Association analysis of common variants of STAT6, GATA3, and STAT4 to asthma and high serum IgE phenotypes

Article Outline

• Abstract
• Methods
  • Study subjects in SNP identification
  • Study subjects in the association analyses
  • SNP screening
  • SNP genotyping
  • Statistical analysis
• Results
  • Identification of polymorphisms
  • Association analysis
• Discussion
• Acknowledgment
• Appendix. Supplementary data
• References
• Copyright

Background

Immune responses characterized by T_H2 type cells and IgE are important for the development of asthma and atopy. The transcription factors STAT6, GATA3, and STAT4 mediate the cytokine-induced development of naïve CD4⁺ T cells into either T_H1 or T_H2 type.

Objective

We studied genetic variation of the STAT6, GATA3, and STAT4 genes and examined whether single nucleotide polymorphisms (SNPs) in these loci were associated with asthma or serum high IgE levels in the Finnish asthmatic families.

Methods

With denaturing high-performance liquid chromatography we screened all exons and exon-intron boundaries of the genes in 14 to 22 patients. All identified SNPs were genotyped in 120 nuclear families, and the haplotypes were analyzed by Haplotype Pattern Mining based statistical analysis. When potential association was observed, the analysis was replicated among 245 asthmatic patients and 405 population-based control subjects.

Results

A total of 23 SNPs were identified, of which 8 were not previously listed in the SNP database. Interestingly, a haplotype analysis of GATA3 showed 3 related haplotypes that associated with different asthma and atopy related phenotypes among both the family and case-control data sets. For STAT6 and STAT4, no significant association to asthma or serum total IgE levels was observed.

Conclusions

We identified a panel of novel SNPs in genes coding for proteins important in the T_H1/T_H2 cell differentiation. SNPs of the GATA3 gene showed an initial association to asthma-related phenotypes. Elucidation of the importance of the identified panel of SNPs in other T_H1/T_H2 mediated diseases will be of great interest.

Key words: Association analysis, common variant, STAT6, GATA3, STAT4, asthma, serum IgE, haplotype

Abbreviations used: DHPLC, Denaturing high-performance liquid chromatography, GATA3, GATA-binding protein 3, HPM, Haplotype Pattern Mining, ILR, Interleukin receptor, SNP, Single nucleotide polymorphism, STAT4, Signal transductor and activator of transcription 4, STAT6, Signal transductor and activator of transcription 6, UTR, Untranslated region

Specific activation of STAT6 is critical for a number of IL-4 mediated effects, including induction of T_H2 and IgE responses, and antigen-induced airway inflammation and hyperresponsiveness.¹, ², ³, ⁴, ⁵ Human STAT6 (12q13.3-14.1) spans 19 kb of genomic DNA containing 23 exons.⁶ Several genome-wide linkage studies in both asthma and atopy have shown linkage to 12q, suggesting that STAT6 is a functional and positional candidate gene for asthma-related traits.⁷, ⁸, ⁹, ¹⁰, ¹¹, ¹² The most profound association study was performed among the white asthmatic sibpairs. In the study, 14 markers were screened, of which the one in the intron 18 showed an association (P=.007) to serum IgE level and the GT repeat polymorphism in the exon 1 (P=.001) to elevated eosinophils.¹³ The significance of STAT6 variations was investigated also in Japanese studies; one showed an association between the GT repeat and allergic phenotypes,¹⁴ and another reported the same repeat to be associated with Japanese childhood asthma.¹⁵ In another Japanese asthma study, an SNP in the 3′ untranslated region (UTR) of the STAT6 gene was shown to be associated with mild atopic asthma (P=.004); this association was not, however, replicated among the British patients¹⁶ or 2 other studies among Japanese patients.¹⁴, ¹⁵

Human GATA3 (10p15) consists of 6 exons spanning 19 kb of genomic DNA.¹⁷ GATA3 belongs to the highly conserved family of transcription activators of which only GATA3 is expressed in T cells.¹⁸, ¹⁹, ²⁰ GATA3 is a key factor of T_H2 cell development by regulating the production of T_H2-specific cytokines IL-4, IL-5, and IL-13.²¹, ²², ²³ GATA3 also has a STAT6-independent autoactivation pathway.²⁴ Recently, an alternative form of GATA3 promoter and an alternative first exon (exon 1a) were identified.²⁵

