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Polymorphisms in CLEC16A and CIITA at 16p13 Are Associated with Primary Adrenal Insufficiency

Department of Medical Genetics (B.S., K.B., T.E., D.E.U.), Ullevål University Hospital, N-0407 Oslo, Norway; Institute of Medical Genetics (B.S., D.E.U.), University of Oslo, N-0315 Oslo, Norway; Section of Endocrinology (E.S.H., A.B.W., K.L.), Institute of Medicine, University of Bergen, N-5021 Bergen, Norway; Department of Medicine (E.S.H., K.L.), Haukeland University Hospital, N-5021 Bergen, Norway; and Institute of Human Genetics (S.H.P., D.O.M.), Newcastle University, Newcastle upon Tyne NE1 3BZ, United Kingdom

Address all correspondence and requests for reprints to: Beate Skinningsrud, Department of Medical Genetics, Ullevål University Hospital, University of Oslo, Kirkeveien 166, N-0407 Oslo, Norway. E-mail: beate.skinningsrud{at}medisin.uio.no.

	Abstract

Top Abstract Introduction Patients and Methods Results Discussion References

 
Context/Objectives: It is known that different autoimmune diseases often share the same susceptibility genes. In this study we aimed to investigate if loci found associated with common autoimmune diseases in recent genome-wide association studies also could be susceptibility loci for autoimmune Addison’s disease (primary adrenal insufficiency).

Design/Patients: A total of 139 tagging single nucleotide polymorphisms (SNPs) in 11 candidate genes (IL2, IL21, IL2RA, CLEC2D, CD69, ERBB3, PTPN11, SH2B3, CLEC16A, CIITA, and PTPN2) were genotyped in a case/control study design consisting of Norwegian Addison’s disease patients (n = 332) and Norwegian healthy control individuals (n = 1029). Five SNPs were subsequently selected for analysis in a United Kingdom sample set consisting of Addison’s disease patients (n = 210) and controls (n = 191).

Results: Polymorphisms in CLEC16A and CIITA remained significantly associated with Addison’s disease in the Norwegian sample set at the 0.05 level, even after correction for multiple testing. CLEC16A and CIITA are both located at 16p13, but linkage disequilibrium patterns and logistical regression analyses suggest that SNPs in these two genes are independently associated with Addison’s disease. We were not able to confirm these associations in the United Kingdom material, however, this may well be due to the limited sample size and lack of statistical power.

Conclusion: Two alleles at 16p13 are independently associated with the risk of Addison’s disease in the Norwegian population, suggesting this chromosomal region to harbor common autoimmunity gene(s), CLEC16A and CIITA being possible independent candidates.

	Introduction

Top Abstract Introduction Patients and Methods Results Discussion References

 
Several studies have shown that many susceptibility genes are common for several different autoimmune disorders. For example, human leukocyte antigen (HLA) class II genes, cytotoxic T lymphocyte-associated protein 4 (CTLA4), and protein tyrosine phosphatase, nonreceptor type 22 (PTPN22) alleles are associated with numerous such diseases (1). Autoimmune Addison’s disease is caused by autoimmune destruction of the adrenal cortex. A distinct feature of autoimmune Addison’s disease is the high frequency (45–70%) of coexisting nonadrenal organ-specific autoimmunity, most often endocrine diseases (2). Addison’s disease with concomitant thyroid autoimmunity and/or type 1 diabetes is the most common syndrome referred to as autoimmune polyendocrine syndrome type II (3). Addison’s disease patients’ strong proneness to autoimmunity makes the disease an interesting model disease in the search for common autoimmunity susceptibility genes. Addison’s disease is a rare autoimmune disease with a prevalence of 14 per 100,000 in Norway (4). Low prevalence has precluded the collection of large Addison’s disease cohorts, and so far no material has been presented in the literature that would have sufficient statistical power to perform genome-wide association (GWA) analysis.

