Skip to main content
Download PDF
  • Methodology article
  • Open access
  • Published:

Pooled genome wide association detects association upstream of FCRL3 with Graves’ disease

BMC Genomics volume 17, Article number: 939 (2016) Cite this article

Abstract

Background

Graves’ disease is an autoimmune thyroid disease of complex inheritance. Multiple genetic susceptibility loci are thought to be involved in Graves’ disease and it is therefore likely that these can be identified by genome wide association studies. This study aimed to determine if a genome wide association study, using a pooling methodology, could detect genomic loci associated with Graves’ disease.

Results

Nineteen of the top ranking single nucleotide polymorphisms including HLA-DQA1 and C6orf10, were clustered within the Major Histo-compatibility Complex region on chromosome 6p21, with rs1613056 reaching genome wide significance (p = 5 × 10−8). Technical validation of top ranking non-Major Histo-compatablity complex single nucleotide polymorphisms with individual genotyping in the discovery cohort revealed four single nucleotide polymorphisms with p ≤ 10−4. Rs17676303 on chromosome 1q23.1, located upstream of FCRL3, showed evidence of association with Graves’ disease across the discovery, replication and combined cohorts. A second single nucleotide polymorphism rs9644119 downstream of DPYSL2 showed some evidence of association supported by finding in the replication cohort that warrants further study.

Conclusions

Pooled genome wide association study identified a genetic variant upstream of FCRL3 as a susceptibility locus for Graves’ disease in addition to those identified in the Major Histo-compatibility Complex. A second locus downstream of DPYSL2 is potentially a novel genetic variant in Graves’ disease that requires further confirmation.

Background

Graves’ disease (GD) is an autoimmune thyroid disease which typically presents as thyrotoxicosis and goitre; other clinical findings include Graves’ orbitopathy, dermopathy and acropachy. The incidence rate of GD is 15 per 100, 000 persons per year with an overall mean prevalence of 1152 per 100, 000 population in the United States [1, 2]. Women are disproportionately more affected with the female to male ratio reported at 7.3:1 [1]. The disease also occurs in children at one tenth the prevalence rate of all ages [1].

GD is a complex disease with a strong familial predisposition; [3–7] the triggers are poorly understood but various environmental factors were linked to onset of GD such as stress, smoking and Yersinia infection in genetically susceptible individuals [8, 9]. GD shows a strong familial predisposition. The concordance rate for GD in monozygotic twins is significantly higher than dizygotic twins and model-fitting analysis predicted that genetic factors account for 79 % of disease heritability in twins [10]. With a concordance rate of 22–35 % for monozygotic twins, environmental and modifier factors are likely to play a key role [10, 11]. Clustering of GD with other autoimmune conditions including type I diabetes mellitus, myasthenia gravis, polymyositis, lupus erythematosus and Addison’s disease suggest genes regulating the immune system shared by these diseases might also contribute towards the genetic susceptibility for GD [3].

The genetic susceptibility of GD can be broadly categorized into genes involved in immune regulation, and thyroid specific genes. Genes confirmed to be associated with GD include the following: multiple loci within the Human leukocyte antigen (HLA) I and II region, as well as non-HLA loci CTLA4, PTPN22, CD40, IL2A, FCRL3 and TSHR [12–26]. The contributions from each of these genetic variants are modest. From candidate gene studies, HLA class II alleles DRB1*03(DR3), DQA1*0501, DQB1*02 were significantly associated with GD with odd ratios of 2 to 3 for DR3 [5, 15, 27, 28]. CTLA4 A49G, and CT60 3′UTR polymorphisms were more frequent in GD with an odds ratio of 1.4 to 2, [12, 13, 29–31] and association with PTPN22 polymorphism has an odds ratio of 1.5 to 1.9 [14, 16, 17].

Genome wide association studies (GWAS) have replicated earlier candidate gene study findings and identified additional new genetic loci. The earliest GWAS studies on autoimmune thyroid disease showed association of GD with multiple loci within the HLA class I and II regions, and also in TSHR and FCRL3 [26, 32]. More recently two new risk loci for GD, the RNASET2-FGFR1OP-CCR6 region at 6q27 and an intergenic region at 4p14 were reported, along with confirmation of positive association of GD with SNPs in HLA-DPB1, TSHR, CLTA4 and FCRL3 [33]. Using a custom array, association of SNPs near PTPN22, CTLA4, TSHR, RNASET2-FGFR1OP-CCR6 loci with GD were replicated, and seven novel susceptibility loci, MMEL1, LPP, BACH2, PRICKLE1, ITGAM, rs1534422 at 2p25.1 and rs4409785 at 11q21 were identified but the results require confirmation in replication studies [24].

A pooled GWAS strategy can effectively identify gene variants in multiple diseases. Using relatively small numbers of cases and controls, pooled GWAS has previously been used to identify the known susceptibility loci CFH and ARMS2/HTRA1 in age related macular degeneration and LOXL1 in pseudoexfoliation syndrome [34]. A pooled DNA study has also identified a new susceptibility locus, HGF, in keratoconus (a progressive corneal degeneration) and additional novel candidate genes related to platelet reactivity in diabetic patients [34–36]. The study design must be carefully considered to include multiple pools to minimise pooling errors and an appropriate array platform to extract maximal available information from the pooled DNA [37, 38].

The aim of this study was to determine if GWAS using DNA pooling methodology could replicate genomic loci associated with GD and discover new genetic loci.

Methods

Definition of Graves’ disease

GD cases were participants with biochemically confirmed hyperthyroidism on thyroid function tests; and had either the presence of Graves’ orbitopathy, or positive thyrotropin receptor (TSHR) antibodies or uniform increase in uptake on technetium-99 m pertechnetate thyroid scan.

Discovery cohort

The Australian Thyroid-associated Orbitopathy Research (ATOR) discovery cohort consisting of 412 GD cases with and without Graves’ orbitopathy of European descent was recruited from endocrinology and ophthalmology clinics in the hospitals and private practices from Victoria and South Australia.