STAT4 deficient mice have markedly reduced IL-12–induced T_H1 responses.²⁶ While promoting T_H1-type cell development STAT4 is a negative regulator for T_H2 cell differentiation.²⁷, ²⁸ STAT4 spans 120 kb on chromosome 2q32.2-q32.3 across one large intron.²⁹ The gene consists of 24 exons. Also 2q has been reported to be linked to asthma-related phenotypes.⁹, ³⁰

In this study, we investigated the role of genetic variations in the 3 genes, STAT6, STAT4, and GATA3, central for T-helper cell differentiation in the predisposition to asthma or high levels of serum IgE in the Finnish asthmatic families.

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Methods 

Study subjects in SNP identification 

Fourteen Finnish unrelated study subjects were screened to detect polymorphisms in the STAT6 gene and 22 subjects for STAT4 and GATA3 loci. Seven were non-atopic control individuals (average IgE, 143 kU/L; range, 50-310 kU/L). They had no history of asthma or atopy, had normal pulmonary function, showed no bronchial hyperreactivity in histamine challenge test, and had negative RAST and skin prick test results. The rest were young atopic adults (average IgE, 4697 kU/L; range, 1,143 to 27,716 kU/L) of which 10 had asthmatic symptoms. These patients were tested as the non-atopic group. They had a history of asthma and atopy, a clinically diagnosed significant reversible bronchus obstruction, and/or bronchial hyperreactivity during histamine challenge test, and at least one positive RAST and/or skin prick test result. Genomic DNA was extracted from peripheral white blood cells by propanol/ethanol-extraction. Serum total IgE level was measured by Diagnostics CAP FEIA (Kabi Pharmacia, Uppsala, Sweden).

Study subjects in the association analyses 

For association study, our DNA material consisted of Finnish study families recruited on the basis of asthma. The details of the recruitment and diagnostic testing have been described previously.³¹, ³² Phenotypes available for this cohort (average age, 43 years; 37 years among individuals with high and 45 years among individuals with low serum IgE) were asthma status, serum total IgE level (Diagnostics CAP FEIA), and blood eosinophil level. Among the genotyped samples there were no non-atopic individuals with high IgE values. For STAT6, STAT4, and GATA3, an association analysis was performed with a subset of the cohort including all the trios with full phenotypic and genotypic information (a total of 120 trios and 360 study subjects). For GATA3 the initial association was replicated in a population-based case-control sample including 245 Finnish asthmatic patients and 405 control subjects. Details of the recruitment and clinical evaluation of the patients have been described.³³ Studied phenotypes for this cohort were asthma status, serum total IgE level, skin prick test results, and blood eosinophil level. Total serum IgE was measured by the immunoluminometric method (Ciba Corning Diagnostics, Halsted, United Kingdom). Genomic DNA of all study individuals was extracted from peripheral white blood cells by using a standard nonenzymatic method.

SNP screening 

For STAT6, GATA3, and STAT4, all the exons, 1 to 2 kb of the 5′ and 3′ flanking sequences of the genes, and intron-exon boundaries were screened for polymorphisms by using temperature-modulated heteroduplex analysis based denaturing high-performance liquid chromatography (DHPLC) method and carried out on the automatic Wave DNA Fragment Analysis System (Transgenomic, San Jose, Calif).³⁴ The overlapping PCR fragments covered 9.8 kb (~51.6%) of the STAT6, 8 kb (~42.1 %) of the GATA3, and 11 kb (~9.1 %) of STAT4 loci. Screening covered Gene bank sequences NT_029419 for STAT6, NT_077569 for GATA3, and NT_005403 for STAT4. PCR primers (given as requested) were designed by the Primer Express software (Applied Biosystems, Foster City, Calif); each amplicon was 300 to 800 bp. When possible, the amplicons were designed to overlap 50 to 200 bp. PCR reactions were carried out in 50- to 100-μL volumes containing 100 to 200 ng of template DNA, 0.2 mmol/L deoxyribonucleoside triphosphate (MBI Fermentas, Vilnius, Lithuania), 400 to 800 nmol/L of each PCR primer (Medprobe, Oslo, Norway), 2 to 3.5 U of Dynazyme EXT polymerase, 10 X Dynazyme Tritonfree polymerase buffer (Finnzymes, Espoo, Finland), and 5% dimethyl sulfoxide (Finnzymes). For DHPLC analysis the melting temperatures for each fragment were predicted with WAVEMAKER software (Transgenomic) and Stanford's DHPLC Melt Program (http://insertion.stanford.edu/melt.html). Observed heteroduplexes were confirmed with direct sequencing, both the group of samples giving the heteroduplex as well as those giving homoduplex profile (ABI PRISM 3100 Genetic Analyzer using BigDye Terminator Cycle Sequencing Kit; Applied Biosystems).