We hypothesized that some recently identified susceptibility loci in other autoimmune diseases could also be involved in autoimmune Addison’s disease. To test this hypothesis, we performed a candidate gene study of selected autoimmunity associated loci. Selection of candidate genes was based on the recently reported genetic associations with type 1 diabetes, rheumatoid arthritis, and Crohn’s disease detected in a GWA scan by The Wellcome Trust Case Control Consortium (WTCCC) (5), and the follow-up study of Todd et al. (6) in type 1 diabetes and Graves’ disease. We selected 11 candidate genes (Table 1) for analysis in a total of 332 Norwegian Addison’s disease patients and 1029 healthy controls. Three of these regions were associated with type 1 diabetes, rheumatoid arthritis, and Crohn’s disease (10p15, 12q24, and 18q11) in the WTCCC study, indicating the presence of common autoimmunity susceptibility loci. The other selected regions represented the strongest associations with type 1 diabetes (4q27, 12p13, 12q13, and 16p13), on which 12q13 and 16p13 were also discovered in another GWA study on type 1 diabetes (7, 8). Todd et al. (6) confirmed unequivocally associations of 12q24, 12q13, 16p13, and 18p11 with type 1 diabetes, whereas the other regions all showed smaller effects. Of these, 4q27, 10p15, and 18p11 were also associated with Graves’ disease. Subsequently to the fine mapping in the Norwegian samples, five single nucleotide polymorphisms (SNPs) were selected for analysis in a smaller, statistically less well-powered United Kingdom cohort.

	Patients and Methods

Top Abstract Introduction Patients and Methods Results Discussion References

 
Patients and controls

From Norway 332 patients with Addison’s disease were recruited from a Norwegian registry of organ-specific autoimmune diseases. A total of 1029 anonymous healthy Norwegian blood donors was used as controls. Further details on cases and controls can be found elsewhere (11). The 210 British patients were obtained at the Newcastle upon Tyne Hospitals Trusts, and surrounding district endocrine services. Details of diagnostic criteria have been reported previously (15). The 191 United Kingdom controls were recruited from the local population and had no clinical features or family history of autoimmune disease. Informed consent was given by all participants.

SNP selection

There were 11 genes in seven genomic regions selected for SNP tagging. These were: interleukin-2 (IL2); interleukin-21 (IL21); interleukin-2 receptor chain, also known as CD25 (IL2RA); C-type lectin domain family 2 member D (CLEC2D); CD69 antigen (p60, early T-cell activation antigen) (CD69); receptor tyrosine-protein kinase erbB-3 precursor (ERBB3); protein tyrosine phosphatase, non-receptor type 11 (PTPN11); SH2B adaptor protein 3 (SH2B3); CLEC16A; CIITA; and protein tyrosine phosphatase, non-receptor type 2 (PTPN2) (Table 1). Tagging SNP (tagSNP) selection was based on the genotyping data from the HapMap phase II CEPH (Utah residents with ancestry from northern and western Europe) (CEU) population (16). We aimed to capture 100% of HapMap SNPs with minor allele frequency (MAF) more than 5% in the selected candidate genes, given a linkage disequilibrium (LD) cutoff at r² > 0.8. Altogether, 150 SNPs were selected for genotyping in the first sample set of Norwegian cases and controls. Only the most associated SNPs in the five genes that showed some evidence of association in the Norwegian sample set were genotyped in the British cases and controls. These are listed in Table 2.

Genotyping

The genotyping of the Norwegian sample set was performed with the SNPlex Genotyping System (Applied Biosystems, Foster City, CA). Four SNPs that did not meet the requirement of SNPlex assay design were analyzed by TaqMan SNP Genotyping Assays (Applied Biosystems). The TaqMan technology was also used for genotyping the five SNPs analyzed in the British sample set (Table 2), and to reanalyze all samples for the rs8048002 SNP to evaluate any occurrence of genotyping errors in this low-frequency SNP, even though the plots generated by SNPlex were robust. All plots were inspected manually after automatic calling. All TaqMan SNP Genotyping Assays were predeveloped by the manufacturer (http://www.appliedbiosystems.com).

