Immunogenetics

, Volume 66, Issue 3, pp 143–151 | Cite as

Polymorphisms in recent GWA identified asthma genes CA10, SGK493, and CTNNA3 are associated with disease severity and treatment response in childhood asthma

Original Paper

Abstract

Recent genome-wide association studies (GWAs) have identified several new genetic risk factors for asthma; however, their influence on disease behavior and treatment response is still unclear. The aim of our study was the association analysis of the most significant single nucleotide polymorphisms (SNPs) recently reported by GWAs in different phenotypes of childhood asthma and analysis of correlation between these SNPs and clinical parameters. We have genotyped 288 children with asthma and 276 healthy controls. We provided here first replication of bivariate associations between CA10 (p = 0.001) and SGK493 (p = 0.011) with asthma. In addition, we have identified new correlation between SNPs in CA10, SGK493, and CTNNA3 with asthma behavior and glucocorticoid treatment response. Asthma patients who carried G allele in SNP rs967676 in gene CA10 were associated with more pronounced airway obstruction, higher bronchial hyper-reactivity, and increased inflammation. Higher bronchial hyper-reactivity was also associated with C allele in SNP rs1440095 in gene SGK493 but only in nonatopic asthmatics. In addition, we found that patients who carried at least one T allele in SNP rs1786929 in CTNNA3 (p = 0.022) and atopic patients who carried at least one G allele in SNP rs967676 in gene CA10 (p = 0.034) had higher increase in pulmonary function after glucocorticoid therapy. Our results suggest genetic heterogeneity between atopic and nonatopic asthma. We provided further evidence that treatment response in childhood asthma is genetically predisposed, and we report here two novel SNPs in genes CA10 and CTNNA3 as potential pharmacogenetic biomarkers that could be used in personalized treatment in childhood asthma.

Keywords

Asthma Association study Single nucleotide polymorphism Pharmacogenomics 

Introduction

Asthma is the most common serious chronic respiratory disease of childhood and affects about 10 % of children younger than 18 (Busse and Lemanske 2001). It is a complex, polygenetic disease, and its pathogenesis cannot be explained by a single mechanism or gene (Berce and Potocnik 2010a, b).

Several candidate genes for asthma were suggested by candidate gene association studies focused primarily on genes that encode proteins of immune response or allergic inflammation (Hoffjan and Ober 2002). The most replicated asthma associated genes so far include TNFα, IL4, IL13, ADAM33, GSTP1, and CD14 gene (reviewed in March et al. 2011 and Gu and Zhao 2011). Recent genome-wide association (GWAs) studies have identified several new genetic risk factors and suggested new biological pathways that were previously not associated with asthma (Hancock et al. 2009; Castro-Giner et al. 2009; Kim et al. 2009; Li et al. 2010; Sleiman et al. 2010). For more powerful genetic evidence however, the GWA study’s findings need to be replicated (Madore and Laprise 2010). The first gene identified by GWA study in asthma ORMDL3 (Moffatt et al. 2007), suggested to play role in sphyngolipid metabolism (Cantero-Recasens et al. 2010), was subsequently well replicated in several independent studies (Galanter et al. 2008; Wu et al. 2009; Hrdlickova and Holla 2011). On the other hand, many of the highly significant asthma candidate genes identified by more recent GWAs including carbonic anhydrase X (CA10), forkhead box D3 (FOXD3) (Li et al. 2010), sugen kinase 493 (SGK493) (Castro-Giner et al. 2009), DENN/MADD domain containing 1B (DENND1B) (Sleiman et al. 2010), transducin-like enhancer of split 4 (TLE4) (Hancock et al. 2009), and catenin cadherin-associated protein alpha 3 (CTNNA3 ) (Kim et al. 2009), however, still lack replication in independent populations.

There is strong evidence that asthma genetics is distinctive in that pathogenesis depends on phenotype (Anderson 2008). Many association studies in asthma also indicate the importance of analyzing candidate genes separately in different subtypes of asthma, such as atopic/nonatopic and childhood/adult form of disease; for example, gene CD14 was identified to be associated with adult (Smit et al. 2009) but not with childhood asthma (Perin et al. 2011). Gene CCR5 was associated with nonatopic asthmatics (Berce et al. 2008) but not with atopics (Nagy et al. 2002). In addition to risk factors, association studies also provide information about correlation between genes and clinical characteristics of patients including disease severity and progression. The most interesting correlations include airway hipper reactivity, pulmonary function, airway obstruction, serum immunoglobulin E (IgE) and blood eosinophil count (Ober and Yao 2011). Hancock et al. (2010) have identified multiple loci associated with pulmonary function in meta-analysis. Polymorphisms in genes for some interleukins, including IL4 and IL13, influence the total serum immunoglobulin E concentrations (Marsh et al. 1994; Kabesch et al. 2006) in asthma patients.

Unfortunately, most of the recent GWA studies in asthma did not analyze correlations between genotype and clinical characteristics of asthma patients. There are a lot of differences in the response to antiasthmatic therapy between individuals, and there is evidence that genetic polymorphisms could explain the great proportion of different response in asthma therapy (Berce and Potocnik 2010a, b). Genes involved in asthma pathogenesis often play important role in response to therapy as well as in disease behavior (Tantisira and Weiss 2009).

The aim of our study was the association analysis of single nucleotide polymorphisms (SNPs) identified in recent GWAs with asthma and asthma traits in children. We have selected SNPs and genes that are most significantly associated with asthma and have not yet been replicated in independent association studies. Moreover, most of the recent GWAs were performed in adult asthma so the aim of our study was also to see if the same SNPs are associated also with childhood asthma. Our study was the first to investigate correlation among selected recently identified asthma SNPs, clinical data, asthma phenotype (atopic vs. nonatopic) and treatment response to inhaled corticosteroids and antileukotriens.

Materials and methods

Patients and clinical measurements

Between January 1, 2007 and January 5, 2009, 288 children with asthma with median age of 11 years were enrolled in this study. All the children had mild or moderate persistent asthma. Of the these children, 50.7 % were treated in Pulmonary and Allergic Outpatients, Department of Pediatric Medicine, General Hospital Murska Sobota. The other 49.3 % were treated in the University Clinical Center Maribor. Asthma was diagnosed according to the National Asthma Education and Prevention Program (NAEPP) and American Thoracic Society (ATS) criteria (AST 1987; NAEPP 1991).

