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The Journal of Clinical Endocrinology & Metabolism Vol. 88, No. 5 2274-2280
Copyright © 2003 by The Endocrine Society

PDS Is a New Susceptibility Gene to Autoimmune Thyroid Diseases: Association and Linkage Study

Hassen Hadj Kacem, Ahmed Rebai, Noureddine Kaffel, Saber Masmoudi, Mohamed Abid and Hammadi Ayadi

Laboratoire de Génétique Moléculaire Humaine (H.H.K., S.M., H.A.), Faculté de Médecine de Sfax; Centre de Biotechnologie de Sfax (A.R.); and Service d’Endocrinologie (N.K., M.A.), CHU Hédi Chaker de Sfax, 3029 Sfax, Tunisia

Address all correspondence and requests for reprints to: Prof. Hammadi Ayadi, Faculté de Médecine, Laboratoire de Génétique Moléculaire Humaine, Av. Majida Boulila, 3029 Sfax, Tunisia. E-mail: hammadi.ayadi{at}fmsf.rnu.tn.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Autoimmune thyroid disease (AITD), including Graves’ disease (GD), Hashimoto thyroiditis (HT), and primary idiopathic myxedema, is caused by multiple genetic and environmental factors. Genes involved in immune response and/or thyroid physiology appear to influence susceptibility to disease. The PDS gene (7q31), responsible for Pendred syndrome (congenital sensorineural hearing loss and goiter), encodes a transmembrane protein known as pendrin. Pendrin is an apical porter of iodide in the thyroid. To evaluate the contribution of PDS gene in the genetic susceptibility of AITD, we examined four microsatellite markers in the gene region. Two hundred thirty-three unrelated patients (GD,141; HT, 54; primary idiopathic myxedema, 38), 15 multiplex AITD families (104 individuals/46 patients) and 154 normal controls were genotyped. Analysis of case-control data showed a significant association of D7S496 and D7S2459 with GD (P = 10-3) and HT (P = 1.07 10-24), respectively. The family-based association test showed significant association and linkage between AITDs and alleles 121 bp of D7S496 and 173 bp of D7S501. Results obtained by transmission disequilibrium test are in good agreement with those obtained by the family-based association test. Indeed, evidence for linkage and association of allele 121 bp of D7S496 with AITD was confirmed (P = 0.0114). Multipoint nonparametric linkage analysis using MERLIN showed intriguing evidence for linkage with marker D7S496 in families with only GD patients [Z = 2.12, LOD = 0.81, P = 0.026]. Single-point and multipoint parametric LOD score linkage analysis was also performed. Again, the highest multipoint parametric LOD score was found for marker D7S496 (LOD = 1.23; P = 0.0086) in families segregating for GD under a dominant model. This work suggests that the PDS gene should be considered a new susceptibility gene to AITDs with varying contributions in each pathology.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
THE AUTOIMMUNE THYROID diseases (AITDs), which include Graves’ disease (GD), Hashimoto’s thyroiditis (HT), and primary idiopathic myxedema (PIM), are multifactorial diseases with both important genetic and environmental components. In GD the autoimmune process results in the production of thyroid-stimulating antibodies and lead to hyperthyroidism, whereas in HT the end result is destruction of thyroid cells and hypothyroidism. Several full genome screens, using different methods of linkage analysis, have shown varying degrees of evidence for linkage between susceptibility genes for AITDs and markers on the long arms of chromosomes 14q31, 20q11, Xq21, 2q33, 13q32, 12q22, 20q13, 18q21, 5q31–33, 8q23–24, 11q11–13, and 9q13–22 (1, 2, 3, 4, 5, 6, 7, 8, 9, 10) and on the short arms of chromosomes Xp11 (11), 2p21 (12), and six close to, but distinct from, the human leukocyte antigen region (6). Linkage evidence for a susceptibility gene for GD to the major histocompatibility complex (MHC) has also been found in some populations (13, 14, 15, 16), but this has been difficult to reproduce in others (1, 17). This suggests different susceptibility genes may exist and may differ among ethnic groups. A full genome screening in a large Tunisian family affected with AITDs showed only one significant linkage in chromosome 2p21 (12), and the analysis of MHC, CTLA-4, Ig VH, and Cß TCR genes with several polymorphic markers did not show any evidence of linkage. However, allelic association studies using case-control or family-based studies showed a significant association between CTLA-4 marker alleles and HLA B37*01 allele with GD in our population (18, 19).

