SEARCH

SEARCH BY CITATION

Keywords:

  • SLE;
  • ETS1;
  • epistasis;
  • IL-17

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and Discussion
  6. Acknowledgement
  7. Conflict of Interests
  8. References
  9. Supporting Information

T-helper cells that produce IL-17 (Th17 cells) are a subset of CD4+ T-cells with pathological roles in autoimmune diseases including systemic lupus erythematosus (SLE), and ETS1 is a negative regulator of Th17 cell differentiation. Our previous work on genome-wide association study (GWAS) identified two variants in the ETS1 gene (rs10893872 and rs1128334) as being associated with SLE. However, like many other risk alleles for complex diseases, little is known on how these genetic variants might affect disease pathogenesis. In this study, we examined serum IL-17 levels from 283 SLE cases and observed a significant correlation between risk variants in ETS1 and serum IL-17 concentration in patients, which suggests a potential mechanistic link between these variants and the disease. Furthermore, we found that the two variants act synergistically in influencing IL-17 production, with evidence of significant genetic interaction between them as well as higher correlation between the haplotype formed by the risk alleles and IL-17 level in patient serum. In addition, the correlation between ETS1 variants and IL-17 level seems to be more significant in SLE patients manifesting renal involvement, dsDNA autoantibody production or early-onset.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and Discussion
  6. Acknowledgement
  7. Conflict of Interests
  8. References
  9. Supporting Information

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with a complex etiology and diverse clinical manifestations (Rahman & Isenberg, 2008). Recently, Th17, rather than Th1 cells, have been demonstrated to be closely related with autoimmunity (Dong, 2006). Several groups reported hyper-activity of interleukin-17 (IL-17) in patients with SLE (Wong et al., 2008; Yang et al., 2009), and evidence of IL-17 working on other cell types to exacerbate disease severity (Nakae et al., 2007). For example, IL-17 was shown to induce peripheral blood mononuclear cells (PBMCs) from SLE patients to produce more autoantibodies and IL-6. IL-17 may also act in synergy with B-cell activating factor (BAFF/BLys) to increase the survival and proliferation of human B cells and their differentiation into Ig-secreting cells (Doreau et al., 2009). ETS1, v-ets erythroblastosis virus E26 oncogene homolog 1 (avian), is a negative regulator of Th17 cell differentiation. Naïve CD4+ T cells deficient in ETS1 underwent greatly enhanced differentiation into Th17 cells. ETS1-deficient mice presented abnormally high levels of IL-17 transcripts in their lungs and exhibited increased mucus production in an IL-17-dependent manner (Moisan et al., 2007). In addition, Ets-1-deficient mice also developed a lupus-like phenotype, supporting this gene's important role in regulating immune system development and function (Wang et al., 2005).

Genetic contributions of ETS1 to lupus susceptibility have also been revealed by two recently published genome-wide association studies (GWASs). Yang et al. (2010) conducted a GWAS in a Hong Kong Chinese population, and followed up the findings by replication in three other cohorts from Anhui, Shanghai and Thailand, which involved a total of 3300 SLE patients and 4200 controls. Two SNPs, rs10893872 and rs1128334 located in or near the ETS1 gene, were found to be highly associated with lupus, with the joint p value reaching 1.8E-07 (odds ratio, OR = 1.21) and 2.3E-11(OR = 1.29), respectively. The same locus has also been revealed by an independent GWAS on Chinese populations (Han et al., 2009). However, like many other risk alleles for complex diseases, little is known about the mechanism(s) through which these genetic variants affect SLE patients. Finding the missing pieces that may bridge the gap between genetic susceptibility and cellular abnormality is an important task and may significantly improve our understanding of this complicated disease.

In this study, we first examined the SNPs rs10893872 and rs1128334 in ETS1 to investigate their epistatic interaction on disease association in a case control study. Considering the role of ETS1 in lupus pathogenesis and development of Th17 cells, the serum IL-17 profile was then investigated in 283 Hong Kong SLE cases. Our results demonstrated that there was significant interaction between the two variants in this locus, and the ETS1 haplotypes defined by the two synergistically interactive variants were associated with serum IL-17 levels in SLE cases, especially in those with specific clinical manifestations.