A total of 498 controls were recruited from two Australian populations. These comprised 198 adult patients with keratoconus, a non-autoimmune eye disease unrelated to GD, who were recruited from eye clinics in Melbourne, and 300 healthy controls aged over 50 years recruited from retirement villages and the general community in South Australia. The control cohort was not actively screened for GD, but none of the controls reported having this condition.

Replication cohort

An independent group of 539 GD cases and 1230 controls were recruited to form a replication cohort. The GD cases were new cases recruited into the ATOR cohort (n = 373) from Melbourne, and endocrine and ophthalmology clinics from Western Australia (n = 166). The controls were research participants of European descent with eye diseases without recognizable association with GD or other autoimmune diseases (angle closure glaucoma or suspect, pigment dispersion syndrome, steroid responsive ocular hypertension, myopia and keratoconus) recruited in a collaborative effort from South Australia and Victoria to study the genetics of these eye diseases (age ranging from 9 to 92 years).

DNA pooling for GWAS study

Two methods of DNA pooling were used. For freshly collected samples, whole blood was pooled directly (pool 1) and for archived DNA samples, equimolar DNA pools were created (pools 2 and 3). Venous blood or DNA aliquots from all participants were thawed for 3 h before construction of 3 pool comparisons. Pool number 1 contained 154 GD cases and 198 controls, pool number 2 had 218 GD cases and 213 controls, and pool number 3 had 40 GD cases and 87 controls, giving a total of 412 cases and 498 controls from the discovery cohort. For pool 1, 100 μl of whole blood from each sample was mixed with protease and lysis buffer, vortexed and incubated, then samples were combined in one tube for whole blood pool construction, 1 tube each for cases and controls. DNA was extracted from the whole blood pool by using QIA-amp DNA blood maxi kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s protocol. For pool 2 and 3 samples, whole blood was lysed and DNA was extracted from each sample. Equimolar DNA pools were constructed in duplicate by step-wise dilutions of genomic DNA using QIA-amp DNA blood maxi kit (Qiagen, Valencia, CA, USA) to 75 ng/μl. We used Fluoroskan Ascent microplate fluorometer (ThermoFisher Scientific, Waltham, MA, USA) coupled with Picogreen reagents (Invitrogen, Carlsbad, CA) for DNA quantitation. Comparisons were made to a standard curve generated from serial dilutions of Lambda DNA (Invitrogen, Carlsbad, CA). A total of 451 ng of DNA from each sample was added to the equimolar DNA pool.

Pooling error

Previous studies showed that the majority of pooling errors arise from errors on the arrays rather than pool construction errors, [39] hence in this pooling design, multiple Illumina HumanOmni5-Quad arrays were used for each pool to reduce array errors. The pooling standard deviation is very small for the Illumina bead arrays, pooling standard deviation for HumanHap300 was estimated between 0.007 and 0.011, [38] and variance for the 1M array was 6.8 × 10−5 [34]. This encompasses both assays and pool construction errors. The Illumina HumanOmni5-Quad beadchip array supersedes its predecessor arrays; the HumanHap300 and 1M array, by virtue of its comprehensive coverage of the human genome but is similar in the way of the bead array design.

Pooled genotyping and analysis

Genome-wide genotyping was conducted at Queensland Institute of Medical Research (QIMR) Berghofer Medical Research Institute, using HumanOmni5-Quad beadchip microarray (Illumina, San Diego, CA, USA) which contained approximately 4.3 million markers. Three case and control pools, each replicated 2 to 4 times to minimise measurement error, were generated for comparisons.

The output of the raw beadscores from Illumina Beadstation were subjected to a series of data cleaning and quality control measures, as described previously for array-based DNA pooling studies [38, 40, 41]. SNPs with minor allele frequency (MAF) greater than 1 % from the reference panel of EUR samples in the 1000 Genomes Project were retained for subsequent analysis (i.e. 2.5 million SNPs). We excluded SNPs with more than 10 % negative beadscores, and SNPs with the sum of mean red and green scores smaller than 1200 across each array. A correction factor (corr) was calculated to rescale the green beadscore to calibrate the mean value of the pooling allele frequency (PAF) at 0.5 over all SNPs on each strand of the array. The PAF was computed based on the raw red intensities and the corrected green intensities as PAF = red/(red + green/corr) [40]. We further filtered non-autosomal SNPs and any SNPs with a significant variance difference between cases and controls.

A final set of autosomal SNPs meeting the following criteria: (1) contain sufficient number of probes (i.e., over ½ of expected number of probes for SNPs with MAF 1–5 %, or over 1/3 of expected number of probes for SNPs with MAF >5 %; (2) display no larger than 0.3 differences between PAF and reference allele frequency from EUR samples in the 1000 Genomes Project were taken forward for association testing. After data cleaning and quality control, 2.2M SNPs were left for association testing. A linear model was used to test for allelic association in the three case-control pools from GD discovery cohort. The test statistic corrected for pooling error was used to rank SNPs; with p values derived from chi-squared distribution with 1 ° of freedom to assess the significance of allele frequency difference between cases and controls. We then conducted a meta-analysis weighted by inverse variance to combine the results of the three case-control pool comparisons. The top-ranking SNPs in the meta-analysis were counter checked for proxies (i.e., linkage disequilibrium r 2 >0.5 with the index SNPs in the EUR populations). If proxy SNPs were available, they should also show evidence of association to be considered valid. A genome-wide significance threshold was set at 5 × 10−8 to account for multiple testing but SNPs with p < 1 × 10−6 were also included for further evaluation.

Individual genotyping and analysis

Top-ranked non-major histo-compatability complex (MHC) SNPs with p-value ≤1 × 10−6 were selected for technical validation genotyping in individual DNA samples that made up the pool as well as for genotyping in the replication cohorts. The top ranking SNPs were individually genotyped in the discovery cohort in 412 GD cases and 480 normal controls. A subset of 18 controls had insufficient residual samples for individual genotyping, hence the discrepancy in the final pooling and validation sample sizes.

