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International Journal of Molecular Sciences
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20 April 2023

SNP in PTPN22, PADI4, and STAT4 but Not TRAF1 and CD40 Increase the Risk of Rheumatoid Arthritis in Polish Population

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1
Department of Rheumatology, Medical University of Lodz, 92-115 Lodz, Poland
2
Department of Clinical Chemistry and Biochemistry, Medical University of Lodz, 92-215 Lodz, Poland
3
Doctoral Study in Molecular Genetics, Cytogenetics and Medical Biophysics, Faculty of Biology and Environmental Protection, University of Lodz, 90-236 Lodz, Poland
4
Biobank, Department of Immunology and Allergy, Medical University of Lodz, 92-213 Lodz, Poland
Int. J. Mol. Sci.2023, 24(8), 7586;https://doi.org/10.3390/ijms24087586
This article belongs to the Special Issue State-of-the-Art Molecular Genetics and Genomics in Poland
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Abstract

Single nucleotide polymorphisms in non-HLA genes are involved in the development of rheumatoid arthritis (RA). SNPS in genes: PADI4 (rs2240340), STAT4 (rs7574865), CD40 (rs4810485), PTPN22 (rs2476601), and TRAF1 (rs3761847) have been described as risk factors for the development of autoimmune diseases, including RA. This study aimed to assess the prevalence of polymorphisms of these genes in the Polish population of patients with rheumatoid arthritis as compared to healthy controls. 324 subjects were included in the study: 153 healthy subjects and 181 patients from the Department of Rheumatology, Medical University of Lodz who fulfilled the criteria of rheumatoid arthritis diagnosis. Genotypes were determined by Taqman SNP Genotyping Assay. rs2476601 (G/A, OR = 2.16, CI = 1.27–3.66; A/A, OR = 10.35, CI = 1.27–84.21), rs2240340 (C/T, OR = 4.35, CI = 2.55–7.42; T/T, OR = 2.80, CI = 1.43–4.10) and rs7574865 (G/T, OR = 1.97, CI = 1.21–3.21; T/T, OR = 3.33, CI = 1.01–11.02) were associated with RA in the Polish population. Rs4810485 was also associated with RA, however after Bonferroni’s correction was statistically insignificant. We also found an association between minor alleles of rs2476601, rs2240340, and rs7574865 and RA (OR = 2.32, CI = 1.47–3.66; OR = 2.335, CI = 1.64–3.31; OR = 1.88, CI = 1.27–2.79, respectively). Multilocus analysis revealed an association between CGGGT and rare (below 0.02 frequency) haplotypes (OR = 12.28, CI = 2.65–56.91; OR = 3.23, CI = 1.63–6.39). In the Polish population, polymorphisms of the PADI4, PTPN22, and STAT4 genes have been detected, which are also known risk factors for RA in various other populations.

1. Introduction

Rheumatoid arthritis (RA) is a chronic inflammatory disorder with a multifactorial basis; however, the etiology of RA remains unknown. It affects approximately 1% of the population and is manifested by the destruction of synovial joints, joint swelling, bone erosion, and joint tenderness [1]. It can lead to severe disability or even premature mortality [2]. The prevalence of RA varies between population [3], most likely due to population variations arising from diverse genetic backgrounds as genetic factors play an essential role and are likely 70% responsible for susceptibility to RA and expression of symptoms [3]. The increased risk of the occurrence of RA has been associated mainly with HLA-DRB1 alleles, which are described in most genetic studies as the predominant cause of RA and are responsible for at least 30% of the total genetic basis of RA [4,5]. However, it has been suggested that single nucleotide polymorphisms (SNPs) in non-HLA genes can also be involved in RA pathogenesis. Large-scale genome-wide association studies (GWAS) identified risk factors for RA and described to be risk loci for also other autoimmune diseases several SNPs in genes including: protein tyrosine phosphatase non-receptor type 22 (PTPN22, rs2476601), peptidylarginine deiminase type 4 (PADI4, rs2240340), tumor necrosis factor receptor-associated factor 1 (TRAF1, rs3761847), signal transducer and activator of transcription 4 (STAT4, rs7574865), and cluster of differentiation 40 (CD40, rs4810485) [4,6]. The products of these genes are involved in the pathogenesis of RA; therefore, the role of all SNPs in RA has a rational explanation. In RA patients the serum level of TRAF1 is described as elevated and correlated with disease activity or the presence of RF antibodies [7]. PADI4 is a gene encoding PADI4 protein, which is involved in the process of citrullination, leading possibly to the formation of autoantigens in RA patients. Anti-citrullinated protein antibodies (ACPA) are directed against posttranslational modified, citrullinated proteins. The presence of ACPA precedes the clinical onset of RA, and the level of ACPA correlates with the patient’s prognosis [8]. CD40 is a transmembrane protein expressed on antigen-presenting cells, which binds to CD40 ligand expressed on lymphocytes T. Expression of CD40 is crucial for a threshold of activation of lymphocytes T and further steps of the immune response leading to autoantibody production and activation of macrophages [9,10]. The pathogenesis of RA can be linked to the STAT4 gene through participation in Th17 and Th1 lineages, which are known as crucial effectors in chronic inflammatory disorders [11,12,13]. PTPN22 is known to be a master regulator of the immune system, it mediates relevant immune responses and plays a role as a negative regulator of signaling pathways mediated through the T cell receptor (TCR) and B cell receptor (BCR) [14,15].
Following these findings, an analysis of the prevalence of the mentioned polymorphisms in the Polish population is justified, since these SNPs have been evaluated in other populations and the quantity of data from the Central European population is rather limited. Therefore, assuming ethnic genetic variation, the contribution of genetic factors to the development of RA may not be identical between populations [16,17,18,19,20,21,22,23,24]. In this study, we are the first to analyze the prevalence of 5 SNPs associated with RA in the Polish population with one exception. PADI4.rs2240340 was assessed in previous research [25], however, this study has a number of flaws, the most important of which is the abnormal disproportion between controls and patients (25 vs. 122) and the resulting very low power.

