Abstract
Background
Rheumatoid arthritis (RA) is the most common autoimmune system diseases in our world. More studies in recent years have shown that FCRL gene polymorphisms is closely related to autoimmune diseases. It is suggested that genetic factors play a crucial role in the pathogenesis of this disease. In this study, we aimed to investigate the relationship between FCRL1 rs2050568, FCRL3 rs2317230 and FCRL6 rs58240276 polymorphisms and RA risk in the Chinese Han population. 506 with RA patients and 509 healthy controls were recruited in this study, and the single nucleotide polymorphisms (SNPs) was successfully genotyped using the Agena MassARRAY platform. Odds ratios (ORs) and 95% confidence intervals (95% CIs) after adjusting for age and gender were conducted to assess these SNPs polymorphisms and RA risk. The multifactor dimensionality reduction (MDR) method was conducted to analyze SNP-SNP interaction.
Results
Our results revealed that there no significant association was observed between the allele and genotype frequencies among these SNPs and RA risk (all p > 0.05). Straified analysis by age and gender, the results confirmed that FCRL1 rs2050568 T/T genotype enhanced the risk of RA in females (p = 0.014). The G/T - T/T genotype of FCRL3 rs2317230 was correlated with a decreased RA risk in males (p = 0.021). We also observed that the C/T-T/T genotype of FCRL6 rs58240276 was increased the risk of RA in the group at age > 54 years (p = 0.016). In addition, FCRL1 rs2050568-TT, FCRL6 rs58240276-TT and FCRL1 rs2050568-TT, FCRL3 rs2317230-TT, FCRL6 rs58240276-TT are the best models for multi-site MDR analysis (p < 0.05), and the two best models mentioned above and classes RA have the most significant correlation.
Conclusions
Our study demonstrated that FCRL1 rs2050568, FCRL3 rs2317230, and FCRL6 rs58240276 polymorphisms were correlated with RA susceptibility in the Chinese Han population.
Supplementary Information
The online version contains supplementary material available at 10.1186/s41021-021-00213-2.
Keywords: RA, FCRL1, FCRL3, FCRL6, Polymorphisms
Introduction
Rheumatoid arthritis (RA) one of the most concerning inflammatory diseases with its frequency, chronicity, and system characterized by synovial destruction and joint inflammation, which leads to reduce the quality of life and even causes to the disability [1, 2]. It was estimated that the prevalence of RA was more than 0.8% in the our world [3]. Up to now, the etiologies of RA remain unknown. Recently, numerous epidemiologic studies confirmed that genetic factorsone of the two essential and fundamental factors that environment factor is the other, may play an important role in the occurrence and development of RA [4, 5]. Moreover, increasing genome-wide association studies (GWAS) have been identified more than hundreds of single-nucleotide polymorphisms (SNPs) correlated with autoimmune diseases, such as ankylosing spondylitis (AS), juvenile idiopathic arthritis (JIA), and RA [6–8].
Fc receptor-like (FCRL) gene family a new member of the Ig superfamily, which encodes the protein may play an essential role in regulating B signaling [9]. It is reported that the extracellular part of FCRL molecules contains multiples numbers of Ig-like domains and their cytoplasmictail contains immunoreceptor tyrosine-based activation motifs (ITAMs) and immunoreceptor tyrosine-based inhibitory motifs (ITIMs) [10]. In addition, the FCRL gene family contained six members with FCRL1, FCRL2, FCRL3, FCRL4, FCRL5 and FCRL6. FCRL1–5 was significantly expressed by B cells. However FCRL6 was expressed by T cells and NK cells [11]. To date, the FCRL gene expression has been extensively assessed in human malignancies involving mantle cell lymphoma and multiple myeloma [12, 13]. Nowadays, increasing researches have been provided many evidences which demonstrated that FCRL gene polymorphisms, especially FCRL3, were associated with various autoimmune diseases including AS and RA [14–16]. Okada et al. [17] conducted a European-specific and Asian-specific GWAS meta-analysis by evaluating about 10 million SNPs, and found 42 new RA risk sites at the genome-wide significance level. Among them, the T/G genotype of FCRL3 rs2317230 polymorphism significantly increased the risk of RA in the European and Asian populations (OR = 1.07, 95% CI = 1.04–1.10, p = 1.0E-05, OR = 1.10, 95%CI = 1.04–1.16, p = 3.1E-04, respectively). Therefore, the FCRL3 rs2317230 polymorphism is associated with the risk of RA in the Europeans and Asians populations.
