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Abstract

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES
  9. Supporting Information

Objective

Systemic sclerosis (SSc) is an autoimmune disease for which multiple susceptibility genes have been reported. Genome-wide association studies have shown that large numbers of susceptibility genes are shared among autoimmune diseases. Recently, our group identified 9 novel susceptibility genes associated with rheumatoid arthritis (RA) in a Japanese population. The aim of this study was to elucidate whether the 18 genes that displayed associations or suggestive associations for RA in our previous study are associated with SSc in Japanese.

Methods

We performed an association study that included 415 patients with SSc and 16,891 control subjects, followed by a replication study that included 315 patients and 21,054 control subjects. The 18 markers reported to display association with RA were analyzed for their associations with SSc in the first study, and 5 markers were further analyzed in the replication study. The inverse variance method was used to evaluate the associations of these markers with SSc in a combined study.

Results

In the phospholipase D4 gene (PLD4), rs2841277 displayed a significant association with SSc in Japanese patients (P = 0.00017). We observed that rs2841280 in exon 2 of PLD4 was in strong linkage disequilibrium with rs2841277 and introduced an amino acid alteration. We also observed associations between SSc and rs6932056 in TNFAIP3 and rs2280381 in IRF8 (P = 0.0000095 and P = 0.0030, respectively), both of which displayed associations with SSc in a European population.

Conclusion

We determined that PLD4 is a novel susceptibility gene for SSc in Japanese, thus confirming the involvement of PLD4 in autoimmunity. Associations between SSc and TNFAIP3 or IRF8 were also detected in our Japanese population. SSc and RA appear to share relatively large proportions of their genetic backgrounds.

Systemic sclerosis (SSc) is a connective tissue disease that affects 7–489 individuals per million worldwide and is characterized by the excess production of extracellular matrix molecules and fibrosis (1). Patients with SSc display skin sclerosis, obliterative microvasculopathy such as Raynaud's phenomenon, and multiorgan involvement. Severe complications of SSc sometimes develop, including interstitial lung disease, pulmonary hypertension, and renal crisis. These severe symptoms and complications of SSc result in a poor prognosis and a shortened lifespan (2, 3). No effective method for preventing or curing SSc has been established (4).

It is well known that SSc has genetic components (5); for example, a US study revealed that the incidence of SSc was much higher among the families of patients with SSc compared with the general population (6). Recent technologic developments enabled the use of genome-wide association studies (GWAS) to identify novel susceptibility loci for autoimmune diseases (7). GWAS of European patients with SSc revealed that CD247 (8), HLA (8), TNIP1, PSORS1C1, and RHOB (9) are susceptibility loci for SSc. In addition, another GWAS identified associations between IRF8, GRB10, and SOX5 and limited cutaneous SSc (lcSSc) in a European population (10). Furthermore, studies adopting a candidate gene approach based on subjecting genes to functional inference analysis led to the identification of STAT4 (11), IRF5 (12), TBX21 (13), NLRP1 (14), TNFSF4 (15), CD226 (16), BLK (17), and TNFAIP3 (18) as novel susceptibility genes for SSc in Europeans. SSc association studies in Japanese populations confirmed that STAT4 (19), IRF5 (20), and BLK (21) are associated with SSc and identified UBE2L3 as a susceptibility gene for diffuse cutaneous SSc (dcSSc) (22). An association between HLA and SSc was also detected in Asians (23). These findings suggest a clear overlap in the genetic background of SSc between different populations.

It is well known that susceptibility genes are shared by various autoimmune diseases (24). In fact, HLA (25), STAT4 (26), and TNFAIP3 (27,28), which are susceptibility genes for SSc, have also been reported to be associated with rheumatoid arthritis (RA). In addition, PTPN22, which was shown to be strongly associated with RA in a European population (29), showed a suggestive association with SSc in Europeans (30). The sharing of these susceptibility genes between RA and SSc raises the possibility that newly identified susceptibility genes for RA could also be susceptibility genes for SSc. Recently, a large Japanese consortium, the Genetic and Allied research in Rheumatic diseases Networking consortium, identified 9 novel susceptibility genes and 6 candidate susceptibility genes for RA using a meta-analysis of GWAS and replication studies (31). Four other genes, namely, HLA, PADI4, CCR6, and TNFAIP3, were also confirmed to display associations with RA. Here, we performed a 2-stage association study of Japanese patients with SSc, in which we genotyped these genes as candidate susceptibility loci.