2. Results

2.1. Characteristics of the Study Population

We analyzed a cohort of 181 RA patients and 153 healthy controls (HC) for RA association, (clinical characteristic is presented in Table 1). The mean time of disease duration was 19 ± 10 years (from 1 to 39 years). A total of 132 patients were currently (for at least one month before blood collection) treated with methotrexate, 6 patients with sulfasalazine, and 37 patients did not receive disease-modifying anti-rheumatic drugs (DMARDs) within the last month. A total of 82 patients were taking glucocorticosteroids within the last week. In 128 patients, rheumatoid factor was positive, and anti-citrullinated protein antibodies were increased in 108 patients. In 43 patients, neither RF nor aCCP were increased. The disease activity was also assessed based on Disease Activity Score 28-joint count C reactive protein (DAS28)—CRP score (DAS < 1.7 was defined as remission, DAS > 1.7 and <2.6 was defined as low disease activity and DAS28 above 5.1 as high disease activity). All control patients had ESR and CRP within normal limits and did not have any chronic disease of inflammatory background. The genotype of 31 patients and 3 healthy subjects were unsuccessful, so the final number of subjects was 150 in both groups.
Table 1. Data present the clinical characteristics of the studied group with the mean values of parameters (±SD).

2.2. The Hardy–Weinberg Equilibrium Analysis

Testing for the Hardy–Weinberg (HW) principle is shown in Table 2. The genotypic distributions of all analyzed SNPs except PADI4.rs2240340 were in accordance with Hardy–Weinberg equilibrium (p > 0.05).
Table 2. The Hardy–Weinberg equilibrium analysis.

2.3. Analysis of a Relationship between the Occurrence of RA and the Studied Polymorphic Variants of PTPN22, PADI4, TRAF1, STAT4, and CD40 Genes

Association studies were used for the analysis. These are population-based studies that determine, in part, whether a particular allele of a gene is more common in RA patients than in healthy individuals. For each polymorphism, the prevalence of each genotype compared to the general population is presented in Table 3.
Table 3. Basic information and allele frequencies of the 5 selected SNPs in PTPN22, PADI4, TRAF1, STAT4, and CD40 genes.
We have taken into account only the most general, codominant genetic model as the mode of inheritance in RA is rather unknown and varies from autosomal dominant to recessive. The distribution of the PTPN22.rs2476601, PADI4.rs2240340, TRAF1.rs3761847, STAT4.rs7574865, CD40.rs4810485 genotypes in the HC and RA groups are listed in Table 4. We observed a relationship between the occurrence of RA and almost all analyzed SNPs except TRAF1.rs3761847. PTPN22.rs2476601, PADI4.rs2240340, and STAT4.rs7574865 increased the RA risk about two times. CD40.rs4810485 decreased the RA risk, however, the association was not significant after Bonferroni correction.
Table 4. Association of RA with the frequency of genotypes of the PTPN22, PADI4, TRAF1, STAT4, and CD40 genes.
No significant association was observed between the TRAF1.rs3761847 and the incidence of RA.

