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The genetics of autoimmune thyroid disease
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TRENDS in Endocrinology and Metabolism
Vol.15 No.5 July 2004
The genetics of autoimmune thyroid disease Hammadi Ayadi1, Hassen Hadj Kacem1, Ahmed Rebai2 and Nadir R. Farid3 1
Laboratoire de Ge´ne´tique Mole´culaire Humaine, Faculte´ de Me´decine, 3029 Sfax, Tunisia Centre de Biotechnologie, 3018 Sfax, Tunisia 3 Osancor Biotech Inc., Watford, Herts, WD17 3BY, UK 2
Autoimmune thyroid diseases (AITDs), such as Graves’ hyperthyroidism, are common disorders involving multiple genes and the environment. Some pathogenetic genes are probably shared between these diseases and non-endocrine autoimmune diseases, whereas others are disease specific. Population studies show that major histocompatibility complex alleles and CTLA4 confer risk for AITDs. Genetic studies have identified over 20 potential loci; only one, mapping to 5q31, has been convincingly replicated. Despite its recent emergence as an autoimmunity gatekeeper gene, linkage of CLTA4 to AITDs was described in only one Caucasian population subset. Like in the case of many complex genetic disorders, identifying AITD pathogenetic genes is limited by the ability of data analysis methods to discern the influence of genes of minor effect in a relatively small database. The decoding of the human genome has drawn public attention to the central role of genetic factors in human health and disease. Genes determining monogenic disorders were discovered long before the completion of the first draft of the human genome. The hope that this would extend to include genes involved in common multigenic disorders, such as type 1 diabetes mellitus or coronary artery disease, has not been fully realized for a variety of technical and analytical reasons. Autoimmune thyroid diseases (AITDs) are common polygenic multifactorial disorders that are caused by multiple genetic and environmental factors [1], including stress, smoking and intercurrent infections [1]. Genes involved in immune response regulation and/or thyroid physiology appear to influence susceptibility to such diseases. AITDs include Graves’ disease (GD), Hashimoto’s thyroiditis (HT), atrophic autoimmune thyroiditis (AAH), postpartum thyroiditis (PPT), sporadic painless thyroiditis and thyroid-associated ophthalmopathy (TAO). These disorders vary in terms of thyroid function, disease duration and spread of manifestations to other anatomical locations. Here, we critically examine the evidence for the contribution of genes to AITD, the loci involved in disease pathogenesis, the methods used to discern these loci and the strengths and limitations of current methods used to determine transmission of genetic risk. Three genetic Corresponding author: N.R. Farid ([email protected]).
regions, the major histocompatibility complex (MHC) region, cytotoxic T-lymphocyte antigen-4 (CTLA4) and a locus at 5q31 have been shown to be important in the pathogenesis of AITD. The genetic basis of AITD The evidence for a genetic involvement in the pathogenesis of AITD comes from the study of twins, in addition to the observation of familial clustering of these diseases. The largest multiplex family was reported in Tunisia in 1996 [2], and is currently composed of 300 members and more than 60 individuals affected with AITDs. The largest study assessing heredity in GD had indicated that more than 40% of patients had a family history of thyroid disease (E.D. Bartels, Heredity in Graves’ disease, PhD thesis, Einar Munksgaard, Copenhagen, 1941). The hereditary aspect of GD was first reported more than a century ago, and since then many studies have tried to estimate the risk to siblings and the offspring of parents with AITDs of developing the disease (reviewed in [3]). The sibling risk ratio relative to the background population is estimated to be between 10 and 15 [4 – 6]. Analysis of disease concordance (appearance in the twin of the proband) in monozygotic (MZ) versus dizygotic (DZ) twins provides a measure of the relative contribution of genetic as opposed to environmental factors to disease susceptibility. A study of 8966 pairs of twins showed that 35% of MZ twins compared with 3% of DZ twins were concordant for GD [7]. Similarly, a concordance rate of 55% in MZ twins compared with 0% in DZ twins was found for autoimmune hypothyroidism in 2945 twins from the same population [8]. 79% of the predisposition to GD is the result of genetic factors [7]. Candidate genes and regions Although several genes have been found to be linked and/or associated with AITDs, none was useful in disease prediction or therapy. The genes involved in the pathogenesis of AITD fall into two main categories: (i) genes involved in immune system regulation and (ii) genes involved in thyroid physiology. Genes involved in immune function The immunological dysfunction in AITD results in the disruption of immune tolerance mechanisms that control, in the physiological state, the activation level of T cells,
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their regulation by other T cells and the generation of cellmediated and humoral responses to thyroid autoantigens. Because immune regulation involves complex interactions between different elements, it is not surprising that autoimmunity involves many gene products. Only a few might be crucial to the process. Many proteins of immunological relevance, which interact both positively and negatively to produce AITD pathology, have been and are being explored [1,6]. The genes involved in immune regulation include: (i) genes involved directly in antigen presentation; (ii) genes involved in immunoglobulin production; and (iii) genes involved in lymphocyte proliferation and suppression. The contribution of polymorphisms in these genes to AITD susceptibility has been studied in many populations and with the use of different approaches (Box 1). Genes in the MHC region, and those encoding CTLA-4, cytokines, immunoglobulins, tumor necrosis factor receptor superfamily member 5 (TNFRSF5; CD40) and the vitamin D receptor (VDR) were found to be associated with and/or linked to AITD genetic susceptibility. Some of these genes were also thought to be involved
