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Endocrinology and Diabetes Research Group, Hospital de Cruces, Barakaldo-Bizkaia, SpainDepartment of Pediatrics, Physical Anthropology and Animal Physiology, University of the Basque Country, Bilbao, Spain
Endocrinology and Diabetes Research Group, Hospital de Cruces, Barakaldo-Bizkaia, SpainDepartment of Pediatrics, Physical Anthropology and Animal Physiology, University of the Basque Country, Bilbao, Spain
Endocrinology and Diabetes Research Group, Hospital de Cruces, Barakaldo-Bizkaia, SpainDepartment of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country, Bilbao, Spain
Background & Aims: Celiac disease is a complex, immune-mediated disorder of the intestinal mucosa with a strong genetic component. HLA-DQ2 is the major determinant of risk, but other minor genes, still to be identified, also are involved. Methods: We designed a strategy that combines gene expression profiling of intestinal biopsy specimens, linkage region information, and different bioinformatics tools for the selection of potentially regulatory single-nucleotide polymorphisms (SNPs) involved in the disease. We selected 361 SNPs from 71 genes that fulfilled stringent functional (changes in expression level) and positional criteria (located in regions that have been linked to the disease, other than HLA). These polymorphisms were genotyped in 262 celiac patients and 214 controls. Results: We detected strong evidence of association with several SNPs (the most significant were rs6747096, P = 2.38 × 10−5; rs7040561, P = 6.55 × 10−5; and rs458046, P = 1.35 × 10−4) that pinpoint novel candidate determinants of predisposition to the disease in previously identified linkage regions (eg, SERPINE2 in 2q33, and PBX3 or PPP6C in 9q34). Conclusions: Our study shows that the combination of function and position is a valid strategy for the genetic dissection of complex traits.
Celiac disease (CD [OMIM# 212750]) is a chronic, immune-mediated enteropathy caused by intolerance to ingested gluten that develops in genetically susceptible individuals. It is one of the most common lifelong disorders affecting Caucasians and recent studies have estimated its prevalence at close to 1:120.
CD is a complex multifactorial disease, and familial clustering has been known to occur for a long time. The major genetic determinant of CD maps to the HLA class II region on 6p21, and more than 90% of celiac patients carry at least one copy of the HLA-DQ2 heterodimer (encoded by alleles HLA-DQA1*05 and HLA-DQB1*02, either in cis or in trans), or less frequently (∼6%) the HLA-DQ8 molecule encoded by HLA-DQA1*03 and HLA-DQB1*0302.
These associations are concordant with the ability of these HLA molecules to present immunogenic, deamidated gliadin peptides to T lymphocytes, which accounts for their central role in the pathogenesis of the disease.
Nevertheless, the HLA-DQ2 variant also is frequent in the general population, and is present in more than 30% of Caucasians, suggesting that HLA alone is not sufficient to explain all the genetic susceptibility to CD. Current knowledge suggests that HLA is responsible for around 40% of the heritability of the disease, and that several other minor genetic determinants are contributing to the genetic predisposition. It has been suggested that there may be additional loci in the major histocompatibility complex (MHC) region that modify disease risk independently from HLA-DQ.
The profusion of potentially relevant immune response genes in this region, together with the differential frequency of A1-B8-DR3-DQ2 and A30-B18-DR3-DQ2 extended haplotypes in DQ2-homozygous patients with either CD or type 1 diabetes, support the presence of non-HLA disease-specific variants in 6p21.
However, the strong linkage disequilibrium within the MHC complicates enormously the search for candidate genes, and the issue is still an open question. Candidate genes outside 6p21, especially those involved in the immune response, such as CTLA4 or IFNG, also have been investigated, but, in general, contradictory results have been obtained.
Discrepancies between studies in candidate gene association analyses could be caused by population heterogeneity, but also could reflect the lack of power to detect small gene effects because of undersized studies. A recently completed whole-genome association study in CD (>300,000 genotyped SNPs) found very strong evidence of association in a region spanning interleukin-2 (IL-2) genes and IL-21 on chromosome 4p27.
In general, however, there is a marked lack of consistency across linkage studies, and the genuine contributors to disease susceptibility are difficult to identify.
Gene expression profiling analyses of the intestinal mucosa also have been performed in CD, and the 2 microarray-based studies performed to date have contributed to our understanding of the functional mechanisms of disease progression.