Materials and Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and Discussion
  6. Acknowledgement
  7. Conflict of Interests
  8. References
  9. Supporting Information

Subjects and the Genotyping Procedure

Detailed description of the subjects and the genotyping procedure of rs10893872 and rs1128334 can be found in our previous report (Yang et al., 2010). Briefly, in the Hong Kong cohort, a total of 1130 cases and 1173 controls were used for the epistasis analysis. Hong Kong SLE cases were collected from four hospitals in Hong Kong Island and the New Territories: Queen Mary Hospital, Tuen Mun Hospital, Queen Elizabeth Hospital and Pamela Youde Nethersole Eastern Hospital. The patients were all of self-reported Chinese ethnicity living in Hong Kong. Medical records were reviewed to confirm that all the subjects met the revised criteria of the American College of Rheumatology for SLE diagnoses (Hochberg, 1997). Clinical and serological data and autoantibody profiles were recorded at the time of diagnosis and also reviewed at the time of sample collection. Hong Kong controls were selected from a pool of healthy blood donors kindly contributed by the Hong Kong Red Cross, with an effort to match for the age and sex of corresponding SLE cases. Hong Kong samples were mainly genotyped by the TaqMan SNP genotyping method using Assays-on-Demand probes and primers (Applied Biosystems, Foster City, CA, USA). For SNP rs10893872, genotypes of 28% of Hong Kong cases (314/1130) were extracted from SNP chip in our previous work in the GWAS stage. Genotyping accuracy was confirmed by direct sequencing of PCR products for a number of randomly chosen samples. The study in Hong Kong was approved by the Institutional Review Board of the University of Hong Kong and Hospital Authority, Hong Kong West Cluster, New Territory West Cluster and Hong Kong East Cluster, and all patients gave informed consent for the collection of samples and subsequent analysis. Detailed description of the subjects and the genotyping procedure in Shanghai, Anhui and Thailand cohort can be found in the Supporting Information.

Assay of Serum IL-17

Two hundred and eighty-three Hong Kong SLE cases were included in the study of serum IL-17 assay. Twenty mL of peripheral blood was collected from each patient for serum extraction at the time of sample collection. The serum samples were divided into aliquots and stored at −80°C, avoiding repeated freeze-thaw cycles. Serum samples were examined within 1 year after collection for the purpose of maintaining stability as suggested by a previous study (de Jager et al., 2009). Concentration of IL-17 in serum was measured by enzyme-linked immunosorbent assay (ELISA) using Human IL-17A ELISA Kit (eBioscience, San Diego, CA) according to the manufacturer's protocol.

Statistical Analysis

Epistasis test (case-control analysis) by logistic regression was adopted here for parametric analysis of genetic interaction using PLINK (Purcell et al., 2007). PLINK uses a model according to allele dosage ranging from 0 to 2 indicating the number of risk alleles for each SNP, A and B, and fits the model in the form of inline image. The parameters b1, b2 and b3 indicate the contribution of SNP A and SNP B and interaction between A and B. The test for interaction is based on the coefficient b3. A p value of <0.05 was considered statistically significant.

Secondly, a pairwise non-parametric epistasis test was also applied using multifactor dimensionality reduction (MDR) analysis (Hahn et al., 2003). Briefly, MDR was used to determine the genetic model that could most successfully predict the disease status or phenotype from several loci. This method includes a combined cross-validation (CV)/permutation-testing procedure that minimizes false positive results by multiple examinations of the data. CV divides the data into a training set and a testing set. For 10-fold cross-validation, the data are divided into 10 equal parts, of which 9 folds are used for model training and the 10th-fold is used for testing. This process is repeated for each possible 9 of 10 and 1 of 10 folds of the data, and all 10 resulting prediction errors are averaged. Consequently, the model with the highest accuracy and maximal CV consistency was considered to be the best. We determined the statistical significance by comparing the average prediction error from the observed data with the distribution of average prediction errors under the null hypothesis of no association derived empirically from 10,000 permutations. The null hypothesis was rejected when the p value derived from the permutation test was 0.05 or lower. The specific high- and low-risk genotypes in the model were also determined by MDR. The high-risk genotypes for SLE were defined as if the ratio of the number of patients to control subjects was equal to or greater than the threshold of 1.0, whereas the low-risk groups were defined as if the threshold was lower than 1.0. The MDR analysis was carried out using version 2.0 of the open-source MDR software package that is freely available online (http://www.epistasis.org).