Box 1. Approaches to study the genetic component Population-based control studies, intrafamilial linkage disequilibrium and classic linkage analysis were used in the study of autoimmune thyroid disease genetics. The limited success attained with linkage analysis might be attributed to the unknown mode of inheritance, incomplete penetrance, phenocopies and the difficulty in collecting enough sibpairs. The results of association studies are difficult to replicate [59] because of (i) genetic and allelic heterogeneity, (ii) predominance of genes with a small effect, and (iii) the epistatic (more than additive) nature of their action. The application of new family-based association tests and the improvement in case –control analysis [60,61] have contributed to a better understanding of the genetics of complex disorders, but less than was expected [63 –65]. The promise of non-parametric and association methods [62] was exaggerated, whereas classic linkage analysis has proved to be robust in the treatment of multifactorial traits. Early on, it was believed [46] that family-based studies would overcome the weaknesses of linkage analysis. Based on that notion, many methods have been described, ranging from testing transmission/non-transmission of alleles (transmission disequilibrium test; TDT) [66] to associating multilocus haplotypes to disease phenotype (e.g. [67]) but no ‘method of choice’ has emerged. Most available tests have limited statistical power where the contributing genes are of minor effect. Thus, a TDT would need 484 families (and . 1000 case –control pairs) to achieve 80% power for a gene with genotypic relative risk (GRR) of 1.5 and a multiplicative inheritance mode [46]. Moreover, association studies are sensitive to allelic heterogeneity [68], in addition to other factors, such as undetected genotypic errors [69] or missing informative parental genotype [58]. These factors might explain why associations are difficult to detect or replicate, particularly for genes of small effect (GRR of 1.1 – 2.0). The submission of a steadily increasing number of markers and candidate genes to multiple testing is bound to produce some false positive results, an issue that might be overcome by using different types of statistical methods and a systematic meta-analytic approach [70]. Similarly, concordance of results from different methods, each with its range of validity and power, might provide some evidence for linkage or association notwithstanding the inherent weakness; for example, correcting for type I error in multiple testing. The effectiveness and validity of different methods are often based on opinion rather than actual performance. www.sciencedirect.com
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in other organ-specific and non-organ-specific autoimmune diseases, such as type I diabetes and rheumatoid arthritis. Associations between AITDs and MHC loci have long been recognized [1,9,10]. An association with an MHC allele was the first genetic association reported for GD and one of the few found in all populations studied. Susceptibility to AITDs maps to the MHC class II region, specifically with the HLA-DRB1 locus. The inability to demonstrate this association in some ethnic groups probably reflects the lack of reagents specific for the susceptibility alleles. In Caucasians, GD is associated with HLA-DRB1*03, HT with DRB1*011/012, 4 or 3 in different populations and PPT (which is closely allied to HT) with HLA-DRB1*04. However, TAO shows no clear MHC association, apart from that of GD itself. No genetic basis for forecasting GD remission after treatment with antithyroid drugs has been substantiated. However, when large populations of GD patients were stratified by cluster analysis, clear subgroups emerged corresponding to GD severity. The subgroup with severe disease had a higher prevalence of HLA-DR3 than did the subgroup with mild disease [1]. The strong linkage disequilibrium of MHC-linked loci within the HLA-DRB1*03 positive haplotype showed an association of alleles at these loci with GD when these were examined in isolation [1,9,10]. An MHC allele dose effect suggested by earlier studies [1,9] was not specifically examined later. In some populations, at least, two HLA-DRB1 alleles might contribute independently to GD susceptibility [11]. Within the crystal structure of HLA-DRB1, residues characteristic of alleles that are positively and negatively associated with GD have been identified. Indeed, an HLDRB1*03 subtype characterized by arginine at residue 74 confers the highest relative risk for GD yet recorded (Y. Ban et al. (2002) Potential role of HLA-DR-74 in the genetic susceptibility of Graves’ disease. Program of the 84th Annual Meeting of the Endocrine Society, San Francisco, CA, USA, 19 – 22 June 2002, Abstract OR22-5). The low genotypic relative risk found in AITDs with MHC alleles prompted researchers to look for of the influence of immune system genes localized elsewhere. CTLA4 is one such gene. Although the association between CTLA4 polymorphisms and AITD was established by several, but not all case – control studies (reviewed in [10,12]), linkage was detected in just two publications. Analysis of UK AITD sibpairs showed evidence of linkage at the CTLA4 gene locus [D2S117; non-parametric log of odds (LOD) (NPL) ¼ 3.43; P ¼ 0.0004] [13]. CTLA4 gene polymorphism was also found to be linked to thyroid autoantibody production [12,14], emphasizing the broad role of CLTLA-4 in immune modulation as opposed to being specific for GD. In some GD pedigrees, CTLA4 polymorphism synergistically enhanced the risk conferred by MHC alleles [15]. Apparently, CTLA4 splice variants regulate immunoregulatory pathways that are essential (but not sufficient) for autoimmunity in both mouse and humans [16] (see below). The CTLA4 splice variant might be an important ‘gatekeeper’ of autoimmunity. Local release of cytokines within the thyroid gland is important in regulating antigen presentation and
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lymphocyte trafficking by enhancing the expression of MHC class II and adhesion molecules on thyroid follicular cells [17]. Many cytokine gene polymorphisms were examined among patients with AITD (GD and AAH). Thus, Hunt et al. examined 14 gene polymorphisms in the genes encoding interleukin 1a (IL-1a; IL1A gene), IL-1b, IL-1 receptor antagonist (IL1RN), IL-1 receptor (IL1R), IL-4 receptor (IL4R), IL-6, IL-10 and tumor necrosis factor b. A significant reduction in the frequency of the variant T allele at the IL4 promoter [C/T single nucleotide polymorphism (SNP); position 2590] was found in AITD and GD [18]. These findings regarding IL4R promoter polymorphism [18] have not been replicated, and neither has variation in IL1RN (exon 2), which was previously reported [19] to be associated with GD [20– 23]. Similarly, association or linkage between polymorphisms of the TNFRSF5 and VDR genes and AITD found in some reports have been difficult to replicate in other populations [24– 28]. However, the VDR gene was found to be associated with type 1 diabetes mellitus and multiple sclerosis in Indian and Japanese populations, respectively [29,30]. That this gene might be important in the autoimmune process in Asian populations requires further investigation. Likewise, the contribution of IgG heavy chain (Gm) allotypes conferred small risks for AITD genetic susceptibility [31,32]. However, the suggestion that two major genes linked to Gm and the MHC contributed to GD susceptibility [33] was questioned [34]. Linkage to germ line minigenes contributing to the Ig (and possibly the T-cell receptor) variable region are attractive targets for further exploring the contribution of these genes to AITD susceptibility. In conclusion, genes involved in immune system regulation continue to be important in the genetic susceptibility to AITD. Genes involved in thyroid physiology The fact that autoantibodies are produced against thyroidstimulating hormone receptor (TSHR), thyroid peroxidase (TPO) and thyroglobulin (Tg) suggests that the genes encoding these antigens might be relevant to AITD. Several studies reported an association and/or linkage between polymorphisms of these genes and AITD segregation. De Roux et al. first evaluated TSHR as a candidate gene in familial GD [35], and an association was found in some populations [36,37], but not in others [38]. An association between polymorphism in the extracellular domain of TSHR was reported in female patients with GD [36,37], whereas others found this not to be the case [38]. Although Tg, the glycoprotein precursor of the thyroid hormones, is a useful antigen in inducing experimental thyroiditis, it does not appear to be important in the pathogenesis of human autoimmune disease. Strong evidence for linkage of AITD to the TG region (8q23 – 24) was recently demonstrated [39], as was both linkage and association of the TG gene with AITD using intragenic microsatellites. This region was found to be linked to autoimmune hypothyroidism (AIH) in a genome screen study using 123 Japanese sibpairs affected by AITD [40] (Table 1). www.sciencedirect.com
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The roles of the TPO gene and Pendred’s syndrome (PDS) gene (SLC26A4) in AITD susceptibility have been explored by one study each [41,42]. No association was found between microsatellite polymorphisms of the TPO gene and GD [41], whereas the SLC26A4 gene appears to be involved in genetic susceptibility to AITD [42]. Indeed, by several statistical methods, this Tunisian case-control and family-based study has shown both an association and linkage of the SLC26A4 gene with AITD. Genome scans results Linkage analysis using the genome scan approach has identified many candidate chromosomal regions that appear to be linked with AITD in some populations but not in others (Table 1). Studies of nine different samples were analyzed using families with different ethnic origins and revealed more than 20 different loci that could harbor candidate genes for AITD genetic susceptibility. Surprisingly, some of the candidate genes that were found to be involved in AITD genetic susceptibility by the candidate gene approach were not revealed by genome scanning, even in the same population. These discordant results could be explained by the genetic heterogeneity of AITDs and/or missed chromosomal regions because of the long genetic distance between markers in genome scan analysis. In addition, the results from only three loci were replicated (chromosomes 20, 8 and 5) and only one replication (5q31), from Japanese and Chinese populations, was convincing [40,43]. These data suggest a possible common genetic susceptibility background to AITD in Far Eastern populations but questions the universal relevance of these loci to AITD susceptibility [44] Exploration of these candidate regions reveals some genes that are relevant to AITD physiopathology, such as TSHR, TNFRSF5, BTK (encoding Bruton agammaglobulinemia protein kinase), MAPK4 (encoding mitogenactivated protein kinase 4), TF4 (encoding transcription factor 4), BCL2 (encoding B-cell/lymphoma 2), FKBP1A (encoding FK506 binding protein 1A), IL3, IL4, IL9, IL12B, IL13 and TG. CTLA4 was only found to be involved to GD genetic susceptibility in a subset of UK patients [13]. Linkage and intrafamilial association analysis of CTLA4 polymorphisms were statistically significant, a result confirmed by different case – control studies in the same population. Ueda et al. [16] showed that an untranslated region in the 30 region of CLTA4 carried a risk for GD, autoimmune hypothyroidism and type 1 diabetes mellitus. A common allelic variant of CTLA4 is correlated with lower mRNA levels of the soluble alternative splice variant of the CTLA-4 protein. In addition, analysis of the mouse model of type I diabetes showed an association between the Ctla4 gene splice variant and reduced production of a splice form encoding a molecule lacking the CD80/CD86 ligand-binding domain, suggesting a mechanism for CTLA4 gene involvement in autoimmunity. Therefore, linkage analysis is useful for screening the genome for genes of ‘major effect’, but has limited power to detect genes of modest effect in the genetics of complex diseases, especially those with a LOD score that is less than the threshold (LOD score ¼ 3.3) of significant linkage determined by Lander and Kruglyak [45].
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Table 1. Genome scan results of AITDa Chromosomes
Phenotypes
Markers
Positionsb cM
Statistical analyses
Refs
14q24.3 – 31
GD
D14S81
98.8c
20q11.2
GD
D20S195
50.2c
[48] [49] [24]
Xq21.33 – 22 2q33
GD GD
DXS8020 D2S117
113.6c 201.4c
20q13
GD
D20S106
50.2
18q21
GD
D18S487
74.7c
Xp11
GD
DXS8083
75.1c
5q31
GD
D5S2090
149.9c
7q 6p11 18q21
GD AITD AITD
D7S502 D6S257 D18S487
79.6c 80c 74.7c
2p21 8q24 11q12 9q21
AITD AITD AITD AITD
D2S171 D8S284 D11S4191 D9S175
48.5 142.7c 63.4c 68.8
3q27 5q11.2 –14.3 6p11 10q23.2 13q32 12q22
AITD AITD AITD-hypothyroidism AITD AITD HT HT
D3S1580 D5S647 D5S428 D6S422 D10S537 D13S173 D12S351
213.7 74.7 95.4 35.7c 93.8 95.9c 97.1
8q23 –4 6q14 –22
HT HT
D8S272 D6S289 – 422
152.5 29.6c –35.7c
2q33
Autoantibody production
D2S155
209.8
MLS ¼ 2.5 LOD ¼ 2.1 MLS ¼ 3.2 LOD ¼ 3.2 NPL ¼ 2.4 P ¼ 0.00043 MLS ¼ 2.5 NPL ¼ 3.46 P ¼ 0.0003 NPL ¼ 2.02 P ¼ 0.023 NPL ¼ 3.09 P ¼ 0.001 NPL ¼ 2.21 P ¼ 0.014 LOD ¼ 4.31 MLS ¼ 4.12 NPL ¼ 2.66; P ¼ 0.001 HLOD ¼ 2.3 MLS ¼ 2.9 NPL ¼ 3.46 P ¼ 0.0003 MLS-c ¼ 3.03 MLS ¼ 3.5 MLS ¼ 2.13 NPL ¼ 6.1 MLS ¼ 2.01 NPL ¼ 7.5 LOD ¼ 2.1 MLS ¼ 2.3 MLS ¼ 1.46 HLOD ¼ 2 HLOD ¼ 4.1 MLS ¼ 2.1 HLOD ¼ 2.3 HOLD ¼ 3.4 MLS ¼ 3.77 LOD ¼ 1.52 NPL ¼ 7.53 MLS ¼ 4.2
[50] [50] [51] [52] [53] [43]
[54] [49] [52] [2] [39] [55]
[56] [44] [54] [54] [49] [49] [54] [40] [57] [14]
a
Abbreviations: AITD, autoimmune thyroid disease; GD, Graves’ disease; HLOD, heterogeneity LOD; HT, Hashimoto’s thyroiditis; MLS, maximum LOD score; NPL, nonparametric LOD. b The distance of the marker from the p-terminal end of the chromosome using Gen’Map 99 and Genethon genetic maps. c The position using Genethon genetic map. LOD or MLS . 2.2, NPL . 3.2 and HLOD . 2.5 are equivalent to P , 0.0007 and are suggestive of linkage; this should be confirmed by replication. LOD or MLS . 5.4, HLOD . 6.0 and NPL . 7.1 are equivalent to P , 0.0000003 and are considered highly significant [58]. The data have been arranged by AITD phenotype.
