rs8124875, rs614971 and rs17548816) by using both iPLEX® and an alternative genotyping method (see the Supplementary methods in Text S1). Only one tSNP (rs12361312 at MS4A2) deviated significantly from HWE expectations in the control group and was discarded from further analyses (Table S2). Therefore, a total of 75 tSNPs, which maintained an adequate coverage for all genes (r²≥0.85), and 211 imputed SNPs were considered for association studies in 1,865 samples (606 cases and 1,259 controls) (Table S3). The mean completion rate among the 75 tSNPs was 98.5% (P₂₅–P₇₅ = 98.7–100.0%), and the estimated overall genotype discordance rate among duplicated samples was 0.30% (95% CI  = 0.08%–1.09%).

Association testing revealed a total of 35 SNPs (16 tSNPs and 19 imputed) that were associated with either asthma or atopic asthma at nominal significance, although these were not considered significant in the context of the multiple comparisons (p-values >0.0012) (Table S3). Based on the premise that incorporating the age-at-onset in the analyses might increase the power to detect association [24], [25], we next used SA of asthma patients to estimate the age at diagnosis cutoff maximizing allele effects. For that, among the 35 SNPs that reached nominal significance, we first excluded the redundant SNPs from each gene using conditional logistic regressions for asthma or atopic asthma. We identified the following SNPs as the ones showing independent nominal associations: rs1800925 (−1112 C/T) in IL13–IL4 (only for atopic asthma), rs2071590 in LTA-TNF (only for asthma), rs569108 (Gly237Glu) in MS4A2, and rs1805015 (Ser478Pro) in IL4R (both for asthma and atopic asthma) and rs2787095 in ADAM33 (only for asthma) (data not shown). These 5 SNPs represented independent associations for each gene and coincidentally, these 5 SNPs had been associated with asthma or related traits in previous studies, but in this study we extended their association to a Southwestern European population with noticeable North African influences.

SA did not show any age at diagnosis cutoff that significantly maximized the association of rs1800925 (in IL13–IL4) with atopic asthma (lowest p-perm  = 0.063). In contrast, SA revealed allele effects peaking at the same age at diagnosis, 39 years (number of cases  = 427), for SNPs from MS4A2 and IL4R: rs569108 in MS4A2 (p-perm  = 0.005), and rs1805015 in IL4R (p-perm  = 0.001). However, SA revealed allele effects peaking at a different age at diagnosis for the SNPs from the other two genes: rs2071590 in LTA-TNF showing a maximum at 26 years (p-perm  = 0.002, number of cases  = 291), and rs2787095 in ADAM33 with a maximum at 14 years (p-perm  = 3.0E–04, number of cases  = 155). The results obtained from the SA analyses using the quartiles of the distribution of the age at diagnosis were equivalent to those obtained using it as a continuous variable (data not shown).

Testing associations on the sub-sample of cases with the age at diagnosis of asthma before the maxima determined by SA for each gene revealed 18 additional SNPs reaching nominal significance (Table S3). Five of these SNPs (0.013≤ p-value ≤0.050) were located in IL4R gene and only one of them constituted a positive finding in previous studies. After conditioning these new associations from IL4R to the SNP rs1805015, only one SNP showed independent association (rs3024676, p-value  = 0.021). The remaining 13 were all SNPs from ADAM33 (3.8E-5≤ p-value ≤0.039), and 6 of them have been associated in at least one previous study (Table S3 and Figure 1). After adjusting the association of these 13 SNPs in ADAM33 that emerged with the age at diagnosis cutoff for the SNP rs2787095, 7 SNPs (rs2787093, rs628965, rs628977, rs630712, rs598418, rs2853209, and rs603112) resulted independently associated from this SNP (0.012≤ p-value ≤0.048). Therefore, the advantages of taking into account the age at diagnosis varying effects for replication studies in asthma were clearly evidenced in ADAM33, a gene for which SNP-level replications are scarce in the literature [5], [43]. Otherwise, we would have missed >50% of SNPs of this gene that showed association in previous studies. For the LTA-TNF and MS4A2 genes, we only observed subtle increases of effect sizes for the SNPs that were revealed in our previous analyses, but did not evidence more SNPs reaching nominal significance (Table S3 and Figure 1). After a global FDR assessment accounting for all comparisons performed, only 10 SNPs in MS4A2, IL4R and ADAM33 genes showed an FDR <5%, which were considered associated at study-wise significance (Table 2). Among these, 7 SNPs were identified to be functional, as they were either predicted to cause missense changes in the protein encoded or had empirically demonstrated regulatory roles as deduced from ENCODE project experimental data [42] (Table S4).