2.4. Haplotype Analysis

Haplotypic analysis indicated a low degree of linkage disequilibrium (LD) of studied polymorphisms (Table 5).
Table 5. Linkage disequilibrium analysis of the 5 selected SNPs in PTPN22, PADI4, TRAF1, STAT4, and CD40 genes. D’ statistic is presented. *** are placed instead of repeated genes or values.
Of the 29 possible haplotypes, 17 were present at a frequency below 0.02 and were therefore classified as rare haplotypes (Table 6). The frequencies of the other haplotypes ranged from 0.0203 to 0.24.
Table 6. Haplotype frequencies of the 5 selected SNPs in PTPN22, PADI4, TRAF1, STAT4, and CD40 genes estimation. * described haplotypes present at frequency below 0.02.
In multilocus analysis, the frequency of four haplotypes (TGGGG; CGGGT; TTGGG, and rare) was significantly higher in RA patients in comparison to controls (Table 7). The association remained significant even after Bonferroni correction (p = 0.0017) for the two haplotypes (CGGGT and rare).
Table 7. Association of RA with the frequency of haplotype of the 5 selected SNPs in PTPN22, PADI4, TRAF1, STAT4, and CD40. * described haplotypes present at frequency below 0.02.

2.5. Sample Power

The power of the sample size was estimated using a QUANTO (https://bio.tools/QUANTO, accessed on 1 May 2009) [26]. When the conditions of the sample power (α = 0.05, risk = two-fold, the number of cases = 150) were employed, we had 0.917 for rs2240340, 0.8855 for rs7574865, 0.8793 for rs2476601, 0.8906 for rs3761847 and 0.9999 for rs4810485. Therefore, our case–control study was sufficiently powerful to study a positive correlation.

3. Discussion

In this study, we evaluated five SNPs occurring worldwide in different populations in genes that were previously associated with RA. We found an association between PADI4.rs2240340, PTPN22.rs2476601 and STAT4.rs7574865 SNPs and RA.
PADI4.rs2240340 is located in the intronic region of the gene and its variants could affect the stability of the PADI4 transcript or may alter transcriptional regulation which may shift it into its pathological activity [27]. These findings confirm those of earlier studies, such as the association with RA occurrence in different European and other populations. In 2003, Suzuki et al. for the first time described the association between PADI4 gene polymorphisms and RA susceptibility in the Japanese population [27]. Next, PADI4 was associated with RA occurrence in the German population [28]. A result of the meta-analysis provided by Burr et al. reported that PADI4.rs2240340 was stronger associated with RA in the Asian population rather than in the European population [17]. However, there are discrepancies not only between but also within one ethnic group. For example, in the Chinese Han population, there were no significant differences between healthy control and RA [29].
STAT4 belongs to the STATs family which are latent cytoplasmic transcription factors [30]. Studies on STAT4 expression showed that compared to the rs7574865 G allele, the presence of the T allele can significantly promote STAT4 mRNA transcription and protein expression [31]. Our results are in line with others [22], wherein the Slovak population the association between STAT4.rs7574865 and RA was found. The meta-analysis provided by Gao et al. based on 37 articles revealed, that when we evaluate results in terms of ethnicity STAT4.rs7574865 was associated with the RA risk in Caucasians, Asians, Africans, and Mexicans [32]. Another, current meta-analysis performed by Ebrahimiyan et al. describes that the TT genotype was presented mainly in RA patients. The dominant and recessive genetic models of TT + GT vs. GG and TT vs. GT + GG both were associated with higher RA risk, respectively [33].
PTPN22 gene encodes a protein which is known as lymphoid tyrosine phosphatase (LYP) also called protein tyrosine phosphatase non-receptor type 22 [34]. PTPN22.rs2476601 (also known as C1858T, R620) leads to the alteration of amino acid residue 620 from arginine to tryptophan in the mature protein. However, its influence on the function of LYP described as a possible loss of negative regulation of T cell signaling protein remains debatable and both gain and loss of function have been suggested [14,34,35,36]. The first association with RA was described by Begovich minor T allele and RA [14]. The meta-analysis showed that there are statistically significant results in all genetic models: dominant model, recessive model, allelic model, TT vs. CC model, and CT vs. CC in Caucasians and Africans [37]. In Asian studies on the Chinese Han population by Tang et al. rs2476601 was lacking in polymorphism with a ≤ 0.1% frequency in RA patients and healthy controls and it is suggested that is extremely rare in Asians in the context of RA studies [38]. PTPN22 is described as one of the strongest non-HLA genetic predisposition factors in the RA [39] so obtaining the highest OR for this polymorphism in our Polish cohort was not surprising.
We also found an association between rs4810485 located within the CD40 gene and RA, however after Bonferroni’s correction it was statistically insignificant. rs4810485.CD40 is located in the second intron of the CD40 gene. It is suggested that this SNP can affect CD40 mRNA and protein expression in B cells and monocytes, however, these results are not consistent [40,41]. rs4810485.CD40 GG genotype is associated with the destruction of the joints in the ACPA-positive RA [42]. Protective effect against the development of RA has a common variant (the minor T allele) at the CD40 and it was also observed in European populations based on meta-analysis [43]. However, other meta-analyses show that the T allele was found to be associated with RA risk in Europeans (OR 0.879 95% CI 0.848–0.901, p = 3 × 10−9) but in Asian studies show no correlation including recessive, dominant, and additive models [44]. In our results, there was no correlation in the Polish population observed under any genetic model between rs4810485.CD40 and RA occurrence. Similar results were assessed in the Western Mexico population [41]. We cannot exclude that study on a larger group would pass Bonferroni’s correction.