Some of the factors that need to be taken into account when interpreting genome scan data are that some studies have included families from distinct populations and none have been followed up by confirmation in independent family cohorts [6,46]. It is certainly not appropriate to re-analyze an expanded cohort that was previously used to derive loci of interest as an independent dataset providing confirmation of linkage evidence, as was performed in one study [39]. Conclusions and perspectives A recent meta-analysis of 25 different reported associations [47] concluded that there are probably many common variants in the human genome with modest but real effects on common disease risk, and that large samples are needed for them to be identified. This puts improving study design and statistical methodology at the top of list of priorities in the analysis of genetic determinants of multifactorial disease. www.sciencedirect.com
Similar to most multifactorial diseases, AITDs do not segregate in families according to simple modes of inheritance. Indeed, the interaction between the effects of multiple genes and environmental factors characterizes the susceptibility to AITD. The genetic pathogenesis of AITD is established by the use of data gathered from twin studies, familial clustering, heritability of thyroid autoantibodies, the association of AITDs with other autoimmune diseases and the expression of the disease in some cases of chromosomal aberration. However, the genetic components of the disease remain unknown. The unknown mode of inheritance, incomplete penetrance, phenocopies and the difficulty of collecting an adequate number of sibpairs and multiplex families from the same population emphasize the difficulties of the genetic analysis of such diseases. Some changes need to be made to the classic approaches to circumvent these problems. First, to simplify the phenotype characterization and to avoid clinical errors, the genetic analysis should be performed with some
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clinical subgroups (to increase the genetic homogeneity), and to identify specific genetic variants (especially in case – control studies). Second, the investigation of individual loci (candidate gene approaches using linkage disequilibrium analysis) should be preceded by bibliographical and bioinformatic studies to gather the maximum amount of information about gene polymorphisms (mutations, SNP and other intragenic polymorphic genetic markers) and protein functions (suggestive of a distinct pathophysiological pathway). Third, positive results should be confirmed by more than one statistical method and replicated in additional datasets. Lastly, new genes previously not known to be involved in AITD pathogenesis and encountered through empirical testing (e.g. by gene profiling; L.G. Puskas, et al. (2003) The distinction between Graves’ disease and Hashimoto’s thyroiditis by gene profiling. Program of the 85th Annual Meeting of the Endocrine Society, Philadelphia, PA, USA, 19 – 22 June, 2003, Abstract P2-1) will have to be added to further genetic analysis consideration. In addition, identifying the genetic component of AITD is not sufficient to understand the aetiology of the disease. Multivariate analysis using functional contribution (gene expression and protein activity) and environmental interaction data must be performed to evaluate the role of each susceptibility gene in the development of AITDs. Applying new methods of analysis in large, well designed studies should confirm the relevance of some of the genes implicated in the pathogenesis of AITDs and might add new genes to the list.
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40 Sakai, K. et al. (2001) Identification of susceptibility loci for autoimmune thyroid disease to 5q31 – q33 and Hashimoto’s thyroiditis to 8q23 – q24 by multipoint affected sib-pair linkage analysis in Japanese. Hum. Mol. Genet. 10, 1379 – 1386 41 Pirro, M.T. et al. (1995) Thyroperoxidase microsatellite polymorphism in thyroid diseases. Thyroid 5, 461 – 464 42 Hadj Kacem, H. et al. (2003) PDS is a new susceptibility gene to autoimmune thyroid diseases: association and linkage study. J. Clin. Endocrinol. Metab. 88, 2274 – 2280 43 Allen, E.M. et al. (2003) genome-wide scan for autoimmune thyroiditis in the Old Order Amish: replication of genetic linkage on chromosome 5q11.2 – q14.3. J. Clin. Endocrinol. Metab. 88, 1292– 1296 44 Jin, Y. et al. (2003) Genome-wide scan of Graves’ disease: evidence for linkage on chromosome 5q31 in Chinese Han pedigrees. J. Clin. Endocrinol. Metab. 88, 1798 – 1803 45 Lander, E. and Kruglyak, L. (1995) Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat. Genet. 11, 241 – 247 46 Risch, N.J. (2000) Searching for genetic determinants in the new millennium. Nature 405, 847 – 856 47 Lohmueller, K.E. et al. (2003) Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat. Genet. 33, 177– 182 48 Tomer, Y. et al. (1997) Mapping of a major susceptibility locus for Graves’ disease (GD-1) to chromosome 14q31. J. Clin. Endocrinol. Metab. 82, 1645 – 1648 49 Tomer, Y. et al. (1999) Mapping the major susceptibility loci for familial Graves’ and Hashimoto’s diseases: evidence for genetic heterogeneity and gene interactions. J. Clin. Endocrinol. Metab. 84, 4656– 4664 50 Barbesino, G. et al. (1998) Linkage analysis of candidate genes in autoimmune thyroid disease. II. Selected gender-related genes and the X chromosome. International Consortium for the Genetics of Autoimmune Thyroid Disease. J. Clin. Endocrinol. Metab. 83, 3290 – 3295 51 Pearce, S.H. et al. (1999) Further evidence for a susceptibility locus on chromosome 20q13.11 in families with dominant transmission of Graves disease. Am. J. Hum. Genet. 65, 1462– 1465 52 Vaidya, B. et al. (2000) Evidence for a new Graves disease susceptibility locus at chromosome 18q21. Am. J. Hum. Genet. 66, 1710– 1714 53 Imrie, H. et al. (2001) Evidence for a Graves’ disease susceptibility locus at chromosome Xp11 in a United Kingdom population. J. Clin. Endocrinol. Metab. 86, 626 – 630
