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Original Studies |
Division of Endocrinology and Metabolism, Department of Medicine (Y.T., G.B., E.C., T.F.D.), and Departments of Psychiatry and Biomathematics (D.A.G.), Mount Sinai School of Medicine, New York, New York 10029
Address all correspondence and requests for reprints to: Yaron Tomer, M.D., Division of Endocrinology and Metabolism, Box 1055, Mount Sinai Medical Center, One Gustave L. Levy Place, New York, New York 10029. E-mail: Yaron.Tomer{at}mssm.edu
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Abstract |
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Introduction |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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s) of more than 15 (6); 2) a high concordance
rate has been reported for monozygotic twins compared to dizygotic
twins (7, 8); and 3) thyroid autoantibodies, which may be markers of
subclinical AITD, have been reported in up to 50% of siblings of
patients with AITD (9, 10). Although the clinical presentations of GD and HT are different, they share many features in common: 1) humoral and cellular immune reaction to thyroid antigens (11), 2) infiltration of the thyroid by T cells which are biased in their V gene use (12), 3) female preponderance of the diseases (13), and 4) strong familial predisposition (reviewed in Ref. 3). Moreover, 1) GD and HT cluster in families (14); 2) there are reports of identical twins and triplets with GD and HT (15); and 3) in the same individual, GD can evolve into HT and vice versa (16). Thus, it is possible that the genetic susceptibility to GD and HT is conferred by similar or related genes. However, the genes causing the AITDs have not been identified. Several candidate genes have been examined in the past for a possible contribution to genetic susceptibility to AITD, including human leukocyte antigen (HLA), but none of them proved to be a major susceptibility gene for AITD (for a review, see Ref. 3).
By using the candidate gene approach, we and others have to date failed to identify major susceptibility loci for familial AITD. Therefore, we decided to screen the whole human genome. In a preliminary screen of candidate chromosomes, three loci were found to be linked with Graves’ disease (17, 18, 19). We have now extended these studies and completed a whole genome screen of a larger group of families with GD and HT. The aims of the present study were: 1) to identify the susceptibility loci for familial GD and HT using microsatellite-based whole genome screening; 2) to analyze the interactions between these loci, and 3) to study the genetic relationship between GD and HT. Our results suggested that the major genetic susceptibility to familial GD was conferred by three interacting loci with additive effects, and that the susceptibility to familial HT was mostly conferred by distinctive loci that did not interact with the GD loci. Only one locus showed evidence of linkage to both GD and HT, suggesting that this locus may harbor a gene conferring susceptibility to both diseases.
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Subjects and Methods |
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The project was approved by the institutional review board. Fifty-six families (354 individuals) were analyzed in the current study (28 from the U.S., 9 from Italy, 10 from Israel, and 9 from the UK). All families enrolled in the study were multiplex for AITD (>1 affected) and multigenerational. To study the genetic relationship between GD and HT, we included 20 families (36%) that were mixed with GD and HT first degree relatives.
Family ascertainment
Families were ascertained only through a patient with AITD, who confirmed having at least one other first degree relative with AITD. Although as many relatives as possible were recruited from each family, the minimum requirement for participation in the study was a family consisting of 4 first degree relatives (including the proband) from 2 generations. On the average, our families had 6.2 members.
Clinical assessment
The AITDs include GD and HT. GD was diagnosed by 1) documented clinical and biochemical hyperthyroidism requiring treatment, 2) diffuse goiter, 3) presence of TSH receptor antibodies, and/or 4) diffusely increased 131I uptake in the thyroid gland. HT was diagnosed by 1) documented clinical and biochemical hypothyroidism requiring thyroid hormone replacement, and 2) presence of autoantibodies to thyroid peroxidase, with or without antibodies to thyroglobulin. Antithyroglobulin and anti-thyroid peroxidase antibodies were measured by specific RIA (Kronus, San Clemente, CA). All other family members, whether thyroid autoantibody positive or negative, were defined for this study as unaffected. For all subjects, phenotype was determined with the clinician blinded to the individual’s genotype. Each participant was interviewed and examined, and gave written informed consent before participating. All pertinent clinical and laboratory data were recorded and stored in our database. At the time of the interview blood was collected for DNA purification as well as for thyroid function tests and thyroid antibody testing.