Thirteen SNPS were genotyped on iPLEX Gold assay (Sequenom, San Diego, CA, USA) by using Sequenom MassARRAY® Analyzer in two batches. The ATOR discovery cohort and Western Australia replication cohort genotyping was performed at the Australian Genome Research Facility (Brisbane, Australia) and the ATOR replication cohort at GeneWorks Pty Ltd (Adelaide, Australia). The allele association analysis was performed using chi-square test (−−assoc) in PLINK, [42] independently for technical validation in the ATOR pooled discovery cohort, the replication cohorts and the overall in the discovery and replication cohorts. The odds ratio and 95 % confidence interval for allelic association was further adjusted for covariates age and sex using logistic regression analysis (−−logistic −−covar). SNPs with p ≤ 1 × 10−4 were considered validated in validation genotyping. For replication genotyping, p <0.05 was considered significant.

Results

The GD cases were predominantly female and younger when compared with controls (Table 1), hence subsequent individual genotyping analysis was adjusted for age and sex.

Table 1 Demographics of Graves’ disease cases and controls in the discovery and replication cohorts
Full size table

In the discovery GWAS, 19 of the top 30 ranking SNPs clustered within the MHC region on chromosome 6p21 including rs9272937 in HLA-DQA1 (p 10−7), two SNPs within C6orf10 (p 10−7), and rs1613056 reaching genome wide significance (p = 5 × 10−8) (Additional file 1: Table S1). One SNP, rs9676286 on chromosome 19, reached genome-wide significance in the pooled GWAS (p = 1.08 × 10−8) for association with GD. However rs9676286 was not as strongly supported when we conducted technical validation genotyping of the individual DNA samples from the pools (p = 5.54 × 10−3).

An additional 12 top ranked non-MHC SNPs suggestive of association (p ~ 10−6) were also selected for validation. Of these, four SNPs showed evidence of association with p < 1 × 10−4 when we conducted technical validation genotyping in the individual DNA samples. They are rs1469893 on chr 15, rs17676303 on chr 1, rs78542322 on chr 6 and rs62469516 on chr 7 (Table 2). Amongst these four, rs1767303 was most strongly associated with GD with a P value at 1.14 × 10−8 (OR = 2.05, 95 % CI 1.57–2.67). When correlating odds ratio of effect allele frequencies in pooled GWAS with individual validation genotyping, the results showed strong correlation (r = 0.89, p <0.001) (Additional file 2: Figure S1).

Table 2 Top ranking SNPs identified from pooled genome wide association study and validation genotyping in the discovery cohort
Full size table

Genotyping in the replication cohort showed positive association of GD with rs17676303, located upstream of FCRL3 on chromosome 1q23.1 (OR = 1.22, 95 % CI 1.01–1.46, p = 0.04). The minor T allele was more frequent in GD cases than in normal controls (OR = 1.42, 95 % CI 1.22–1.64, p = 3.41 × 10−6) in the combined discovery and replication cohorts, adjusted for sex and age. Rs62469516, rs1469893 and rs78542322 minor alleles were not associated with GD in the replication cohort, or the combined cohorts (Table 3).

Table 3 Genotyping results for top ranking single nucleotide polymorphisms in replication cohorts, and combined analysis of discovery and replication cohorts using individual genotyping
Full size table

We further examined the relationship between SNP rs17676303 and other annotated SNPs in FCRL3 gene or its promoter region, namely rs7528684, rs3761969 and rs11264798, which have previously been identified as positively associated with GD, rheumatoid arthritis and systemic lupus erythematosus [25, 33, 43]. All three SNPs previously associated with GD showed high linkage disequilibrium with rs17676303 in the Caucasian HapMap population (Table 4). The result suggests that these SNPs reported with various auto-immune diseases may be tagging the same functional genetic variants around the FCRL3 gene locus.

Table 4 Haploview linkage disequilibrium between known susceptibility loci in Graves’ disease and rs17676303 identified in pooled GWAS for Graves’ disease in Hapmap CEU population data
Full size table

A second SNP, rs9644119, located downstream of DPYSL2 on chromosome 8p21 is highly ranked for its association with GD in pooled GWAS with a P value approaching genome wide significance (p = 9.10 × 10−8). The association was not validated in the discovery cohort and for the combined cohort the association with GD is less strong than the discovery pooled GWAS would suggest (p = 4.02 × 10−3). In the replication cohort, the minor T allele conferred increased odds for GD compared to controls (OR = 1.33, 95 % CI 1.11–1.59, p = 1.95 × 10−3); after adjusting for sex and age, the result remained significant. (OR = 1.28, 95 % CI 1.06–1.54, p = 9.34 × 10−3). The results suggested this allele might be a genetic locus of interest for GD, but will need further confirmation in future studies.

Discussion

This study investigated the genetic associations of GD using a pooled genome wide association study (GWAS) in 910 samples. Despite the relatively small number of samples for discovery GWAS, the pooled strategy in combination with increased sample size in a second stage of individual genotyping in both the validation and replication cohorts, readily detected evidence of association of a genetic locus upstream of FCRL3 on chromosome 1q23. The association of FCRL3 with GD has previously been identified in large scale GWAS studies [25, 26, 33]. The pooled GWAS also detected association of GD with multiple SNPs within the MHC I and MHC II regions on chromosome 6p21. Susceptibility loci within both these regions which contain HLA genes involved in antigen presentation, are well established for GD by association studies, candidate gene analysis and GWAS [5, 25–27, 44]. Rs9272937 in the un-translated region of HLA-DQA1 within the MHC class II region showed evidence of association with GD (p = 4.43 × 10−7) in our study. The positive association of HLA-DQA1 with GD has been replicated in multiple studies [15, 44, 45]. Two SNPs within the intron of the C6orf10 gene suggesting association with GD (p 10−7), were also independently validated as a locus of susceptibility in GD in a previous GWAS study [26].

These replicative findings in GD prove the robustness of the two-stage study design for pooled GWAS. The pooled GWAS study also found evidence of association for a gene locus marked by rs9644119, downstream of DPYSL2 on chromosome 8, but the significance is less certain. The risk attributions from individual genetic variants for GD are known to be small with odds ratios <1.5, [23] therefore large sample sizes in the thousands will be needed to increase power for detecting significant association, and may in the future prove or disprove the significance for the rs9644119 locus.