Statistics

Tests for allelic associations were based on 2 x 2 contingency tables of allele frequencies and ² tests. To correct for multiple comparisons, 10,000 permutations of the data were performed, including all 139 SNPs that were successfully genotyped. These analyses were performed using PLINK version 1.01 (19). The significance level was 0.05 after multiple testing corrections. Overall P values were calculated by treating the allele variable and population variable as covariates in logistical regression analyses. TagSNP selection, Hardy-Weinberg tests, and LD calculations were performed by Haploview versions 3.32 and 4.0 (20). Power calculations (Fig. 1), logistical regression analyses, and graphics were done in R version 2.6.1 (21). Forward stepwise logistical regression was performed to evaluate the independency of the associations at 16p13, whereby a SNP was added to the model provided it contributed significantly conditionally on the SNP already present. Genotypes were entered into the logistical regression model as ordered variables coded zero, one, or two, representing the number of occurrences of the risk allele per marker. The equality of odds ratios (ORs) was checked using the Breslow-Day test.

View larger version (7K): [in this window] [in a new window]   FIG. 1. Power calculations for allelic association of the Norwegian and United Kingdom sample sizes. The solid line represents the Norwegian sample size, whereas the dashed line represents the United Kingdom sample size. The calculations assume a SNP with risk allele frequency of 0.1, which is in complete LD with the causative variant and a significance level of 0.05.

	Results

Top Abstract Introduction Patients and Methods Results Discussion References

Four SNPs were excluded because they did not fulfill criteria for SNPlex assay design, and were untaggable by any other SNP. Seven SNPs were removed due to poor genotyping quality, which included those that deviated from Hardy-Weinberg equilibrium in controls (P < 0.01). In total we were able to analyze 139 of the 150 selected SNPs in the Norwegian sample set, which gave a 93% average coverage (r² > 0.8) of HapMap SNPs in the 11 tagged genes (Table 1). The SNPlex Genotyping technology requires higher DNA quality than the TaqMan SNP Genotyping assays. A total of 63 Norwegian controls had too-low DNA quality for SNPlex analysis. After eliminating these samples from the data analyzed by the SNPlex Genotyping technology, we had an overall 94% genotyping call rate in the Norwegian and British sample sets. Two different control samples were genotyped twice on every 384-well plate. Neither of them showed any genotype discrepancies, nor were genotyping discrepancies detected when rs8048002 was reanalyzed with TaqMan.

SNPs in CLEC16A and CIITA at 16p13 are associated with Addison’s disease

SNPs in five genes showed some evidence of association with Addison’s disease in the Norwegian population, these were: CIITA and CLEC16A at 16p13, IL2RA at 10p13, CLEC2D at 12p13, and ERBB3 at 12q13. All, but IL2RA, showed a deviation in the same direction as previously reported in type 1 diabetes (5). Two of these SNPs, rs8048002 and rs12917716, located in CIITA and CLEC16A, respectively, remained significant at the 0.05 level after correcting for multiple testing of the 139 good-quality SNPs (Table 2).

LD at 16p13

Because CLEC16A and CIITA are located only 20 kilobases (kbs) apart, it is conceivable that these two associations reflect a common underlying association and that the association of one of the genes is secondary to LD with one "true" susceptibility gene. To address this question, we first studied the LD pattern in the region (supplemental Fig. S1, which is published as supplemental data on The Endocrine Society’s Journals Online web site at http://jcem.endojournals.org). CLEC16A resides in a 233-kb block of LD that also contains DEXI (dexamethasone-induced transcript), whereas CIITA spans at least two LD blocks. CLEC16A and CIITA display little LD in the Norwegian population, which is illustrated by the low LD between rs8048002 and rs12917716 (D' = 0.08 and r² = 0.001). Logistical regression analysis also confirmed these two SNPs to contribute with independent effect (P = 0.0003).