Genes and pathways that might be important in the pathogenesis of the disease have been highlighted, but again, the genetic implication of these findings, in terms of discovering disease-associated variants within those particular genes, still is pending. The observed alterations in expression levels are, in the majority of cases, secondary to (and not causative of) the disease process, and do not correspond with structural changes in the genomic sequence. CD is the result of a combination of mutations (in more than one gene) upon which environmental factors act, altogether modulating the degree of predisposition to, as well as the severity and clinical manifestations of, the disease. Each single, primary alteration of gene expression levels associated with the disease thus is expected to be modest. Although some changes might be caused by SNPs located within coding regions, and cause amino acid substitutions or stop codons that have a dramatic impact on protein function, gene variants associated with complex diseases are likely to be located in noncoding regulatory regions of genes and produce more subtle effects on the intensity or timing of gene expression. These regulatory SNPs might alter the level of gene expression because they modify transcription factor binding sites or because they affect the structure of messenger RNA (mRNA) in ways that alter splicing, posttranscriptional processing, and stability.
In the present work, we have taken advantage of the extraordinary power of whole-genome expression profiling for the identification of genes and pathways involved in CD and combined these results with the genomic regions that previously have shown strong evidence of linkage to CD. Our aim was to identify target genes for disease-association studies on the basis of functional and positional criteria. Finally, we selected regulatory SNPs that might be responsible for the changes observed in expression and performed a case-control, disease-association study. The study was approved by the institutional ethics board and informed consent was obtained from all subjects or their parents. An outline of the strategy used in this study is shown in Figure 1.
Materials and Methods
Gene Expression Profiling
CD was diagnosed according to the European Society of Pediatric Gastroenterology Hepatology and Nutrition criteria, including antigliadin, anti-endomysium, and antitransglutaminase antibody determinations as well as a confirmatory small bowel biopsy. Two biopsy specimens from the distal duodenum of each patient were obtained using a double-port small intestinal pediatric capsule; one of the samples served for clinical pathology examination and diagnosis of CD and the other sample was used in the present study.
We investigated changes in gene expression profiles of the intestinal mucosa induced by chronic exposure to dietary gluten, as well as the acute effects of gliadin on CD-susceptible intestinal epithelium using an in vitro stimulation model of intestinal biopsy specimens. For the analysis of the effects of chronic, or long-term, exposure to gliadin, biopsy specimens from 9 newly diagnosed CD patients with clinically active disease (positive for CD-associated antibodies and presenting atrophy of intestinal villi with crypt hyperplasia) who were on a nonrestricted (gluten-containing) diet at that time, were compared with tissue samples from 9 normalized CD patients (asymptomatic, antibody-negative, and with a recovered intestinal epithelium) who had been on a strict gluten-free diet for more than 2 years. Biopsy samples were immediately frozen and stored in liquid nitrogen until RNA was extracted. For the acute response experiment, duodenal biopsy specimens were obtained from 10 gluten-free diet–treated CD patients (as described earlier) and each tissue sample was cut into 2 portions, which were incubated separately in 1 mL of RPMI medium, with and without the addition of 10 μg/mL gliadin (cat no. G3375; Sigma, St. Louis, MO) at 37°C and 5% CO2 for 4 hours.
Samples subsequently were frozen and stored in liquid nitrogen until RNA was extracted. Frozen tissue samples were disrupted with disposable plastic pellet pestles (Kontes, Vineland, NJ) in 1.5-mL microcentrifuge tubes and homogenized using a QIAshredder column (QIAGEN Gmbh, Hilden, Germany). Total RNA was isolated using the RNeasy-Micro kit (QIAGEN) treated with DNase I and stored at −70°C until use. RNA was quantified by ultraviolet spectrophotometry and the quality of the sample was verified using the RNA 6000 NanoAssay on a BioAnalyzer system (Agilent Technologies Inc., Santa Clara, CA). On average, 3 μg of total RNA were isolated from each whole biopsy specimen, which was used for the chronic exposure experiment, and 0.5 μg from each incubated portion, which was used for the acute response experiment.