For the two compelling signals we have detected in the vicinity of FCRL3 and DPYSL2, genetic variants in FCRL3 were associated with a modest increase in odds for GD at 1.2 to 1.35 [25, 33, 46]. Genetic variation in the promoter region of FCRL3 i.e. FCRL3 -169T>C allele is reportedly associated with other autoimmune diseases such as rheumatoid arthritis and systemic lupus erythematosus, [43, 47, 48] suggesting that the FCRL3 locus may influence the susceptibility to multiple autoimmune diseases in a similar manner to the immune regulating CTLA4, PTPN22 and MHC genes [24, 25, 32]. To pinpoint the exact locus associated with FCRL3, the entire FCRL region on chromosome 1q21 was previously investigated using imputation, and the strongest signals associated with GD were located in a cluster of SNPs including rs7528684 and rs3761959 [49]. SNP rs7528684 in the promoter region of FCRL3 was also previously reported as positively associated with rheumatoid arthritis, systemic lupus erythematosus and GD [43]. Rs3761959 tagging rs7522061 and rs7528684 in the FCRL3 gene showed association with GD in the Wellcome Trust Case Control Consortium GWAS study (p = 9.4 × 10−3), and the association was further confirmed by The China Consortium for the Genetics of Autoimmune Thyroid disease, reaching genome wide significance (p = 2.22 × 10−8) [25, 33]. In addition, rs11264798 in FCRL3 was also associated with GD (p = 1.6 × 10−5) [25]. We found high linkage disequilibrium of rs17676303, the SNP associated with GD in our study, with rs7528684, rs3761959 and rs11264798, that strongly suggest these SNPs were tagging the same functional variants in the vicinity of the FCRL3 gene.

Functional analysis suggests FCRL3 has immune regulating roles. FCRL3 is an Fc receptor like molecule that shares a genomic location (chr 1q21-23) and gene structure as a receptor for the Fc portion of immunoglobulin with other members of the immunoglobulin superfamily [50]. Unlike the Fc receptor, the FCRL molecule has not been shown biochemically to bind immunoglobulin [50]. FCRL3 molecules are preferentially expressed by B cells [51, 52]. FCRL3 possesses both tyrosine based activating and inhibitory motifs in its cytoplasmic tail, indicating these may be used to transmit immune-modulatory signals via tyrosine phorphorylation in regulating B cell differentiation and response [50]. The strongest evidence linking FCRL3 polymorphisms with autoimmunity is derived from linkage disequilibrium mapping of the chr1q21-23 region in rheumatoid arthritis, which identified 4 SNPs (rs7528684, rs11264799, rs945635 and rs3761959) in a Japanese population [43]. Of the 4 SNPs positively associated with rheumatoid arthritis, FCRL3 -169T>C (marked by rs7528684 in the promoter region of FCRL3) substitution substantially increased promoter activity in FCRL3 and enhanced binding affinity to the consensus NF-κβ binding motif, where NF-κβ widely regulates genes involved in immune response [43]. In addition, FCRL3 transcripts in peripheral B cells and genomic DNA are higher in heterozygous individuals with genotype -169C/T +358C/G, and increased FCRL3 expression in synovial tissue and peripheral CD19+ B cell population suggest that higher expression of FCRL3 is potentially pathogenic [43]. Further correlation of B cell abnormalities with FCRL3 expression is afforded by significant positive correlation of rheumatoid factor autoantibody titre in individuals with rheumatoid arthritis with the number of susceptible −169 C alleles [43]. However not all studies showed consistent association with polymorphisms in FCRL3, specifically no discernible association with FCRL3 were noted in some other rheumatoid arthritis populations including Caucasians in North American and European countries [53–55]. When the susceptibility effect of FCRL3 was stratified by NF-κβ1 genotypes, association of FCRL3 polymorphisms with rheumatoid arthritis was observed in patients heterozygous for the NF-κβ1 promoter in a Spanish population [54].

The second gene of interest, DPYSL2, produces collapsin response mediator protein 2 (CRMP2). It was first described in the central nervous system as an effector cytosolic protein for semaphorin mediated axonal growth cones navigation during neural development and plasticity [56]. Dysregulation of CRMP2 phosphorylation and expression, have been associated with neuropsychiatric diseases including Alzheimer’s disease and schizophrenia [56]. CRMP2 is also highly expressed in activated T lymphocytes from peripheral blood bearing CD69 and HLA-DR markers. CRMP2 is distributed in the trailing uropod in polarized T cells binding to cytoskeletal structures particularly to vimentin [57]. The observations of enhanced spontaneous and chemokine-directed T cell transmigration by induced expression of CRMP2 in Jurkat T cells, and high expression of CRMP2 in migrating T cells suggest CRMP2 may regulate T cell motility and migration [57]. The mechanism underlying CRMP2 induced T cell migration seems to involve phosphorylation of CRMP2 Tyr-479 residue, acting as transducer for chemokine stromal cell-derived factor-1α (CXCL12) signaling [58]. The discovery of CRMP2 cellular function in T cells defines its potential role in the pathogenesis of immune mediated inflammation, specifically neuroinflammation [59].

Conclusions

In conclusion, the pooled GWAS is an effective methodology for detecting common genetic variants in GD. The study confirmed FCRL3 region as a susceptibility locus for GD in addition to MHC. A second locus downstream of DPYSL2 is potentially a novel genetic variant in GD that requires further confirmation.

Abbreviations

ATOR:

Australian thyroid-associated orbitopathy research

GD:

Graves’ disease

GWAS:

Genome wide association study

HLA:

Human leukocyte antigen

MAF:

Minor allele frequency

MHC:

Major histo-compatibility complex

PAF:

Pooling allele frequency

SNP:

Single nucleotide polymorphism

TSHR:

Thyrotropin receptor

References

  1. Jacobson DL, Gange SJ, Rose NR, Graham NM. Epidemiology and estimated population burden of selected autoimmune diseases in the United States. Clin Immunol Immunopathol. 1997;84(3):223–43.