Candidate gene studies have proven very effective in detecting susceptibility genes for many diseases as well as genes important for disease progression and severity. Population-based, case-control studies have shown a consistent association of AITDs with many candidate genes involved in immune response or thyroid physiology. Immunoglobulin heavy chain (Gm) (15), IL-1 receptor antagonist (20), T-cell antigen receptor ß (21), and some MHC haplotypes have been found to be associated with GD in some populations but not in others (13, 14, 22, 23, 24, 25). Many thyroid-restricted genes have been tested, such as the TSH receptor gene (TSHR) (26), thyroid peroxidase (27), and thyroglobulin (Tg) (28). Association of GD to TSHR gene has been found in some populations, but this has been difficult to reproduce in others (26, 29). Only the Tg gene has been found to be linked and associated with AITD (28).

The PDS gene (7q31), responsible for Pendred syndrome (congenital sensorineural hearing loss and goiter) (OMIM 274600), was identified in 1997 (30). The PDS gene encodes a transmembrane protein known as pendrin, which is expressed in the thyroid, kidney, and fetal cochlea (30). Pendrin functions as a transporter of iodide and chloride (31). Iodide is of known importance in the thyroid, thereby emphasizing a potential critical role for pendrin in thyroid physiology. In vitro and in vivo findings showed that iodine organification was altered in thyroid cells obtained from a patient with Pendred syndrome (32). Interestingly, significantly greater amounts of pendrin were encountered in thyroid tissue from patients with GD (33). In addition, concurrence of Pendred syndrome, autoimmune thyroiditis, and simple goiter in one family has been described (34). These findings prompted us to analyze the possible contribution of the PDS gene in the genetic susceptibility of AITDs.

In this study, we analyzed polymorphic microsatellite markers around the PDS gene, using case-control and families-based designs, to evaluate the role of PDS in the genetic control of AITDs in a sample from Tunisia. Our findings suggest PDS may be a susceptibility gene to AITDs.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
Unrelated patients and controls

One hundred forty-one unrelated patients (30 men and 111 women) with GD, 54 unrelated patients (50 women and 4 men) with HT, and 38 (31 women and 7 men) unrelated patients with PIM were included in this study. Data gathered from the patient group were compared with those obtained from a control population of 154 unrelated healthy subjects (75 men and 79 women) originating from the same area with no clinical evidence or family history of AITD and inflammatory joint disease.

Families

Fifteen families (104 individuals containing 46 patients) were analyzed in the current study. Three families were simplex and the others contained more than two patients. Six families were mixed containing both GD and HT patients and the remaining families contained only one pathology (seven with GD and two with HT). On the average, the families had 6.9 members.

Clinical assessment

GD was diagnosed on the basis of clinical and laboratory evidence of hyperthyroidism requiring treatment, palpable diffuse goiter, high thyroid hormonal rates, positive anti-TSHR, antithyroid peroxidase, and/or antithyroglobulin antibodies. HT was diagnosed by documented clinical and biochemical hypothyroidism requiring thyroid hormone replacement and presence of autoantibodies to thyroid peroxidase, with or without antibodies to thyroglobulin. PIM was diagnosed by the presence of hypothyroidism requiring T3 or T4 replacement. Patients with PIM have an atrophic gland.