PATIENTS AND METHODS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES
  9. Supporting Information

Study subjects.

DNA samples were obtained from 415 patients with SSc at Kyoto University Hospital and Tokyo Women's Medical University; these samples comprised the first set. Independent DNA samples were obtained from 315 patients with SSc at Keio University Hospital, Sagamihara National Hospital, and Kanazawa University Hospital; these samples were used as the replication set. All patients were Japanese, all had a diagnosis of SSc as determined by a rheumatologist, and all fulfilled the 1980 American College of Rheumatology classification criteria for SSc (32). The patients with SSc for whom clinical information was available were classified as having lcSSc or dcSSc, according to the definitions developed by LeRoy et al (33). The control samples were described in detail in our previous study (31). The current study was approved by the local ethics committees at each institution, and written informed consent was obtained from all subjects. The basic characteristics of the study subjects are shown in Table 1.

Table 1. Characteristics of the study population*
 PatientsControls
  • *

    The first set included 415 patients with systemic sclerosis (SSc) and 16,891 control subjects. The replication set included 315 patients with SSc and 21,054 control subjects. Anti–topo I = anti–topoisomerase I; ACA = anticentromere antibody.

First set  
 InstitutionsKyoto University, Tokyo Women's Medical UniversityKyoto University, Tokyo Women's Medical University, BioBank Japan
 TypingTaqMan assayIllumina HumanHap610 Quad BeadChip, Illumina HumanHap550 BeadChip, Affymetrix Genome-Wide Human SNP Array 6.0
 Limited SSc/diffuse SSc, %49.6/50.4Not applicable
 Anti–topo I/ACA, %30.6/31.1Not applicable
 Interstitial lung disease, %48.9Not applicable
 Age, mean ± SD years50.9 ± 14.760.9 ± 12.5
 Female, %91.344.9
Replication set  
 InstitutionsKeio University, Sagamihara National Hospital, Kanazawa UniversityKyoto University, BioBank Japan
 TypingTaqMan assayIllumina HumanHap550 BeadChip, Illumina HumanHap610 Quad BeadChip
 Limited SSc/diffuse SSc, %63.8/34.6Not applicable
 Anti–topo I/ACA, %29.5/35.2Not applicable
 Interstitial lung disease, %43.2Not applicable
 Age, mean ± SD years51.4 ± 14.159.3 ± 14.2
 Female, %87.348.4

Genotyping.

The 9 novel susceptibility markers, 6 potentially associated markers, and 4 confirmed markers of RA that were identified in our previous study in a Japanese population (31) were chosen as candidate susceptibility markers for SSc in Japanese. Eighteen of the 19 markers (HLA was excluded; see Results), none of which had previously been reported to be associated with SSc in Japanese individuals, were genotyped in the current study. The 5 candidate markers in the first set that showed associations with P values less than 0.1 were further genotyped in the replication study. Single-nucleotide polymorphisms (SNPs) rs2841280 and rs894037 were chosen as candidate causative variants in the phospholipase D4 gene (PLD4) region. Because rs894037 was shown to be monomorphic in Japanese, rs2841280 was genotyped in 334 control subjects, in addition to all patients, for imputation reference. The patients in the first and replication studies were genotyped at Kyoto University or Tokyo Women's Medical University and at Keio University or University of Tsukuba, respectively, using TaqMan assays (Applied Biosystems). The genotyping methods in control subjects were described in detail in our previous study (31).

Briefly, control genotypes in the first set were imputed based on the genome-scanning data, using mach2dat software with HapMap Phase II East Asian Populations as reference. The control genotypes for the replication study were extracted from genome-scanning data for the markers included on Illumina HumanHap610 Quad BeadChips. The genotypes for rs6932056 (which is not included in the array) were imputed based on the genome-scanning data, using mach2dat software with HapMap Phase II East Asian Populations as reference, and were used as control data for the replication set. The genotypes for rs2841280 (which is not included in the HapMap data or the array) were also imputed in control subjects, based on the genome-scanning data, using mach2dat software. Genotyping data for the 334 control subjects as determined by TaqMan assay in combination with genome-scanning data were used as reference.

Statistical analysis.