Gene expression profiling was performed using the Human U133 Plus 2.0 array (Affymetrix, Santa Clara, CA). In the chronic exposure experiment, double-stranded complementary DNA (cDNA) and biotinylated complementary RNA were synthesized from 2 μg of total RNA using the One-Cycle cDNA synthesis kit and the IVT labeling kit (both from Affymetrix). For the acute response experiment, the Two-Cycle cDNA synthesis kit (Affymetrix) was used, starting with 200 ng of total RNA. Array hybridization, washing, and staining procedures were performed according to the manufacturer’s protocols. By using the Gene Chip Operating Software v1.2 (Affymetrix), results from each individual microarray were scaled so that the average probe set hybridization signal intensity value (target intensity) was 50, and raw image files were processed to .CEL files, which were used for the comparison of gene expression profiles. Experimental results (in .CEL file format) are available from the authors upon request. A more extensive description of genes and biological pathways identified by microarray experiments is currently in preparation.
Differentially expressed transcripts were identified using MUSC ArrayQuest, a web-accessible (http://proteogenomics.musc.edu/arrayquest.html) genomic analysis process controller from the Medical University of South Carolina, where .CEL files were uploaded and user-specified analysis methods are performed on computers loaded with the R programming language and Bioconductor software packages (available at http://www.bioconductor.org/).
For the present study, method no. 12 of the MUSC Array Quest Methods Library was selected: a detailed description is available at the web site, but, in brief, this method normalizes hybridization data using Robust Multichip Average, and differentially expressed genes can be filtered based on fold-change, t test, and/or false discovery rate thresholds, all of which can be adjusted by the user to obtain a reasonable output in terms of the number and characteristics of the genes that are considered differentially expressed.
Quantitative reverse-transcription polymerase chain reaction was used to replicate the microarray results in independent RNA samples in a randomly selected set of altered genes. For the acute experiment, total RNA was isolated from biopsy specimens from 6 additional patients with active CD and 6 other patients with treated CD. The mRNA levels of INFG, UBD, EPHX1, TAP1, ACAA2, CD47, RNASE4, ACOT7, HIP1R, NOD27, and PSME2 were measured in individual samples. In the acute response experiment, expression levels of HDAC4, TMEM37, PDZK1, TREH, RAB6IP2, ALDOB, SSX2IP, SLC38A1, SLC25A16, and APOC2 were analyzed in RNA pools prepared from 10 gliadin-stimulated and nonstimulated biopsy pairs from treated CD patients, to ensure enough material for replication of all the genes. Primers and probes for each gene were purchased as commercial Assay-on-Demand sets (Applied Biosystems, Foster City, CA) and RT-PCR reactions were performed in duplicate on an ABI PRISM 7900 Sequence Detection System (Applied Biosystems), with a single-reaction enzyme mixture using QuantiTect Probe reverse-transcription polymerase chain reaction (QIAGEN Gmbh) as previously described.
The expression of the housekeeping gene RPLPO (large ribosomal protein) was quantified simultaneously in each experiment and used as an endogenous control of input RNA, and relative expression of each gene was calculated using the accurate cycle threshold method.
Candidate Gene and SNP Selection
For the selection of candidate genes for the genetic association study, we selected chromosomal positions that had been identified either by at least 2 independent genome-wide linkage studies, or those that although described only once, showed a logarithm of odds score greater than 2.
were adjusted to prioritize putative regulatory SNPs, including those located in predicted transcription factor binding sites, CpG islands, exon–intron boundaries, promoter regions, as well as splicing enhancers, and those forming triplex DNA structures (Figure 1). In addition, only SNPs having a unique mapping location in the NCBI b35 human genome assembly and a minor allele frequency greater than 5% in Caucasians (according to genotyping results deposited in dbSNP [http://www.ncbi.nlm.nih.gov/SNP/index.html] release 125) were included. SNPs that could not be designed for an Illumina GoldenGate assay (Illumina Inc., San Diego, CA) were replaced, when possible, by tag SNPs from release 21 of the HapMap project (http://www.hapmap.org/).
Genotyping and Disease Association Analysis
The study population for genetic association studies consisted of 264 patients with CD (152 girls and 112 boys; mean age at diagnosis, 3.2 y; range, 0.7–13.7 y) who currently are being followed up at the Pediatric Gastroenterology Units of the 2 participating hospitals, and 214 healthy adult volunteers from the general population (110 women and 104 men) with no personal or family history of CD. Both patients and controls were residents in the Basque Country and of Caucasian ethnic origin. Oligonucleotide pools for 361 SNPs were purchased as a GoldenGate Custom Panel (Illumina) and DNA samples were genotyped and plates were processed following the manufacturer’s instructions (http://www.illumina.com/downloads/GOLDENGATEASSAY.pdf). The genotype confidence score of the assay for keeping allele calls was set to 0.25. SNPs with ambiguous genotype in more than 10% of the samples were removed from subsequent analysis. Interassay reproducibility was assessed by including 1 of the samples in the 5 genotyping experiments performed.