PCR amplification of microsatellite markers
DNA was extracted from whole blood as previously described (20). For the whole genome screening we used the Perkin-Elmer Corp. microsatellite panels (version 1.0, panels 1–4, 20, and 25–28; version 2.0, panels 5–19 and 21–24; a total of 387 markers). In addition, oligonucleotides for amplification of the microsatellites used for fine mapping were designed according to published sequences in the genome database (http://gdbwww.gdb.org/). Microsatellite markers were selected from the Genethon linkage maps (21) and were analyzed according to the method of Weber (22). Fluorescent-labeled primers were purchased from PE Applied Biosystems (Foster City, CA). PCR were performed in 15-µL reaction volumes containing 50 ng of genomic DNA, 5 pmol of each primer (one of which was fluorescent labeled), PCR buffer containing 50 mmol/L KCl; 10 mmol/L Tris-HCl (pH 8.3); 1.5 mmol/L MgCl2; 200 µmol/L each of deoxy (d)-ATP, dGTP, dTTP, and dCTP; and 1 U of AmpliTaq DNA polymerase (Perkin-Elmer Corp., PE Applied Biosystems). Reaction mixtures were heated to 94 C for 7 min, and then cycled 30 times as follows: 30 s at 94 C, 30 s at 55 C, and 30 s at 72 C. The PCR products were diluted 1:20 in ddH2O and pooled. Two microliters of the pooled products were mixed with 0.5 µL internal size standard and 10 µL deionized formamide, denatured, and separated using an ABI 310 genetic analyzer (PE Applied Biosystems). Allele calling was performed using Genotyper 2.0 software. The marker data were then automatically exported to our database (Ingres database), where they were integrated with the already existing phenotype information and prepared for linkage analysis.
Linkage analysis
Linkage analysis was performed using both model-free [nonparametric (NPL)] and model-based (parametric) methods of linkage analysis.
Two-point linkage analysis
Two-point LOD scores for the different markers studied were computed using LIPED software (23) assuming both dominant and recessive models. For each model, three levels of penetrance were tested (30%, 50%, and 80%). According to recently published guidelines (24) we used a LOD score of 1.9 or more in our whole genome screen as evidence for linkage and a LOD score above 3.3 as evidence for significant linkage. All linkage analyses were performed assuming a population prevalence of 1% for both diseases, based on the disease prevalence data in the literature (25, 26). Based on the assumed disease prevalence of 0.01, the gene frequency was adjusted according to the model used (dominant or recessive) and the penetrance used, assuming Hardy-Weinberg equilibrium.
Multipoint linkage analysis
Multipoint LOD scores were computed by the GeneHunter program (27) using all the markers on each chromosome. Multipoint linkage analysis yields the maximum information for each individual for the area of interest. Using GeneHunter, we set the inheritance parameters identical to those that gave the maximum LOD scores (MLS) in the two-point analyses. Marker placement and distances for the multipoint analysis were obtained from the Genethon maps (21). For the multipoint linkage analysis, we assumed a population prevalence of 1% for both diseases and adjusted the gene frequency accordingly.
NPL analysis
Our families were multiplex, with 57% unaffected individuals
(Table 1
). Under such conditions some
information is lost in the NPL analysis (i.e. the haplotype
information of the unaffected individuals is not used), and the
resulting NPL scores are lower. However, as the mode of inheritance of
the AITDs is not known, we performed NPL analysis as a check. NPL
scores were computed using the GeneHunter algorithm for multipoint NPL
scores. Whole chromosomes were analyzed, and the marker mapping
information was obtained from the Genethon maps (21).