All the children treated in the outpatient clinic during the period of the study were included according to the inclusion and exclusion criteria. Patients with other chronic inflammatory diseases except asthma and atopic diseases were excluded from the study. Patients were free from any acute diseases or asthma exacerbation at the time when blood samples were taken. Parents signed informed consent for children younger than 15 years, while older children gave informed consent themselves. Genotype data from 276 nonatopic nonasthmatic age and sex-matched healthy individuals with median age of 13 years served as a control group.

Allergic status of asthmatics was determined with the skin prick tests to most common aeroallergens (Allergopharma, Reinbek, Germany) and with the specific IgE measurement (CAP-RAST Pharmacia&Upjohn, Freiburg, Germany).

Pulmonary function was measured with a Vitalograph 2150 spirometer (Compact, Buckingham, UK). For the purpose of our study, the values of forced vital capacity (FVC) and forced expiratory volume in the first second of expiration (FEV1) were recorded. We calculated the FEV1/FVC ratio and used it as a measure of bronchial obstruction. FEV1 and FVC were expressed as a percentage of the predicted normal value for sex, height, and age (AST 1987). All patients underwent spirometry before the treatment and repeated the test 4 weeks later. Change of FEV1 and also FEV1/FVC ratio was used as a measure of response to antiasthmatic treatment. One hundred ninety-nine of the patients were treated with glucocorticoids (fluticasone dry powder; Flixotide Discus. Glaxo), 89 with antileukotrien drug Singular.

Bronchial hyper-reactivity was assessed with a methacholine bronchoprovocation test before the institution of the antiasthmatic treatment in all 288 patients. We calculated provocative concentration of methacholine (PC20) causing a fall of FEV1 of 20 % from the initial value.

We measured fraction of exhaled nitric oxide (FENO) before the institution of antiasthmatic treatment with a Nixon analyzer (Aerocrine Inc., USA) in all 288 patients. The analyzer uses a chemiluminescence method for gas analysis.

A 12-ml sample of blood was drawn from each patient into tubes EDTA for genetic analysis and specific IgE measurement. In 146 patients, an eosinophil count and total IgE were measured in the blood.

Demographic data and clinical characteristics of asthmatics are presented in Table 1.
Table 1

Clinical and laboratory parameters in all asthmatics

Parameter

N

Minimum

Maximum

Median

Mean

Standard deviation

Age

288

5

19

11.00

11.45

3.28

FEV1 (%)

288

13

124

90.00

89.38

14.84

dFEV1 (%)

288

−28

61

3.00

4.85

13.29

FEV1/FVC

288

42

126

89.00

89.33

7.25

PC20 (mg/ml)

288

0.02

8.0

0.29

0.79

1.09

FENO (ppb)

288

5

125

35.00

42.62

30.88

Total IgE (IU/ml)

146

2

3673

361.00

551.18

640.80

Eosinophils

146

40

1520

444.00

514.69

327.14

FEV1 forced expiratory flow in first second before the treatment (in percent of predicted for age. height and sex), dFEV1 change of FEV1 in 4 weeks of antiasthmatic therapy, FVC force vital capacity, PC20 concentration of metaholine that caused 20 % fall of FEV1, FENO fraction of exhaled nitric oxide, Total IgE concentration of total immunoglobulin class E in peripheral blood, Eosinophils number of eosinophils in cubic millimeters of blood

Selection of candidate SNPs and PCR-RFLP test designing

We have performed in silico analysis of data from recent GWA studies. Using the web database HuGe Navigator, we determinate the number of independent replication studies of genes, which the top GWA SNPs are located on.

We choose six SNPs, i.e., rs967676 (CA10), rs1440095 (SGK493), rs2378383 (TLE4), rs10493343 (FOXD3), rs1786929 (CTNNA3), and rs2786098 (DENND1B), which were strongly associated with asthma in GWA studies and located in different independent loci. In addition, there was no previous studies of role of selected genes in therapy response in asthmatic patients and also almost no correlations with clinical parameters which describe asthma behavior.

We designed primers using bioinformatics tool Primer3 and check it with IDT oligo analyzer. Restriction enzymes were selected using the program GeneRunner. Because there is no enzyme, which would enable genotyping using restriction fragment length polymorphism (RFLP) method for SNPs rs2378383 and rs10493343, we have selected another two SNPs, which are in LD (D′ = 1, r2 > 0.8) with SNPs previously analyzed in GWA study (rs13298282 instead rs2378383 and rs832521 instead rs10493343).

DNA extraction and genotyping

Whole blood from patients and controls was used for isolation of the total genomic DNA. First, we isolated lymphocytes using Ficoll-Paque Plus (GE Healtcare, Uppsala, Sweden) according to the manufacturer’s instruction. With TRI reagent (Sigma, Steinheim, Germany) we isolated DNA and dissolved it in water at final concentration of 25 ng/μl.

Using bioinformatics tools, we dimensioned primers (internet application Primer3). Restriction enzymes were chosen using program GenneRunner. Genotyping of SNPs was performed by polymerase chain reaction (PCR) followed by RFLP.

The PCR reaction was carried out in a 10-μl reaction volume containing 50 ng of genomic DNA, 250 nM of each oligonucleotide primer, 1.5 mM MgCl2, 0.2 mM dNTP mix, 10 mM Tris–HCl and 0.5 U Taq polymerase (Fermentas, Vilinus, Lithuania). PCR conditions were as follows: preincubation at 95 °C for 10 min followed by 35 cycles of 1 min denaturation at 95 °C, 30 s annealing at 58–64 °C (depend on primers), and 30 s extensions at 72 °C for 5 min. PCR products were digested using restriction enzymes (Fermentas) at 37 °C overnight. The PCR products were electrophoresed on 2 % agarose gel and visualized using ethidium bromide under UV fluorescence. Supplementary Table 1 shows the optimal PCR conditionals appropriate restriction enzymes and size of PCR products before and after restriction.

Genotype and allele frequencies of all six analyzed candidate SNPs were in Hardy–Weinberg equilibrium in all groups of asthmatics and in the control group. In the association study, we compared the allele and genotype frequencies between each group of asthma patients and control subjects. We analyzed the genotype frequencies under dominant model (heterozygotes were analyzed together with homozygotes for minor allele) and recessive model (heterozygotes were analyzed together with homozygotes for major allele).

Statistical analysis

Data analysis was carried out using SPSS version 17.0 (SPSS Inc., Chicago, IL, USA).