The associations between the genotyped markers and SSc were analyzed using a Cochran-Armitage trend test in both the first and replication studies. Subanalyses were performed by comparing the genotypes of the control subjects with those of patients in the SSc subgroups based on the disease phenotypes. The subanalyses used the same control subjects as were used in the association studies. Intracase analyses based on phenotypes were also performed.

Odds ratios (ORs) and 95% confidence intervals were also calculated. The associations detected in the first and replication studies were then meta-analyzed using the inverse variance method. The resultant P values were corrected using the Benjamini-Hochberg false discovery rate (FDR) criterion, and corrected P values less than 0.05 were regarded as significant in both the combined study and the subanalyses. The efficiency of the current study was estimated by calculating the likelihood of detecting 3 significant markers (after correcting the P values using the FDR method) among 18 randomly selected markers. After the statistically significant markers were identified, the best-fit model for each association was analyzed using dominant, recessive, trend, and allelic chi-square tests or models. Statistical analyses were performed using R or SPSS (version 18) software.

RESULTS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES
  9. Supporting Information

Analyses of candidate genes for SSc in a Japanese population.

The 415 patients with SSc and 16,891 control subjects in the first set were genotyped for the 18 markers that were shown to have associations or suspected associations with RA in our previous study. The HLA region was excluded from the genotyped markers, because this region has already been shown to be associated with SSc in Asians. The allele frequencies of the patients were compared with those of the control subjects, using a Cochran-Armitage trend test.

As a result, 3 markers that demonstrated associations with P values less than 0.01 in the first set (Table 2) were identified, namely, rs6932056 in the TNFAIP3 region (P = 0.0000038, OR 1.69), rs10821944 in the ARID5B region (P = 0.0025, OR 1.25), and rs2841277 in the PLD4 region (P = 0.0054, OR 1.25). Two loci that showed suggestive associations with P values less than 0.1 (Table 2) were also identified, namely, rs12529514 in the CD83 region (P = 0.083, OR 1.18) and rs2280381 in the IRF8 region (P = 0.095, OR 1.19). The TNFAIP3 and IRF8 regions were previously reported to display associations with SSc and lcSSc, respectively, in European populations (10, 18). These 5 markers were selected as candidate susceptibility markers for SSc in Japanese and were subjected to validation.

Table 2. Association studies of Japanese patients with SSc*
SNPChrGeneAllele 1/2Allele 1 frequency
First setReplication setCombined study
ControlsPatientsPControlsPatientsPP, patients vs. controlsOR (95% CI)P, patients without overlapping RA vs. controls
  • *

    SSc = systemic sclerosis; SNP = single-nucleotide polymorphism; Chr = chromosome; OR = odds ratio; 95% CI = 95% confidence interval; RA = rheumatoid arthritis.

  • The control rs6932056 genotypes used in the replication study were imputed using genome-scanning data obtained for 3,765 subjects.

rs7664491PADI4T/C0.400.370.12
rs119006732B3GNT2T/C0.290.280.65
rs28674614ANXA3A/G0.440.430.57
rs6570755IL3-CSF2A/G0.360.340.25
rs125295146CD83C/T0.140.160.0830.150.160.310.0461.15 (1.00−1.33)0.040
rs15718786CCR6C/T0.490.470.28
rs69320566TNFAIP3C/T0.0690.113.8 × 10−60.0670.0790.239.5 × 10−61.50 (1.25−1.80)5.4 × 10−6
rs22334346NFKBIEG/A0.210.210.93
rs1082194410ARID5BG/T0.360.410.00250.360.370.640.00731.16 (1.04−1.29)0.010
rs378191311PDE2A-CENTD2T/G0.690.690.91
rs493736211ETS1-FLI1T/C0.680.680.88
rs284127714PLD4T/C0.690.740.00540.690.730.0120.000171.25 (1.11−1.41)0.00052
rs378363714GCH1C/T0.740.730.54
rs195789514PRKCHG/T0.390.410.26
rs649666715ZNF774A/C0.350.370.33
rs740492816PRKCB1T/C0.620.630.51
rs228038116IRF8T/C0.840.860.0950.830.870.00990.00301.26 (1.08−1.47)0.0021
rs284729718PTPN2G/A0.340.340.85

Next, a replication study consisting of 315 patients with SSc and 21,054 control subjects was performed to validate the associations of the 5 markers with SSc. The patients were genotyped for the 5 markers. The genotypes of the control subjects for the 5 markers, except rs6932056, were extracted from the Illumina Infinium HumanHap610 Quad array, as reported previously (31). The genotypes for rs6932056 were imputed based on genome-scanning data using mach2dat software, because rs6932056 was not included in the array. As a result, rs2841277 in the PLD4 region and rs2280381 in the IRF8 region showed relatively strong associations with SSc (P = 0.012, OR 1.25 and P = 0.0099, OR 1.37, respectively) (Table 2). Interestingly, we observed that all 5 of the markers that displayed associations in the first study also demonstrated the same association directions in the replication study.