    Article  CAS  PubMed  Google Scholar 

  2. Cooper GS, Stroehla BC. The epidemiology of autoimmune diseases. Autoimmun Rev. 2003;2(3):119–25.

    Article  PubMed  Google Scholar 

  3. Hemminki K, Li X, Sundquist J, Sundquist K. The epidemiology of Graves’ disease: evidence of a genetic and an environmental contribution. J Autoimmun. 2010;34(3):J307–13.

    Article  CAS  PubMed  Google Scholar 

  4. Manji N, Carr-Smith JD, Boelaert K, Allahabadia A, Armitage M, Chatterjee VK, Lazarus JH, Pearce SH, Vaidya B, Gough SC, et al. Influences of age, gender, smoking, and family history on autoimmune thyroid disease phenotype. J Clin Endocrinol Metab. 2006;91(12):4873–80.

    Article  CAS  PubMed  Google Scholar 

  5. Tomer Y, Davies TF. Searching for the autoimmune thyroid disease susceptibility genes: from gene mapping to gene function. Endocr Rev. 2003;24(5):694–717.

    Article  CAS  PubMed  Google Scholar 

  6. Villanueva R, Greenberg DA, Davies TF, Tomer Y. Sibling recurrence risk in autoimmune thyroid disease. Thyroid. 2003;13(8):761–4.

    Article  CAS  PubMed  Google Scholar 

  7. Strieder TG, Prummel MF, Tijssen JG, Endert E, Wiersinga WM. Risk factors for and prevalence of thyroid disorders in a cross-sectional study among healthy female relatives of patients with autoimmune thyroid disease. Clin Endocrinol (Oxf). 2003;59(3):396–401.

    Article  Google Scholar 

  8. Prummel MF, Strieder T, Wiersinga WM. The environment and autoimmune thyroid diseases. Eur J Endocrinol. 2004;150(5):605–18.

    Article  CAS  PubMed  Google Scholar 

  9. Prummel MF, Wiersinga WM. Smoking and risk of Graves’ disease. JAMA. 1993;269(4):479–82.

    Article  CAS  PubMed  Google Scholar 

  10. Brix TH, Kyvik KO, Christensen K, Hegedus L. Evidence for a major role of heredity in Graves’ disease: a population-based study of two Danish twin cohorts. J Clin Endocrinol Metab. 2001;86(2):930–4.

    CAS  PubMed  Google Scholar 

  11. Brix TH, Christensen K, Holm NV, Harvald B, Hegedus L. A population-based study of Graves’ disease in Danish twins. Clin Endocrinol (Oxf). 1998;48(4):397–400.

    Article  CAS  Google Scholar 

  12. Kavvoura FK, Akamizu T, Awata T, Ban Y, Chistiakov DA, Frydecka I, Ghaderi A, Gough SC, Hiromatsu Y, Ploski R, et al. Cytotoxic T-lymphocyte associated antigen 4 gene polymorphisms and autoimmune thyroid disease: a meta-analysis. J Clin Endocrinol Metab. 2007;92(8):3162–70.

    Article  CAS  PubMed  Google Scholar 

  13. Furugaki K, Shirasawa S, Ishikawa N, Ito K, Kubota S, Kuma K, Tamai H, Akamizu T, Hiratani H, Tanaka M, et al. Association of the T-cell regulatory gene CTLA4 with Graves’ disease and autoimmune thyroid disease in the Japanese. J Hum Genet. 2004;49(3):166–8.

    Article  CAS  PubMed  Google Scholar 

  14. Velaga MR, Wilson V, Jennings CE, Owen CJ, Herington S, Donaldson PT, Ball SG, James RA, Quinton R, Perros P, et al. The codon 620 tryptophan allele of the lymphoid tyrosine phosphatase (LYP) gene is a major determinant of Graves’ disease. J Clin Endocrinol Metab. 2004;89(11):5862–5.

    Article  CAS  PubMed  Google Scholar 

  15. Allahabadia A, Heward JM, Nithiyananthan R, Gibson SM, Reuser TT, Dodson PM, Franklyn JA, Gough SC. MHC class II region, CTLA4 gene, and ophthalmopathy in patients with Graves’ disease. Lancet. 2001;358(9286):984–5.

    Article  CAS  PubMed  Google Scholar 

  16. Skorka A, Bednarczuk T, Bar-Andziak E, Nauman J, Ploski R. Lymphoid tyrosine phosphatase (PTPN22/LYP) variant and Graves’ disease in a Polish population: association and gene dose-dependent correlation with age of onset. Clin Endocrinol (Oxf). 2005;62(6):679–82.

    Article  CAS  Google Scholar 

  17. Ichimura M, Kaku H, Fukutani T, Koga H, Mukai T, Miyake I, Yamada K, Koda Y, Hiromatsu Y. Associations of protein tyrosine phosphatase nonreceptor 22 (PTPN22) gene polymorphisms with susceptibility to Graves’ disease in a Japanese population. Thyroid. 2008;18(6):625–30.

    Article  CAS  PubMed  Google Scholar 

  18. Kurylowicz A, Kula D, Ploski R, Skorka A, Jurecka-Lubieniecka B, Zebracka J, Steinhof-Radwanska K, Hasse-Lazar K, Hiromatsu Y, Jarzab B, et al. Association of CD40 gene polymorphism (C-1 T) with susceptibility and phenotype of Graves’ disease. Thyroid. 2005;15(10):1119–24.

    Article  CAS  PubMed  Google Scholar 

  19. Tomer Y, Concepcion E, Greenberg DA. A C/T single-nucleotide polymorphism in the region of the CD40 gene is associated with Graves’ disease. Thyroid. 2002;12(12):1129–35.

    Article  CAS  PubMed  Google Scholar 

  20. Tomer Y, Huber A. The etiology of autoimmune thyroid disease: a story of genes and environment. J Autoimmun. 2009;32(3–4):231–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Brand OJ, Lowe CE, Heward JM, Franklyn JA, Cooper JD, Todd JA, Gough SC. Association of the interleukin-2 receptor alpha (IL-2Ralpha)/CD25 gene region with Graves’ disease using a multilocus test and tag SNPs. Clin Endocrinol (Oxf). 2007;66(4):508–12.