Genotype and allele frequencies were calculated for the patients and control group. The χ2 test and two-sided Fisher’s exact test were used to calculate the significance of the difference in allele and genotype frequencies between asthmatic and controls. We calculated the odds ratio (OR) for asthma with 95 % confidence intervals (CI). Genotype interactions in connection on disease development were conducted by log-linear analysis.

With the t test for two independent samples, we analyzed the influence of genotype on clinical parameters, which are normally distributed quantitative traits: FEV1 and change of FEV1 after inhaled corticosteroid treatment (dFEV1). With Mann–Whitney test, we analyzed the influence of genotype on quantitative traits, which are not normally distributed: FEV1/FVC ratio, blood eosinophil count, total serum IgE, PC20, and FENO. Distribution was determinate by Kolmogorov–Smirnov test for groups larger than 50 individuals or Shapiro–Wilk test for smaller groups. Continuous variables were made dichotomous using maximum log likelihood analysis as suggested by Lück et al. (2009). Genotype interactions in connection on clinical characteristics and treatment response were also conducted by log-linear analysis. In all tests, p < 0.05 was considered to indicate statistical significance and are evaluated according to the BADGE guidelines.

Results

Association analysis

Tables 2 and 3 summarize the p values obtained after genotype and allele frequency comparison between patents and controls.
Table 2

SNPs locations and p values obtained after comparison of genotypes between patients and controls using different models (dominant, recessive, codominant); the data (p value, OR and CI) for the model with the best p value is included in the table

SNP (GENE)

Gen. dis. controls

Gen. dis. all patients

Gen. dis. atopics

Gen. dis. nonatopics

Model

All patients

Atopics

Non-atopics

rs967676a (CA10)

AA: 25.0 %

AG: 55.6 %

GG: 19.4 %

A: 52.8 %

G: 47.2 %

AA: 38.3 %

AG: 46.6 %

GG: 15.1 %

A: 61.6 %

G: 38.4 %

AA: 35.6 %

AG: 47.1 %

GG: 17.3 %

A: 59.2 %

G: 40.8 %

AA: 42.7 %

AG: 45.3 %

GG: 12.0 %

A: 65.4 %

G: 34.6 %

Dominant

AA vs. AG + GG

p = 0.001

OR = 1.88

(1.14–2.50)

p = 0.009

OR = 1.73

(1.14–2.56)

p = 0.007

OR = 2.16

(1.25–3.57)

rs832521b (FOXD3)

CC: 25.2 %

CT: 48.7 %

TT: 26.1 %

C: 49.6 %

T: 50.4 %

CC: 26.0 %

CT: 45.1 %

TT: 28.9 %

C: 48.6 %

T: 51.4 %

CC: 29.6 %

CT: 41.3 %

TT: 29.1 %

C: 50.3 %

T: 49.7 %

CC: 19.7 %

CT: 48.7 %

TT: 31.6 %

C: 44.1 %

T: 55.9 %

Dominant

CC + CT vs. TT

p = 0.490

OR = 1.15

(0.78–1.70)

p = 0.503

OR = 1.16

(0.75–1.50)

p = 0.376

OR = 1.31

(0.75–2.30)

rs1440095a (SGK439)

CC: 9.8 %

CT: 44.1 %

TT: 46.1 %

C: 31.9 %

T: 68.1 %

CC: 13.0 %

CT: 48.9 %

TT: 38.1 %

C: 37.5 %

T: 62.5 %

CC: 7.7 %

CT: 54.8 %

TT: 37.5 %

C: 35.1 %

T: 64.9 %

CC: 21.3 %

CT: 39.9 %

TT: 38.8 %

C: 41.3 %

T: 58.7 %

Recessive

CC vs. CT + TT

p = 0.277

OR = 1.37

(0.8–2.38)

p = 0.603

OR = 1.29

(0.38–1.49)

p = 0.011

OR = 2.49

(1.32–5.00)

(0.20–0.76)

rs2786098a (DENND1B)

GG: 59.6 %

GT: 36.6 %

TT: 3.8 %

G: 77.9 %

T: 22.1 %

GG: 67.2 %

GT: 30.7 %

TT: 2.1 %

G: 82.6 %

T: 17.4 %

GG: 65.9 %

GT: 32.4 %

TT: 1.7 %

G: 82.1 %

T: 17.9 %

GG: 67.2 %

GT: 28.9 %

TT: 3.9 %

G: 81.6 %

T: 18.4 %

Dominant

GG vs. GT + TT

p = 0.075

OR = 1.38

(0.97–1.96)

p = 0.192

OR = 1.31

(0.88–1.96)

p = 0.285

OR = 1.38

(0.81–2.38)

rs13298282b

(TLE4)

CC: 0.0 %

CT: 19.4 %

TT: 80.6 %

C: 9.7 %

T: 90.3 %

CC: 1.1 %

CT: 17.4 %

TT: 81.5 %

C: 9.8 %

T: 90.2 %

CC: 1.2 %

CT: 20.9 %

TT: 77.9 %

C: 11.7 %

T: 88.3 %

CC: 0.0 %

CT: 12.2 %

TT: 87.8 %

C: 6.1 %

T: 93.9 %

Recessive

CC vs. CT + TT

p = 0.255

OR = /c

( / )

p = 0.185

OR = /c

( / )

p = /c

OR = /c

( / )

rs1786929a (CTNNA3)

CC: 6.9 %

CT: 49.6 %

TT: 44.5 %

C: 31.7 %

T: 68.3 %

CC: 10.2 %

CT: 44.7 %

TT: 45.1 %

C: 32.6 %

T: 67.4 %

CC: 11.3 %

CT: 41.7 %

TT: 47.0 %

C: 32.2 %

T: 67.8 %

CC: 10.2 %

CT: 44.9 %

TT: 44.9 %

C: 32.7 %

T: 67.3 %

Recessive

CC vs. CT + TT

p = 0.213

OR = 1.47

(0.81–2.86)

p = 0.158

OR =1.16

(0.58–1.28)

p = 0.442

OR = 1.52

(0.61–3.85)

Gen. dis. distribution of genotypes, p p value calculated by chi-square test, () % confidence interval, (/) value cannot be calculated

aSNP is previously associated with asthma in GWA study

bSNP is in LD with previously associated SNPs in GWA study

cComparison is not possible because there are no homozygotes for minor allele C in group of nonatopic asthmatics

Table 3

Genotype and allele frequencies for SNP rs967676 in CA10 gene and statistical significance; summation of patients with atopic and those with nonatopic asthma is not equal to all asthmatics because for 15 of patients atopic status could not be determinate