SNP genotyping 

Genotyping was performed by SNaPshot (Applied Biosystems)³⁵ according to the manufacturer's directions. Primer sequences are available as requested. PCR samples were purified with Shrimp Alkaline Phosphatase (SAP) and Exonuclease I (ExoI) enzymes (UBS, Cleveland, Ohio). The SnaPshot reactions were carried out in multiplex reactions in a total volume of 10 μL containing 5 μL of ready reaction mix (diluted 2:3 with 20 mmol/L Mg2+ and 800 mmol/L Tris-HCl, pH 9.0), 0.1 to 2.8 μmol/L of each genotyping primer, and 1:10 to 1:20 dilutions of the purified PCR products. The thermal cycling step consisted of the denaturing step at 96°C for 10 seconds, the annealing step at 50°C for 5 seconds, and 25 cycles of the extension step at 60°C for 30 seconds. For post-extension treatment the samples were purified with 1 U SAP. The samples were run with the ABI PRISM 3100, and obtained genotype data were analyzed with GeneScan 2.1 software (Applied Biosystems).

The genotyping success rate varied from 94.8% to 99.1%. All the markers were in Hardy-Weinberg equilibrium, and Mendel errors were found <0.01%.³⁶

Statistical analysis 

Haplotype analysis with Haplotype Pattern Mining (HPM) algorithm³⁷ was performed for dichotomized traits: asthma (affected and control) and high serum total IgE level (serum IgE level>100 kU/L as high responder and serum IgE level ≤ 100 kU/L as low responder), or the combinations of asthma with different serum total IgE levels and associated blood eosinophil accounts. Haplotyping was done within each trio, and 4 independent chromosomes were obtained. In case of ambiguities resulting from missing genotype data, identical heterozygotic genotypes in all of the family members, or mendelian incompatibilities, the alleles were discarded. If the child was affected, the transmitted chromosomes were considered disease-associated and the nontransmitted chromosomes as controls. If one of the parents was affected, his/her chromosomes were considered disease-associated and the spouse's chromosomes as controls. When both the parent and the child were affected, only the nontransmitted chromosome of the unaffected parent was considered as the control and the remaining 3 as disease-associated ones. The input for HPM consists of these haplotypes.³⁷ The maximum length of the haplotype patterns and the number of gaps allowed were given as parameters for the algorithm. For population-based case-control replication data set, the haplotypes were constructed of the data from unrelated individuals by using SNPhap algorithm (http://www-gene.cimr.cam.ac.uk/clayton/software/), and those haplotypes were used in the similar way as input data for HPM. The HPM finds all the phenotype-associated haplotype patterns that exceed a given threshold value by a χ² test. Each marker is given a marker-wise score based on the number of qualified patterns spanning across the marker. The significance of the observed P value can be tested by permutation where the grouping of chromosomes is done randomly, and the scores are recalculated several times to obtain marker-wise P values that are comparable with each other.³⁷ Quantitative statistical analysis methods were used to evaluate associations between specific haplotype carrier status and elevated total serum IgE levels detected by HPM. For this purpose, total serum IgE measurements were transformed into a normally distributed form by log_e transformation and adjusted for age and gender. Resulting new variable was analyzed with independent samples t test and linear regression analysis.