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54 Tomer, Y. et al. (2003) Common and unique susceptibility loci in Graves and Hashimoto diseases: results of whole-genome screening in a data set of 102 multiplex families. Am. J. Hum. Genet. 73, 736– 747 55 Villanueva, R. et al. (2002) Autoimmune thyroid disease susceptibility loci in a large Chinese family. Clin. Endocrinol. (Oxf.) 56, 45 – 51 56 Ban, Y. et al. (2002) The influence of human leucocyte antigen (HLA) genes on autoimmune thyroid disease (AITD): results of studies in HLA-DR3 positive AITD families. Clin. Endocrinol. (Oxf.) 57, 81 – 88 57 Alkhateeb, A. et al. (2002) Mapping of an autoimmunity susceptibility locus (AIS1) to chromosome 1p31.3 – p32.2. Hum. Mol. Genet. 11, 661– 667 58 Allen, A.S. et al. (2003) Informative missingness in genetic association studies: case – parent designs. Am. J. Hum. Genet. 72, 671– 680 59 Emahazion, T. et al. (2001) SNP association studies in Alzheimer’s disease highlight problems for complex disease analysis. Trends Genet. 17, 407 – 413 60 Pritchard, J.K. et al. (2000) Association mapping in structured populations. Am. J. Hum. Genet. 67, 170 – 181 61 Devlin, B. and Roeder, K. (1999) Genomic control for association studies. Biometrics 55, 997 – 1004 62 Greenberg, D.A. et al. (1998) Power, mode of inheritance, and type I error in LOD scores and affecteds-only methods: reply to Kruglyak. Am. J. Hum. Genet. 62, 202– 204 63 Abreu, P.C. et al. (1999) Direct power comparisons between simple LOD scores and NPL scores for linkage analysis in complex diseases. Am. J. Hum. Genet. 65, 847– 857 64 Sham, P.C. et al. (2000) Power comparison of parametric and nonparametric linkage tests in small pedigrees. Am. J. Hum. Genet. 66, 1661– 1668 65 Xiong, M. and Guo, S.W. (1998) The power of linkage detection by the transmission/disequilibrium tests. Hum. Hered. 48, 295– 312 66 Spielman, R.S. et al. (1993) Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am. J. Hum. Genet. 52, 506– 516 67 Fan, R. and Knapp, M. (2003) Genome association studies of complex diseases by case – control designs. Am. J. Hum. Genet. 72, 850 – 868 68 Slager, S.L. et al. (2000) Effect of allelic heterogeneity on the power of the transmission disequilibrium test. Genet. Epidemiol. 18, 143 – 156 69 Mitchell, A.A. et al. (2003) Undetected genotyping errors cause apparent overtransmission of common alleles in the transmission/ disequilibrium test. Am. J. Hum. Genet. 72, 598 – 610 70 Ioannidis, J.P. et al. (2001) Replication validity of genetic association studies. Nat. Genet. 29, 306 – 309
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The genetics of autoimmune thyroid disease Hammadi Ayadi1, Hassen Hadj Kacem1, Ahmed Rebai2 and Nadir R. Farid3 1
Laboratoire de Ge´ne´tique Mole´culaire Humaine, Faculte´ de Me´decine, 3029 Sfax, Tunisia Centre de Biotechnologie, 3018 Sfax, Tunisia 3 Osancor Biotech Inc., Watford, Herts, WD17 3BY, UK 2
Autoimmune thyroid diseases (AITDs), such as Graves’ hyperthyroidism, are common disorders involving multiple genes and the environment. Some pathogenetic genes are probably shared between these diseases and non-endocrine autoimmune diseases, whereas others are disease specific. Population studies show that major histocompatibility complex alleles and CTLA4 confer risk for AITDs. Genetic studies have identified over 20 potential loci; only one, mapping to 5q31, has been convincingly replicated. Despite its recent emergence as an autoimmunity gatekeeper gene, linkage of CLTA4 to AITDs was described in only one Caucasian population subset. Like in the case of many complex genetic disorders, identifying AITD pathogenetic genes is limited by the ability of data analysis methods to discern the influence of genes of minor effect in a relatively small database. The decoding of the human genome has drawn public attention to the central role of genetic factors in human health and disease. Genes determining monogenic disorders were discovered long before the completion of the first draft of the human genome. The hope that this would extend to include genes involved in common multigenic disorders, such as type 1 diabetes mellitus or coronary artery disease, has not been fully realized for a variety of technical and analytical reasons. Autoimmune thyroid diseases (AITDs) are common polygenic multifactorial disorders that are caused by multiple genetic and environmental factors [1], including stress, smoking and intercurrent infections [1]. Genes involved in immune response regulation and/or thyroid physiology appear to influence susceptibility to such diseases. AITDs include Graves’ disease (GD), Hashimoto’s thyroiditis (HT), atrophic autoimmune thyroiditis (AAH), postpartum thyroiditis (PPT), sporadic painless thyroiditis and thyroid-associated ophthalmopathy (TAO). These disorders vary in terms of thyroid function, disease duration and spread of manifestations to other anatomical locations. Here, we critically examine the evidence for the contribution of genes to AITD, the loci involved in disease pathogenesis, the methods used to discern these loci and the strengths and limitations of current methods used to determine transmission of genetic risk. Three genetic Corresponding author: N.R. Farid ([email protected]).