Genotype frequencies for SNP rs967676 (CA10 gene)

All asthmatics n = 288

AA /AG + GG

AA + AG/GG

Atopic asthmatics n = 189

AA/AG + GG

AA + AG/GG

Non-atopic asthmatics n = 84

AA/AG + GG

AA + AG/GG

Controls n = 276

 AA: 38.3 %

p < 0.01

p = 0.21

AA: 35.6 %

p < 0.01

p = 0.81

AA: 42.7 %

p < 0.01

p = 0.06

AA: 25.0 %

 AG: 46.6 %

OR = 1.88

OR = 1.35

AG: 47.1 %

OR = 1.73

OR = 1.12

AG: 45.3 %

OR = 2.11

OR = 2.16

AG: 55.6 %

 GG: 15.1 %

(1.14–2.50)

(0.96–2.70)

GG: 17.3 %

(1.14–2.56)

(0.68–1.79)

GG: 12.0 %

(1.25–3.57)

(0.98–3.57)

GG: 19.4 %

Allele frequencies for SNP rs967676 (CA10 gene)

All asthmatics n = 288

A/G

Atopic asthmatics n = 189

A/G

Non-atopic asthmatics n = 84

A/G

Controls n = 276

   

 A: 61.6 %

p < 0.01

A: 59.2 %

p = 0.06

A: 65.3 %

p < 0.01

A: 52.8 %

   

 G; 38.4 %

OR = 1.45 (1.14–1.82)

G; 4 0.8 %

OR = 1.30 (0.99–1.69)

G; 34.7 %

OR = 1.71 (1.18–2.44)

G; 47.2 %

   

p value calculated by chi-square test

n number of individuals included in the analysis, OR odds ratio values in () is 95 % confidence interval

Frequency of AA homozygotes for SNP rs967676 in gene CA10 was significantly higher in asthma patients (38.27 %) compared to control group (25.00 %. p = 0.001, p corrected for multiple testing = 0.14; OR = 1.88). We confirmed the association in both subgroups, atopic (p = 0.009. OR = 1.73) and nonatopic asthmatic (p = 0.007, OR = 2.11) (Table 4).
Table 4

Association of SNPs rs967676, rs1440095, rs2244012, and clinical parameters

Gene (SNP)

Subgroup

FEV1 (%)

FEV1/FVC

FeNO (ppb)

PC20 (mg/ml)

Total IgE

Eosinophils

CA10 (rs967676) AA vs. AG + GG

All patients

0.664

0.014

0.292

0.035

0.035

0.004

Atopic asthma

0.300

0.357

0.746

0.940

0.301

0.065

Nonatopic asthma

0.093

0.020

0.068

0.029

0.130

0.022

SGK439 (rs1440095) CC+CT vs TT

All patients

0.459

0.159

0.769

0.532

0.711

0.913

Atopic asthma

0.267

0.696

0.942

0.439

0.751

0.761

Nonatopic asthma

0.245

0.119

0.228

0.015

0.442

0.445

CTNNA3 (rs1786929) CC+CT vs TT

All patients

0.584

0.025

0.211

0.123

0.542

0.220

Atopic asthma

0.648

0.188

0.070

0.225

0.106

0.470

Nonatopic asthma

0.572

0.061

0.773

0.196

0.651

0.329

The values in the table are p values calculated by t test for two independent samples for FEV1 and with nonparametric Mann–Whitney test for all other parameters

FEV11 forced expiratory flow in first second before the treatment (in percent of predicted for age, height, and sex), FVC force vital capacity, FENO fraction of exhaled nitric oxide, PC20 concentration of metaholine that caused 20 % fall of FEV1, total IgE concentration of total immunoglobulin class E in peripheral blood, Eosinophils number of eosinophils in mm3 of blood

We found allele C for SNP rs1440095 in gene SGK493 as a risk allele for asthma development, but only in nonatopic phenotype. We found higher frequency of individuals with CC genotype (recessive model for minor allele C) in nonatopic asthma patients (21.25 %) compared to frequency of individuals with the same genotype in the group of healthy individuals (9.77 %, p = 0.011, OR = 2.49).

We found no significant influence of polymorphisms rs278609 (TLE4), rs832521 (FOXD3), rs1786929 (CTNNA3) and rs13289282 (DENND1B) on risk for asthma.

We did not identify any significant interactions among loci in connection with risk for asthma development. The lowest but still insignificant association that we obtain for interaction of genes CA10 and DENN1B (p = 0.129) in analysis of subgroup of patients with atopic asthma compared to controls.

Influence of SNPs on clinical parameters

We also analyzed the effect of genotype at selected SNPs on disease behavior. We found the impact of SNPs rs967676 (CA10), rs1440095 (SGK493), and rs1786929 (CTNNA3) on clinical parameters important in asthma. p values and genotype/clinical parameters correlations are summarized in Table 4.

Allele G of SNP rs967676 is associated with more severe asthma. Carriers of one or two G alleles have significantly lower FEV1/FVC ratio (median, 88), lower provocative concentration of metaholin PC20 (median, 0.27 mg/ml), higher total IgE value (median, 440 IU/ml), and higher eosinophil count (median, 508 mm−3) compared to homozygote for allele A (medians: FEV1/FVC = 91, p = 0.014; PC20 = 0.33 mg/ml, p = 0.035; total IgE = 296 UI/ml, p = 0.035; eosinophil count = 370 mm−3, p = 0.004). Impact of this SNP on bronchial hyper-reactivity, measured by PC20 value, is even more significant in subgroup of patients with nonatopic asthma phenotype (p = 0.029).

Carriers of TT genotype for rs1786929 have significantly higher FEV1/FVC ratio (median, 90) compared to heterozygotes or CC homozygotes (median, 88, p = 0.025).

In addition, we found that SNP rs1440095 has impact on airway hyper-reactivity. Homozygote for allele T or heterozygote have lower value PC20 (median, 0.26 mg/ml) compared to carriers of two C alleles (median, 0.4 mg/ml, p = 0.015).

In addition, log linear analysis shows significant interaction of SNPs rs967676 in gene CA10 and rs1786929 in gene CTNNA3in connection with FEV1 (p = 0.048). Combination of AA genotype for rs967676 and CC or CT genotype for rs1786929 influences on higher FEV1 and so milder form of asthma.