    CAS  Google Scholar 

  22. Brand OJ, Barrett JC, Simmonds MJ, Newby PR, McCabe CJ, Bruce CK, Kysela B, Carr-Smith JD, Brix T, Hunt PJ, et al. Association of the thyroid stimulating hormone receptor gene (TSHR) with Graves’ disease. Hum Mol Genet. 2009;18(9):1704–13.

    Article  CAS  PubMed  Google Scholar 

  23. Simmonds MJ. GWAS in autoimmune thyroid disease: redefining our understanding of pathogenesis. Nat Rev Endocrinol. 2013;9(5):277–87.

    Article  CAS  PubMed  Google Scholar 

  24. Cooper JD, Simmonds MJ, Walker NM, Burren O, Brand OJ, Guo H, Wallace C, Stevens H, Coleman G, Franklyn JA, et al. Seven newly identified loci for autoimmune thyroid disease. Hum Mol Genet. 2012;21(23):5202–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Burton PR, Clayton DG, Cardon LR, Craddock N, Deloukas P, Duncanson A, Kwiatkowski DP, McCarthy MI, Ouwehand WH, Samani NJ, et al. Association scan of 14,500 nonsynonymous SNPs in four diseases identifies autoimmunity variants. Nat Genet. 2007;39(11):1329–37.

    Article  CAS  PubMed  Google Scholar 

  26. Nakabayashi K, Tajima A, Yamamoto K, Takahashi A, Hata K, Takashima Y, Koyanagi M, Nakaoka H, Akamizu T, Ishikawa N, et al. Identification of independent risk loci for Graves’ disease within the MHC in the Japanese population. J Hum Genet. 2011;56:772–8.

    Article  CAS  PubMed  Google Scholar 

  27. Yarman S, Oguz F, Carin M. HLA-DRB1*03 is a susceptibility gene in patients with Graves’ disease with and without ophthalmopathy. Int J Immunogenet. 2007;34(1):23–5.

    Article  CAS  PubMed  Google Scholar 

  28. Bednarczuk T, Gopinath B, Ploski R, Wall JR. Susceptibility genes in Graves’ ophthalmopathy: searching for a needle in a haystack? Clin Endocrinol (Oxf). 2007;67(1):3–19.

    Article  CAS  Google Scholar 

  29. Esteghamati A, Khalilzadeh O, Mobarra Z, Anvari M, Tahvildari M, Amiri HM, Rashidi A, Solgi G, Parivar K, Nikbin B, et al. Association of CTLA-4 gene polymorphism with Graves’ disease and ophthalmopathy in Iranian patients. Eur J Intern Med. 2009;20(4):424–8.

    Article  CAS  PubMed  Google Scholar 

  30. Buzzetti R, Nistico L, Signore A, Cascino I. CTLA-4 and HLA gene susceptibility to thyroid-associated orbitopathy. Lancet. 1999;354(9192):1824.

    Article  CAS  PubMed  Google Scholar 

  31. Bednarczuk T, Hiromatsu Y, Fukutani T, Jazdzewski K, Miskiewicz P, Osikowska M, Nauman J. Association of cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4) gene polymorphism and non-genetic factors with Graves’ ophthalmopathy in European and Japanese populations. Eur J Endocrinol. 2003;148(1):13–8.

    Article  CAS  PubMed  Google Scholar 

  32. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447(7145):661–78.

    Article  Google Scholar 

  33. Chu X, Pan CM, Zhao SX, Liang J, Gao GQ, Zhang XM, Yuan GY, Li CG, Xue LQ, Shen M, et al. A genome-wide association study identifies two new risk loci for Graves’ disease. Nat Genet. 2011;43(9):897–901.

    Article  CAS  PubMed  Google Scholar 

  34. Craig JE, Hewitt AW, McMellon AE, Henders AK, Ma L, Wallace L, Sharma S, Burdon KP, Visscher PM, Montgomery GW, et al. Rapid inexpensive genome-wide association using pooled whole blood. Genome Res. 2009;19(11):2075–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Burdon KP, Macgregor S, Bykhovskaya Y, Javadiyan S, Li X, Laurie KJ, Muszynska D, Lindsay R, Lechner J, Haritunians T, et al. Association of polymorphisms in the hepatocyte growth factor gene promoter with keratoconus. Invest Ophthalmol Vis Sci. 2011;52(11):8514–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Postula M, Janicki PK, Rosiak M, Kaplon-Cieslicka A, Trzepla E, Filipiak KJ, Kosior DA, Czlonkowski A, Opolski G. New single nucleotide polymorphisms associated with differences in platelets reactivity in patients with type 2 diabetes treated with acetylsalicylic acid: genome-wide association approach and pooled DNA strategy. J Thromb Thrombolysis. 2013;36(1):65–73.

    Article  CAS  PubMed  Google Scholar 

  37. Teumer A, Ernst FD, Wiechert A, Uhr K, Nauck M, Petersmann A, Volzke H, Volker U, Homuth G. Comparison of genotyping using pooled DNA samples (allelotyping) and individual genotyping using the affymetrix genome-wide human SNP array 6.0. BMC Genomics. 2013;14:506.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Macgregor S, Zhao ZZ, Henders A, Nicholas MG, Montgomery GW, Visscher PM. Highly cost-efficient genome-wide association studies using DNA pools and dense SNP arrays. Nucleic Acids Res. 2008;36(6):e35.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Macgregor S. Most pooling variation in array-based DNA pooling is attributable to array error rather than pool construction error. Eur J Hum Genet. 2007;15(4):501–4.