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The AITDs include GD and HT. It was not clear whether the
susceptibility genes for these two disorders were unique or common to
both diseases. Indeed, both disorders occur in the same family, and in
our dataset 36% of the families included first degree relatives with
GD and HT. Therefore, we analyzed the data using three models: 1) all
AITD patients were considered as affected (loci identified using this
model would confer susceptibility for both GD and HT); 2) only GD
patients were considered as affected (under this model HT patients were
considered as unaffected even if they had relatives with GD); and 3)
only HT patients were considered as affected (under this model GD
patients were considered as unaffected even if they had relatives with
HT). Family members with thyroid autoantibodies alone were classified
as unaffected. In addition, we tested the dataset for heterogeneity.
Heterogeneity testing (
) was also performed using GeneHunter
(27).
Power calculations
Simulation studies were performed to assess the power of our 56
families to detect linkage, and to assess the maximum attainable LOD
scores for our dataset. We assumed a penetrance of 30% for our
simulations based on the reported 30% concordance rate in monozygotic
twins (8). The simulation software used for the power calculations (28)
generated 56 families that were homologous to the families that we
included in our study. The simulations demonstrated that using a
dataset of 56 families gave statistical power to reject linkage out to
10 centimorgans (cM; 
= 0.1), at a penetrance as low as 0.3.
Our families were, therefore, sufficient to reject linkage for the
tested markers. Simulations also showed that we had the power to detect
linkage at a penetrance of 0.3 using the 56 families. The MLSs were
more than 4.0 for data generated with a marker at a recombination
fraction () of 0.01 from the disease gene, and more than 2.8 for a
marker at = 0.05 from the disease gene. The theoretical
maximum attainable LOD score in our dataset was 6.7, assuming the
recessive model and = 0.01.
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Results |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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Table 1
shows the clinical characteristics of the 56 families
studied. Fourteen (25%) had only GD-affected members, 22 (39%) had
only HT-affected members, and 20 (36%) had both GD- and HT-affected
first degree relatives. Of the 151 affected individuals, 132 (87.4%)
were females, and the affected female/male (F:M) ratio (7:1) was in
accordance with that reported in the literature (29). Thirty-four
percent of the clinically and biochemically unaffected family members
had thyroid antibodies, similar to the incidence reported in previous
studies (10, 30). Interestingly, the F:M ratio in the thyroid
antibody-positive, unaffected individuals was 1:1, i.e. much
lower than the F:M ratio in the affected family members.
Whole genome screening for the AITD genes
For mapping the AITD genes (i.e. genes causing both GD
and HT), individuals with either GD or HT were considered affected.
Whole genome screening for the AITD genes revealed only one locus on
chromosome 6 that showed evidence for linkage with both GD and HT (Fig. 1A). This locus was designated AITD-1.
The two-point MLS was 2.2 for marker D6S257 (80 cM) obtained for the
recessive model at a penetrance of 30% and a recombination fraction of
0.01 (Fig. 1A and Table 2). To fine-map
AITD-1 we performed a multipoint analysis using a genetic map for
chromosome 6 based on the Genethon maps (http://www.genethon.fr). The
order of the markers and recombination fractions of the Genethon maps
were verified in our dataset. Multipoint linkage analysis localized
AITD-1 to within an approximate interval of 8 cM between markers
D6S1610 and D6S257. The multipoint MLS was 2.9 (Fig. 1B and Table 2),
which demonstrated strong evidence for linkage (24). Further testing
showed little evidence for heterogeneity in our dataset ( =
0.92; maximum heterogeneity LOD score, 2.96). Nonparametric LOD score
analysis was performed using the GeneHunter program and gave a maximum
multipoint NPL score of 2.3 (P = 0.0024) at the same
locus. Thus, the NPL analysis supported the evidence for linkage in
that region.
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For screening the unique GD genes (i.e. genes causing
GD but not HT), we defined as affected only those individuals with GD.