Before disease-association studies, SNPs with a minor allele frequency lower than 0.01 in the complete dataset and those that failed the Hardy–Weinberg equilibrium test (P < .001) in either cases or controls were eliminated. The Cochran–Armitage case/control genotypic test for trend was used for single-marker disease-association analysis, and P values (uncorrected and after adjusting for multiple testing using the Bonferroni correction [number of tests = number of SNPs = 330]), odds ratios, and 95% confidence intervals are reported. To perform gene-based multimarker association analyses, the sliding-window option in PLINK was used. In this approach, haplotypes in sliding windows of a fixed number of SNPs, shifting 1 SNP at a time (haplotype length ranging from 2 SNPs to the total number of SNPs in each particular gene), were inferred for every individual using the expectation-maximization algorithm. Subsequently, a case/control disease association test was performed with each haplotype, and asymptotic P values were calculated.
Nonsupervised hierarchic clustering of microarray results of intestinal biopsy specimens from newly diagnosed CD patients and from patients on a gluten-free diet could separate both conditions efficiently, indicating that the 2 groups are relatively homogeneous and uniform, regardless of background expression levels in each of the individuals (data not shown). In consequence, stringent selection parameters based on false discovery rates could be used to identify differentially expressed genes: fixing a P value of .01, false discovery rate thresholds of 0.025, 0.05, and 0.1 yielded 1453, 1647, and 3305 differentially expressed sequences, respectively. In contrast, the 2 portions of the same biopsy specimen (incubated with and without gliadin in the acute experiment) tended to cluster together, indicating that background similarities in each sample were stronger than the differences provoked by the gliadin insult. Consequently, less stringent thresholds, based on fold-change ratio, were used. With a fixed P value of .01, tweaking the fold-change ratio between 2 and 1.2, resulted in a number of sequences with altered expression levels ranging from 11 to 137, respectively. To obtain a reasonable list of genes for SNP selection and disease-association studies, a threshold of false discovery rates less than 0.05 and fold-change ratios greater than 1.3 were established, respectively, resulting in a list of 1647 differentially expressed sequences in the chronic exposure experiment and 96 in the in vitro stimulated biopsy specimens (including 1287 and 87 known genes, respectively) (supplementary Table 1; see supplementary material online at www.gastrojournal.org). Thirty-four genes were common to both experiments, so a final list of 1340 genes was introduced into our SNP-selection algorithm. Of the genes that were replicated by reverse-transcription polymerase chain reaction, the expression levels of all but HIP1R and PDZK1 were concordant with what had been observed in the microarray experiments.
Crossing these 1340 altered genes with the 9 chromosomal regions that showed strong evidence of linkage with CD, the initial list of candidates was restricted to 76 genes. In addition, 2 genes that were located in one of the selected linkage regions and whose products were predicted to be transcription factors of several functional candidate genes also were included (Figure 2). The regulatory SNP selection criteria resulted in a list of 362 SNPs from 71 genes, of which 305 could be designed for genotyping with an Illumina GoldenGate assay (Illumina Inc.). It was possible to replace several undesignable SNPs with 50 tag SNPs (r2 > 0.8) from the HapMap project and with 6 proxy SNPs, the closest neighbor of a candidate SNP (supplementary Table 2; see supplementary material online at www.gastrojournal.org).
After applying the quality-control parameters to the raw genotyping results from the 361 SNPs, 6 SNPs were removed because more than 10% of the samples had ambiguous genotype calls. All of the samples had a valid genotype call for at least 95% of the SNPs. Consistency between the 5 replicates of the same sample was 100% for the 355 SNPs analyzed. Replication of genotyping by Taqman Allelic Discrimination assays was 100% concordant. Allele frequencies for each SNP were calculated and those with minor allele frequency less than 0.01 in the complete sample set (4 SNPs) were eliminated from further analysis. Genotype frequencies of 21 SNPs were not in Hardy–Weinberg equilibrium (P < .001) and those SNPs also were excluded.