    Article  CAS  PubMed  Google Scholar 

  40. Lu Y, Dimasi DP, Hysi PG, Hewitt AW, Burdon KP, Toh T, Ruddle JB, Li YJ, Mitchell P, Healey PR, et al. Common genetic variants near the Brittle Cornea Syndrome locus ZNF469 influence the blinding disease risk factor central corneal thickness. PLoS Genet. 2010;6(5):e1000947.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Brown KM, Macgregor S, Montgomery GW, Craig DW, Zhao ZZ, Iyadurai K, Henders AK, Homer N, Campbell MJ, Stark M, et al. Common sequence variants on 20q11.22 confer melanoma susceptibility. Nat Genet. 2008;40(7):838–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Kochi Y, Yamada R, Suzuki A, Harley JB, Shirasawa S, Sawada T, Bae SC, Tokuhiro S, Chang X, Sekine A, et al. A functional variant in FCRL3, encoding Fc receptor-like 3, is associated with rheumatoid arthritis and several autoimmunities. Nat Genet. 2005;37(5):478–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Maciel LM, Rodrigues SS, Dibbern RS, Navarro PA, Donadi EA. Association of the HLA-DRB1*0301 and HLA-DQA1*0501 alleles with Graves’ disease in a population representing the gene contribution from several ethnic backgrounds. Thyroid. 2001;11(1):31–5.

    Article  CAS  PubMed  Google Scholar 

  45. Barlow AB, Wheatcroft N, Watson P, Weetman AP. Association of HLA-DQA1*0501 with Graves’ disease in English Caucasian men and women. Clin Endocrinol (Oxf). 1996;44(1):73–7.

    Article  CAS  Google Scholar 

  46. Simmonds MJ, Heward JM, Carr-Smith J, Foxall H, Franklyn JA, Gough SC. Contribution of single nucleotide polymorphisms within FCRL3 and MAP3K7IP2 to the pathogenesis of Graves’ disease. J Clin Endocrinol Metab. 2006;91(3):1056–61.

    Article  CAS  PubMed  Google Scholar 

  47. Newman WG, Zhang Q, Liu X, Walker E, Ternan H, Owen J, Johnson B, Greer W, Mosher DP, Maksymowych WP, et al. Rheumatoid arthritis association with the FCRL3 -169C polymorphism is restricted to PTPN22 1858 T-homozygous individuals in a Canadian population. Arthritis Rheum. 2006;54(12):3820–7.

    Article  CAS  PubMed  Google Scholar 

  48. Eike MC, Nordang GB, Karlsen TH, Boberg KM, Vatn MH, Dahl-Jorgensen K, Ronningen KS, Joner G, Flato B, Bergquist A, et al. The FCRL3 -169 T > C polymorphism is associated with rheumatoid arthritis and shows suggestive evidence of involvement with juvenile idiopathic arthritis in a Scandinavian panel of autoimmune diseases. Ann Rheum Dis. 2008;67(9):1287–91.

    Article  CAS  PubMed  Google Scholar 

  49. Zhao SX, Liu W, Zhan M, Song ZY, Yang SY, Xue LQ, Pan CM, Gu ZH, Liu BL, Wang HN, et al. A refined study of FCRL genes from a genome-wide association study for Graves’ disease. PLoS One. 2013;8(3):e57758.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Davis RS. Fc receptor-like molecules. Annu Rev Immunol. 2007;25:525–60.

    Article  CAS  PubMed  Google Scholar 

  51. Miller I, Hatzivassiliou G, Cattoretti G, Mendelsohn C, Dalla-Favera R. IRTAs: a new family of immunoglobulinlike receptors differentially expressed in B cells. Blood. 2002;99(8):2662–9.

    Article  CAS  PubMed  Google Scholar 

  52. Davis RS, Wang YH, Kubagawa H, Cooper MD. Identification of a family of Fc receptor homologs with preferential B cell expression. Proc Natl Acad Sci U S A. 2001;98(17):9772–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Choi CB, Kang CP, Seong SS, Bae SC, Kang C. The -169C/T polymorphism in FCRL3 is not associated with susceptibility to rheumatoid arthritis or systemic lupus erythematosus in a case–control study of Koreans. Arthritis Rheum. 2006;54(12):3838–41.

    Article  CAS  PubMed  Google Scholar 

  54. Martinez A, Sanchez E, Valdivia A, Orozco G, Lopez-Nevot MA, Pascual-Salcedo D, Balsa A, Fernandez-Gutierrez B, de la Concha EG, Garcia-Sanchez A, et al. Epistatic interaction between FCRL3 and NFkappaB1 genes in Spanish patients with rheumatoid arthritis. Ann Rheum Dis. 2006;65(9):1188–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Hu X, Chang M, Saiki RK, Cargill MA, Begovich AB, Ardlie KG, Criswell LA, Seldin MF, Amos CI, Gregersen PK, et al. The functional -169 T-- > C single-nucleotide polymorphism in FCRL3 is not associated with rheumatoid arthritis in white North Americans. Arthritis Rheum. 2006;54(3):1022–5.

    Article  PubMed  Google Scholar 

  56. Hensley K, Venkova K, Christov A, Gunning W, Park J. Collapsin response mediator protein-2: an emerging pathologic feature and therapeutic target for neurodisease indications. Mol Neurobiol. 2011;43(3):180–91.

    Article  CAS  PubMed  Google Scholar 

  57. Vincent P, Collette Y, Marignier R, Vuaillat C, Rogemond V, Davoust N, Malcus C, Cavagna S, Gessain A, Machuca-Gayet I, et al. A role for the neuronal protein collapsin response mediator protein 2 in T lymphocyte polarization and migration. J Immunol. 2005;175(11):7650–60.

    Article  CAS  PubMed  Google Scholar 

  58. Varrin-Doyer M, Vincent P, Cavagna S, Auvergnon N, Noraz N, Rogemond V, Honnorat J, Moradi-Ameli M, Giraudon P. Phosphorylation of collapsin response mediator protein 2 on Tyr-479 regulates CXCL12-induced T lymphocyte migration. J Biol Chem. 2009;284(19):13265–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Varrin-Doyer M, Nicolle A, Marignier R, Cavagna S, Benetollo C, Wattel E, Giraudon P. Human T lymphotropic virus type 1 increases T lymphocyte migration by recruiting the cytoskeleton organizer CRMP2. J Immunol. 2012;188(3):1222–33.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Drs Peter Colman, Duncan Topliss, Mark Stein, Cherie Chiang, Jennifer Conn, Alan McNab, Thomas Hardy, Richard Stawell, Garry Davis for referring patients to the study. Prof Duncan Topliss for assisting with ethics application at Alfred Health. Ms Stacey Rylander and Mr Chamika De Silva for patients recruitment.