In addition to rs8048002, four SNPs in CIITA showed some evidence of association (supplemental Table S1, which is published as supplemental data on The Endocrine Society’s Journals Online web site at http://jcem.endojournals.org). One of them, the promoter polymorphism of CIITA (rs3087456), previously reported associated with Addison’s disease (18) and rheumatoid arthritis (22), was in strong, but incomplete LD with rs8048002 (D' = 0.99 and r² = 0.21). In Norwegian Addison’s disease patients, the G-risk allele of rs3087456 gave an OR of 1.23 [95% confidence interval (CI) 1.01–1.50], a risk that just reflected a LD hitchhiking effect of the C-risk allele of rs8048002, which had an OR estimate of 1.79 (95% CI 1.31–2.45). Logistical regression analysis also indicated their dependency. When the SNPs initially were chosen for tagging the CIITA gene, rs8048002 was tagging only one additional HapMap SNP, rs6498119, both located in intron 3. However, according to the HapMap data, the LD block harboring these two SNPs also spans the 12-kb upstream promoter region (23). Interestingly, three linked SNPs (rs6498126, rs11074938, and rs4781020) in CIITA with individual P values less than 0.05 were in low LD with rs8048002 (D' 0.3 and r² 0.002) (supplemental Table S1 and supplemental Fig. S1). However, logistical regression analysis showed that they were not significantly independent of the strongest association in CLEC16A, rs12917716.

In CLEC16A, rs12917716 was tagging 31 HapMap SNPs (16) when the initial selection of tagSNPs was performed. There were 10 tagSNPs in CLEC16A, in addition to rs12917716, significantly associated with Addison’s disease before multiple testing corrections (supplemental Table S1). Our genotyping data revealed a high LD (D' > 0.9) between rs12917716 and all these SNPs, except for rs9806963 (D' = 0.37 and r² = 0.04). These correlated SNPs also embraced the SNPs with the strongest associations with type 1 diabetes reported by the WTCCC (rs12708716) (5) and Hakonarson et al. (7) (rs2903692). However, logistical regression analysis indicated that rs9806963 might contribute with an independent effect of both rs8048002 and rs12917716 (P = 0.05).

Association analysis of the six other genotyped regions

However, none of the other loci demonstrated significant associations with Addison’s disease after multiple testing corrections. SNPs in IL2RA, CLEC2D, and ERBB3 did harbor SNPs with uncorrected significances/borderline significances (Fig. 2 and Table 2). Therefore, it could very well be that SNPs in these genes are Addison’s disease susceptibility variants but that the present study is not powered to detect it. All associations, but IL2RA, went in the same direction in the Norwegian Addison’s disease patients as reported in type 1 diabetes (5). There was no evidence of association between Addison’s disease and SNPs in the other genes analyzed: IL2; IL21; CD69; SH2B3; PTPN11; and PTPN2; or the three individual SNPs each located in Tenr, 5' of SOCS1 and intergenic of SH2B3 and PTPN11 (Fig. 2 and supplemental Table S1).

View larger version (15K): [in this window] [in a new window]   FIG. 2. ORs for the SNPs located in seven different loci that were analyzed in 332 Norwegian cases and 1029 controls. The vertical lines indicate 95% CIs.

The estimated powers to confirm the rs8048002 and rs12917716 allelic associations in the United Kingdom sample set were 59 and 62%, respectively, given the same effect sizes and MAFs as in the Norwegian sample set. Despite this limited statistical power, we decided to analyze five SNPs in the United Kingdom sample set. None of the associations could be confirmed in the United Kingdom sample set. rs7093069 in IL2RA stood out with the most surprising allelic distribution, by showing an opposite direction of the associated allele compared with the Norwegian sample set, i.e. in the same direction as observed in type 1 diabetes (5). The other four SNPs had either an OR close to 1.00, or showed a tendency for deviation in the same direction as observed in the Norwegian material. However, after combining the two cohorts, both rs8048002 and rs12917716 showed evidence of association (Table 2).

	Discussion

Top Abstract Introduction Patients and Methods Results Discussion References

 
Based on findings in GWA studies (5, 7, 8, 24, 25) in related autoimmune diseases, we used a candidate-gene approach and identified association between Addison’s disease and polymorphisms in two genes at 16p13: CIITA and CLEC16A. LD patterns and logistical regression analyses suggest that these significant associations (rs8048002 and rs12917716) are independent.