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Results 

Identification of polymorphisms 

We screened all the exons and the exon-intron boundaries of STAT6, GATA3, and STAT4 for polymorphisms in 14 to 22 Finnish study subjects. To improve our chances to find functionally important SNPs in IgE-mediated disorders, the pool of screened individuals was enriched with atopic patients with exceptionally high serum total IgE values. All identified heteroduplexes were verified with direct sequencing. Among these individuals, a total of 5 SNPs in STAT6, 9 SNPs in GATA3, and 9 SNPs in STAT4 were identified (Fig 1), of which 8 were not previously reported into the SNP database (dbSNP/NCBI, http://www.ncbi.nlm.nih.gov/SNP/). Two SNPs identified in STAT6 were intronic, and 3 were located in the 3′UTR region of the gene. Nine non-coding variants of GATA3 were observed. One STAT4 polymorphism causing an amino acid substitution (C42113438T, located in the DNA-binding domain of the gene) in exon 16 was identified in one of the study subjects. This SNP was identified as a heteroduplex in DHPLC screening and verified by sequencing.

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• Fig 1. 

  Genomic structure of the (A) STAT6, (B) GATA3, and (C) STAT4 and identified polymorphisms (not in scale). The SNPs are named according to their position in NT_029419 (STAT6), NT_077569 (GATA3), and NT_005403 (STAT4), respectively. Novel SNPs not previously reported in the database are marked with an asterisk.

These 23 SNPs identified were chosen for the haplotype association analysis.

Association analysis 

To analyze the association of 23 SNPs with either asthma or high (>100 kU/L) serum total IgE level, the allele/haplotype association analysis was performed among Finnish nuclear families recruited on the basis of asthma. The data set revealed 199 affected and 161 control chromosomes for asthma; moreover, there were 273 high serum IgE response and 199 low IgE response associated chromosomes.

We found no significant allele association for any of the loci studied (Table I). All the SNPs detected in STAT6 gene were relatively common among the genotyped subjects (28% to 50%). In GATA3 three of 9 SNPs were rare (<5%), whereas in STAT4 all except 1 SNP were rare (<6%). For haplotype analysis we allowed 1 wild card (1 error or missing allele) in the haplotype patterns. For either STAT6 or STAT4, no haplotype association was found (χ² values in all 2 × 2 contingency tables < 4.9, data not shown). The most common haplotypes explaining the majority of haplotype diversity of both loci are shown in Tables E1 through E3 (in the Journal's Online repository at www.mosby.com/jaci). Interestingly, for GATA3 the haplotype analysis identified 3 potentially associating haplotypes (Table II). One of the haplotypes (CGTAGCTCC) was found to be associated with high total serum IgE levels and was studied further. Subjects were divided into haplotype carriers and noncarriers; their total serum IgE values were transformed into a normally distributed form and analyzed with quantitative analysis methods. Haplotype (CGTAGCTCC) carriers showed significant association to elevated total serum IgE levels in t test (Table III) and in linear regression analysis (P=.01) when age, gender, and asthma status were used as covariates.

Table I. Comparisons of allele frequencies of the SNPs in STAT6, GATA3, and STAT4 in high IgE (273 high IgE response associated vs 161 low response chromosomes) and asthma associated chromosome (199 asthma associated vs 161 control chromosomes), respectively