Genotyping

DNA from patients and controls were obtained from peripheral blood as previously described (35). DNAs from families were genotyped with four microsatellite markers of CA repeat (order of the markers: D7S501-D7S496-D7S2459-D7S692) localized in the PDS chromosomal region with one intragenic marker (D7S2459). The map position and order of markers were published by Everett et al. (30). The distance between the 5' end (D7S501) and the 3' end (D7S692) of the PDS region is 1.7 cM. DNAs from unrelated patients and controls were genotyped with only three markers (D7S496, D7S2459, and D7S692).

Genotyping was undertaken using a radioactive detection system. Microsatellite markers were amplified in 96-well Falcon flexi plates by the PCR using Techne thermocyclers. PCRs were performed in a final volume of 50 ml, containing 60 ng genomic DNA, 50 pmol of each primer, 1.25 mM deoxynucleotide triphosphate, 10 mM Tris pH 9, 50 mM KCl, 1.5 mM MgCl2, 0.1% triton X-100, 0.01% gelatin, and 1 U Taq DNA polymerase.

Each PCR was performed using a hot-start procedure, and amplification was carried out using 35 cycles of denaturation at 94 C for 40 sec, with annealing at 55 C for 40 sec, and elongation at 72 C for 40 sec, followed by a final elongation at 72 C for 2 min. Five milliliters of each sample were denatured at 96 C for 2 min and resolved on 5–7% denaturing polyacrylamide gel with 8.3 M urea. The separated bands were transferred onto nylon membrane (Hybond N+) and visualized by autoradiography, after exposition to XAR-5 film (Kodak, Rochester, NY) for 2–48 h at -70 C.

Disease models

Until now it was not clear whether GD and HT are two distinct disorders with different or common etiologies. To evaluate the contribution of the PDS gene in each pathology, we analyzed the data under six models (Table 1Go): 1, all AITD patients were considered affected; 2, just GD patients were considered affected (under this model HT patients were considered as unaffected even if they had relatives with GD); 3, just HT patients were considered affected (under this model GD patients were considered as unaffected even if they had relatives with HT); 4, only mixed families were analyzed (AITD patients were considered affected); and 5, families with only GD were analyzed; and 6, families with only HT were analyzed).


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  		Table 1. Characteristics of the study sample in each disease model

 
Statistical analyses

Case-control association study. The distribution of the alleles in unrelated patients with AITDs vs. controls were compared using the {chi}2 test. The association was evaluated using relative predispositional effect methods (23). Statistical significance was reached when P was less than 0.05, and Fisher’s exact test was used when necessary. Odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were calculated according to Woolf’s formula (36).

Family-based association tests. We used the family-based association test (FBAT) package (37) to test for association in the presence of linkage (38). FBAT provides a {chi}2 test statistic that tests the composite null hypothesis of no linkage or no disequilibrium without concerns about confounding because of admixture. FBAT has the advantage over the conventional transmission disequilibrium test (TDT) of being able to use data from all family members, not just case-parent trios.

We also used the TDT approach (39) as implemented in GeneHunter (version 1.3, http://waldo.wi.mit.edu/ftp/distribution/software/genehunter/gh2/) to test for association. In this implementation cases in which one parent is missing are used only when the genotyped parent and the proband are both distinct heterozygotes (40). This program also allows the use of multilocus TDT to test for transmission disequilibrium of haplotypes for two to four markers.

Linkage

We performed multipoint nonparametric linkage analysis using Merlin (41). Merlin implements a new algorithm using sparse gene flow trees, which perform exact and approximate likelihood calculations for single-point and multipoint linkage analysis. Merlin allows time saving in computation comparatively to GeneHunter (42) and Allegro (43) for some family settings in complex pedigrees. Merlin outputs both NPLall (Z) scores and their corresponding allele sharing LOD scores obtained by the Kong and Cox {delta} method under exponential model (44) to test for allele sharing among affected individuals with their asymptotic P values. It also gives the maximum possible Kong and Cox LOD scores (LODmax) one might get if fully informative markers linked to an unobserved disease locus of maximal effect were available, conditional on observed phenotypes and family structure.