Results and Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and Discussion
  6. Acknowledgement
  7. Conflict of Interests
  8. References
  9. Supporting Information

Epistatic Interaction between the Two Risk Variants in ETS1

In our previous GWAS report, the most significant variant rs1128334 was identified as a major contributor of lupus susceptibility (Yang et al., 2010). However, the other reported SNP, rs10893872, was not in very high LD with rs1128334 (r2 = 0.68 in the HK cohort), suggesting that the less significant variant, rs10893872, may provide an additional contribution to disease susceptibility. Besides, haplotype analysis indicated that the TA haplotype formed by the two variants was the major risk haplotype, whilst the CG haplotype was the major protective haplotype with other haplotypes having very low allele frequencies (Table 1). It seems that the TA haplotype has a greater effect size and more significant disease association than rs1128334 alone (OR 1.36 for the haplotype vs. 1.27 for the SNP, and p = 5.9E−6 vs. p = 1.5E−4). Thus, it would be of value to exam whether epistatic interaction exists between rs10893872 and rs1128334. Epistasis test using PLINK (based on logistic regression analysis) was performed on 1130 HK cases and 1173 HK controls. As shown in Table 2 (left panel), the results suggested a significant effect from epistatic interaction between the two variants in the HK samples (p = 0.001, OR = 1.35). Epistasis analyses on the other three cohorts also supported such an effect (Table S1), although the epistatic interaction in the Anhui cohort was somehow complicated, probably due to the complexity of heterogeneity (Table S2, shown as the value I).

Table 1. Summary of the results from previous studies in Hong Kong samples
Association result (Yang et al., 2010)
  1. MAF: Minor allele frequency.

  2. a

    P_values and ORs showed here were the association results in HK cohort.

MarkerMAF_caseMAF_controlP_valueaORa
rs112833440.635.11.5E-041.27
rs1089387247.342.80.0201.20
Haplotype analysis (Yang et al., 2010)
rs10893872-rs1128334MAF_caseMAF_controlP_valueaORa
TA haplotype40.433.25.9E-061.36
CG haplotype51.956.60.00350.83
Prevalence of some clinical manifestations in Hong Kong Chinese patients (Li et al., 2012)
Age of onset, mean (S.D.), years29.8 (13)
Anti-dsDNA (%)71.6
Renal disorder (%)55.4
Table 2. Epistatic interaction results between two variants in ETS1 done by PLINK and MDR
InteractionLogistic regressionMDR analysis
  1. OR means odds ratio for interaction, and a value of 1.0 indicates no effect.

  2. Cross-validation consistency reflects the number of times MDR analysis identified the same model as the data were divided into different segments. Balanced accuracy is defined as (sensitivity + specificity)/2. This gives an accuracy estimate that is not biased by the larger class.

SNP1SNP2OR (95% CI)PCVCBalanced accuracyPOR (95% CI)
rs10893872rs11283341.35 (1.13∼1.61)0.001010/100.54490.00011.89 (1.51∼2.36)

Another statistical method for testing epistatic interaction was also applied here for further validation. A pairwise MDR analysis was adopted here to test epistatic interaction between the two variants. Table 2 (right panel) showed the results of cross-validation consistency (CVC) and balanced accuracy obtained from MDR analysis of the two-locus model, which was significant and had a maximum CVC and highest accuracy. The combinations of low- and high-risk groups were classified in this model (Fig. 1). In agreement with the results obtained by logistic regression, a significant effect from a synergistically epistatic interaction (p = 0.0001) was observed, and the OR for this model was 1.89 (95% CI: 1.51–2.36). Similar results were obtained from the other three cohorts (Table S3 and Fig. S1).

image

Figure 1. The optimal two-locus model as determined by MDR analysis on ETS1 variants in a Hong Kong population. (0 = no risk alleles, 1 = 1 risk allele, 2 = 2 risk alleles, −9 = undetermined genotype). The numbers within each small square represent the number of cases (left) and controls (right). For each square, dark-shading indicates high risk of disease, whereas light shading represents low risk of disease. Boxes were labeled as high risk if the ratio of the number of patients to controls met or exceeded the threshold of 1.0.