regions, the major histocompatibility complex (MHC) region, cytotoxic T-lymphocyte antigen-4 (CTLA4) and a locus at 5q31 have been shown to be important in the pathogenesis of AITD. The genetic basis of AITD The evidence for a genetic involvement in the pathogenesis of AITD comes from the study of twins, in addition to the observation of familial clustering of these diseases. The largest multiplex family was reported in Tunisia in 1996 [2], and is currently composed of 300 members and more than 60 individuals affected with AITDs. The largest study assessing heredity in GD had indicated that more than 40% of patients had a family history of thyroid disease (E.D. Bartels, Heredity in Graves’ disease, PhD thesis, Einar Munksgaard, Copenhagen, 1941). The hereditary aspect of GD was first reported more than a century ago, and since then many studies have tried to estimate the risk to siblings and the offspring of parents with AITDs of developing the disease (reviewed in [3]). The sibling risk ratio relative to the background population is estimated to be between 10 and 15 [4 – 6]. Analysis of disease concordance (appearance in the twin of the proband) in monozygotic (MZ) versus dizygotic (DZ) twins provides a measure of the relative contribution of genetic as opposed to environmental factors to disease susceptibility. A study of 8966 pairs of twins showed that 35% of MZ twins compared with 3% of DZ twins were concordant for GD [7]. Similarly, a concordance rate of 55% in MZ twins compared with 0% in DZ twins was found for autoimmune hypothyroidism in 2945 twins from the same population [8]. 79% of the predisposition to GD is the result of genetic factors [7]. Candidate genes and regions Although several genes have been found to be linked and/or associated with AITDs, none was useful in disease prediction or therapy. The genes involved in the pathogenesis of AITD fall into two main categories: (i) genes involved in immune system regulation and (ii) genes involved in thyroid physiology. Genes involved in immune function The immunological dysfunction in AITD results in the disruption of immune tolerance mechanisms that control, in the physiological state, the activation level of T cells,
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their regulation by other T cells and the generation of cellmediated and humoral responses to thyroid autoantigens. Because immune regulation involves complex interactions between different elements, it is not surprising that autoimmunity involves many gene products. Only a few might be crucial to the process. Many proteins of immunological relevance, which interact both positively and negatively to produce AITD pathology, have been and are being explored [1,6]. The genes involved in immune regulation include: (i) genes involved directly in antigen presentation; (ii) genes involved in immunoglobulin production; and (iii) genes involved in lymphocyte proliferation and suppression. The contribution of polymorphisms in these genes to AITD susceptibility has been studied in many populations and with the use of different approaches (Box 1). Genes in the MHC region, and those encoding CTLA-4, cytokines, immunoglobulins, tumor necrosis factor receptor superfamily member 5 (TNFRSF5; CD40) and the vitamin D receptor (VDR) were found to be associated with and/or linked to AITD genetic susceptibility. Some of these genes were also thought to be involved
Box 1. Approaches to study the genetic component Population-based control studies, intrafamilial linkage disequilibrium and classic linkage analysis were used in the study of autoimmune thyroid disease genetics. The limited success attained with linkage analysis might be attributed to the unknown mode of inheritance, incomplete penetrance, phenocopies and the difficulty in collecting enough sibpairs. The results of association studies are difficult to replicate [59] because of (i) genetic and allelic heterogeneity, (ii) predominance of genes with a small effect, and (iii) the epistatic (more than additive) nature of their action. The application of new family-based association tests and the improvement in case –control analysis [60,61] have contributed to a better understanding of the genetics of complex disorders, but less than was expected [63 –65]. The promise of non-parametric and association methods [62] was exaggerated, whereas classic linkage analysis has proved to be robust in the treatment of multifactorial traits. Early on, it was believed [46] that family-based studies would overcome the weaknesses of linkage analysis. Based on that notion, many methods have been described, ranging from testing transmission/non-transmission of alleles (transmission disequilibrium test; TDT) [66] to associating multilocus haplotypes to disease phenotype (e.g. [67]) but no ‘method of choice’ has emerged. Most available tests have limited statistical power where the contributing genes are of minor effect. Thus, a TDT would need 484 families (and . 1000 case –control pairs) to achieve 80% power for a gene with genotypic relative risk (GRR) of 1.5 and a multiplicative inheritance mode [46]. Moreover, association studies are sensitive to allelic heterogeneity [68], in addition to other factors, such as undetected genotypic errors [69] or missing informative parental genotype [58]. These factors might explain why associations are difficult to detect or replicate, particularly for genes of small effect (GRR of 1.1 – 2.0). The submission of a steadily increasing number of markers and candidate genes to multiple testing is bound to produce some false positive results, an issue that might be overcome by using different types of statistical methods and a systematic meta-analytic approach [70]. Similarly, concordance of results from different methods, each with its range of validity and power, might provide some evidence for linkage or association notwithstanding the inherent weakness; for example, correcting for type I error in multiple testing. The effectiveness and validity of different methods are often based on opinion rather than actual performance. www.sciencedirect.com
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in other organ-specific and non-organ-specific autoimmune diseases, such as type I diabetes and rheumatoid arthritis. Associations between AITDs and MHC loci have long been recognized [1,9,10]. An association with an MHC allele was the first genetic association reported for GD and one of the few found in all populations studied. Susceptibility to AITDs maps to the MHC class II region, specifically with the HLA-DRB1 locus. The inability to demonstrate this association in some ethnic groups probably reflects the lack of reagents specific for the susceptibility alleles. In Caucasians, GD is associated with HLA-DRB1*03, HT with DRB1*011/012, 4 or 3 in different populations and PPT (which is closely allied to HT) with HLA-DRB1*04. However, TAO shows no clear MHC association, apart from that of GD itself. No genetic basis for forecasting GD remission after treatment with antithyroid drugs has been substantiated. However, when large populations of GD patients were stratified by cluster analysis, clear subgroups emerged corresponding to GD severity. The subgroup with severe disease had a higher prevalence of HLA-DR3 than did the subgroup with mild disease [1]. The strong linkage disequilibrium of MHC-linked loci within the HLA-DRB1*03 positive haplotype showed an association of alleles at these loci with GD when these were examined in isolation [1,9,10]. An MHC allele dose effect suggested by earlier studies [1,9] was not specifically examined later. In some populations, at least, two HLA-DRB1 alleles might contribute independently to GD susceptibility [11]. Within the crystal structure of HLA-DRB1, residues characteristic of alleles that are positively and negatively associated with GD have been identified. Indeed, an HLDRB1*03 subtype