Through performed a case-control analysis comprising 506 patients with RA and 509 healthy controls in the Chinese Han population, our study is the first to elucidate the relationship between FCRL1 rs2050568, FCRL3 rs2317230, FCRL6 rs58240276 polymorphisms and RA in the Chinese Han population.
Material and methods
Subjects
All individuals including 506 patients with RA and 509 healthy controls were enrolled from the Yanliang 630 hospital from the October 2016 to January 2019. All the individuals were unrelated Chinese Han people. In the current study, we performed the same exclusion criterion: any patients and healthy controls having cancer, transplantation or other autoimmune diseases were excluded. In addition, this study was conducted in accordance with the Declaration of Helsinki and the protocol was approved by the Ethics Committee of the Affiliated Hospital of Xizang Minzu University. In the same vein, written informed consents were obtained from all subjects.
DNA extraction and genotyping
Five whole blood samples were available from each participants. According to the manufacturer’s instructions, genomic DNA was extracted from whole blood samples using the GoldMag-Mini Whole Blood Genomic DNA Purification Kit (GoldMag. Co. Ltd., Xi’an, China). The DNA concentration and purity were assessed using spectrophotometer (NanoDrop 2000; Thermo Fisher Scientific, Waltham, MA, USA). In this study, FCRL1 rs2050568, FCRL3rs2317230, and FCRL6 rs58240276 were chose to investigate the influence on the risk of RA from the 1000 Genomes Project (http://www.1000genomes.org/) with the minor allele frequency (MAF) > 5% [18]. The amplification and extension primers were performed through the Agena Bioscience Assay Design Suite V2.0 software (https://agenacx.com/online-tools/), following the guideline. Subsequently, SNPs genotyping and data analysis were conducted using the Agena MassARRAY platform (Agena Bioscience, San Diego, CA, USA) and Agena Bioscience TYPER version 4.0, respectively.
Statistical analyses
SPSS 20.0 (SPSS, Chicago, IL, USA) software was used for statistical analysis [19]. For all subjects, the difference of age and gender was applied by the Pearson’s chi-square test and independent sample Student’s t-test. Among the healthy controls, the genotype frequencies were calculated to evaluate the departure from Hardy-Weinberg equilibrium (HWE) using the chi-square test. In addition, the relationship between these SNPs and RA risk was estimated with the values of odds ratios (ORs) and 95% confidence intervals (CIs) using the logistic regression analysis after adjusting for age and gender. The analysis was conducted by the PLINK 1.07 software [20]. All p values were two-sided, and p < 0.05 was considered to be statistically significant. In addition, we used multifactor dimensionality reduction (MDR, version 3.0.2) to evaluate the impact of SNP - SNP interaction on the risk of RA. This method can reveal the high-order interactions between genes related to a specific phenotype, thereby identifying multilocus genotype combinations which are related a high or low risk of disease. The p value analyzed using MDR software was calculated using the χ2 test.
Results
Characteristics of subjects
In the current study, 506 patients with RA (135 males and 371 females, mean age 54.35 ± 11.69 years) and 509 unrelated healthy controls (134 males and 375 females, mean age: 54.39 ± 12.02 years) were recruited. No significant difference in the distribution of gender between the patients and healthy controls was observed (p > 0.958). However, we found that the distribution of age between the two groups was significant difference (p = 0.038). Subsequently, we further analyzed the clinical parameters among 506 patients with RA. The mean ± SD of CRP, RF, ESR, and CCP were 31.05 ± 40.25 mg/L, 164.09 ± 147.21 KIU/L, 44.28 ± 30.86 mm/h and 75.11 ± 60.78 RU/ml, respectively. The characteristics of patients and healthy controls are summarized in Table 1.
Table 1.