Influence of SNPs on response to antiasthmatic therapy

We found an association of SNPs rs967676 (CA10) and rs1786929 (CTNNA3) with response on inhaled glucocorticoids as it is shown in Table 5.
Table 5

Association of SNPs rs967676 and rs1786929 with response to the inhaled corticosteroids therapy

Gene (SNP)

Subgroup

dFEV1 (%)—p value

CA10 (rs967676) AA vs AG + GG

All patients

0.293

Atopic asthma

0.034

Nonatopic asthma

0.207

CTNNA3 (rs1786929) CC vs CT + TT

All patients

0.022

Atopic asthma

0.027

Nonatopic asthma

0.161

dFEV1 change of FEV1 in 4 weeks of antiasthmatic therapy; p values are estimated using t test for two independent samples

Pulmonary function (FEV1) in asthmatics with CC genotype for SNP rs1786929 decrease (dFEV1 = −0.14 %), but in those with CT or CT genotype, FEV1 increase (dFEV1 = 7.75 %, p = 0.022). We found that SNP rs967676 in CA10 gene affects asthma treatment response, but only in patients with atopic asthma phenotype (p = 0.034). Atopic asthmatic with AG or GG genotype had higher increase of FEV1 (8.54 %) compared to AA homozygotes (3.89 %).

Discussion

In this study, we analyzed the association of most significant asthma associated SNPs recently reported by GWA studies, with different phenotypes of childhood asthma. We also provide first analysis of correlation between recently reported GWA asthma SNPs and clinical parameters, including disease severity and treatment outcome. We report here new correlation between SNPs in CA10, SGK493, and CTNNA3 with asthma behavior and glucocorticoid treatment response.

We found in our study T allele at SNP rs1440095 in gene SGK493 as a risk allele for nonatopic asthma and identify new association between this SNP and severity of airway hyper-reactivity observed only in nonatopic asthmatics. We found in our study also that SNP rs967676 in CA10 is associated with treatment outcome only in atopic asthmatics; however, its influence on disease behavior parameters (FEV1/FVC ratio, PC20, and eosinophil count) was observed only in nonatopic asthmatics. Our results suggest genetic heterogeneity between atopic and nonatopic asthma. Previously meta-analysis showed that gene IL4 represents a risk factor mainly for atopic asthma (Liu et al. 2012a, b). A mutation in gene CCR5 is protective factor only for nonatopic asthma (Berce et al. 2008). The impact of CD14 gene on clinical characteristic was observed only in nonatopic asthma patients (Perin et al. 2011). Genetic studies in atopic and nonatopic asthmatics are in the line with functional studies, which reported many differences in molecular pathogenesis between atopic and nonatopic asthma (Walker et al. 1994; Hashimoto et al. 2005; Bottini et al. 2005).

Asthmatics who do not respond to glucocorticoid treatments represent up to 10 % of all patients affected with asthma (Ito et al. 2006). Many studies, including our previous, confirmed that mechanisms of glucocorticoids is complex and depends on many genes (Tantisira et al. 2004; Poon et al. 2008; Berce and Potocnik 2010a, b; Berce et al. 2012). SNPs in genes CRHR, TBX21, and FCER2 have been confirmed to have impact on response to inhaled corticosteroids (ICS) (Tantisira et al. 2004; Poon et al. 2008). We previously identified genes ORMDL3 and CTLA4 as important factors in response to anti-asthmatic treatment with ICS (Berce et al. 2012; Berce and Potocnik 2010a, b). In this study, we have identified two new associations between SNPs in CTNNA3 and CA10 genes and response to ICS treatment. We found that asthmatics with CT or TT genotype in SNP rs1786929 in gene CTNNA3 have significantly higher increase in forced expiratory flow in first second after therapy and thus better respond to ICS compared to homozygote for allele C in the same SNP. The CC homozygotes in CTNNA3 did not benefit from ICS treatment at all. CTNNA3 is a key protein of the adherence junction complex in epithelial cells and plays an important role in cellular adherence (Liu et al. 2012a, b). Although the putative role of this protein in the lungs or airways is unknown, antibodies to “self-antigens” including alpha-catenin have been identified in serum of asthmatic patients (Liu et al. 2012a, b; Bernstein et al. 2013). In addition to identified pharmacogenetic association with CTNNA3, we found that atopic asthmatics with AG or GG genotype in SNP rs967676 in CA10 gene had higher increase in pulmonary function and thus better respond to ICS compared to AA homozygotes. The gene CA10 encodes a protein that belongs to the carbonic anhydrase family of zinc metalloenzymes, which catalyze the reversible hydration of carbon dioxide in various biological processes (Mori et al. 2009). Although CA10, according to our result, represents a genetic risk for asthma independent of disease phenotype, its role in treatment with glucocorticoids seems to be important only in atopic patients.

In our study, we also found the influence of SNPs in genes CA10, CTNNA3, and SGK493 on asthma severity. Asthma characteristics that describe the severity of disease phenotype are intensity of inflammation, pulmonary function, intermittent bronchial obstruction, bronchial hyper-reactivity, and allergy (Madore and Laprise 2010). Among asthma patients, carriers of at least one G allele at SNP rs967676 in gene CA10 have significantly more pronounced bronchial obstruction (FEV1/FVC ratio). Similarly, those asthmatics with two G alleles have higher IgE level, which means that G allele is associated with atopy. The same allele is associated with airway hyper-reactivity, which we determinate by measurement of provocative concentration of metaholine (PC20) and also with higher eosinophil count. There is also a significant interaction of rs967676 in gene CA10 and rs1786929 in gene CTNNA3 in connection with pulmonary function, which confirmed the influence of the same allele on more severe asthma phenotype. We have also confirmed the effect of SNP in CA10 gene on three disease behavior parameters (FEV1/FVC ratio, PC20, and eosinophil count) in a subgroup of nonatopic asthmatic but not in the group of patients with atopic phenotype of disease. Interestingly, we found allele A of SNP in CA10 gene as susceptibility allele for asthma and associated with milder form of disease. Similar virtually controversial observations of the opposite effect of same genotype on disease subphenotypes has been observed previously in association studies in many complex diseases, for example, in inflammatory bowel disease, it was reported that same alleles in genes PTPN22 and NOD2 increase risk for Crohn’s disease but, on the other hand, show significant protective effects in ulcerative colitis, exceptions that may reflect biological differences between the two sub phenotypes (Jostins et al. 2012). Ulcerative colitis (UC) and Crohn’s disease (CD) are closely related subphenotypes of the inflammatory bowel disease and share most genes with some risk genes that are specific for UC or CD. We could give similar logical hypothesis that there are two subphenotypes in asthma, “mild asthma subphenotype” and “severe asthma subphenotype,” that share most genes but which have some biological differences resulting from subphenotype specific genes and biological pathways. Having this in mind, we propose logical hypothesis AA genotype in CA10 gene is risk genotype for asthma, but only for “mild asthma sub phenotype” and has limited influence in “severe asthma sub phenotype”. In addition, many association studies in asthma show that genetic influence on disease behavior is often independent of their influence on disease susceptibility; for example, SNPs in IL10 were found to be associated with asthma severity although the frequencies of alleles of those SNPs in asthmatics were similar as in control group (Karjalainen et al. 2003). On the other hand, GWA study found a strong association between rs2897443 on RAD50 intron, but it does not influence on major asthma characteristic such as reduced FEV1 or FVC (Li et al. 2010). More precise mechanistic explanations for the role of CA10 in asthma pathogenesis is yet to be determined when more functional data will be available for CA10 gene. Currently, very limited functional data for CA10 suggest CA10 plays an important role in the central nervous system and brain development (Taniuchi et al. 2002); however, evidences shows that the gene is expressed also in lung (EST Database 2013) and therefore may have influence on respiratory diseases.