characterized by arginine at residue 74 confers the highest relative risk for GD yet recorded (Y. Ban et al. (2002) Potential role of HLA-DR-74 in the genetic susceptibility of Graves’ disease. Program of the 84th Annual Meeting of the Endocrine Society, San Francisco, CA, USA, 19 – 22 June 2002, Abstract OR22-5). The low genotypic relative risk found in AITDs with MHC alleles prompted researchers to look for of the influence of immune system genes localized elsewhere. CTLA4 is one such gene. Although the association between CTLA4 polymorphisms and AITD was established by several, but not all case – control studies (reviewed in [10,12]), linkage was detected in just two publications. Analysis of UK AITD sibpairs showed evidence of linkage at the CTLA4 gene locus [D2S117; non-parametric log of odds (LOD) (NPL) ¼ 3.43; P ¼ 0.0004] [13]. CTLA4 gene polymorphism was also found to be linked to thyroid autoantibody production [12,14], emphasizing the broad role of CLTLA-4 in immune modulation as opposed to being specific for GD. In some GD pedigrees, CTLA4 polymorphism synergistically enhanced the risk conferred by MHC alleles [15]. Apparently, CTLA4 splice variants regulate immunoregulatory pathways that are essential (but not sufficient) for autoimmunity in both mouse and humans [16] (see below). The CTLA4 splice variant might be an important ‘gatekeeper’ of autoimmunity. Local release of cytokines within the thyroid gland is important in regulating antigen presentation and
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lymphocyte trafficking by enhancing the expression of MHC class II and adhesion molecules on thyroid follicular cells [17]. Many cytokine gene polymorphisms were examined among patients with AITD (GD and AAH). Thus, Hunt et al. examined 14 gene polymorphisms in the genes encoding interleukin 1a (IL-1a; IL1A gene), IL-1b, IL-1 receptor antagonist (IL1RN), IL-1 receptor (IL1R), IL-4 receptor (IL4R), IL-6, IL-10 and tumor necrosis factor b. A significant reduction in the frequency of the variant T allele at the IL4 promoter [C/T single nucleotide polymorphism (SNP); position 2590] was found in AITD and GD [18]. These findings regarding IL4R promoter polymorphism [18] have not been replicated, and neither has variation in IL1RN (exon 2), which was previously reported [19] to be associated with GD [20– 23]. Similarly, association or linkage between polymorphisms of the TNFRSF5 and VDR genes and AITD found in some reports have been difficult to replicate in other populations [24– 28]. However, the VDR gene was found to be associated with type 1 diabetes mellitus and multiple sclerosis in Indian and Japanese populations, respectively [29,30]. That this gene might be important in the autoimmune process in Asian populations requires further investigation. Likewise, the contribution of IgG heavy chain (Gm) allotypes conferred small risks for AITD genetic susceptibility [31,32]. However, the suggestion that two major genes linked to Gm and the MHC contributed to GD susceptibility [33] was questioned [34]. Linkage to germ line minigenes contributing to the Ig (and possibly the T-cell receptor) variable region are attractive targets for further exploring the contribution of these genes to AITD susceptibility. In conclusion, genes involved in immune system regulation continue to be important in the genetic susceptibility to AITD. Genes involved in thyroid physiology The fact that autoantibodies are produced against thyroidstimulating hormone receptor (TSHR), thyroid peroxidase (TPO) and thyroglobulin (Tg) suggests that the genes encoding these antigens might be relevant to AITD. Several studies reported an association and/or linkage between polymorphisms of these genes and AITD segregation. De Roux et al. first evaluated TSHR as a candidate gene in familial GD [35], and an association was found in some populations [36,37], but not in others [38]. An association between polymorphism in the extracellular domain of TSHR was reported in female patients with GD [36,37], whereas others found this not to be the case [38]. Although Tg, the glycoprotein precursor of the thyroid hormones, is a useful antigen in inducing experimental thyroiditis, it does not appear to be important in the pathogenesis of human autoimmune disease. Strong evidence for linkage of AITD to the TG region (8q23 – 24) was recently demonstrated [39], as was both linkage and association of the TG gene with AITD using intragenic microsatellites. This region was found to be linked to autoimmune hypothyroidism (AIH) in a genome screen study using 123 Japanese sibpairs affected by AITD [40] (Table 1). www.sciencedirect.com
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The roles of the TPO gene and Pendred’s syndrome (PDS) gene (SLC26A4) in AITD susceptibility have been explored by one study each [41,42]. No association was found between microsatellite polymorphisms of the TPO gene and GD [41], whereas the SLC26A4 gene appears to be involved in genetic susceptibility to AITD [42]. Indeed, by several statistical methods, this Tunisian case-control and family-based study has shown both an association and linkage of the SLC26A4 gene with AITD. Genome scans results Linkage analysis using the genome scan approach has identified many candidate chromosomal regions that appear to be linked with AITD in some populations but not in others (Table 1). Studies of nine different samples were analyzed using families with different ethnic origins and revealed more than 20 different loci that could harbor candidate genes for AITD genetic susceptibility. Surprisingly, some of the candidate genes that were found to be involved in AITD genetic susceptibility by the candidate gene approach were not revealed by genome scanning, even in the same population. These discordant results could be explained by the genetic heterogeneity of AITDs and/or missed chromosomal regions because of the long genetic distance between markers in genome scan analysis. In addition, the results from only three loci were replicated (chromosomes 20, 8 and 5) and only one replication (5q31), from Japanese and Chinese populations, was convincing [40,43]. These data suggest a possible common genetic susceptibility background to AITD in Far Eastern populations but questions the universal relevance of these loci to AITD susceptibility [44] Exploration of these candidate regions reveals some genes that are relevant to AITD physiopathology, such as TSHR, TNFRSF5, BTK (encoding Bruton agammaglobulinemia protein kinase), MAPK4 (encoding mitogenactivated protein kinase 4), TF4 (encoding transcription factor 4), BCL2 (encoding B-cell/lymphoma 2), FKBP1A (encoding FK506 binding protein 1A), IL3, IL4, IL9, IL12B, IL13 and TG. CTLA4 was only found to be involved to GD genetic susceptibility in a subset of UK patients [13]. Linkage and intrafamilial association analysis of CTLA4 polymorphisms were statistically significant, a result confirmed by different case – control studies in the same population. Ueda et al. [16] showed that an untranslated region in the 30 region of CLTA4 carried a risk for GD, autoimmune hypothyroidism and type 1 diabetes mellitus. A common allelic variant of CTLA4 is correlated with lower mRNA levels of the soluble alternative splice variant of the CTLA-4 protein. In addition, analysis of the mouse model of type I diabetes showed an association between the Ctla4 gene splice variant and reduced production of a splice form encoding a molecule lacking the CD80/CD86 ligand-binding domain, suggesting a mechanism for CTLA4 gene involvement in autoimmunity. Therefore, linkage analysis is useful for screening the genome for genes of ‘major effect’, but has limited power to detect genes of modest effect in the genetics of complex diseases, especially those with a LOD score that is less than the threshold (LOD score ¼ 3.3) of significant linkage determined by Lander and Kruglyak [45].