Characteristics of cases and controls
| Variables | Cases (n = 506) | Controls (n = 509) | p |
|---|---|---|---|
| Age, years (mean ± SD) | 59.80 ± 9.08 | 59.80 ± 10.63 | 0.038* |
| ≤ 54 | 262 (51.78%) | 260 (51.08%) | |
| > 54 | 244 (48.22%) | 249 (48.92%) | |
| Gender | 0.958 | ||
| Male | 135 (26.68%) | 134 (26.33%) | |
| Female | 371 (73.32%) | 375 (73.67%) | |
| Clinical parameters | |||
| CRP (mg/L) | 31.05 ± 40.25 | ||
| RF (KIU/L) | 164.09 ± 147.21 | ||
| ESR (mm/h) | 44.28 ± 30.86 | ||
| CCP (RU/ml) | 75.11 ± 60.78 | ||
SD: standard deviation; RA: rheumatoid arthritis; SD: standard deviation; CRP: C-reaction protein; RF: rheumatoid factor; ESR: erythrocyte sedimentation rate; CCP: anti-cyclic citrullinated peptide
p values were calculated from χ2 test
*p < 0.05 indicates statistical significance
Association between SNPs and RA risk
The detailed information including SNP ID, position, gene, allele, MAF, and HWE p-value is displayed in Table 2. Our results suggested that the genotype distribution of all candidate SNPs was in accordance with HWE among the healthy controls group (all p > 0.05). Furthermore, the MAF of these SNPs was not significantly associated with RA risk in the Chinese Han population (p > 0.05). In order to further evaluate the relationship between FCRL1 rs2050568, FCRL3 rs2317230, FCRL6 rs58240276 polymorphisms and RA risk, three different genetic models such as the dominant, the recessive and the log-additive models after adjusting for age and gender were performed. Nevertheless, no significant correlation between the three SNPs polymorphisms and the risk of RA was observed in this study (Table 3).
Table 2.
Basic characteristics and allele frequencies among these SNPs
| SNP | Gene | Chr | Allele | MAF | HWE p–Value | OR(95% CI) | p | |
|---|---|---|---|---|---|---|---|---|
| Case | Control | |||||||
| rs2050568 | FCRL1 | 1 | T/C | 0.23 | 0.20 | 0.891 | 1.17(0.94–1.44) | 0.155 |
| rs2317230 | FCRL3 | 1 | T/G | 0.37 | 0.37 | 0.924 | 1.03 (0.86–1.23) | 0.775 |
| rs58240276 | FCRL6 | 1 | T/C | 0.23 | 0.20 | 0.891 | 1.17(0.94–1.44) | 0.155 |
CI: confidence interval; HWE: Hardy–Weinberg equilibrium; MAF: minor allele frequency; OR: odds ratio; SNP: single nucleotide polymorphism
*Sites with HWE (p < 0.05) are excluded
Pa values is calculated with two–sided χ2
*pa < 0.05 indicates statistical significance
Table 3.
Association between SNPs genotypes and RA risk
| SNP | Model | Genotype | Cases | Controls | OR (95%CI) | P |
|---|---|---|---|---|---|---|
| rs2050568 | Dominant | C/C | 184 | 184 | 1 | |
| FCRL1 | C/T –T/T | 322 | 325 | 0.99 (0.77–1.28) | 0.945 | |
| Recessive | C/C – C/T | 436 | 441 | 1 | ||
| T/T | 70 | 68 | 1.40 (0.99–1.98) | 0.060 | ||
| Log-additive | 1.09 (0.91–1.31) | 0.348 | ||||
| rs2317230 | Dominant | G/G | 197 | 202 | 1 | |
| FCRL3 | G/T - T/T | 309 | 307 | 1.03 (0.80–1.33) | 0.806 | |
| Recessive | G/G - G/T | 419 | 443 | 1 | ||
| T/T | 87 | 66 | 1.04 (0.73–1.49) | 0.821 | ||
| Log-additive | 1.03 (0.86–1.23) | 0.771 | ||||
| rs58240276 | Dominant | C/C | 300 | 325 | 1 | |
| FCRL6 | C/T –T/T | 206 | 184 | 1.21 (0.94–1.56) | 0.137 | |
| Recessive | C/C – C/T | 482 | 488 | 1 | ||
| T/T | 24 | 21 | 1.16 (0.63–2.11) | 0.636 | ||
| Log-additive | 1.17(0.94–1.45) | 0.155 |
CI: confidence interval; OR, odds ratio; SNP: single nucleotide polymorphism
*p < 0.05 indicates statistical significance
Stratification analysis on the association between SNPs and RA risk
When the stratification analysis by age was conducted, the results suggested that the C/T – T/T genotype of FCRL6 rs58240276 polymorphism was significant increased the risk of RA in the old group at age > 54 years in under the dominant model (OR = 1.54, 95% CI = 1.08–2.19, p = 0.016). We also observed that FCRL6 rs58240276 polymorphism was associated with an increased improved the risk of RA in the old group at age > 54 years in the log-additive model (OR = 1.41, 95% CI = 1.05–1.89, p = 0.021). However, no significant differences were found between FCRL1 rs2050568 and FCRL3 rs2317230 polymorphism and RA risk under the stratification analysis among three genetic models. The results are presented in Table 4.