In addition, we were the first to found the association between SGK493 and airway hyper-reactivity, but only in nonatopic asthmatics, what is consistent with our findings about effect of this gene on nonatopic asthma development. Function of the gene SGK493, which we have confirmed as asthma gene only in group of nonatopic asthmatics, is unknown. It could be involved in pathological states as protein kinases mediate most of the signal transduction in eukaryotic cells (Cohen 2002; Kim et al. 2009). In this study, we are also the first to found that SNP rs1786929 (CTNNA3) effect on bronchial obstruction in asthma in general.

Our results provide first independent replication of GWA identified asthma associated SNPs in genes CA10 and SGK493. We did not, however, confirm the associations of other 4 GWA reported asthma genes (FOXD3, CTNNA3, DENND1B, and TLE4). The GWAs, which were the source for our candidate SNP selection, included patients from different populations with different phenotypes and severity of disease, what might explain why we did not replicate these associations. Researchers agree that asthma is not a single disease but rather an array of disorders that share common characteristic (Kiley et al. 2007). Three of the GWA studies from which we extracted candidate SNPs and genes for our association study were performed in populations of adult asthma patients (Castro-Giner et al. 2009; Kim et al. 2009; Li et al. 2010), while the other two GWA studies, Sleiman et al. (2010) and Hancock et al. (2009), were studying childhood asthma. There are many studies showing differences in genetic risk factors between childhood and adult asthma (Bottema et al. 2005; Heinzmann et al. 2000). For example, associations of SNPs in IL13 gene and adult asthma are highly significant in the studies of Heinzmann et al. (2000) and Howard et al. (2001),, but it was not confirmed by Demeo et al. (2002) and He et al. (2003) in children. Similarly, the impact of SNPs in genes CD14 (O’Donnell et al. 2004) and GSTP1 (Child et al. 2003) on risk for asthma is age dependent. Our results suggest that genes FOXD3 and CTNNA3 do not represent a risk factor for asthma in children although they may have an important role in adults. Another way to classify asthma is according to the severity of phenotype. Children in our study had mild or moderate persistent asthma, but in above-mentioned studies, patients with severe types of asthma were predominant (Kim et al. 2009). Gene DENND1B may be significantly associated with severe form of asthma, but not with asthma in general. In our study, we found shows significant association when we compare genotype frequencies between patients and controls using likelihood ratio test, but the p value was higher than 0.05 for all comparisons using chi-square and Fisher exact tests. There is substantial genetic heterogeneity in asthma genetic architecture among different populations (Fryer et al. 2000; Muro et al. 2008). Hancock et al. (2009) associated the TLE4 gene with childhood asthma, but in this study, only children from Mexico City were included. Mexico City is one of the most polluted cities in the world, so the role of TLE4 may be important in asthma phenotype caused by environmental factor such as pollution. Using the multivariate models, we sorted out interactions between SNPs rs967676 in gene CA10 and rs1786929 in gene CTNNA3 in connection with FEV1 (p = 0.048). Multivariate models, however, revealed that most of the loci independently contribute to disease risk and clinical characteristics.

The potential limitation of our study is lower number of patents and controls enrolled compared to GWA studies that usually require large number of patients, so additional independent association studies are highly warranted to replicate candidate SNPs and genes that we fail to replicate in our study as well as our novel findings of correlations between genotypes and clinical characteristics of asthma patients, including treatment outcome. The independent replication studies providing new association data are necessary to enable subsequent meta-statistical analysis in large number of patients and controls for confirmation of disease associated genes and clearer picture of asthma genetics.

Anyway, we can say that our results suggest that genes CA10 and also SGK493 may be an important risk factor for asthma development, especially for a nonatopic phenotype. CTNNA3 is not major contributor or genetic risk factor for childhood asthma but rather influence the disease expression and response to therapy. CTNNA3 could also be an important biomarker to predict the treatment outcome with ICS. Our results also provide additional genetic evidence, suggesting different role of genes in atopic and nonatopic asthma behavior and contribute to our understanding of the asthma pathogenesis.

Notes

Acknowledgments

This study was supported by the Slovenian research agency. Many thanks to Dr. Vojko Berce and Dr. Maja Kavalar for patients’ samples and clinical data. Special thanks to Alojz Tapajner for his help and advices in statistical analysis.