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Figure 1. P-values of association by chromosome position with A) asthma ≤14years for ADAM33, B) asthma ≤39 years for MS4A2 and, C) asthma ≤39 years for IL4R.

P-values are expressed in –log₁₀ scale. The SNP number shown on the plot denotes the result for the most significant SNP for each gene and the results for the remaining were color coded to reflect their LD with this SNP based on pairwise r² values from the 1KGP. Estimated recombination rates (from 1KGP) were also plotted on the right axis to reflect the local LD structure.

https://doi.org/10.1371/journal.pone.0073157.g001

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Table 2. Association summary of the 10 SNPs that resulted significantly associated with asthma after adjustments for the multiple comparisons.

https://doi.org/10.1371/journal.pone.0073157.t002

In order to provide evidence for replication at these loci, we accessed the GABRIEL data, the largest GWAS meta-analysis in asthma performed in Europeans [17]. There, we were able to allocate 11 out of the 51 SNPs that reached nominal significance with asthma in our study (Table S5). Only the SNP rs1805012, located in IL4R, demonstrated in silico replication in GABRIEL (p = 5.7E-04), showing the same direction of effects as in our study.

Discussion

In this study, we have comprehensively analyzed the association of 286 common variants of eight candidate genes with asthma and atopic asthma in a case-control Spanish sample and found associations for 10 SNPs in three of them (MS4A2, IL4R and ADAM33) after considering all tests performed. We additionally provided in silico replication for IL4R with GWAS data from the GABRIEL study.

It is well known that the age-at-onset of asthma is associated with different phenotypic characteristics [44], and it has recently evidenced that age-varying genetic associations can cause non-replication and, consequently, lead to missing important genetic associations [45]. Therefore, here we re-evaluated the association of these genes by restricting the analysis to case subjects with an age at diagnosis of asthma before a cutoff that maximized allele effects of replicated variants. This allowed us to verify that association improved for certain genes, such as ADAM33, as recently supported for other firm candidates [25], [46], [47], and also to gain insight in the genetic complexity of asthma associations at these candidate genes. Intriguingly, many of their effects peaked in the range of age at diagnosis between 20 and 45 years, coincidental with the age range with the maximum expression of the disease [48], [49]. It remains to be solved whether or not true biological mechanisms underlie this and previous observations [17], [25], [46]. Nevertheless, our results suggest that it will be worth considering the disease age at diagnosis in further studies, as well as in the research of improved asthma treatment and prevention.

To identify firm susceptibility genes and understand the biological processes underlying the development of the disease, replication in independent well-powered studies is essential, regardless of whether the first evidence of association was provided by a GWAS study or a candidate gene survey [50]. Besides, replication efforts allow testing the generalizability of findings in other populations, and discovering novel genetic loci contributing to phenotypic trait variability [51]. Particularly, testing the associations in populations of recent African ancestry will likely improve the detection of new risk variants [52], as they may offer the opportunity to refine the signal or to allocate the causal variants [53]. Our study aligns with these considerations, as it was performed in a population with sizeable North African genetic influences [28], [54], and with a sample size representing a substantially larger population of cases (>97%) than the vast majority of prior published case-control studies of these genes in unrelated individuals, although still far from optimal to detect weak effects.