Table 4.
Association between SNPs and RA risk were stratified for age
| SNP | Model | Genotype | ≤ 54 | > 54 | ||
|---|---|---|---|---|---|---|
| OR (95% CI) | P | OR (95% CI) | P | |||
| rs2050568 | Dominant | C/C | 1 | 1 | ||
| FCRL1 | C/T –T/T | 0.90 (0.62–1.31) | 0.592 | 1.08 (0.76–1.54) | 0.656 | |
| Recessive | C/C – C/T | 1 | 1 | |||
| T/T | 1.42 (0.87–2.34) | 0.165 | 1.43 (0.87–2.34) | 0.154 | ||
| Log-additive | 1.05 (0.81–1.37) | 0.715 | 1.14 (0.89–1.47) | 0.296 | ||
| rs2317230 | Dominant | G/G | 1 | |||
| FCRL3 | G/T - T/T | 1.10 (0.76–1.59) | 0.605 | 0.98 (0.69–1.40) | 0.929 | |
| Recessive | G/G - G/T | 1 | ||||
| T/T | 0.85 (0.50–1.46) | 0.561 | 1.26 (0.77–2.05) | 0.360 | ||
| Log-additive | 1.01 (0.77–1.33) | 0.930 | 1.05 (0.82–1.35) | 0.689 | ||
| rs58240276 | Dominant | C/C | 1 | 1 | ||
| FCRL6 | C/T –T/T | 0.93 (0.64–1.34) | 0.697 | 1.54 (1.08–2.19) | 0.016 | |
| Recessive | C/C – C/T | 1 | 1 | |||
| T/T | 0.80 (0.29–2.18) | 0.662 | 1.43 (0.67–3.06) | 0.355 | ||
| Log-additive | 0.92 (0.67–1.28) | 0.631 | 1.41 (1.05–1.89) | 0.021 | ||
CI: confidence interval; OR: odds ratio; SNP: single nucleotide polymorphism
*p < 0.05 indicates statistical significance
Subsequently, stratification analysis by gender was performed. Our results confirmed that FCRL1 rs2050568 T/T genotype enhanced the risk of RA in females under the recessive model (OR = 1.64, 95% CI = 1.10–2.45, p = 0.014). On the contrary, the G/T - T/T genotype of FCRL3 rs2317230 was correlated with decreased RA risk in males in the dominant model (OR = 0.56 95% CI = 0.34–0.92, p = 0.021). For FCRL6 rs58240276, we not observed that any association between the SNP polymorphism and RA risk in gender stratification (Table 5).
Table 5.
Association between SNPs and RA risk were stratified for gender
| SNP | Model | Genotype | Male | Female | ||
|---|---|---|---|---|---|---|
| OR (95% CI) | P | OR (95% CI) | P | |||
| rs2050568 | Dominant | C/C | 1 | 1 | ||
| FCRL1 | C/T –T/T | 0.89 (0.54–1.46) | 0.648 | 1.03 (0.76–1.39) | 0.842 | |
| Recessive | C/C – C/T | 1 | 1 | |||
| T/T | 0.80 (0.38–1.69) | 0.564 | 1.64 (1.10–2.45) | 0.014 | ||
| Log-additive | 0.89 (0.62–1.28) | 0.533 | 1.17(0.95–1.43) | 0.150 | ||
| rs2317230 | Dominant | G/G | 1 | |||
| FCRL3 | G/T - T/T | 0.56 (0.34–0.92) | 0.021 | 1.29 (0.96–1.73) | 0.092 | |
| Recessive | G/G - G/T | 1 | ||||
| T/T | 0.85 (0.38–1.87) | 0.678 | 1.10 (0.74–1.65) | 0.636 | ||
| Log-additive | 0.69 (0.47–1.00) | 0.051 | 1.16 (0.95–1.43) | 0.153 | ||
| rs58240276 | Dominant | C/C | 1 | 1 | ||
| FCRL6 | C/T –T/T | 1.30 (0.80–2.11) | 0.292 | 1.18 (0.88–1.59) | 0.274 | |
| Recessive | C/C – C/T | 1 | 1 | |||
| T/T | 1.35 (0.45–3.99) | 0.593 | 1.08 (0.53–2.22) | 0.835 | ||
| Log-additive | 1.25 (0.83–1.87) | 0.282 | 1.14 (0.88–1.47) | 0.317 | ||
CI: confidence interval; OR: odds ratio; SNP: single nucleotide polymorphism
*p < 0.05 indicates statistical significance
SNP - SNP interactions using MDR analysis with RA risk