4. Materials and Methods

4.1. Patients and Control Group

The study group included 181 patients with RA (145 women and 36 men; mean age 61 ± 13) selected from patients of the Department of Rheumatology, Medical University of Lodz, and outpatient clinic. A control group of 153 volunteers (117 women and 36 men; mean age 45 ± 16) was recruited from healthy people. The exclusion criteria only for control group were no symptoms of chronic inflammatory conditions and no RA history and the exclusion criteria for both the study and control groups were past or present malignancy. This cohort study was approved by the Institutional Bioethics Committee of the Medical University of Lodz (Lodz, Poland) (no. RNN/07/18/KE, approved date: 16 January 2018). All patients fulfilled the EULAR/ACR 2010 diagnostic criteria of RA.

4.2. Genotyping

Genomic DNA (gDNA) was isolated by using GeneMatrix Blood DNA Purification Kit (EURx, Gdansk, Poland) according to the manufacturer protocol. To determine the purity and concentration of gDNA we used TAKE3 plate and multi-plate reader (BioTek, Hong Kong, China) for photometric measurement. Genotypes were determined by Taqman SNP Genotyping Assay (Applied Biosystems, Foster City, CA, USA) and HOT FIREPol® Probe qPCR Mix (Solis, Tartu, Estonia). Analysis was made in Bio-Rad CFX96 system (BioRad, Hercules, CA, USA) according to the manufacturer protocol. The analyzed SNPs are presented in Table 8.
Table 8. Single nucleotide polymorphisms analyzed in this study.

4.3. Data Analysis

A statistical analysis of genotype frequencies, Hardy–Weinberg equilibrium, linkage disequilibrium (LD) analysis between the five SNPs, and haplotype analysis was assessed using SNPStats (https://www.snpstats.net/start.htm, accessed on 20 February 2018) [45]. Results with p < 0.05 were considered statistically significant. Bonferroni’s correction was applied to reduce type I error in multiple testing by multiplying the p values by the number of SNPs or haplotypes (five SNP—significant value was set at 0.05/5 = 0.01 for genotype analyses; 29 haplotypes—significant value was set at 0.05/29 = 0.0017 for genotype analyses). The statistical analysis determined the risk of an event (odds ratio—OR) and the confidence interval (95% CI) with the use of a linear regression model. Power calculations were conducted using Quanto 1.2.4 software [46].

5. Conclusions

Our study confirmed that polymorphisms involving the PADI4 gene, whose protein is the main factor of citrullination, also occurred in the Polish RA population. Similarly, the STAT4 gene, presented in macrophages involved in the synovitis of the joints. Moreover, in PTPN22 loss of negative regulation of T cell signaling protein has been reported.
Our study has some limitations. First of all, our study had pilot character and further research, performed on a larger group, is needed to establish more in-depth an association between RA and studied SNPs. We cannot make an analysis on RA subgroups related to different RA features due to no desirable statistical power (>0.8). Moreover, the average age of the patient group is about 61 years, while it is 45 years for the control group. Usually, the control group and the patient should be matched in terms of age, in addition to being matched in terms of gender. However, the mean age at the time of onset of the RA is about 45 y [47] and this value is similar to our control mean age. We have an older patients group because it contains patients treated for RA for many years at our clinic but not many newly diagnosed, “fresh” RA.
In the future, to confirm our results, replication studies of a large number of cases and/or additional studies of other SNPs within five genes would be required. Moreover, it would be excellent to understand the role of these SNPs in the clinical characteristics of RA in the Polish population.
Nevertheless, our study provides possible biomarkers for RA diagnosis and provides clues for further mechanism study, especially in the Polish population.