		High IgE		Asthma
	Allele	Associated(%)	Not associated(%)	Associated(%)	Not associated(%)
STAT6 specific markers named on basis of their position on NT_029419
19647081	G	55.8	65.5	56.9	61.3
	A	44.2	34.5	43.1	38.7
19645488	G	55.7	63.3	56.2	60.3
	A	44.3	36.7	43.8	39.7
19633406	G	77.3	72.8	76.0	74.2
	A	22.7	27.2	24.0	25.8
19633134	A	51.8	51.5	50.2	51.2
	G	48.2	48.5	49.8	48.8
19632954	A	54.8	52.4	57.3	51.5
	G	45.2	47.6	42.7	48.5
GATA3 specific markers named on basis of their position on NT_077569
2450477	C	92.1	86.2	90.4	88.4
	T	7.9	13.8	9.6	11.6
2458625	G	99.3	99.5	100.0	98.8
	A	0.7	0.5	0.0	1.2
2458701	T	98.5	97.5	98.5	98.1
	G	1.5	2.5	1.5	1.9
2459095	A	21.5	16.0	20.9	19.3
	G	78.5	84.0	79.1	80.7
2460264	G	60.6	59.1	63.8	58.3
	A	39.4	40.9	36.2	41.7
2460466	C	98.9	100.0	99.0	99.4
	T	1.1	0.0	1.0	0.6
2463543	C	91.6	91.3	91.8	90.5
	T	8.4	8.7	8.2	9.5
2474305	C	15.9	15.5	17.2	14.6
	T	84.1	84.5	82.8	85.4
2478975	C	89.3	88.4	85.9	91.4
	T	10.7	11.6	14.1	8.6
STAT4 specific markers named on basis of their position on NT_005403
42225344	G	98.9	95.5	98.5	96.3
	A	1.1	4.5	1.5	3.7
42132257	C	34.9	33.8	39.9	35.0
	T	65.1	66.2	60.1	65.0
42128770	T	97.8	96.5	96.5	96.9
	C	2.2	3.5	3.5	3.1
42113438∗	C	100.0	100.0	100.0	100.0
	T	0.0	0.0	0.0	0.0
42110400	T	98.9	98.0	98.5	97.6
	C	1.1	2.0	1.5	2.4
42105770	C	99.6	97.5	99.0	98.2
	T	0.4	2.5	1.0	1.8
42105023	C	97.0	94.5	97.4	94.4
	T	3.0	5.5	2.6	5.6
42103557	T	98.1	95.4	97.9	95.6
	C	1.9	4.6	2.1	4.4
42103821	T	100.0	100.0	100.0	100.0
	C	0.0	0.0	0.0	0.0

Table II. The best associating haplotypes of GATA3 gene detected by HPM in different phenotypic comparisons in both the primary (family collection) and replication (case-control) data sets

The alleles shared between the haplotypes are shown in bold.

I data set, Primary data set; II data set, replication data set.

Table III. Independent 2-sample t test of serum total IgE levels among haplotype CGTAGCTCC carriers in data set I

The alleles shared between the haplotypes are shown in bold.

I data set, Primary data set.

The comparisons with related phenotypes identified partially different GATA3 haplotypes. Six of 9 alleles were shared between the haplotypes. Most of the results remained statistically significant after 10,000 permutations. Obviously we have tested multiple phenotypes, genes, and markers simultaneously. Therefore, the marker-wise P values should be corrected with number of independent tests performed. However, because the phenotypes are closely related and the gene-specific markers are inherited in linkage disequilibrium, it is difficult to determine the appropriate statistical correction. To evaluate the biologic significance of our findings, we decided to replicate the genotyping of GATA3 in another, independent data set. The Finnish case-control data set (245 asthmatic patients and 405 age- and gender-matched control subjects) expresses very similar clinical characteristics (onset of chronic asthma in adulthood and demand for daily medication) as the previously analyzed family collection. The frequencies of the observed haplotypes in the family data set and predicted haplotypes in the case-control data set were comparable. Interestingly, 1 of the 3 haplotypes showing association already in the family data was also observed to associate in the replication data (Table II).

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Discussion 

On the basis of chromosomal linkage studies and their function, STAT6 and STAT4 are positional candidates for asthma and atopy.⁸, ⁹, ¹⁰, ¹¹, ¹², ³⁰ GATA3 and T-BET are essential transcription factors in the differentiation of T helper lymphocytes. T-BET was not associated with asthma-related traits on the basis of 15 SNPs genotyped in this same patient cohort used in the present study.³⁸