Allele frequencies in cases and controls for the 330 SNPs included in the analysis, as well as the complete results of the association test, are included in supplementary Table 3 (see supplementary material online at www.gastrojournal.org). The 10 SNPs in 6 different genes that showed the strongest evidence of association (uncorrected P < .005) with CD are shown in Table 1. Haplotype analysis did not detect association in any additional candidate gene (complete results not shown) and, as could be expected, alleles of SNPs that had shown association in the single-marker analysis were present in associated haplotypes. Evidence for a more significant association with haplotypes compared with single SNPs was observed only in the case of 2- and 3-SNP haplotypes in CUTL1 and YIPF5 (Table 2).
Table 1Results of the Association Analysis Showing the Most Significant SNPs
CD is a highly prevalent, complex, immune-mediated enteropathy that is associated strongly to HLA-DQ2, but that also is influenced by other genetic determinants and environmental factors. During the past few years, numerous candidate gene association analyses, together with whole-genome linkage studies and microarray-based expression profiling experiments, have investigated the genetic susceptibility to CD, but the picture is still far from completion. In an attempt to contribute to the genetic dissection of CD, we designed a 3-step polymorphism selection strategy before a case-control association analysis to identify SNPs that may be contributing to disease susceptibility. Of the 1340 genes that showed altered expression levels in intestinal biopsy specimens of CD patients in response to a chronic or acute challenge to gliadin, we picked 78 genes that mapped to chromosomal regions with strong evidence of linkage to the diseases. Bioinformatic selection tools were used to identify SNPs with a theoretic implication in the observed changes in gene function, and a genotyping assay was designed to identify disease-associated variants. It finally was possible to analyze the genotyping results from 330 SNPs in 71 genes and several association signals were detected.
The most significant association signal (Table 1) corresponds to rs6747096 (P = 2.38 × 10−5), a synonymous SNP located in exon 3 (10 bp upstream of the splice site) of gene SERPINE2 (serpin peptidase inhibitor, clade E). According to SNP selector online tool, rs6747096 had a very high regulatory score. It has been shown that SERPINE2 is important in the initial stages of extracellular matrix production,
and, in fact, we have detected alterations in the expression levels of several genes involved in this pathway in biopsy specimens from newly diagnosed CD patients (supplementary Table 1; see supplementary material online at www.gastrojournal.org). The observed down-regulation of SERPINE2 gene expression is coherent with the loss of extracellular matrix, a feature of active CD that participates in the marked villous atrophy observed at diagnosis of active disease.
Four additional SNPs elsewhere in the gene also were genotyped, but no evidence of association was detected. SERPINE2 maps to the CELIAC3 linkage region on 2q33, which has been replicated in various genome-wide studies. The T-lymphocyte inhibitor gene CTLA4 maps to this region and is considered a general autoimmunity gene that has been implicated in several diseases,
positive findings have not been replicated consistently. More recent studies have extended the region to the CD28/CTLA4/ICOS T-lymphocyte regulatory cluster, and disease-associated haplotypes also have been proposed.
Functional results of these associated variants and haplotypes still are pending, but, overall, CD risk owing to the 2q33 genomic region seems complex and different genes could be involved in particular autoimmune diseases.
Linkage of CD to region 9q34.11 has been identified only in the most recent genome-wide study,
although with a LOD score of 2.47. This region contained the next 5 most significant SNPs, located in 2 different genes that are 0.5 Mb apart: rs7040561 is an intronic SNP in PBX3 (pre–B-cell leukemia homeobox 3), a member of the pre–B-cell leukemia homeobox transcription factors that are implicated in developmental and transcriptional gene regulation in numerous cell types, including immune cell maturation.
Putative binding sites for PBX hetero-oligomers were present in 101 genes of the chronic response list and in 6 of those identified in the acute response experiment. The other gene in the region, PPP6C (protein phosphatase 6, catalytic subunit), contained 4 SNPs (rs1048251, rs7019234, rs459311, and rs458046) that showed significant association, a result that could be expected in view of the strong linkage disequalilibrium (r2 = 1) among those SNPs in the Caucasian HapMap samples. This gene is a protein phosphatase involved in the G1/S transition of mitotic cell cycle
The pathologic consequences of the changes in expression levels of genes involved in this pathway need to be investigated further, but they could explain the increase in crypt cell proliferation rate, a phenomenon that occurs as a compensatory reaction to the gluten-induced loss of enterocytes.