Download figure to PowerPoint

Interaction of the Two Risk Variants in ETS1 Correlates with IL-17 Profile in SLE Patients

Previously, significantly lower expression from the risk “A” allele of rs1128334 than from the protective “G” allele was observed in PBMCs of healthy individuals heterozygous for the variant (Yang et al., 2010). Thus, samples from 283 HK SLE cases were firstly divided according to the three different genotypes of rs1128334 (AA, AG and GG), and serum IL-17 level was measured by ELISA. Average IL-17 profiles were compared using one-way ANOVA, and the unpaired t-test was used for the comparisons between every two groups. The comparisons among serum IL-17 level from healthy individuals with different genotypes of rs1128334 were not performed due to low concentrations of IL-17 in the serum of healthy individuals as demonstrated previously (Wong et al., 2008; Zhao et al., 2010). We observed a difference in average IL-17 levels from lupus patients among the three groups, with an increase in IL-17 expression corresponding to the number of risk “A” alleles, although the difference did not reach significance level between the genotypes. In addition, comparisons of IL-17 levels from patients with different genotypes of rs10893872 further excluded the possibility of potential dominant contribution from rs10893872 (Fig. S2).

In order to examine whether epistatic interaction of the risk variants in ETS1 may contribute to subsequent cellular events, such as cytokine production, the other variant rs10893872 was included here for the analysis. SLE individuals were grouped according to the carrier status of the risk TA haplotype formed by rs10893872 and rs1128334 (n = 0, 1 and 2). For individuals with TC and AG genotypes formed by these two variants, it was difficult to distinguish the exact haplotypes. Thus, 60 individuals were left out of the analysis. Results in Figure 2(A) showed that the serum IL-17 level of the individuals that carrying two copies of the risk TA haplotype were significantly higher than those from the non-risk haplotype carriers (121.5 ± 16.70 pg/ml vs. 60.58 ± 11.19 pg/ml, p = 0.0028, unpaired t-test with Welch's correction). The individuals with one copy of the risk haplotype showed intermediate expression of IL-17.

image

Figure 2. Serum IL-17 concentration in SLE patients stratified by carrier status of risk haplotype (TA haplotype) formed by rs10893872 and rs1128334. Each dot represents an individual, and the numbers in brackets indicate sample size. (A) All the patients examined in this experiment are included except those with an unidentifiable haplotype. (B) Patients with anti-dsDNA antibodies positive manifestation. (C) Patients with renal involvement positive manifestation. (D) Patients with disease onset younger than 20 year of age. The results are shown as mean ± S.E.M, and the p values were calculated using an unpaired t test with Welch's correction (**, p < 0.01; *, p < 0.05).

Download figure to PowerPoint

We further investigated the correlation between the risk haplotype in ETS1 and serum IL-17 level in patients with different clinical manifestations. Anti-double-stranded DNA (anti-dsDNA) antibodies are highly specific for lupus patients, and levels of anti-dsDNA antibodies in patients’ serum tend to reflect disease activity (Rahman & Isenberg, 2008). As shown in Figure 2(B), in this group of patients, the serum IL-17 profiles from individuals carrying two copies of the risk haplotype were significantly higher than those of the non-carriers (119.6 ± 18.61 pg/ml vs. 49.23 ± 11.78 pg/ml, p = 0.0017), a larger difference compared to what was seen in the whole patient pool. We also carried out the analysis in patients with another common but severe clinical manifestation, renal involvement. A similar result was observed, with the homozygous carriers showing a much higher IL-17 level (122.0 ± 23.88 pg/ml vs. 42.40 ± 17.03 pg/ml, p = 0.0080, Fig. 2C). Lupus is a complex disease that is affected by both genetic and environmental factors; earlier age of onset of the disease is suggested to correlate with a higher genetic contribution (Velazquez-Cruz et al., 2007; Webb et al., 2011). Analysis of the patient group with early onset age (younger than 20 year) showed the same trend (150.7 ± 29.02 pg/ml vs. 58.71 ± 18.44 pg/ml, p = 0.0108, Fig. 2D). However, for patients with other clinical manifestations including arthritis, serositis and oral ulcers, no significant correlation between the ETS1 haplotype and IL-17 level was observed. Haplotype-based association was also examined in a patient-only analysis on the potential effect of the haplotypes on these sub-phenotypes. A higher TA risk haplotype was observed in patients positive for renal involvement, anti-dsDNA autoantibodies, or early-onset, than in those negative for these features or late onset, although only for early-onset analysis was the difference nominally significant (TA haplotype increased from 39% in late-onset patients to 48% in patients with onset age < 20, p = 0.015).