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Table 1. Genome scan results of AITDa Chromosomes
Phenotypes
Markers
Positionsb cM
Statistical analyses
Refs
14q24.3 – 31
GD
D14S81
98.8c
20q11.2
GD
D20S195
50.2c
[48] [49] [24]
Xq21.33 – 22 2q33
GD GD
DXS8020 D2S117
113.6c 201.4c
20q13
GD
D20S106
50.2
18q21
GD
D18S487
74.7c
Xp11
GD
DXS8083
75.1c
5q31
GD
D5S2090
149.9c
7q 6p11 18q21
GD AITD AITD
D7S502 D6S257 D18S487
79.6c 80c 74.7c
2p21 8q24 11q12 9q21
AITD AITD AITD AITD
D2S171 D8S284 D11S4191 D9S175
48.5 142.7c 63.4c 68.8
3q27 5q11.2 –14.3 6p11 10q23.2 13q32 12q22
AITD AITD AITD-hypothyroidism AITD AITD HT HT
D3S1580 D5S647 D5S428 D6S422 D10S537 D13S173 D12S351
213.7 74.7 95.4 35.7c 93.8 95.9c 97.1
8q23 –4 6q14 –22
HT HT
D8S272 D6S289 – 422
152.5 29.6c –35.7c
2q33
Autoantibody production
D2S155
209.8
MLS ¼ 2.5 LOD ¼ 2.1 MLS ¼ 3.2 LOD ¼ 3.2 NPL ¼ 2.4 P ¼ 0.00043 MLS ¼ 2.5 NPL ¼ 3.46 P ¼ 0.0003 NPL ¼ 2.02 P ¼ 0.023 NPL ¼ 3.09 P ¼ 0.001 NPL ¼ 2.21 P ¼ 0.014 LOD ¼ 4.31 MLS ¼ 4.12 NPL ¼ 2.66; P ¼ 0.001 HLOD ¼ 2.3 MLS ¼ 2.9 NPL ¼ 3.46 P ¼ 0.0003 MLS-c ¼ 3.03 MLS ¼ 3.5 MLS ¼ 2.13 NPL ¼ 6.1 MLS ¼ 2.01 NPL ¼ 7.5 LOD ¼ 2.1 MLS ¼ 2.3 MLS ¼ 1.46 HLOD ¼ 2 HLOD ¼ 4.1 MLS ¼ 2.1 HLOD ¼ 2.3 HOLD ¼ 3.4 MLS ¼ 3.77 LOD ¼ 1.52 NPL ¼ 7.53 MLS ¼ 4.2
[50] [50] [51] [52] [53] [43]
[54] [49] [52] [2] [39] [55]
[56] [44] [54] [54] [49] [49] [54] [40] [57] [14]
a
Abbreviations: AITD, autoimmune thyroid disease; GD, Graves’ disease; HLOD, heterogeneity LOD; HT, Hashimoto’s thyroiditis; MLS, maximum LOD score; NPL, nonparametric LOD. b The distance of the marker from the p-terminal end of the chromosome using Gen’Map 99 and Genethon genetic maps. c The position using Genethon genetic map. LOD or MLS . 2.2, NPL . 3.2 and HLOD . 2.5 are equivalent to P , 0.0007 and are suggestive of linkage; this should be confirmed by replication. LOD or MLS . 5.4, HLOD . 6.0 and NPL . 7.1 are equivalent to P , 0.0000003 and are considered highly significant [58]. The data have been arranged by AITD phenotype.
Some of the factors that need to be taken into account when interpreting genome scan data are that some studies have included families from distinct populations and none have been followed up by confirmation in independent family cohorts [6,46]. It is certainly not appropriate to re-analyze an expanded cohort that was previously used to derive loci of interest as an independent dataset providing confirmation of linkage evidence, as was performed in one study [39]. Conclusions and perspectives A recent meta-analysis of 25 different reported associations [47] concluded that there are probably many common variants in the human genome with modest but real effects on common disease risk, and that large samples are needed for them to be identified. This puts improving study design and statistical methodology at the top of list of priorities in the analysis of genetic determinants of multifactorial disease. www.sciencedirect.com
Similar to most multifactorial diseases, AITDs do not segregate in families according to simple modes of inheritance. Indeed, the interaction between the effects of multiple genes and environmental factors characterizes the susceptibility to AITD. The genetic pathogenesis of AITD is established by the use of data gathered from twin studies, familial clustering, heritability of thyroid autoantibodies, the association of AITDs with other autoimmune diseases and the expression of the disease in some cases of chromosomal aberration. However, the genetic components of the disease remain unknown. The unknown mode of inheritance, incomplete penetrance, phenocopies and the difficulty of collecting an adequate number of sibpairs and multiplex families from the same population emphasize the difficulties of the genetic analysis of such diseases. Some changes need to be made to the classic approaches to circumvent these problems. First, to simplify the phenotype characterization and to avoid clinical errors, the genetic analysis should be performed with some
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clinical subgroups (to increase the genetic homogeneity), and to identify specific genetic variants (especially in case – control studies). Second, the investigation of individual loci (candidate gene approaches using linkage disequilibrium analysis) should be preceded by bibliographical and bioinformatic studies to gather the maximum amount of information about gene polymorphisms (mutations, SNP and other intragenic polymorphic genetic markers) and protein functions (suggestive of a distinct pathophysiological pathway). Third, positive results should be confirmed by more than one statistical method and replicated in additional datasets. Lastly, new genes previously not known to be involved in AITD pathogenesis and encountered through empirical testing (e.g. by gene profiling; L.G. Puskas, et al. (2003) The distinction between Graves’ disease and Hashimoto’s thyroiditis by gene profiling. Program of the 85th Annual Meeting of the Endocrine Society, Philadelphia, PA, USA, 19 – 22 June, 2003, Abstract P2-1) will have to be added to further genetic analysis consideration. In addition, identifying the genetic component of AITD is not sufficient to understand the aetiology of the disease. Multivariate analysis using functional contribution (gene expression and protein activity) and environmental interaction data must be performed to evaluate the role of each susceptibility gene in the development of AITDs. Applying new methods of analysis in large, well designed studies should confirm the relevance of some of the genes implicated in the pathogenesis of AITDs and might add new genes to the list.
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