Supplementary material

251_2013_755_MOESM1_ESM.docx (16 kb)
Supplementary Table 1Conditions for PCR-RFLP reactions and fragment size after completion of test. (DOCX 16 kb)

References

  1. Anderson GP (2008) Endotyping asthma: new insights into key pathogenic mechanisms in a complex, heterogeneous disease. Lancet 372:1107–1119PubMedCrossRefGoogle Scholar
  2. AST - American Thoraric Society (1987) Standards for the diagnosis and care of patients with chronic obstructive pulmonary disease (COPD) and asthma. Am Rev Respir Dis 136:225–244CrossRefGoogle Scholar
  3. Berce V, Potocnik U (2010a) Association of Q551R polymorphism in the interleukin 4 receptor gene with nonatopic asthma in Slovenian children. Wien Klin Wochenschr 122:11–18PubMedCrossRefGoogle Scholar
  4. Berce V, Potocnik U (2010b) Functional polymorphism in CTLA4 gene influences the response to therapy with inhaled corticosteroids in Slovenian children with atopic asthma. Biomarkers 15:158–166PubMedCrossRefGoogle Scholar
  5. Berce V, Repnik K, Potocnik U (2008) Association of CCR5-delta32 mutation with reduced risk of nonatopic asthma in Slovenian children. J Asthma 45:780–784PubMedCrossRefGoogle Scholar
  6. Berce V, Kozmus CE, Potocnik U (2012) Association among ORMDL3 gene expression, 17q21 polymorphism and response to treatment with inhaled corticosteroids in children with asthma. Pharmacogenomics J. doi:10.1038/tpj.2012.36 PubMedGoogle Scholar
  7. Bernstein DI, Kashon L, Lummus ZL et al (2013) CTNNA3 (α-catenin) gene variants are associated with diisocyanate asthma: a replication study in a Caucasian worker population. Toxicol Sci 131:242–246PubMedCrossRefGoogle Scholar
  8. Bottema RW, Reijmerink NE, Koppelman GH, Kerkhof M, Postma DS (2005) Phenotype definition, age, and gender in the genetics of asthma and atopy. Immunol Allergy Clin North Am 25:621–639PubMedCrossRefGoogle Scholar
  9. Bottini N, Ronchetti F, Gloria-Bottini F, Stefanini L, Bottini E, Lucarini N (2005) Atopic and nonatopic asthma in children. J Asthma 42:25–28PubMedCrossRefGoogle Scholar
  10. Busse WW, Lemanske Jr RF (2001) Asthma. N Engl J Med 344:350–362PubMedCrossRefGoogle Scholar
  11. Cantero-Recasens G, Fandos C, Rubio-Moscardo F, Valverde MA, Vicente R (2010) The asthma-associated ORMDL3 gene product regulates endoplasmic reticulum-mediated calcium signaling and cellular stress. Hum Mol Genet 19:111–121PubMedCrossRefGoogle Scholar
  12. Castro-Giner F, Bustamante M, Ramon GJ et al (2009) A pooling-based genome-wide analysis identifies new potential candidate genes for atopy in the European Community Respiratory Health Survey (ECRHS). BMC Med Genet 10:128PubMedCentralPubMedCrossRefGoogle Scholar
  13. Child F, Lenney W, Clayton S et al (2003) Correction of bronchial challenge data for age and size may affect the results of genetic association studies in children. Pediatr Allergy Immunol 14:193–200PubMedCrossRefGoogle Scholar
  14. Cohen P (2002) Protein kinases—the major drug targets of the twenty-first century? Nat Rev Drug Discov 1:309–315. doi:10.1038/nrd773 PubMedCrossRefGoogle Scholar
  15. Demeo DL, Lange C, Silverman EK et al (2002) Univariate and multivariate family-based association analysis of the IL-13 ARG130GLN polymorphism in the Childhood Asthma Management Program. Genet Epidemiol 23:335–348PubMedCrossRefGoogle Scholar
  16. EST database (2013) http://www.ncbi.nlm.nih.gov/nucest/BX281234?report=genbank. Accessed 8 Dec 2013
  17. Fryer AA, Spiteri MA, Bianco A et al (2000) CCL5/RANTES chemokine gene promoter polymorphisms are not associated with atopic and nonatopic asthma in a Spanish population. Genes Immun 1:509–514PubMedCrossRefGoogle Scholar
  18. Galanter J, Choudhry S, Eng C et al (2008) ORMDL3 gene is associated with asthma in three ethnically diverse populations. Am J Respir Crit Care Med 177:1194–1200PubMedCrossRefGoogle Scholar
  19. Gu ML, Zhao J (2011) Mapping and localization of susceptible genes in asthma. Chin Med J 124:132–143PubMedGoogle Scholar
  20. Hancock DB, Romieu I, Shi M et al (2009) Genome-wide association study implicates chromosome 9q21.31 as a susceptibility locus for asthma in Mexican children. PLoS Genet 5:e1000623PubMedCentralPubMedCrossRefGoogle Scholar
  21. Hancock DB, Eijgelsheim M, Wilk JB et al (2010) Meta-analyses of genome-wide association studies identify multiple loci associated with pulmonary function. Nat Genet 42s:45–52CrossRefGoogle Scholar
  22. Hashimoto T, Akiyama K, Kobayashi N, Mori A (2005) Comparison of IL-17 production by helper T cells among atopic and nonatopic asthmatics and control subjects. Int Arch Allergy Immunol, 137(Suppl1):51–54Google Scholar
  23. He JQ, Chan-Yeung M, Becker AB et al (2003) Genetic variants of the IL13 and IL4 genes and atopic diseases in at-risk children. Genes Immun 4:385–389PubMedCrossRefGoogle Scholar
  24. Heinzmann A, Mao XQ, Akaiwa M et al (2000) Genetic variants of IL-13 signalling and human asthma and atopy. Hum Mol Genet 9:549–559PubMedCrossRefGoogle Scholar
  25. Hoffjan S, Ober C (2002) Present status on the genetic studies of asthma. Curr Opin Immunol 14:709–717PubMedCrossRefGoogle Scholar
  26. Howard TD, Whittaker PA, Zaiman AL et al (2001) Identification and association of polymorphisms in the interleukin-13 gene with asthma and atopy in a Dutch population. Am J Respir Cell Mol Biol 25:377–384PubMedCrossRefGoogle Scholar
  27. Hrdlickova B, Holla LI (2011) Relationship between the 17q21 locus and adult asthma in a Czech population. Hum Immunol 72:921–925PubMedCrossRefGoogle Scholar
  28. Ito K, Chung KF, Adcock IM (2006) Update on glucocorticoid action and resistance. J Allergy Clin Immunol 117:522–543PubMedCrossRefGoogle Scholar
  29. Jostins J, Ripke S, Weersma RK et al (2012) Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491:119–124PubMedCentralPubMedCrossRefGoogle Scholar