Author Contributions

Conceptualization, J.M. and T.P.; data curation, T.B. and G.D.; funding acquisition, T.P. and J.M.; investigation, T.B., G.G., J.S. and G.D.; methodology, T.B., G.D., J.S., J.M. and T.P.; project administration, T.P.; resources, O.B., T.B. and M.P.; software, M.P.; supervision, J.M. and T.P.; validation, T.B., G.D. and J.S.; visualization, G.G. and J.S.; writing—original draft, T.B. and J.S.; writing—review and editing, J.M. and T.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Science Center (NCN, Poland), grant number UMO-2017/25/B/NZ6/01358.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Bioethics Committee of the Medical University of Lodz (Lodz, Poland) (no. RNN/07/18/KE, approved date: 16 January 2018).

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to law regulations in country of corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Alamanos, Y.; Drosos, A.A. Epidemiology of Adult Rheumatoid Arthritis. Autoimmun. Rev. 2005, 4, 130–136. [Google Scholar] [CrossRef] [PubMed]
  2. Toledano, E.; Candelas, G.; Rosales, Z.; Martínez Prada, C.; León, L.; Abásolo, L.; Loza, E.; Carmona, L.; Tobías, A.; Jover, J.Á. A Meta-Analysis of Mortality in Rheumatic Diseases. Reumatol. Clin. 2012, 8, 334–341. [Google Scholar] [CrossRef] [PubMed]
  3. Yip, K.; Navarro-Millán, I. Racial, Ethnic, and Healthcare Disparities in Rheumatoid Arthritis. Curr. Opin. Rheumatol. 2021, 33, 117. [Google Scholar] [CrossRef]
  4. Dedmon, L.E. The Genetics of Rheumatoid Arthritis. Rheumatology 2020, 59, 2661–2670. [Google Scholar] [CrossRef] [PubMed]
  5. van der Helm-van Mil, A.H.M.; Huizinga, T.W.J.; Schreuder, G.M.T.; Breedveld, F.C.; de Vries, R.R.P.; Toes, R.E.M. An Independent Role of Protective HLA Class II Alleles in Rheumatoid Arthritis Severity and Susceptibility. Arthritis Rheum. 2005, 52, 2637–2644. [Google Scholar] [CrossRef]
  6. Stahl, E.A.; Raychaudhuri, S.; Remmers, E.F.; Xie, G.; Eyre, S.; Thomson, B.P.; Li, Y.; Kurreeman, F.A.S.; Zhernakova, A.; Hinks, A.; et al. Genome-Wide Association Study Meta-Analysis Identifies Seven New Rheumatoid Arthritis Risk Loci. Nat. Genet. 2010, 42, 508–514. [Google Scholar] [CrossRef]
  7. Cheng, T.; Sun, X.; Wu, J.; Wang, M.; Eisenberg, R.A.; Chen, Z. Increased Serum Levels of Tumor Necrosis Factor Receptor-Associated Factor 1 (TRAF1) Correlate with Disease Activity and Autoantibodies in Rheumatoid Arthritis. Clin. Chim. Acta 2016, 462, 103–106. [Google Scholar] [CrossRef]
  8. Sarnik, J.; Makowska, J.S. Highlighting the Versatility of the Citrullination Process. Immunobiology 2022, 227, 152233. [Google Scholar] [CrossRef]
  9. Alaaeddine, N.; Hassan, G.S.; Yacoub, D.; Mourad, W. CD154: An Immunoinflammatory Mediator in Systemic Lupus Erythematosus and Rheumatoid Arthritis. Clin. Dev. Immunol. 2012, 2012, 490148. [Google Scholar] [CrossRef]
  10. Cheng, T.; Wang, M.; Chen, L.; Guo, Y.; Chen, Z.; Wu, J. Increased Expression of CD40/TRAF1 and Activation of Nuclear Factor-ΚκB-Dependent Proinflammatory Gene Expression in Collagen-Induced Arthritis. Scand. J. Rheumatol. 2018, 47, 455–460. [Google Scholar] [CrossRef]
  11. Gu, E.; Lu, J.; Xing, D.; Chen, X.; Xie, H.; Liang, J.; Li, L. Rs7574865 Polymorphism in Signal Transducers and Activators of Transcription 4 Gene and Rheumatoid Arthritis: An Updated Meta-Analysis of 28 Case-Control Comparisons. Int. J. Rheum. Dis. 2015, 18, 3–16. [Google Scholar] [CrossRef]