Two GWA studies (5, 7) and a follow-up study (6) have pointed out SNPs in CLEC16A to be strongly associated with type 1 diabetes. However, there was no evidence for association at this locus either with rheumatoid arthritis or Crohn’s disease in the WTCCC GWA study (5), or with Graves’ disease in the follow-up study by Todd et al. (6). CLEC16A SNPs showed risk estimates with ORs of 0.65 (7) to 0.83 (6) in type 1 diabetes, which is comparable to the effect demonstrated in our Norwegian Addison’s disease patients at rs12917716 with an OR of 0.72. Interestingly, this is the first report of an autoimmune disease besides type 1 diabetes that is associated with a SNP in CLEC16A, and is thereby suggesting the region to harbor a common autoimmunity susceptibility gene.

CLEC16A is a hot candidate of being the real susceptibility gene. The gene is classified as a C-type lectin and encodes a protein of unknown function, which is almost exclusively expressed in immune cells, such as dendritic cells, B lymphocytes, and natural killer cells (http://symatlas.gnf.org/SymAtlas/). C-type lectins are known to have several carbohydrate recognition sites, and thereby be able to discriminate between self and nonself in their involvement in the immune system. Their involvements range from extracellular pathogen recognition to pathogen neutralization inside the cells (26). However, CLEC16A seems to lack crucial domains in carbohydrate recognition, but Todd et al. (6) predicted a conserved immunoreceptor tyrosine-based activation motif encoded by exon 12. Several receptors in the immune system (also some c-type lectins) have immunoreceptor tyrosine-based activation motifs responsible for the signal transduction through binding of SH2 domain-containing proteins (27). There is an obvious necessity to obtain more functional data on the protein encoded by CLEC16A to elucidate its potential effect in immune and autoimmune pathways.

CIITA is the only known gene in the LD block harboring rs8048002. CIITA was included in this study based on its close chromosomal localization to CLEC16A, its protein function as a HLA class II transactivator (28), and the fact that a promoter polymorphism in the gene was previously reported associated with Addison’s disease (18). This promoter polymorphism (rs3087456) has also been studied in other autoimmune diseases, most carefully in rheumatoid arthritis, but with diverging conclusions. A recently published metaanalysis rejected its association with rheumatoid arthritis (29), despite that this SNP or a SNP in tight LD with it seemed to influence the expression of HLA class II genes (22). By selecting tagSNPs spanning CIITA, we identified another SNP located in intron 3, which revealed a stronger association with Addison’s disease in our study than rs3087456 (Breslow-Day test P = 0.05).

CIITA encodes the master control factor for HLA class II expression, as a coactivator interacting within a complex of three other proteins: RFXANK, RFX5, and RFXAP. The absence of HLA class II expression can be due to mutations in CIITA or any of the genes coding for these proteins, and is known as bare lymphocyte syndrome. This syndrome leads to severe immunodeficiency and is usually fatal in early childhood (30). We hypothesize that polymorphism(s) in CIITA could influence minor changes in the level or tissue selectivity of HLA class II expression, and thereby contribute as a predisposing risk factor for Addison’s disease, and possibly other related autoimmune diseases. The fact that the strongest associated SNP resides in a common LD block with SNPs covering the entire promoter region (HapMap data) may also suggest that a causative variant can affect only one of the alternative splice-variants controlled by four different promoters (23). The diverging results on associations with rheumatoid arthritis and other autoimmune disease could be due to the fact that the few studied SNPs were only in partial LD with the real causative variant. Our finding of a stronger association with the intron 3 SNP rs8048002 warrants new studies in rheumatoid arthritis and other autoimmune diseases.