  30. Kabesch M, Schedel M, Carr D et al (2006) IL-4/IL-13 pathway genetics strongly influence serum IgE levels and childhood asthma. J Allergy Clin Immunol 117:269–274PubMedCrossRefGoogle Scholar
  31. Karjalainen J, Hulkkonen J, Neiminen MM et al (2003) Interleukin-10 gene promoter region polymorphism is associated with eosinophil count and circulating immunoglobulin E in adult asthma. Clin Exp Allergy 33:78–83PubMedCrossRefGoogle Scholar
  32. Kiley J, Smith R, Noel P (2007) Asthma phenotypes. Curr Opin Pulm Med 13:19–23PubMedGoogle Scholar
  33. Kim SH, Cho BY, Park CS et al (2009) Alpha-T-catenin (CTNNA3) gene was identified as a risk variant for toluene diisocyanate-induced asthma by genome-wide association analysis. Clin Exp Allergy 39:203–212PubMedCrossRefGoogle Scholar
  34. LaBINAEpEPR NH (1991) Guidlines for the diagnosis and management of asthma. J Allergy Clin Immunol 88:425–534CrossRefGoogle Scholar
  35. Li X, Howard TD, Zheng SL et al (2010) Genome-wide association study of asthma identifies RAD50-IL13 and HLA-DR/DQ regions. J Allergy Clin Immunol 125:328–335PubMedCentralPubMedCrossRefGoogle Scholar
  36. Liu S, Li T, Liu J (2012a) Interleukin-4 rs2243250 polymorphism is associated with asthma among Caucasians and related to atopic asthma. Cytokine 59:364–369PubMedCrossRefGoogle Scholar
  37. Liu M, Subramanian V, Christie C, Castro M, Mohanakumar T (2012b) Immune responses to self-antigens in asthma patients: clinical and immunopathological implications. Hum Immunol 73:511–516PubMedCentralPubMedCrossRefGoogle Scholar
  38. Lück H, Kinzig M, Jetter A, Fuhr U, Sörgel F (2009) Mesalazine pharmacokinetics and NAT2 phenotype. Eur J Clin Pharmacol 65:47–54PubMedCrossRefGoogle Scholar
  39. Madore AM, Laprise C (2010) Immunological and genetic aspects of asthma and allergy. J Asthma Allergy 3:107–121PubMedCentralPubMedGoogle Scholar
  40. March ME, Sleiman PM, Hakonarson H (2011) The genetics of asthma and allergic disorders. Discov Med 11(56):35–45PubMedGoogle Scholar
  41. Marsh DG, Neely JD, Breazeale DR et al (1994) Linkage analysis of IL4 and other chromosome 5q31.1 markers and total serum immunoglobulin E concentrations. Science 264:1152–1156PubMedCrossRefGoogle Scholar
  42. Moffatt MF, Kabesch M, Liang L et al (2007) Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma. Nature 448(7152):470–473PubMedCrossRefGoogle Scholar
  43. Mori S, Kou I, Sato H et al (2009) Nucleotide variations in genes encoding carbonic anhydrase 8 and 10 associated with femoral bone mineral density in Japanese female with osteoporosis. J Bone Miner Metab 27(2):213–216PubMedCrossRefGoogle Scholar
  44. Muro M, Marín L, Torio A, Pagan JA, Alvarez-López MR (2008) The −403 G—a promoter polymorphism in the RANTES gene is associated with atopy and asthma. Int J Immunogenet 35(1):19–23PubMedGoogle Scholar
  45. Nagy A, Kozma GT, Bojszko A, Krikovszky D, Falus A, Szalai C (2002) No association between asthma or allergy and the CCR5Delta 32 mutation. Arch Dis Child 86(6):426PubMedCrossRefGoogle Scholar
  46. O’Donnell AR, Toelle BG, Marks GB et al (2004) Age-specific relationship between CD14 and atopy in a cohort assessed from age 8 to 25 years. Am J Respir Crit Care Med 169:615–622PubMedCrossRefGoogle Scholar
  47. Ober C, Yao TC (2011) The genetics of asthma and allergic disease: a 21st century perspective. Immunol Rev 242:10–30PubMedCentralPubMedCrossRefGoogle Scholar
  48. Perin P, Berce V, Potocnik U (2011) CD14 gene polymorphism is not associated with asthma but rather with bronchial obstruction and hyperreactivity in Slovenian children with non-atopic asthma. Respir Med 105:S54–S59PubMedCrossRefGoogle Scholar
  49. Poon AH, Tantisira KG, Litonjua AA et al (2008) Association of corticotropin-releasing hormone receptor-2 genetic variants with acute bronchodilator response in asthma. Pharmacogenet Genomics 18:373–382PubMedCentralPubMedCrossRefGoogle Scholar
  50. Sleiman PM, Flory J, Imielinski M et al (2010) Variants of DENND1B associated with asthma in children. N Engl J Med 362:36–44PubMedCrossRefGoogle Scholar
  51. Smit LA, Siroux V, Bouzigon E et al (2009) CD14 and toll-like receptor gene polymorphisms, country living, and asthma in adults. Am J Respir Crit Care Med 179:363–368PubMedCrossRefGoogle Scholar
  52. Taniuchi K, Nishimori I, Takeuchi T et al (2002) Developmental expression of carbonic anhydrase-related proteins VIII, X, and XI in the human brain. Neuroscience 112:93–99PubMedCrossRefGoogle Scholar
  53. Tantisira K, Weiss S (2009) The pharmacogenetics of asthma treatment. Curr Allergy Asthma Rep 9:10–17PubMedCrossRefGoogle Scholar
  54. Tantisira KG, Hwang ES, Raby BA et al (2004) TBX21: a functional variant predicts improvement in asthma with the use of inhaled corticosteroids. Proc Natl Acad Sci U S A 101:18099–18104PubMedCentralPubMedCrossRefGoogle Scholar
  55. Walker C, Bauer W, Braun RK et al (1994) Activated T cells and cytokines in brnochoalveolar lavages from patients with various lung diseases associated with eosinophilia. Am J Respir Crit Care Med 150:1038–1048PubMedCrossRefGoogle Scholar
  56. Wu H, Romieu I, Sienra-Monge JJ, Li H, del Rio-Navarro BE, London SJ (2009) Genetic variation in ORM1-like 3 (ORMDL3) and gasdermin-like (GSDML) and childhood asthma. Allergy 64:629–635PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Faculty of Medicine, Center for Human Molecular Genetics and PharmacogenomicsUniversity of MariborMariborSlovenia
  2. 2.Faculty of Chemistry and Chemical Technology, Laboratory for Biochemistry Molecular Biology and GenomicsUniversity of MariborMariborSlovenia
  3. 3.Faculty of Health ScienceUniversity of MariborMariborSlovenia

Personalised recommendations