Evidence for epistatic interactions among susceptibility loci for SLE has been provided by many previous studies, and these findings support a role for genetic interaction contributing to the complexity of lupus heritability (Hughes et al., 2012; Zhou et al., 2012). Based on our observations, ETS1 haplotypes, defined by two interactive variants, are associated with serum IL-17 level in lupus patients, especially in individuals with specific clinical manifestations. The two interacting variants might predispose individuals to SLE by affecting the expression of ETS1. SNP rs1128334 was located in the 3′-UTR of the gene, while SNP rs10893872 was in absolute LD (r2 = 1) with rs4937333, another variant that was also located in the 3′-UTR of ETS1. Indeed, ETS1 has been shown to be a putative target of several microRNAs (miRNA), including miR-326, which was shown to play a role in Th17 cell differentiation (Du et al., 2009). In addition, a recent study (Luo et al., 2011) showed that the risk allele of miR-146a variant conferred weaker binding affinity for Ets-1. Their findings pointed out that the reduced expression of Ets-1 and its reduced binding affinity to the miR-146a promoter both may contribute to low levels of this microRNA in SLE patients, which may ultimately lead to cellular abnormalities in SLE patients.

Although the detailed mechanisms still remain largely undetermined, it is likely that the two variants may work in synergy to affect ETS1 expression through RNA-induced silencing machinery or other mechanisms. An alternative explanation is that the haplotype formed by the risk alleles of the two SNPs tags an unknown functional variant that is in high LD and is associated with the disease. To address these possibilities, further functional characterization and sequencing of the region are required in future studies.

In conclusion, we demonstrated that there was significant synergistic epistatic interaction between two risk variants in the ETS1 gene, and the haplotype formed by the two variants correlated significantly with IL-17 levels in SLE patients. Our study proposed a link that may help bridge the gap between genetic susceptibility and cellular abnormality, which is an important next step for association studies in order to better understand disease mechanisms and identify new drug targets.

Acknowledgement

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and Discussion
  6. Acknowledgement
  7. Conflict of Interests
  8. References
  9. Supporting Information