  12. Barton, A.; Thomson, W.; Ke, X.; Eyre, S.; Hinks, A.; Bowes, J.; Gibbons, L.; Plant, D.; Wilson, A.G.; Marinou, I.; et al. Re-Evaluation of Putative Rheumatoid Arthritis Susceptibility Genes in the Post-Genome Wide Association Study Era and Hypothesis of a Key Pathway Underlying Susceptibility. Hum. Mol. Genet. 2008, 17, 2274–2279. [Google Scholar] [CrossRef] [PubMed]
  13. Becskei, A.; Grusby, M.J. Contribution of IL12R Mediated Feedback Loop to Th1 Cell Differentiation. FEBS Lett. 2007, 581, 5199–5206. [Google Scholar] [CrossRef]
  14. Begovich, A.B.; Carlton, V.E.H.; Honigberg, L.A.; Schrodi, S.J.; Chokkalingam, A.P.; Alexander, H.C.; Ardlie, K.G.; Huang, Q.; Smith, A.M.; Spoerke, J.M.; et al. A Missense Single-Nucleotide Polymorphism in a Gene Encoding a Protein Tyrosine Phosphatase (PTPN22) Is Associated with Rheumatoid Arthritis. Am. J. Hum. Genet. 2004, 75, 330–337. [Google Scholar] [CrossRef]
  15. Armitage, L.H.; Wallet, M.A.; Mathews, C.E. Influence of PTPN22 Allotypes on Innate and Adaptive Immune Function in Health and Disease. Front. Immunol. 2021, 12, 636618. [Google Scholar] [CrossRef] [PubMed]
  16. Steer, S.; Lad, B.; Grumley, J.A.; Kingsley, G.H.; Fisher, S.A. Association of R602W in a Protein Tyrosine Phosphatase Gene with a High Risk of Rheumatoid Arthritis in a British Population: Evidence for an Early Onset/Disease Severity Effect. Arthritis Rheum. 2005, 52, 358–360. [Google Scholar] [CrossRef] [PubMed]
  17. Burr, M.L.; Naseem, H.; Hinks, A.; Eyre, S.; Gibbons, L.J.; Bowes, J.; Wilson, A.G.; Maxwell, J.; Morgan, A.W.; Emery, P.; et al. PADI4 Genotype Is Not Associated with Rheumatoid Arthritis in a Large UK Caucasian Population. Ann. Rheum. Dis. 2010, 69, 666–670. [Google Scholar] [CrossRef]
  18. Han, T.-U.; Bang, S.-Y.; Kang, C.; Bae, S.-C. TRAF1 Polymorphisms Associated with Rheumatoid Arthritis Susceptibility in Asians and in Caucasians. Arthritis Rheum. 2009, 60, 2577–2584. [Google Scholar] [CrossRef] [PubMed]
  19. Song, G.G.; Bae, S.-C.; Kim, J.-H.; Lee, Y.H. Associations between TRAF1-C5 Gene Polymorphisms and Rheumatoid Arthritis: A Meta-Analysis. Immunol. Investig. 2014, 43, 97–112. [Google Scholar] [CrossRef] [PubMed]
  20. Elshazli, R.; Settin, A. Association of PTPN22 Rs2476601 and STAT4 Rs7574865 Polymorphisms with Rheumatoid Arthritis: A Meta-Analysis Update. Immunobiology 2015, 220, 1012–1024. [Google Scholar] [CrossRef]
  21. Orozco, G.; Alizadeh, B.Z.; Delgado-Vega, A.M.; González-Gay, M.A.; Balsa, A.; Pascual-Salcedo, D.; Fernández-Gutierrez, B.; González-Escribano, M.F.; Petersson, I.F.; van Riel, P.L.C.M.; et al. Association of STAT4 with Rheumatoid Arthritis: A Replication Study in Three European Populations. Arthritis Rheum. 2008, 58, 1974–1980. [Google Scholar] [CrossRef] [PubMed]
  22. Stark, K.; Rovenský, J.; Blažičková, S.; Grosse-Wilde, H.; Ferencik, S.; Hengstenberg, C.; Straub, R.H. Association of Common Polymorphisms in Known Susceptibility Genes with Rheumatoid Arthritis in a Slovak Population Using Osteoarthritis Patients as Controls. Arthritis Res. Ther. 2009, 11, R70. [Google Scholar] [CrossRef]