The remaining SNPs in Table 1 show more modest evidence of association, and correspond to 3 genes from different linkage regions: rs11954744 and rs6887645 are in YIPF5 (Yip1 domain family member 5), a member of a Rab–guanosine triphosphatase interacting factor protein family that is expressed in all tissues and is induced by transforming growth factor-β1.
YIPF is on 5q33.1-35.3, a region containing a cytokine gene cluster that has been associated with Crohn’s disease, where a putative disease-associated haplotype has been identified. Several polymorphisms in cytokine genes (including IL-12B, IL-4, IL-5, IL-9, IL-13, and IL-17B) have been analyzed in CD patients and controls, but no evidence of association was shown.
In turn, rs365836 is located in gene CUTL1 (cut-like 1, CCAAT displacement protein), a transcriptional repressor involved in epithelial cell differentiation that also is a target of transforming growth factor-β signaling.
This gene is on linkage region 7q22.1, identified in several genome-wide scans but where no associated variant or candidate gene has been identified previously. Statistical significance did not hold for these 3 SNPs after stringent correction for multiple testing using a conservative approach that considers each SNP an independent test, but haplotype analysis also supported the association of both genes and warrants further investigation into their potential contribution to disease susceptibility. Finally, rs12619019 is located on gene ALS2CR13 (amyotrophic lateral sclerosis 2-juvenile, chromosome region, candidate 13). This gene is also on 2q33 (although 21 Mb telomeric from SERPINE2). Interestingly, the gene has been linked to amyotrophic lateral sclerosis (OMIM#205100), a rare, neurodegenerative disorder that shares the production of autoantibodies against TGase2 with CD.
In summary, our study shows the efficiency of combining gene expression and linkage information in the search of candidate genes for CD susceptibility. This particular disease is an excellent study model for autoimmune disorders because routine diagnostic and follow-up intestinal biopsy specimens of affected individuals allow direct investigation of the target organ. This is also the case of other complex diseases of the gastrointestinal tract, and, when tissue samples are not available, other sources of functional information (such as cells or animal models) could be used. Our strategy has allowed the identification of several polymorphisms that are associated with risk to CD. Nevertheless, our selection criteria of polymorphisms were biased subjectively toward SNPs that could influence transcription and also by the technology available in our laboratory. Other classes of polymorphisms (insertion/deletions, tandem repeats, copy number variation, and so forth) also might be implicated in disease susceptibility and different selection algorithms also could be successful. In fact, a recent whole-genome association analysis in CD has shown strong association with CD in variants of the IL-21 and IL-12 gene region on chromosome 4q27, a region not considered high priority by previous linkage studies. The mechanisms by which the associated SNPs might alter the expression of each gene in response to the epithelial exposure to gliadin remain unknown and, in fact, these SNPs might only be hitchhiking polymorphisms and not true etiologic variants. Moreover, these findings need to be replicated in independent populations. For these reasons, and because other polymorphism selection algorithms might identify different genotyping targets, we make our raw data available for any subsequent study that will contribute to our understanding of this disease.
This work was partially funded by Research Project grants 03/11032 and 06/111030 from the Basque Department of Health and PI04/1170 from the Instituto de Salud Carlos III of the Spanish Ministry of Health (J.R.B.). Ainara Castellanos–Rubio and Izortze Santín are predoctoral fellows supported by grants from the University of the Basque Country and the Spanish Ministry of Education, respectively. Jose Ramon Bilbao is co-funded by the Spanish Ministry of Health-Human Resources Stabilization Program (99/3076). CIC bioGUNE support was provided from The Basque Department of Industry, Tourism and Trade (Etortek Research Programs 2006/2007) and from the Innovation Technology Department of the Bizkaia County.
Celiac disease results from a dysregulated immune response to dietary wheat gluten and related cereal proteins.1,2 The disease is an acquired disorder, but with a strong hereditary component. The evidence for the importance of genes comes from familial and twin studies. About 10% of first-degree relatives are affected by the disease, compared with the population prevalence of about 1%; the pairwise concordance rates in monozygotic and dizygotic twins are about 75% and 10%, respectively.3 Already in 1972 the association between celiac disease and the HLA locus had been established.