This study was partially supported by the generous donation from Shun Tak District Min Yuen Tong of Hong Kong. We thank Winnie Lau and her team for collection of samples and clinical records for Hong Kong patients. WY and YLL acknowledge support from the Research Grant Council of the Hong Kong Government (GRF HKU781709M, HKU 784611M and HKU 770411M). JZ, LZ YZ, DY and SZ are supported by the Edward Sai Kim Hotung Pediatric Education and Research Fund.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and Discussion
  6. Acknowledgement
  7. Conflict of Interests
  8. References
  9. Supporting Information
  • De Jager, W., Bourcier, K., Rijkers, G. T., Prakken, B. J., & Seyfert-Margolis, V. (2009) Prerequisites for cytokine measurements in clinical trials with multiplex immunoassays. BMC immunol 10, 52.
  • Dong, C. (2006) Diversification of T-helper-cell lineages: Finding the family root of IL-17-producing cells. Nat Rev Immunol 6, 329333.
  • Doreau, A., Belot, A., Bastid, J., Riche, B., Trescol-Biemont, M. C., Ranchin, B., Fabien, N., Cochat, P., Pouteil-Noble, C., Trolliet, P., Durieu, I., Tebib, J., Kassai, B., Ansieau, S., Puisieux, A., Eliaou, J. F., & Bonnefoy-Berard, N. (2009) Interleukin 17 acts in synergy with B cell-activating factor to influence B cell biology and the pathophysiology of systemic lupus erythematosus. Nat Immunol 10, 778785.
  • Du, C., Liu, C., Kang, J., Zhao, G., Ye, Z., Huang, S., Li, Z., Wu, Z., & Pei, G. (2009) MicroRNA miR-326 regulates TH-17 differentiation and is associated with the pathogenesis of multiple sclerosis. Nat Immunol 10, 12521259.
  • Hahn, L. W., Ritchie, M. D., & Moore, J. H. (2003) Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 19, 376382.
  • Han, J. W., Zheng, H. F., Cui, Y., Sun, L. D., Ye, D. Q., Hu, Z., Xu, J. H., Cai, Z. M., Huang, W., Zhao, G. P., Xie, H. F., Fang, H., Lu, Q. J., Li, X. P., Pan, Y. F., Deng, D. Q., Zeng, F. Q., Ye, Z. Z., Zhang, X. Y., Wang, Q. W., Hao, F., Ma, L., Zuo, X. B., Zhou, F. S., Du, W. H., Cheng, Y. L., Yang, J. Q., Shen, S. K., Li, J., Sheng, Y. J., Zuo, X. X., Zhu, W. F., Gao, F., Zhang, P. L., Guo, Q., Li, B., Gao, M., Xiao, F. L., Quan, C., Zhang, C., Zhang, Z., Zhu, K. J., Li, Y., Hu, D. Y., Lu, W. S., Huang, J. L., Liu, S. X., Li, H., Ren, Y. Q., Wang, Z. X., Yang, C. J., Wang, P. G., Zhou, W. M., Lv, Y. M., Zhang, A. P., Zhang, S. Q., Lin, D., Low, H. Q., Shen, M., Zhai, Z. F., Wang, Y., Zhang, F. Y., Yang, S., Liu, J. J., & Zhang, X. J. (2009) Genome-wide association study in a Chinese Han population identifies nine new susceptibility loci for systemic lupus erythematosus. Nat Genet 41, 12341237.
  • Hochberg, M. C. (1997) Updating the American College of Rheumatology revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum 40, 1725.
  • Hughes, T., Adler, A., Kelly, J. A., Kaufman, K. M., Williams, A. H., Langefeld, C. D., Brown, E. E., Alarcon, G. S., Kimberly, R. P., Edberg, J. C., Ramsey-Goldman, R., Petri, M., Boackle, S. A., Stevens, A. M., Reveille, J. D., Sanchez, E., Martin, J., Niewold, T. B., Vila, L. M., Scofield, R. H., Gilkeson, G. S., Gaffney, P. M., Criswell, L. A., Moser, K. L., Merrill, J. T., Jacob, C. O., Tsao, B. P., James, J. A., Vyse, T. J., Alarcon-Riquelme, M. E., Network, B., Harley, J. B., Richardson, B. C., & Sawalha, A. H. (2012) Evidence for gene-gene epistatic interactions among susceptibility loci for systemic lupus erythematosus. Arthritis Rheum 64, 485492.
  • Li, P. H., Wong, W. H., Lee, T. L., Lau, C. S., Chan, T. M., Leung, A. M., Tong, K. L., Tse, N. K., Mok, C. C., Wong, S. N., Lee, K. W., Ho, M. H., Lee, P. P., Chong, C. Y., Wong, R. W., Mok, M. Y., Ying, S. K., Fung, S. K., Lai, W. M., Yang, W., & Lau, Y. L. (2012) Relationship between autoantibody clustering and clinical subsets in SLE: Cluster and association analyses in Hong Kong Chinese. Rheumatology 52, 337345.
  • Luo, X., Yang, W., Ye, D. Q., Cui, H., Zhang, Y., Hirankarn, N., Qian, X., Tang, Y., Lau, Y. L., De Vries, N., Tak, P. P., Tsao, B. P., & Shen, N. (2011) A functional variant in microRNA-146a promoter modulates its expression and confers disease risk for systemic lupus erythematosus. PLoS Genet 7, e1002128.