The inverse variance method was used to combine the data for the first and replication studies. SNPs rs2841277 in the PLD4 region, rs6932056 in the TNFAIP3 region, and rs2280381 in the IRF8 region showed significant associations with SSc even after correcting the associated P values using the FDR method for multiple testing (Table 2). Importantly, all 3 of these loci shared risk alleles with RA. Although rs6932056 in the TNFAIP3 region did not show a strong association with SSc in the replication study, its association was significant in the combined study. The PLD4 region was shown to be a novel susceptibility gene for SSc, and, for the first time, the TNFAIP3 and IRF8 regions were confirmed to be associated with SSc in Japanese.

The association between rs2841277 and SSc was then investigated in detail. When the 200-kbp region around rs2841277 was evaluated, 2 hypothetical genes and cell division cycle associated 4 gene (CDCA4) were located at the region, in addition to PLD4. PLD4 was the only gene whose region showed moderate to strong linkage disequilibrium (LD) with rs2841277, indicating PLD4 as a susceptibility gene (Figure 1A). We vigorously searched candidate markers in exons of PLD4 that showed strong LD with rs2841277 and selected 2 markers registered in the 1000 Genomes Project (34) that displayed >5% frequency in genotyped subjects, namely, rs2841280 (Figure 1B) and rs894037 in exon 2. Genotyping of these polymorphisms revealed strong LD between rs2841280 (E27Q) and rs2841277 (D′ = 0.98, r2 = 0.75) and monomorphism of rs894037 in Japanese. An association study of rs2841280 using control genotypes obtained by imputation supported association of PLD4 with SSc (P = 6.3 × 10−5) (see Supplementary Tables 1and 2, available on the Arthritis & Rheumatism web site at http://onlinelibrary.wiley.com/doi/10.002/art.37777/abstract).

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Figure 1. Linkage disequilibrium (LD) block around the PLD4 region and the PLD4 structure. A, LD block and genes around PLD4. The LD block is based on HapMap phase 3 data. Asterisk indicates rs2841277. B, Schematic view of PLD4 structure. Rectangles represent exons of PLD4.

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Because the 3 loci were associated with RA in a Japanese population, we analyzed whether the associations with SSc in the current study were contributed by patients with both RA and SSc. When 22 patients who had RA as well as SSc were excluded, significant associations for the 3 loci were still observed (Table 2). A further stringent analysis excluding patients with other autoimmune diseases demonstrated significant associations of the 3 genes (see Supplementary Table 2). When we compared SSc patients with and those without other autoimmune diseases for the associated alleles, no differences were observed (data not shown).

Subanalysis of types of SSc.

Previous studies have revealed that the genetic background of SSc varies between different types of SSc (11, 18). Thus, subanalyses of the 5 regions examined in the combined study were performed, in which the allele frequencies of the control subjects were compared with those of the patients with lcSSc or dcSSc. The control subjects were the same as those used in the first study or the combined study. Although PLD4 and TNFAIP3 did not display a preference for either SSc phenotype, IRF8 and ARID5 showed suggestive preferences for lcSSc, and CD83 showed a suggestive preference for dcSSc (Table 3).

Table 3. Associations of the 2 SSc subtypes*
SNPChrGeneAllele 1/2Controls, allele 1 frequencyLimited cutaneous SSc (n = 408)Diffuse cutaneous SSc (n = 318)
Allele 1 frequencyPOR (95% CI)Allele 1 frequencyPOR (95% CI)
  • *

    SSc = systemic sclerosis; SNP = single-nucleotide polymorphism; Chr = chromosome; OR = odds ratio; 95% CI = 95% confidence interval.