Six loci on chromosomes 1, 2, 3, 6, 9, and 13 gave LOD scores higher
than 1.0 but lower than 1.5. Three loci gave two-point LOD scores of
1.9 or more, which was considered evidence for linkage (24): D14S81
(designated GD-1) on chromosome 14 (two-point MLS, 2.1), D20S195
(designated GD-2) on chromosome 20 (MLS, 3.2), and DXS8020 (designated
GD-3) on chromosome X (MLS, 1.9; Fig. 2A
and Table 2). At two loci (GD-1 and 2) the MLS was obtained for the
recessive model, at a penetrance of 30% and a recombination fraction
of 0.01, and at GD-3 the MLS was obtained at a penetrance of 40%
(Table 2). This suggested that the inheritance of the three GD
susceptibility genes and that of the disease were recessive with
approximately 30% penetrance. These penetrance results were compatible
with twin data showing 30% concordance between identical twins (31).
Multipoint analysis confirmed these results, giving higher multipoint
LOD scores at GD-1, -2, and –3; the maximum multipoint LOD scores were
2.5 for GD-1, 3.5 for GD-2, and 2.5 for GD-3 (Fig. 2, B, C, and D, and
Table 2). The NPL scores were also supportive of linkage (1.9 for GD-1,
2.4 for GD-2 and 1.8 for GD-3; Table 2). The other six loci that gave
positive LOD scores for GD (>1.0) proved to be false positives. They
each gave negative LOD scores in the multipoint analysis that used the
information from all markers on a chromosome.
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GD-2 on chromosome 20 gave the highest parametric LOD
score and NPL score. We, therefore, fine-mapped GD-2 to create a
framework for the identification of the susceptibility gene in this
region. We analyzed 11 closely spaced markers in the region for
critical recombinants in the affected family members. Using this
approach, critical recombinants were found between markers D20S107 and
D20S466 as the centromeric boundary of GD-2, and between D20S855 and
D20S108 as the telomeric boundary of GD-2. Therefore, GD-2 was
localized to a 1.0-cM interval between markers D20S107 (55 cM) and
D20S108 (56 cM; Fig. 3).
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The 3 loci contributing to the susceptibility to GD may each exert
their effects independently in GD or may interact to contribute
synergistically to the genetic susceptibility to GD. We studied the
interactions among the 3 GD loci by performing a regression analysis of
the LOD scores obtained at the 3 loci for each of the families. In the
regression analysis, we tested whether there were correlations between
the individual family LOD scores obtained for GD-1, -2, and -3. This
analysis showed a statistically significant correlation between the
individual family LOD scores obtained for GD-1 and GD-2 (r = 0.7;
P < 0.0001) and between the individual family LOD
scores obtained for GD-1 and GD-3 (r = 0.5; P =
0.008). However, there was no correlation between the LOD scores for
GD-2 and GD-3 (r = 0.1; P = 0.4). This analysis
suggested that in most of the families the genetic susceptibility to GD
was conferred by an interaction between GD-1 and GD-2 or between GD-1
and GD-3. Additionally, when we summed the LOD scores at the 3 loci for
each family, it showed that at least 1 locus was positive in 91% of
the families. This may suggest that GD-1, -2, and -3 were responsible
for most of the genetic susceptibility to GD in our dataset. In only a
minority of the GD families (3 of 34) did 2 of the loci give negative
LOD scores, and negative LOD scores for all 3 loci were not obtained
for any family (Fig. 4).
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For screening the unique HT genes (i.e. genes causing
HT but not GD), we defined as affected only individuals with HT. Whole
genome screening for HT in all of our HT families did not show any
locus giving a LOD score of 1.9 or more (Fig. 5A). However, two loci, D12S351 on
chromosome 12 and D13S173 on chromosome 13, gave LOD scores of 1.7 and
1.8, respectively (Fig. 5A and Table 2). On multipoint analysis D13S173
gave a maximum LOD score of 2.1, which was suggestive of linkage (24)
and was designated HT-1 (Fig. 5B and Table 2), whereas D12S351 gave a
maximum LOD score of -0.8 with evidence of heterogeneity
(heterogeneity LOD score, 2.3; = 0.47; Table 2). To try and
identify the subgroup of HT families linked to the chromosome 12 locus,
we retested chromosome 12 for two subsets of our HT families based on
their geographic origins. This analysis demonstrated that the locus
showed strong evidence of linkage only to the European HT families,
giving a maximum multipoint LOD score at D12S351 of 3.8 for the
recessive model at 80% penetrance (Fig. 5C). The North American HT
families gave a negative multipoint LOD score at D12S351 using the same
parameters for analysis.