Single-point and multipoint parametric LOD score linkage analysis was also performed with GeneHunter (42) under both dominant and recessive genetic models, assuming 50% penetrance, no phenocopy, and 5% gene frequencies as suggested in the MMLS approach of Greenberg et al. (45). An admixture test was also used to assess heterogeneity among families.

To evaluate evidence for linkage of the marker region tested, we used pointwise P values to calculate regionwise P values by applying the formula of Lander and Kruglyak (46) with C = 1 (one chromosome under test) and G = 0.017 (length in morgans of the region tested). Note that genome-wide criteria (given in Ref. 46) should not be used here because we were investigating a new candidate region without any prior information or evidence for linkage (e.g. from a previous genome scan).

The marker mapping information was obtained from the Genethon map (47). The last update of this map (http://www.gdb.org/hugo/chr7/geneticmaps.html) was used. When available, we used in linkage analysis the marker allele frequencies estimated on an independent sample of 154 unaffected individuals drawn from the general population (except for marker D7S501).


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
DNAs from patients and controls were genotyped for microsatellite markers to determine the role of PDS gene in AITD development. Several statistical methods are employed in our analysis because of the genetic complexity of these diseases.

Case-control studies

The D7S2459 microsatellite marker polymorphism analysis revealed nine alleles with sizes ranging from 132 to 152 bp (Table 2Go). When the allelic and genotypic frequencies corresponding to the D7S2459 in GD patients and controls were compared, no significant differences were found (P > 0.05). However, analysis of the allelic frequency distribution in HT and PIM patients showed a significant difference from control data (HT: {chi}2 = 132.03, 8 degrees of freedom, P = 1.07 10-24; PIM: {chi}2 = 35.64, 8 degrees of freedom, P = 2 10-5; HT and PIM: {chi}2 = 87.58, 8 degrees of freedom, P = 1.4 10-15). In addition, the comparison between HT and PIM allele distributions showed a significant difference (P = 1.6 10-13), reflecting a very important allelic heterogeneity between types of AITD. To evaluate the risk of each allele in determining genetic susceptibility of HT and PIM, ORs are shown in Table 2Go. The highest OR was found with the 144-bp and 132-bp alleles respectively (HT: OR = 7.81; 95% CI, 3.96<OR<15.52, P = 2 10-12; PIM: OR = 9.27, 95% CI, 1.29<OR<103.59, Fisher’s exact test: P = 0.01). To reveal the relative effects (predisposing, protective, or neutral) of the D7S2459 alleles, we applied the relative predispositional effects method (23). After removing the 144-bp and 132-bp alleles from both patient (HT and PIM, respectively) and control data, and repeating comparisons, the significance of overall {chi}2 was preserved (HT: {chi}2 = 100.09, 7 degrees of freedom, P = 1.03 10-18; PIM: {chi}2 = 27.16, 7 degrees of freedom, P = 3.1 10-4). Indeed, more than one allele contributes in this significant association with predisposing or protective effects (Table 2Go).


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  		Table 2. D7S2459 polymorphism in unrelated patients and control groups

 
The D7S692 microsatellite marker revealed six alleles with sizes ranging from 159 to169 bp. Association study showed no significant differences of allele distribution between patients and controls (P > 0.05).

The D7S496 microsatellite marker revealed 13 alleles with size ranging from 121 to 145 bp (Table 3Go). Analysis of this polymorphism in GD patients showed a significant difference between patient and control allele frequencies distributions (GD: {chi}2 = 32.99, 12 degrees of freedom, P = 10-3). The highest OR was found with the 145-bp allele (OR = 3.92, 95% CI, 1.21<OR<16.53; P = 0.01) and the smallest significant OR was found with the 131-bp allele (OR = 0.20, 95% CI, 0.05<OR<0.62; P = 0.001). To evaluate the effect of each allele, we applied the relative predispositional effect method. After removing the 145-bp and 131-bp alleles (separately) from patient and control data, and repeating comparisons, the significance of overall {chi}2 was preserved in both comparisons (P = 0.0055 and P = 0.0174, respectively). Analysis of the D7S496 polymorphism in the HT patient showed no significant association between marker and disease (P > 0.05).