  23. Plant, D.; Flynn, E.; Mbarek, H.; Dieudé, P.; Cornelis, F.; Ärlestig, L.; Dahlqvist, S.R.; Goulielmos, G.; Boumpas, D.T.; Sidiropoulos, P.; et al. Investigation of Potential Non-HLA Rheumatoid Arthritis Susceptibility Loci in a European Cohort Increases the Evidence for Nine Markers. Ann. Rheum. Dis. 2010, 69, 1548–1553. [Google Scholar] [CrossRef] [PubMed]
  24. Kurkó, J.; Besenyei, T.; Laki, J.; Glant, T.T.; Mikecz, K.; Szekanecz, Z. Genetics of Rheumatoid Arthritis—A Comprehensive Review. Clin. Rev. Allergy Immunol. 2013, 45, 170–179. [Google Scholar] [CrossRef] [PubMed]
  25. Ciesla, M.; Kolarz, B.; Darmochwal-Kolarz, D. The Lack of Association between PADI4_94 or PADI4_104 Polymorphisms and RF, ACPA and Anti-PAD4 in Patients with Rheumatoid Arthritis. Sci. Rep. 2022, 12, 11882. [Google Scholar] [CrossRef] [PubMed]
  26. Politi, C.; Roumeliotis, S.; Tripepi, G.; Spoto, B. Sample Size Calculation in Genetic Association Studies: A Practical Approach. Life 2023, 13, 235. [Google Scholar] [CrossRef]
  27. Suzuki, A.; Yamada, R.; Chang, X.; Tokuhiro, S.; Sawada, T.; Suzuki, M.; Nagasaki, M.; Nakayama-Hamada, M.; Kawaida, R.; Ono, M.; et al. Functional Haplotypes of PADI4, Encoding Citrullinating Enzyme Peptidylarginine Deiminase 4, Are Associated with Rheumatoid Arthritis. Nat. Genet. 2003, 34, 395–402. [Google Scholar] [CrossRef]
  28. Hoppe, B.; Häupl, T.; Gruber, R.; Kiesewetter, H.; Burmester, G.R.; Salama, A.; Dörner, T. Detailed Analysis of the Variability of Peptidylarginine Deiminase Type 4 in German Patients with Rheumatoid Arthritis: A Case–Control Study. Arthritis Res. Ther. 2006, 8, R34. [Google Scholar] [CrossRef]
  29. Chen, R.; Wei, Y.; Cai, Q.; Duan, S.; Ren, D.; Shen, J.; He, D.; Fang, M.; Lv, K.; Cheng, N.; et al. The PADI4 Gene Does Not Contribute to Genetic Susceptibility to Rheumatoid Arthritis in Chinese Han Population. Rheumatol. Int. 2011, 31, 1631–1634. [Google Scholar] [CrossRef]
  30. Levy, D.E.; Darnell, J.E. Stats: Transcriptional Control and Biological Impact. Nat. Rev. Mol. Cell Biol. 2002, 3, 651–662. [Google Scholar] [CrossRef]
  31. Lamana, A.; López-Santalla, M.; Castillo-González, R.; Ortiz, A.M.; Martín, J.; García-Vicuña, R.; González-Álvaro, I. The Minor Allele of Rs7574865 in the STAT4 Gene Is Associated with Increased MRNA and Protein Expression. PLoS ONE 2015, 10, e0142683. [Google Scholar] [CrossRef]
  32. Gao, W.; Dong, X.; Yang, Z.; Mao, G.; Xing, W. Association between Rs7574865 Polymorphism in STAT4 Gene and Rheumatoid Arthritis: An Updated Meta-Analysis. Eur. J. Intern. Med. 2020, 71, 101–103. [Google Scholar] [CrossRef] [PubMed]
  33. Ebrahimiyan, H.; Mostafaei, S.; Aslani, S.; Jamshidi, A.; Mahmoudi, M. Studying the Association between STAT4 Gene Polymorphism and Susceptibility to Rheumatoid Arthritis Disease: An Updated Meta-Analysis. Iran J. Immunol. 2019, 16, 39408. [Google Scholar] [CrossRef]
  34. Tizaoui, K.; Shin, J.I.; Jeong, G.H.; Yang, J.W.; Park, S.; Kim, J.H.; Hwang, S.Y.; Park, S.J.; Koyanagi, A.; Smith, L. Genetic Polymorphism of PTPN22 in Autoimmune Diseases: A Comprehensive Review. Medicina 2022, 58, 1034. [Google Scholar] [CrossRef] [PubMed]
  35. Vang, T.; Congia, M.; Macis, M.D.; Musumeci, L.; Orrú, V.; Zavattari, P.; Nika, K.; Tautz, L.; Taskén, K.; Cucca, F.; et al. Autoimmune-Associated Lymphoid Tyrosine Phosphatase Is a Gain-of-Function Variant. Nat. Genet. 2005, 37, 1317–1319. [Google Scholar] [CrossRef]