  • Moisan, J., Grenningloh, R., Bettelli, E., Oukka, M., & Ho, I. C. (2007) Ets-1 is a negative regulator of Th17 differentiation. J Exp Med 204, 28252835.
  • Nakae, S., Iwakura, Y., Suto, H., & Galli, S. J. (2007) Phenotypic differences between Th1 and Th17 cells and negative regulation of Th1 cell differentiation by IL-17. J Leukoc Biol 81, 12581268.
  • Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A., Bender, D., Maller, J., Sklar, P., De Bakker, P. I., Daly, M. J., & Sham, P. C. (2007) PLINK: A tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81, 559575.
  • Rahman, A. & Isenberg, D. A. (2008) Systemic lupus erythematosus. N Engl J Med 358, 929939.
  • Velazquez-Cruz, R., Orozco, L., Espinosa-Rosales, F., Carreno-Manjarrez, R., Solis-Vallejo, E., Lopez-Lara, N. D., Ruiz-Lopez, I. K., Rodriguez-Lozano, A. L., Estrada-Gil, J. K., Jimenez-Sanchez, G., & Baca, V. (2007) Association of PDCD1 polymorphisms with childhood-onset systemic lupus erythematosus. Eur J Hum Genet 15, 336341.
  • Wang, D., John, S. A., Clements, J. L., Percy, D. H., Barton, K. P., & Garrett-Sinha, L. A. (2005) Ets-1 deficiency leads to altered B cell differentiation, hyperresponsiveness to TLR9 and autoimmune disease. Int Immunol 17, 11791191.
  • Webb, R., Kelly, J. A., Somers, E. C., Hughes, T., Kaufman, K. M., Sanchez, E., Nath, S. K., Bruner, G., Alarcon-Riquelme, M. E., Gilkeson, G. S., Kamen, D. L., Richardson, B. C., Harley, J. B., & Sawalha, A. H. (2011) Early disease onset is predicted by a higher genetic risk for lupus and is associated with a more severe phenotype in lupus patients. Ann Rheum Dis 70, 151156.
  • Wong, C. K., Lit, L. C., Tam, L. S., Li, E. K., Wong, P. T., & Lam, C. W. (2008) Hyperproduction of IL-23 and IL-17 in patients with systemic lupus erythematosus: Implications for Th17-mediated inflammation in auto-immunity. Clin Immunol 127, 385393.
  • Yang, J., Chu, Y., Yang, X., Gao, D., Zhu, L., Wan, L., & Li, M. (2009) Th17 and natural Treg cell population dynamics in systemic lupus erythematosus. Arthritis Rheum 60, 14721483.
  • Yang, W., Shen, N., Ye, D. Q., Liu, Q., Zhang, Y., Qian, X. X., Hirankarn, N., Ying, D., Pan, H. F., Mok, C. C., Chan, T. M., Wong, R. W., Lee, K. W., Mok, M. Y., Wong, S. N., Leung, A. M., Li, X. P., Avihingsanon, Y., Wong, C. M., Lee, T. L., Ho, M. H., Lee, P. P., Chang, Y. K., Li, P. H., Li, R. J., Zhang, L., Wong, W. H., Ng, I. O., Lau, C. S., Sham, P. C., & Lau, Y. L. (2010) Genome-wide association study in Asian populations identifies variants in ETS1 and WDFY4 associated with systemic lupus erythematosus. PLoS Genet 6, e1000841.
  • Zhao, X. F., Pan, H. F., Yuan, H., Zhang, W. H., Li, X. P., Wang, G. H., Wu, G. C., Su, H., Pan, F. M., Li, W. X., Li, L. H., Chen, G. P., & Ye, D. Q. (2010) Increased serum interleukin 17 in patients with systemic lupus erythematosus. Mol Biol Rep 37, 8185.
  • Zhou, X. J., Lu, X. L., Nath, S. K., Lv, J. C., Zhu, S. N., Yang, H. Z., Qin, L. X., Zhao, M. H., Su, Y., Shen, N., Li, Z. G., Zhang, H., & International Consortium on the Genetics of Systemic Lupus, E. (2012) Gene-gene interaction of BLK, TNFSF4, TRAF1, TNFAIP3, and REL in systemic lupus erythematosus. Arthritis Rheum 64, 222231.

Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and Discussion
  6. Acknowledgement
  7. Conflict of Interests
  8. References
  9. Supporting Information

Disclaimer: Supplementary materials have been peer-reviewed but not copyedited.

FilenameFormatSizeDescription
ahg12018-sup-0001-suppmat.pdf370K

Table S1 Results of epistatic interaction between rs10893872 and rs1128334 in Shanghai, Thailand and Anhui samples, using logistic regression implemented in PLINK.

Table S2 Meta-analysis of association studies in different cohorts of ETS1 variants.

Table S3 Multifactor dimensionality reduction (MDR) analysis for pairwise interactions in SLE patients and controls in Shanghai, Thailand and Anhui cohorts.

Figure S1 The optimal two-locus model as determined by MDR analysis on ETS1 variants in Shanghai, Thailand and Anhui cohorts.

Figure S2 Serum IL-17 concentration in SLE patients stratified by carrier status of rs1128334 genotype (a) or rs10893872 genotype (b).

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.