For the other loci analyzed in this study, no convincing associations with Addison’s disease could be detected. However, nine of the 33 SNPs in IL2RA at 10p15 showed some evidence of association in the Norwegian sample set. However, the genotyping results from the United Kingdom sample set showed an opposite direction of the allelic association compared with Norwegian cases and controls, and thereby weakened the hypothesis of an association between IL2RA and Addison’s disease. Two other regions with interesting allelic frequency deviations in the Norwegian cases and controls were 12p13 and 12q13. LD blocks in these regions contain multiple genes. We chose CLEC2D and CD69 (at 12p13), and ERBB3 (at 12q13) as our candidate genes for fine-mapping analysis because these genes were closest to the strongest associated SNP within type 1 diabetes (5), and also because of their known function in immunity (26, 31). The 12q13 locus has been confirmed to be associated with type 1 diabetes (6, 8), whereas a follow-up on 12p13 showed weaker association (6). Anyhow, our skewed allelic distributions between cases and controls in 10p15, 12p13, and 12q13 are inconclusive, and the absence of significance may just be due to the lack of power. This highlights the importance of collecting larger materials of Addison’s disease patients.

Limitations of this study

Our study is underpowered to exclude minor effect susceptibility genes with an OR less than 1.5 (Fig. 1). If the studied genes have equivalent effects on Addison’s disease as reported in type 1 diabetes with ORs from 0.8–1.3 (5), we cannot exclude that some of our negative associations are due to inadequate sample sizes, rather than being truly negative. Therefore, this study is predominantly suitable to demonstrate genes with stronger effect in Addison’s disease than type 1 diabetes, as would not be unlikely with the knowledge that Addison’s disease patients are particularly prone to organ-specific autoimmunity. In association studies there is also a question if the genotyped SNPs have been able to capture the causative variant. Our high coverage rates of HapMap SNPs in the candidate genes, supported by the knowledge of a general agreement of LD patterns between the HapMap CEU cohort and European populations (32, 33), would suggest that the vast majority of common SNPs (MAF > 5%) were captured in this study. However, if rarer alleles represent the causative variant(s), deep sequencing should be an approach for future studies before any of the candidate genes are rejected as disease susceptibility factors.

The initial SNP screen was performed on the largest sample size to avoid too many false-negative results when analyzing many SNPs for a disease with low prevalence. With power estimates of 60% to reproduce the significances from the Norwegian sample set in the United Kingdom sample set, overinterpreting the lack of associations in this population might be misleading. Therefore, the results provided from the United Kingdom samples should not be seen as real replications but rather as useful contributions to any subsequent metaanalysis.

In conclusion, we have identified SNPs in CIITA and CLEC16A to be associated with autoimmune Addison’s disease. Further studies with extensive resequencing on large samples are needed to identify the causative polymorphisms at 16p13. Although the data strongly suggest the two associations to be independent, such an approach is also required to exclude the possibility that both SNPs are in LD with a single, as yet unidentified causative polymorphism. This together with functional studies of genetic variants could contribute to an increased understanding of the pathogenesis of Addison’s disease.

	Acknowledgments

 
We thank Øivind Skare, Ph.D., for valuable help.

	Footnotes

 
The study was supported by grants form South-Eastern and Western Regional Health Authorities, the Ullevål University Hospital Scientific Advisory Council (VIRUUS), and The Functional Genomics Programe (FUGE) administrated by The Research Council of Norway.

Disclosure Statement: The authors have nothing to disclose.

First Published Online July 1, 2008

Abbreviations: CEU, CEPH (Utah residents with ancestry from northern and western Europe; CI, confidence interval; CIITA, class II, major histocompatibility complex, transactivator; CLEC2D, C-type lectin domain family 2, member D; CLEC16A, C-type lectin domain family 16, member A; CTLA4, cytotoxic T lymphocyte-associated protein 4; GWA, genome-wide association; HLA, human leukocyte antigen; kb, kilobase; LD, linkage disequilibrium; MAF, minor allele frequency; OR, odds ratio; PTPN2, protein tyrosine phosphatase, nonreceptor type 2; PTPN11, protein tyrosine phosphatase, nonreceptor type 11; PTPN22, protein tyrosine phosphatase, nonreceptor type 22; SH2B3, SH2B adaptor protein 3; SNP, single nucleotide polymorphism; tagSNP, tagging single nucleotide polymorphism; WTCCC, Wellcome Trust Case Control Consortium.

Received April 16, 2008.

Accepted June 23, 2008.

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