  36. Perry, D.J.; Peters, L.D.; Lakshmi, P.S.; Zhang, L.; Han, Z.; Wasserfall, C.H.; Mathews, C.E.; Atkinson, M.A.; Brusko, T.M. Overexpression of the PTPN22 Autoimmune Risk Variant LYP-620W Fails to Restrain Human CD4+ T Cell Activation. J. Immunol. 2021, 207, 849–859. [Google Scholar] [CrossRef]
  37. Abbasifard, M.; Imani, D.; Bagheri-Hosseinabadi, Z. PTPN22 Gene Polymorphism and Susceptibility to Rheumatoid Arthritis (RA): Updated Systematic Review and Meta-Analysis. J. Gene Med. 2020, 22, e3204. [Google Scholar] [CrossRef] [PubMed]
  38. Tang, L.; Wang, Y.; Zheng, S.; Bao, M.; Zhang, Q.; Li, J. PTPN22 Polymorphisms, but Not R620W, Were Associated with the Genetic Susceptibility of Systemic Lupus Erythematosus and Rheumatoid Arthritis in a Chinese Han Population. Hum. Immunol. 2016, 77, 692–698. [Google Scholar] [CrossRef]
  39. Stanford, S.M.; Bottini, N. PTPN22: The Archetypal Non-HLA Autoimmunity Gene. Nat. Rev. Rheumatol. 2014, 10, 602–611. [Google Scholar] [CrossRef]
  40. Vazgiourakis, V.M.; Zervou, M.I.; Choulaki, C.; Bertsias, G.; Melissourgaki, M.; Yilmaz, N.; Sidiropoulos, P.; Plant, D.; Trouw, L.A.; Toes, R.E.; et al. A Common SNP in the CD40 Region Is Associated with Systemic Lupus Erythematosus and Correlates with Altered CD40 Expression: Implications for the Pathogenesis. Ann. Rheum. Dis. 2011, 70, 2184–2190. [Google Scholar] [CrossRef]
  41. Román-Fernández, I.V.; Ávila-Castillo, D.F.; Cerpa-Cruz, S.; Gutiérrez-Ureña, S.; Hernández-Bello, J.; Padilla-Gutiérrez, J.R.; Valle, Y.; Ramírez-Dueñas, M.G.; Pereira-Suárez, A.L.; Muñoz-Valle, J.F. CD40 Functional Gene Polymorphisms and MRNA Expression in Rheumatoid Arthritis Patients from Western Mexico. Genet. Mol. Res. 2016, 15, gmr15048775. [Google Scholar] [CrossRef] [PubMed]
  42. van der Linden, M.P.M.; Feitsma, A.L.; le Cessie, S.; Kern, M.; Olsson, L.M.; Raychaudhuri, S.; Begovich, A.B.; Chang, M.; Catanese, J.J.; Kurreeman, F.A.S.; et al. Association of a Single-Nucleotide Polymorphism in CD40 With the Rate of Joint Destruction in Rheumatoid Arthritis. Arthritis Rheum. 2009, 60, 2242–2247. [Google Scholar] [CrossRef]
  43. Raychaudhuri, S.; Remmers, E.F.; Lee, A.T.; Hackett, R.; Guiducci, C.; Burtt, N.P.; Gianniny, L.; Korman, B.D.; Padyukov, L.; Kurreeman, F.A.S.; et al. Common Variants at CD40 and Other Loci Confer Risk of Rheumatoid Arthritis. Nat. Genet. 2008, 40, 1216–1223. [Google Scholar] [CrossRef]
  44. Lee, Y.H.; Bae, S.-C.; Choi, S.J.; Ji, J.D.; Song, G.G. Associations between the Functional CD40 Rs4810485 G/T Polymorphism and Susceptibility to Rheumatoid Arthritis and Systemic Lupus Erythematosus: A Meta-Analysis. Lupus 2015, 24, 1177–1183. [Google Scholar] [CrossRef]
  45. Solé, X.; Guinó, E.; Valls, J.; Iniesta, R.; Moreno, V. SNPStats: A Web Tool for the Analysis of Association Studies. Bioinformatics 2006, 22, 1928–1929. [Google Scholar] [CrossRef] [PubMed]
  46. Gauderman, W.J. Sample Size Requirements for Matched Case-Control Studies of Gene-Environment Interaction. Stat. Med. 2002, 21, 35–50. [Google Scholar] [CrossRef] [PubMed]
  47. Goemaere, S.; Ackerman, C.; Goethals, K.; De Keyser, F.; Van der Straeten, C.; Verbruggen, G.; Mielants, H.; Veys, E.M. Onset of Symptoms of Rheumatoid Arthritis in Relation to Age, Sex and Menopausal Transition. J. Rheumatol. 1990, 17, 1620–1622. [Google Scholar] [CrossRef]
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