Funding

Ophthalmic Research Institute of Australia (JJK), National Health and Medical Research Council of Australia (JC), Australian Research Council Fellowship (SM), National Health and Medical Research Council of Australia Fellowship (GM and 1028444 to PNB), Early Career Fellowship (YL), University of Melbourne Early Career Researcher (SS) and Australian Postgraduate Award (JJK). Centre of Eye Research Australia receives operational infrastructure support from the Victorian Government.

Availability of data and material

The dataset supporting the conclusions of this article were included within the article and the supplementary material.

Authors’ contributions

JJK, contributed to conception and design, acquisition, analysis and interpretation of data and manuscript draft preparation. KPB, YL, GWM, SM, JEC contributed to conception and design of the study, analysis and interpretation of data and draft revision. KL, LL contributed to acquisition and processing of data. PNB, SS, JW, AG contributed to acquisition of additional data, and critical revision of manuscript. PRE, PSH, RW, SF, SF, AR, MD, DS contributed to data acquisition and critical manuscript revision. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

No identifiable individual data were contained in this publication.

Ethics approval and consent to participate

The study was approved at multiple recruitment sites by the Royal Victorian Eye and Ear Hospital Human Research Ethics Committee, Melbourne Health Human Research Ethics Committee and the Alfred Health Human Research Ethics Committee in Melbourne, Victoria, Australia; Southern Adelaide Clinical Human Research Ethics Committee and the Royal Adelaide Hospital Human Research Ethics Committee in Adelaide, South Australia. The genetic research was conducted in accordance with the Declaration of Helsinki. Written consents to participate were obtained from all patients.

Author information

Authors and Affiliations

  1. Melbourne Medical School Western Campus, Department of Medicine, University of Melbourne, Sunshine Hospital, 176 Furlong Road, St Albans, VIC, 3021, Australia

    Jwu Jin Khong & Peter Shane Hamblin

  2. Orbital, Plastics and Lacrimal Unit, The Royal Victorian Eye and Ear Hospital, Heidelberg, VIC, Australia

    Jwu Jin Khong

  3. Department of Ophthalmology and Department of Surgery, University of Melbourne, Austin Health, Heidelberg, VIC, Australia

    Jwu Jin Khong

  4. Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia

    Kathryn P. Burdon

  5. Statistical Genetics, Queensland Institute of Medical Research (QIMR) Berghofer Medical Research Institute, Herston, QLD, Australia

    Yi Lu & Stuart Macgregor

  6. Department of Ophthalmology, Flinders University of South Australia, Bedford Park, South Australia, Australia

    Kate Laurie, Lefta Leonardos & Jamie E. Craig

  7. Department of Surgery, Centre for Eye Research Australia and Ophthalmology, University of Melbourne, East Melbourne, VIC, Australia

    Paul N. Baird & Srujana Sahebjada

  8. Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands, WA, Australia

    John P. Walsh

  9. School of Medicine and Pharmacology, The University of Western Australia, Crawley, WA, Australia

    John P. Walsh

  10. Centre for Ophthalmology and Visual Sciences, University of Western Australia, Western Australia, Australia

    Adam Gajdatsy

  11. Department of Medicine, School of Clinical Sciences, Monash University, Clayton, VIC, Australia

    Peter R. Ebeling

  12. Department of Endocrinology and Diabetes, Western Health, St Albans, VIC, Australia

    Peter Shane Hamblin & Rosemary Wong

  13. Department of Diabetes and Endocrinology, Royal Melbourne Hospital, Parkville, VIC, Australia

    Simon P. Forehan & Spiros Fourlanos

  14. Department of Endocrinology, The Royal Adelaide Hospital, Adelaide, South Australia, Australia

    Anthony P. Roberts

  15. Department of Medicine, University of Otago, Christchurch, New Zealand

    Matthew Doogue

  16. South Australian Institute of Ophthalmology, University of Adelaide, South Australia, Australia

    Dinesh Selva

  17. Molecular Epidemiology, Queensland Institute of Medical Research (QIMR) Berghofer Medical Research Institute, Herston, QLD, Australia

    Grant W. Montgomery

Authors
  1. Jwu Jin Khong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kathryn P. Burdon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kate Laurie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lefta Leonardos
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Paul N. Baird
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Srujana Sahebjada
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. John P. Walsh
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Adam Gajdatsy
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Peter R. Ebeling
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Peter Shane Hamblin
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Rosemary Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Simon P. Forehan
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Spiros Fourlanos
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Anthony P. Roberts
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Matthew Doogue
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Dinesh Selva
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Grant W. Montgomery
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Stuart Macgregor
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Jamie E. Craig
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jwu Jin Khong.

Additional files

Additional file 1: Table S1.

Top 30 single nucleotide polymorphism associated with Graves’ disease compared with controls in genome wide association study. This table denoted the top ranking SNPs and highlighted SNPs in major histocompatibility complex region and non-HLA SNPs that were chosen for subsequent validation. (DOCX 141 kb)

Additional file 2: Figure S1.

Scatterplot for odds ratio of 13 effect allele frequencies comparing pooled GWAS Graves’ disease with individual genotyping in the discovery cohort. This figure showed the correlation between odds ratio derived from pooled GWAS genotyping and individual genotyping in the ATOR discovery cohort; the correlation is high r = 0.89. (TIF 9765 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khong, J.J., Burdon, K.P., Lu, Y. et al. Pooled genome wide association detects association upstream of FCRL3 with Graves’ disease. BMC Genomics 17, 939 (2016). https://doi.org/10.1186/s12864-016-3276-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12864-016-3276-z

Keywords

BMC Genomics

ISSN: 1471-2164

Contact us