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  		Table 3. D7S496 polymorphism in unrelated patients and control groups

 
Family-based association study

Results of allelic tests in FBAT (Table 4Go) showed particularly highly significant association with the combined AITD phenotype (P = 0.0089) and at a lower degree with the GD only model (P = 0.0253) for allele 121 bp of D7S496. Significant association at the 5% level was also found for AITD and mixed disease models with allele 173 bp of D7S501.


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  		Table 4. FBAT with D7S496 and D7S501 microsatellite markers using different disease models

 
Results obtained by TDT are in good agreement with those of FBAT, although evidence for linkage and association for allele 121 bp of D7S496 with AITD is less strong (P = 0.0114) probably because of the small number of families used. On the other hand, similar P values were found for TDT and FBAT tests for families with GD only, meaning that the same amount of information was captured by both methods. For marker D7S501 significant association at 5% was found with GD model contrasting with results of FBAT in which it was significant in mixed and AITD models. TDT with haplotypes for all markers did not give any significant transmission disequilibrium, which could be expected given the large number of haplotypes and the limited sample size (104 individuals). Also, when one considers the haplotype of both alleles of markers D7S496 and D7S501 having shown evidence for association (121 and 173 bp), no transmission disequilibrium was observed because of the small number of transmissions (only two). Note that no other haplotype showed significant transmission disequilibrium.

Linkage analyses

Results of linkage analysis are reported in Table 5Go. We notice that significant evidence for linkage was found with marker D7S496 in the GD only model (Z = 2.12, LOD = 0.81, P = 0.026, regionwise P = 0.033 < 0.05). It is interesting to note that for all cases studied, and for each marker interval, maximum LOD values were reached on marker positions and the highest marker information was found for marker D7S496 (data not shown); this marker has the maximum number of typed individuals and the highest reported LOD score for almost all models (Table 5Go).


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  		Table 5. Nonparametric LOD scores given by MERLIN for different disease models

 
Parametric LOD scores ranged between -10.74 (in marker interval D7S496-D7S2459 under recessive transmission mode in AITD) and 1.23 (P = 0.0086) again on marker D7S496 in GD only families under dominant model (full results not shown). As with nonparametric study, this is the only significant linkage (regionwise P value = 0.011 < 0.05) found in our study. Note that no heterogeneity was detected in these families by the admixture test (heterogeneity LOD equal to LOD suggesting all families were linked). It is interesting to note that under recessive transmission mode, all disease models yielded negative LOD score indicating exclusion of the recessive model. Under dominant transmission mode, the maximum LOD when varying over penetrance (from 0.2 to 1) and gene frequencies (from 0.01 to 0.10) was obtained for 50% penetrance and 1% gene frequency (LOD = 1.375, P = 0.0059). In addition, power calculations indicated that the maximum attainable LOD scores (Kong and Cox LODmax) is reached under AITD model (LODmax = 3.93, P = 10-5).


   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
The clinical and immunological features of GD and HT are distinct; however, there are multiplex families with both GD and HT, and cases in which GD evolves into HT. Thus, there may be both susceptibility genes specific to GD or HT and common genes controlling risk to both.

This study provided strong evidence that the PDS gene is an important susceptibility gene for AITD in our population. Association analysis using case-control study showed a significant association between D7S496 and GD. Direct analysis of the PDS gene using one intragenic microsatellite marker (D7S2459) showed a strong association with HT. Results of FBAT showed particularly highly significant association with AITD and at a lower degree with GD only model for allele 121bp of D7S496. Results obtained by TDT were in good agreement with those of FBAT, and evidence for linkage and association of allele 121 bp of D7S496 with AITD is confirmed. Multipoint nonparametric linkage analysis using Merlin showed an interesting evidence for linkage with marker D7S496 in families with GD only. These results strongly suggest that the PDS gene contributes to AITD emergence.