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As only one locus (AITD-1) was found to confer susceptibility to
both GD and HT, we tested whether this locus contributed equally to the
development of GD and HT in all of our families. For this analysis, we
divided the families into two subsets: 1) families in which all
affected individuals had either GD or HT (the exclusive GD/HT
families), and 2) families in which both GD and HT affected relatives
were found (the mixed families). The analysis showed that the maximum
LOD score obtained for the mixed families was 2.2, whereas the maximum
LOD score obtained for the exclusive GD/HT families was 0.7 (Table 3). This suggested that AITD-1
contributed mostly to the susceptibility to GD and HT in families in
which both diseases coexisted. However, AITD-1 had a less important
contribution, relative to the unique GD and HT loci (i.e.
GD-1, -2, and -3 and HT-1), in families in which exclusively GD- or
HT-affected individuals were found. A common genetic background may,
therefore, exist for GD and HT, but mostly in mixed families. In
GD-only or HT-only families, the genetic susceptibility was mostly
conferred by genes unique to each disease.
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Discussion |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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gene (36), TSH receptor (37, 38), thyroid peroxidase
(39), and CTLA-4 (40) genes (for a review, see Ref. 3). With the
exception of the HLA and CTLA-4 loci, all other candidate genes
examined gave either negative or equivocal results. The association of the HLA genes with the AITDs has received much attention. Patients with GD have been shown to have increased HLA-DR3 (41) and HLA-DQA110501 haplotypes in Caucasians (42, 43), giving relative risks of 2.0–5.0 (43, 44, 45). Recently, Gough et al. reported evidence of association (relative risk , 2.72) between GD and the haplotype DRB110304-DQB1102-DQA110501 using family studies (transmission disequilibrium test) (46). The association of HT with HLA has been weaker (47). However, our own studies (48, 49) and those of others (50) have shown no linkage between AITD and the HLA region, indicating that the HLA genes made only a small contribution to the overall genetic susceptibility to GD (51, 52).
Outside the HLA region, only the CTLA-4 gene has been found to be associated with the AITDs, giving a relative risk of about 2.0 (40, 53, 54). However, the association between CTLA-4 and AITD has not been consistently confirmed in all studies (55). Recently, Pearce et al. (56) reported linkage between the CTLA-4 region and AITD. However, in view of the finding of linkage to the CTLA-4 gene region coupled with relatively weak association of this gene with AITD, it is possible that other genes in the regions are involved in the genetic susceptibility to AITD. The CTLA-4 gene region contains other nearby candidate genes for thyroid autoimmunity, including CD28, STAT-1 and –4 (signal transducer and activator of transcription), caspases 8 and 10, and cAMP response element-binding protein-1 (http://gdbwww. gdb.org/). Indeed, although we found no evidence of linkage between the CTLA-4 gene and the familial AITD, we did obtain low positive LOD scores (1.0) for CTLA-4 at higher recombination fractions, suggesting that a gene in the region (not CTLA-4) may be involved in susceptibility to AITD (52). More studies on the CTLA-4 gene region and fine-mapping of the locus are needed to identify the susceptibility gene in this region.
As only two of the many candidate genes tested (HLA and CTLA-4) gave low relative risk associations, they could not explain the familial clustering of the AITDs. In the present study we used the whole genome screening approach to dissect the genetic susceptibility to familial GD and HT. Six loci were identified that showed evidence of linkage with AITD: AITD-1, which was linked with both GD and HT; GD-1, -2, and –3, which were linked with GD only; HT-1, which was linked with HT in all of the families; and HT-2, which showed evidence for linkage with HT in a subset of the families.