rs7664491PADI4T/C0.400.390.520.94 (0.77−1.14)0.360.110.85 (0.69−1.04)
rs119006732B3GNT2T/C0.290.250.0960.82 (0.66−1.03)0.310.321.11 (0.9−1.38)
rs28674614ANXA3A/G0.440.420.400.92 (0.75−1.12)0.440.971.00 (0.82−1.22)
rs6570755IL3-CSF2A/G0.360.340.540.94 (0.76−1.15)0.330.230.88 (0.72−1.08)
rs125295146CD83C/T0.140.150.791.03 (0.85−1.25)0.180.00751.32 (1.08−1.62)
rs15718786CCR6C/T0.490.480.810.98 (0.80−1.19)0.460.200.88 (0.72−1.07)
rs69320566TNFAIP3C/T0.0690.0930.00621.40 (1.1−1.78)0.100.000631.57 (1.21−2.04)
rs22334346NFKBIEG/A0.210.200.600.94 (0.73−1.20)0.220.701.05 (0.83−1.33)
rs1082194410ARID5BG/T0.360.400.00851.22 (1.05−1.41)0.380.301.09 (0.93−1.29)
rs378191311PDE2A-CENTD2T/G0.690.690.981.00 (0.81−1.24)0.690.901.01 (0.82−1.25)
rs284127714PLD4T/C0.690.730.00671.24 (1.06−1.45)0.740.00491.29 (1.08−1.55)
rs284128014PLD4C/G0.640.690.00111.30 (1.11−1.52)0.690.00861.27 (1.06−1.51)
rs284729718PTPN2G/A0.340.330.670.96 (0.78−1.18)0.340.871.02 (0.83−1.25)
rs493736211ETS1-FLI1T/C0.680.680.750.97 (0.78−1.19)0.690.921.01 (0.82−1.25)
rs378363714GCH1C/T0.740.730.690.96 (0.77−1.19)0.730.650.95 (0.76−1.18)
rs195789514PRKCHG/T0.390.400.841.02 (0.84−1.25)0.420.161.15 (0.95−1.41)
rs649666715ZNF774A/C0.350.390.0881.19 (0.97−1.45)0.340.750.97 (0.79−1.19)
rs740492816PRKCB1T/C0.620.610.600.95 (0.78−1.16)0.660.151.17 (0.95−1.44)
rs228038116IRF8T/C0.840.880.00381.36 (1.11−1.68)0.860.211.16 (0.92−1.45)

We also investigated whether the susceptibility loci affect autoantibody status and severe complications. The association studies revealed an association of TNFAIP3 with SSc patients who possess anticentromere antibodies (ACAs) (see Supplementary Table 3, available on the Arthritis & Rheumatism web site at http://onlinelibrary.wiley.com/doi/10.002/art.37777/abstract), but intracase analyses did not demonstrate clear significance (P = 0.043). We did not observe other associations between the susceptibility loci and clinical phenotypes of SSc, in either case–control analyses or intracase analyses.

Efficacy of the current study.

In the current study, a candidate gene analysis was performed based on a meta-analysis of RA GWAS, because many susceptibility genes for autoimmune disease have been reported to be shared by a wide range of diseases. As a result, 3 susceptibility genes for SSc in Japanese were identified. Thus, we analyzed whether the candidate gene approach taken in the current study for detecting novel susceptibility genes for SSc was effective. When the likelihood of finding 3 susceptibility genes among 18 genes by chance was calculated, the likelihood was determined to be 2.5 × 10−8. These results indicated that our approach to identifying novel susceptibility genes for systemic diseases is effective. It would be interesting to compare the risk direction of the genotyped markers between RA and SSc. Although the 3 susceptibility loci for SSc shared risk direction with RA, no correspondence of the risk directions of the markers between the 2 diseases was detected (Figure 2). This indicated that a large proportion of the 18 RA markers are not shared by SSc, and that the lack of association between the 13 markers and SSc was not attributable to the low power produced by the relatively small number of SSc patients included in this study.

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Figure 2. Comparison of associations for systemic sclerosis (SSc) and rheumatoid arthritis (RA). The odds ratios obtained for 18 genes in association studies of SSc and RA are plotted.

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DISCUSSION

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES
  9. Supporting Information

Because SSc can lead to severe complications, poor quality of life, and shortened survival, clarifying the characteristics of SSc is important. Clarification of the disease would aid the search for novel therapeutic targets and the development of new therapeutic strategies. Detecting susceptibility genes using GWAS or a candidate gene approach would also help to uncover the pathophysiology underlying SSc.