We found 23 SNPs in the STAT6, GATA3, and STAT4 genes. No coding variants leading to amino acid changes were found in the STAT6 or GATA3 genes, suggesting a highly conserved exon structure of the genes. This is consistent with previous SNP screening studies.¹³, ¹⁴, ¹⁶, ³⁹, ⁴⁰ We concentrated our genotyping efforts into the SNPs (5 to 9 markers per locus) found among the Finnish individuals. Therefore, our study does not include all the SNPs described in the public database in the intronic regions that were not covered by our screening process. The genotyped markers, however, were inherited in linkage disequilibrium (Tables E1 through E3 in the Online Repository), and we should have seen a bias in haplotype distribution among affected individuals also in case the causative SNP is missing. All the SNPs found in STAT6 had been previously reported¹³, ¹⁶ and are relatively common (frequency>22 %) (Table I). In the present study we did not genotype the GT repeat in the first exon of STAT6. This GT repeat has been shown to be associated with elevated eosinophil levels among whites¹³ and with allergic phenotypes¹⁴ and childhood asthma¹⁵ among Japanese individuals. SNP in intron 18, shown to associate with total IgE levels (P=.0070) in one study,¹³ did not behave that way in our study. Genotyping of the 5 SNPs identified among the screened Finnish individuals (28 chromosomes) did not support previous reports.¹³, ¹⁶ There was no association of the 3′UTR SNP (G19633406A) described by Gao et al¹⁶ among Finnish families, which, together with previously reported negative associations,¹³, ¹⁴, ¹⁵, ¹⁶ suggests that the 3′UTR variant does not have an important role in the pathogenesis of asthma and allergy. Thus, the biologic importance of described polymorphisms in STAT6 remains unclear. Apparently both ethnic and clinical differences in study populations may add to the complexity of the results.

GATA3 haplotype analysis in both the family and case-control data sets suggested an association with asthma-related phenotypes (Table II). Three partially different (6/9 alleles shared) associating haplotypes were identified. The allelic differences between these haplotypes may be due to the ethnic background of the 2 data sets, family collection and population-based, analyzed. When studying complex disorders such as asthma and atopy, susceptibility alleles predisposing to disease can differ between founder and outbred populations. Because none of the SNPs of GATA3 altered the amino acid sequence, it may be difficult to directly predict whether the SNPs have a functional role in the disease susceptibility. None of the SNPs seemed to locate in the functional domains of GATA3 (TRANSFAC professional 7.4.1; BIOBASE GmbH, Wolfenbüttel, Germany). Still, it is possible that the SNPs relatively near to intron/exon borders have an effect on mRNA splicing, whereas the SNPs at 5′and 3′UTR sequences affect the efficiency of translation or RNA processing. There are studies indicating that this type of noncoding variants causes changes in the stability and splicing of the encoded mRNAs and, hence, has biologic importance.⁴¹, ⁴², ⁴³, ⁴⁴, ⁴⁵, ⁴⁶, ⁴⁷, ⁴⁸ Therefore, the haplotype associations in GATA3 we describe here may be important. The SNPs G2458625A, T2458701G, and A2459095G are all located 1000 to 1500 bp upstream of the coding sequence of GATA3, whereas 2 SNPs in the 5′UTR, G2460264A and C2460466T, are located at position −250 to −50 bp. Being at promoter regions, these may have an unknown effect to transcription. An SNP in the 3′UTR, C2478975T, is located 92 bp downstream of STOP codon. Only 1 SNP of GATA3, C2474305T, is located near the intron/exon border, being 27 bp upstream of the fifth exon. However, further genetic and functional studies are needed for the final conclusions of the functional impact of these SNPs.

In conclusion, we have identified a panel of novel SNPs in genes encoding key proteins that orchestrate immune responses. For STAT6, GATA3, and STAT4 genes this is the first asthma association study among the Finnish asthmatic families. To our knowledge, for STAT4 and GATA3 there are also no previously published association analyses. In addition, a putative association between GATA3 haplotypes and serum high IgE and asthma traits was observed. Elucidation of the importance of this panel of SNPs in a variety of diseases, also other than asthma, characterized by an imbalance between T_H1/T_H2 responses will be of great interest.

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We thank Tia Elo, Heidi Jalonen, Tuija Kyrölä, Tuuli Lappalainen, and Eveliina Virtanen for technical assistance and Emmi Ylikoski for comments and discussions. We also acknowledge Dr Johannes Savolainen for collecting patient samples for SNP screening.

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Appendix. Supplementary data 

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References 

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