Absence of linkage between HT and the PDS gene could be explained by the reduced number of patients in the studied sample, by the weak contribution of the PDS gene in HT development and/or the presence of an additional candidate gene to GD in the PDS region. Indeed, this region contains other nearby candidate genes for autoimmune diseases, including B-cell receptor-associated protein (BAP29). Physical mapping (http://www.genome.ucsc.edu) showed that D7S496 microsatellite marker was very close to the BAP29 gene. Thus, this gene could thereby be another susceptibility gene to GD in the PDS gene region. Moreover, this chromosomal region (7q22–31.1) showed a suggestive linkage to other autoimmune diseases, such as type 1 diabetes (48), multiple sclerosis (49), and inflammatory bowel disease (Crohn’s disease and ulcerative colitis) (50), thereby suggesting that this region may include a common susceptibility gene for these autoimmune diseases. However, the causal gene may just be linked to PDS.

The PDS gene may predispose to AITD by two global mechanisms: level of gene expression and/or protein activity in the thyroid tissue. An important part of both mechanisms are dependent of the genomic sequence of the gene. Indeed, sequence change in or near the PDS gene may alter its expression or pendrin interaction with other molecules. In fact, many studies report the involvement of intronic microsatellites repeat in many gene expressions by the splicing enhancement and/or the tissue-specific alternative splicing (51, 52, 53, 54). The D7S2459 microsatellite marker could play a similar role in PDS gene, and further work is required to evaluate the effect of AC repeat length in gene expression and mRNA stability. In addition, a recent work examining the regulatory network controlling the pendrin synthesis, using a cultured rat thyroid cell line (FRTL-5), showed that PDS expression was found to be significantly induced by low concentration of Tg, and the increase in PDS gene expression was transcriptional (33, 55). However, this regulation is not exclusive for the PDS gene. Indeed, Tg is a transcriptional regulator of many other thyroid-restricted genes (55).

Recently a study using 102 families (264 patients) showed strong evidence for linkage of AITD to the Tg region (8q24) (28). Direct analysis using Tg intragenic microsatellites markers demonstrated that the Tg gene was both linked and associated with AITD. This gene is considered the first thyroid-specific gene to be found linked and associated with AITD (28). But a full genome screening in a large Tunisian family affected with AITDs did not show any linkage with 8q24 chromosome (12). However, direct analysis of Tg gene using intragenic markers is necessary to exclude or involve this gene.

The pendrin and Tg play a dynamic role in iodide flux and thyroid hormone formation (55); therefore, it is easy to imagine the involvement of pendrin in thyroid gland stability, but we need more investigation to explain how pendrin can create an autoimmune process. Therefore, the same problem may exist for each thyroid-specific gene found to be associated or linked with AITD. Furthermore, understanding the interaction of the PDS gene and its product with the whole of molecules expressed in the thyroid tissue is central to elucidate the role of this protein in AITD physiopathology.

In conclusion, we found evidence in these Tunisian samples that PDS may be a susceptibility gene for AITD.


   Acknowledgments
 
We are grateful to Abecasis GR for helpful recommendations about the use and interpretation of Merlin results.


   Footnotes
 
This work was supported by the Ministère de la Recherche Scientifique et de la Technologie (MRST) (Tunisia) and the International Center for Genetic Engineering and Biotechnology (ICGEB) (Italy). A.R. acknowledges Grant C2966 from the International Foundation for Science (Sweden).

Abbreviations: AITD, Autoimmune thyroid disease; CI, confidence interval; FBAT, family-based association test; GD, Graves’ disease; HT, Hashimoto thyroiditis; MHC, major histocompatibility complex; OR, odds ratio; PIM, primary idiopathic myxedema; TDT, transmission disequilibrium test; Tg, thyroglobulin; TSHR, TSH receptor.

Received September 18, 2002.

Accepted February 19, 2003.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

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