Previous studies have revealed that more than 15 markers and loci are associated with SSc. However, the markers detected so far cannot fully explain the genetics of SSc, indicating that many susceptibility genes are yet to be identified. Because a relatively large proportion of RA susceptibility genes are shared by other autoimmune diseases (24), a candidate gene approach using novel markers observed in GWAS of RA is a fascinating way of identifying new SSc markers. In fact, some of the novel susceptibility markers for RA identified in the meta-analysis were shown to be susceptibility markers for systemic lupus erythematosus (SLE) and Graves' disease (31).

In the current study, we successfully identified 3 susceptibility genes for SSc in Japanese. No studies have identified PLD4 as an SSc-associated locus. The current study is also the first to detect TNFAIP3 and IRF8 as susceptibility genes for SSc in a Japanese population. The best-fit models for each association are shown in Supplementary Table 4, available on the Arthritis & Rheumatism web site at http://onlinelibrary.wiley.com/doi/10.002/art.37777/abstract.

It is conceivable that these 3 associations might have been obtained due to the overlap of RA and SSc. Even after excluding the patients with both RA and SSc based on physicians' reports, the significant associations for the 3 loci were still observed (Table 3). Information regarding rheumatoid factor (RF) and anti–citrullinated protein antibody (ACPA) was available for 371 SSc patients without RA and 65 SSc patients without RA, respectively, of whom 21.6% and 10.8% were positive for RF and ACPA, respectively. These prevalences are compatible with those previously observed in SSc patients without RA (35, 36). Moreover, we showed that the effect sizes and risk direction of the markers tested in this study were dissociated between SSc and RA. In addition, further stringent analysis comprising SSc patients without any autoimmune disease also showed the associations of the 3 loci. These results indicate that the associations of the 3 loci are not attributable to overlapping of RA or other diseases.

Although the associations of the ARID5B and CD83 loci with SSc did not reach a stringently significant level in the combined study, the tendencies toward an association with SSc displayed by rs10821944 in the ARID5B locus and rs12529514 in the CD83 region in the first study were maintained in the replication study. This indicates that these loci are potential susceptibility regions for SSc. Further replication studies are needed to address the associations of these 2 loci with SSc in a Japanese population.

Because TNFAIP3 was reported to be strongly associated with SSc in a European population (18), the significant associations detected in the combined study indicate that TNFAIP3 displays general associations with SSc that go beyond ethnic boundaries. In addition, rs6932056, which displayed a strong association with SSc in a European population (18), is in strong LD with rs5029939 (r2 = 0.85) in the Japanese population. SNP rs6932056 also displays strong LD with rs2230926, a missense mutation of TNFAIP3 (r2 = 0.85), in Japanese. The rs2230926 missense mutation leads to an amino acid alteration in the OTU (ovarian tumor) domain of the A20 protein, which is considered to result in decreased NF-κB signaling. Because we did not observe strong associations between rs6932056 and SSc in the replication study, it will be necessary to reexamine the association between TNFAIP3 and SSc using independent sample sets of Japanese patients with SSc, in spite of the significant associations detected in this study.

PLD4 is a recently reported member of the phospholipase family without phospholipase D activity. PLD4 is expressed in the spleen and early postnatal microglia in the white matter of mice (37). The phenotypes of Pld4-deficient mice have not been reported. In addition, little is known about the expression or distribution of PLD4 in humans. Although the functions of PLD4 are also poorly understood, it is known to be involved in the phagocytosis of microglia (38). The expression of PLD4 around the marginal zone in the spleen might support the functional involvement of PLD4 in immunologic systems. It is interesting that rs2841280, which alters an amino acid of PLD-4, is associated with SSc. Minor allele G of rs2841280 is associated in a protective manner. The impact of an amino acid alteration brought by rs2841280 on the effect of PLD-4 protein is not known.

When we analyzed the impact of the amino acid alteration using in silico analysis (SIFT software; http://sift.jcvi.org/), it was shown to result in a small effect. However, the association raises the possibility that this polymorphism leads functional modulation of PLD-4, and it is feasible to analyze the functional change of PLD-4 protein with rs2841280, using animal models of SSc. When we performed an in silico analysis of the effect of rs2841277 and rs2841280 on PLD4 expression, we did not detect any clear associations between the 2 genotypes and PLD4 transcription (P > 0.05) (39). Therefore, in spite of the association of these 2 mutations, it has not been confirmed whether one of these 2 polymorphisms is the causative mutation.