The AITDs are unique. Although their clinical manifestations are different, they share common immunopathogenetic mechanisms. Therefore, it has been proposed that they may share a common genetic susceptibility (57). Our results amplify this simple concept. We found evidence for a shared susceptibility locus (AITD-1) between GD and HT in families in which both diseases occurred. However, in the families in which only GD or HT was found, the genetic susceptibility was mostly unique to GD or HT. These results suggested that the familial forms of GD and HT may present in at least three forms according to their genetic susceptibility: 1) families in which only GD occurs, 2) families in which only HT occurs, and (3) families in which both diseases occur.
AITD-1, which is located close to the HLA region but is distinct from it, may confer susceptibility to both GD and HT. This locus is more likely to harbor a general thyroid autoimmunity gene than a disease-specific gene. Moreover, the finding of a major systemic lupus erythematosus (SLE) locus in the same location as AITD-1 may imply that a general autoimmunity gene may be located in this region. Indeed, SLE and AITD are known to be associated in the same individuals and to run together in families (58, 59).
The results demonstrated that our HT phenotype was heterogeneous and may need to be redefined. In our study we defined all patients with hypothyroidism and positive thyroid antibodies as having HT. However, using this broad definition, only one locus (HT-1) was found to be weakly linked with familial HT (multipoint MLS, 2.1), and one locus (HT-2) showed definite evidence for heterogeneity. It is likely, therefore, that HT is a much more heterogeneous disease than GD and that our current definition of HT included several types of autoimmune thyroiditis with differing immunogenetic etiologies. It may be necessary to modify our current definitions of HT using additional criteria, such as the presence of goiter, the age of onset, ethnicity, and the levels of thyroid antibodies. This conclusion was supported by the finding of a locus on chromosome 12 (HT-2) that was strongly linked with HT only in our European families (MLS 3.8), which indicated a geographic difference or a selection bias. However, our North American families were also of European descent.
Of the three loci found to be linked exclusively with GD, GD-2 gave the highest LOD score of 3.5 and was analyzed further. By fine mapping we were able to limit the boundaries of GD-2 to a 1-cM region. This will provide a framework for the identification of GD-2. If GD-2 represented a gene involved in immune regulation it would be expected to cause susceptibility to other autoimmune diseases. Indeed, another SLE locus was recently mapped to the same region of GD-2 (60, 61). As described previously, it is well known that SLE and other autoimmune conditions are associated with AITD and sometimes present in the same families or in the same individuals. Identification of common autoimmunity genes may help explain this clustering, as seen in autoimmune polyglandular syndrome type I (62).
Complex diseases are likely to be caused by the interaction of several genes, and their combined effects may differ in different individuals and families (6). In type I diabetes mellitus at least 12 susceptibility loci have been identified by several groups (63, 64, 65, 66), and in murine SLE 12 loci have been identified (67, 68). In our dataset, three distinct loci were found to be linked with GD, and our analysis showed evidence for their interaction contributing to the overall susceptibility to GD. Although the combined effects of GD-1, -2, and -3 were not identical in the different GD families, at least one of the loci contributed to the genetic predisposition to GD in 91% of our families. A similar finding of loci interaction was recently reported in murine lupus (69). The molecular basis for the interactions between susceptibility genes in complex diseases is unknown. In type I diabetes, where two of the genes (HLA-DR and insulin VNTR) have been identified, the molecular interaction remains unclear. Is this the cumulative effect of increased statistical risk (similar to multiple environmental factors contributing to the susceptibility to a disease)? Or, are there biological interactions between the susceptibility genes or their products to induce the susceptible phenotype? To better understand these effects we have to identify all of the genes involved and their molecular mechanisms in inducing susceptibility to autoimmune diseases.
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Acknowledgments |
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Footnotes |
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Received July 19, 1999.
Revised September 8, 1999.
Accepted September 17, 1999.
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References |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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gene microsatellite
pollymorphisms in patients with Graves’ disease. Thyroid. 8:1013–1017.[Medline]
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