Under a simplistic scenario assuming complete LD of associated SNPs with causal variants, the analyzed sample size provided a 70% power to detect a minimum risk of 1.45 for a risk allele frequency of 45% with a two-sided p = 0.0012 significance level for the primary outcome (asthma), and ranged from 14.6% to 52.6% for the analyses in subset of cases with atopic asthma and asthma before the age at diagnosis cutoff (Table S6). We acknowledge that risk effects of this range are in the upper bound of those expected for common variants in complex traits [55], which may have contributed to our failure to detect associations for some of the genes tested. Alternatively, our failure to find associations may possibly be attributed to: i) Our impossibility to test their association with more relevant traits or patient sub-samples (e.g. asthma drug responses [56], [57], environmental exposures [58]); ii) The use of controls self-reporting no personal or familiar history of pulmonary or allergic disease, but without a disease confirmation based on a clinical characterization (e.g. lung function measurements, SPT or specific IgE testing); iii) The lack of a true association with asthma susceptibility, as has been suggested for particularly relevant variants by meta-analyses [56]. Whichever is correct, a recently published study with on a similar sample size showed positive and negative association results fully congruent with ours [59]. In support of our results, we were able to replicate in silico the association of a SNP in IL4R in the largest GWAS study published to date that included more than 25,000 Europeans [17]. This SNP from IL4R, as well as few others from the same gene that were found associated in our study (rs1801275, rs1805015, and rs3024676), also demonstrated congruent effects and significant association in a recent GWAS of total IgE levels [60]. This evidence supports that, despite the enormous efforts to disentangle asthma genes such as those entailed by the GABRIEL study [17] or the EVE consortium [19], many more asthma susceptibility genes awaits its discovery.

Some recent replication studies focusing on candidate genes have utilized available arrays for genome wide genotyping [61]–[63] where common variants of many key asthma candidate genes could be insufficiently covered. In this respect, Michel et al. [59] indicated that only 37% of the previously associated SNPs from 14 candidate genes were captured by the array utilized by the same authors on the first GWAS for asthma and, surprisingly, not a single SNP from key asthma genes such as ADAM33, IL4 and CD14 was contained in their array [8]. Only after extending the study by further genotyping (and by imputation) on the same samples of their GWAS, these authors were able to consistently replicate many of the biological candidates that were missing from their GWAS [59]. We confirmed that the coverage of published GWAS for asthma performed in European populations to date has been insufficient for ADAM33 (<30%), even in a best-case scenario using the HapMap phase 2 data as a reference for comparisons (Table S7). If array comparisons were made against the 1KGP sequencing data [38], the coverage would be even lower (Table S7). Besides, it is worth noting that the estimated coverage of these genes might be inflated, as these were implicitly derived for HapMap CEU data and the same data was used to inform the SNP contents of the array, and we have assumed that the 100% of SNPs contained in the array were successfully genotyped. Effects similar to those related to the age-of-onset of asthma, exceptionally explored [17], could have also contributed to find no association for the genes explored here in the published GWAS for asthma.

In conclusion, here we found the association of 10 common variants in three biological candidate genes (MS4A2, IL4R and ADAM33) that attained study-wise significance, and one of them was also supported by in silico replication in GWAS data. Therefore, we provided independent support for their role as risk factors for the amalgam of asthma phenotypes. Moreover, our results evidenced the genetic complexity at some of these susceptibility loci and the importance of considering age-at-onset effects. Given the low statistical power of the present study, particularly limited in the case subset analyses when considering the age at diagnosis, further studies will be needed to identify causal variants and to unravel if these genes are truly associated with asthma, with atopy or with both.

Supporting Information

Acknowledgments

The authors want to thank Tobías Felipe for programming, and María Torres (CeGen, Santiago de Compostela Node) for her exceptional technical support with iPLEX® Gold assays. In addition, we would like to express our gratitude to the Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory (JDRF/WT DIL, Cambridge Institute for Medical Research) for granting the access to the MS4A2 re-sequencing data, and acknowledge that those who carried out the original analysis and collection of the data bear no responsibility for the analysis or interpretations of this study.