Although the detection of a P value less than 5 × 10−8 in a GWAS is stringent evidence of an association between a marker and a particular disease, the detection of suggestive associations between the PLD4 region and SSc in European GWAS would indicate that associations exist between PLD4 and SSc in other populations. However, when we examined the associations between the PLD4 locus or nearby loci and SSc in GWAS involving a European population, we did not detect any strong associations (P < 10−4) (8, 9). According to the HapMap database, the European population displays a higher risk allele frequency for rs2841277 than the Japanese population. In addition, the HapMap database also indicates that the LD block spanning PLD4, which includes rs2841277, is similar in Europeans and Japanese. Nevertheless, a European population did not show a strong association between PLD4 and SSc, suggesting that PLD4 has a stronger effect on autoimmune diseases in Japanese than in Europeans. There is also a possibility that these 2 polymorphisms are only markers, and that a rare variant in LD with the 2 markers affects disease onset. A rare causative variant might explain a different association of PLD4 with SSc between populations.

IRF8 was shown to be associated with SLE in a European population (40). Interferon regulatory factor 8 (IRF-8) protein is a transcription factor involved in the interferon pathway. The interferon pathway has been shown to be involved with a broad range of autoimmune diseases, including SSc (41). Thus, it is interesting that IRF5 and IRF8, both of which belong to the IRF family, displayed associations with SSc. Although a European GWAS of SSc patients revealed suggestive associations between the IRF4 locus and SSc, the results were not successfully replicated (8), indicating that the different functional roles of each IRF family molecule might influence the development of SSc. IRF8 promotes B cell differentiation; however, the roles and importance of B cells in skin fibrosis in SSc patients have not been established (42–44). IRF8 and its mutant variants are also known to be involved in the development of dendritic cells (45). Thus, the association between IRF8 and SSc might indicate the involvement of B cells and dendritic cells in the development of SSc.

When the patients with SSc were classified as having either lcSSc or dcSSc and subanalyses were performed, ARID5B, IRF8, and CD83 displayed stronger associations with one of the 2 phenotypes. However, the associations of these 3 markers with the phenotypes were not strong enough to provide convincing evidence of a clear distinction between the genetic backgrounds of the 2 SSc phenotypes. When the associations of the SSc subtypes with the other 13 markers in the first set were analyzed, no strong association was detected (P > 0.05). Other subanalyses of the susceptibility loci in the combined set did not show significant results between disease phenotypes, due to lack of power. Because classification according to disease phenotypes resulted in limited numbers of subjects in each subset, we conducted this subanalysis only in the combined set. The association between TNFAIP3 and ACAs should be confirmed in a large-scale association study.

Although GWAS are an extremely powerful way to detect novel susceptibility genes for diseases, GWAS of patients with SSc have been performed only in European populations. Our study detected strong evidence for the sharing of susceptibility genes between RA and SSc in a Japanese population. In addition, the current study indicated that a candidate gene approach based on the results of GWAS of other diseases that display pathologic signaling pathways or mechanisms similar to those associated with the disease being examined is an effective approach to identifying novel susceptibility genes.

It will be interesting to perform GWAS of Japanese patients with SSc and analyze the similarities and differences in the detected associations not only between Japanese and Europeans but also between Japanese patients with SSc and Japanese patients with RA.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES
  9. Supporting Information

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Terao had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Terao, Ohmura, Kawaguchi, Nishimoto, Kawasaki, Takehara, Furukawa, Kochi, Ota, Ikari, Sato, Tohma, Yamada, Yamamoto, Kubo, Yamanaka, Kuwana, Tsuchiya, Matsuda, Mimori.

Acquisition of data. Terao, Ohmura, Kawaguchi, Nishimoto, Kawasaki, Takehara, Furukawa, Kochi, Ota, Ikari, Sato, Tohma, Yamada, Yamamoto, Kubo, Yamanaka, Kuwana, Tsuchiya, Matsuda, Mimori.

Analysis and interpretation of data. Terao, Ohmura.

Acknowledgements

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES
  9. Supporting Information

We thank the staff of the BioBank Japan Project for collecting DNA samples from control subjects.

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  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES
  9. Supporting Information

Additional Supporting Information may be found in the online version of this article.

FilenameFormatSizeDescription
ART_37777_sm_SupplTables.doc73KSupplementary Tables 1 through 4

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