The MDR method was conducted to analyze SNP - SNP interaction. We used MDR analysis to assess the impact of the SNP - SNP interaction between the three selected SNPs in the FCRL (Table 6). The higher the “CV Consistency” and “Accuracy” values, the greater the interaction between SNPs. We found three models in total and found that FCRL1 rs2050568-TT, FCRL3 rs2317230-TT, and FCRL6 rs58240276-TT are the best models for multi - site MDR analysis (CV consistency = 10/10, accuracy = 0.541, p < 0.01). In addition, models with FCRL1 rs2050568 and FCRL6 rs58240276 increase the risk of RA. Next, we used the interaction dendrogram of the entire genotype data set to show the SNP - SNP interaction between these two genes. As shown in Fig. 1, the bluer the color, the stronger the redundancy between SNPs. Conversely, the redder the color, the stronger the synergy between the sites. And in the risk analysis of RA, it can be observed that there is a strong redundancy between rs2050568 and rs2317230. It can be seen from Fig. 2 that there is a synergy between rs58240276 and rs2317230.
Table 6.
MDR analysis of SNP-SNP interactions in FCRL gene
| Model | Training Bal. Acc. | Testing Bal. Acc. | CV Consistency | Accuracy | Sensitivity | Specificity | OR (95%CI) | p |
|---|---|---|---|---|---|---|---|---|
| rs58240276 | 0.527 | 0.492 | 6/10 | 0.525 | 0.408 | 0.641 | 1.23 (0.96–1.59) | 0.11 |
| rs2050568,rs58240276 | 0.539 | 0.514 | 10/10 | 0.538 | 0.363 | 0.712 | 1.41 (1.08–1.83) | 0.01* |
| rs2050568,rs2317230,rs58240276 | 0.545 | 0.490 | 10/10 | 0.541 | 0.391 | 0.692 | 1.44 (1.11–1.87) | 0.0057* |
Abbreviation: MDR: multifactor dimensionality reduction; SNP: single nucleotide polymorphism; CV: cross-validation; OR: odds ratio; CI: confidence interval
Note: p is calculated using χ2 test. All p-values in this study are two-tailed. Bold values mean statistical significance.*p < 0.05 indicates statistical significance
Fig. 1.
SNP-SNP Interaction Dendrogram
Fig. 2.
Fruchterman-Reingold, Describing Interactions Between the Three Genetic Variants
Discussion
RA is a most typical autoimmune disease, and affected by various genetic and environmental factors. Multiples studies revealed that genetic factors can lead to the risk of RA and various SNPs, which have been confirmed to be associated with the susceptibility of RA [21]. Up to now, the contribution of the SNPs such as FCRL1 rs2050568, FCRL3 rs2317230, and FCRL6 rs58240276 to RA risk remained unclear. In the current study, we performed a case-control study to clarify the relationship between FCRL1 rs2050568, FCRL3 rs2317230, FCRL6 rs58240276 polymorphisms and RA susceptibility in the Chinese Han population. Eventually, our results suggested that these SNPs were associated with the risk of RA in different stratification by age and gender.
The FCRL family gene is located in 1p21–23 region, and may play a crucial role in regulation of the B cell signaling [22]. Mutations in FCRL gene have been reported to be correlated with numerous human diseases, including grave disease and immune system disease [23–25]. A study reported that the expressions of activating or inhibitory FCRL1, 2, and 4 revealed alterations in graves’ disease patients compared to healthy subjects [26]. Recently, a meta-analysis which including 916 patients with RA and 3746 healthy controls was performed, the results suggested that FCRL3 rs17727339 showed significant correlations with RA risk [16]. In addition, Kochi et al. reported that FCRL3 variant (−169C) was associated with RA risk in the Japanese population [24]. Whereas, a subsequent research suggested that this association was not reproduced among a Spanish and numerous US-based RA case-control subjects [25, 27]. There are differences in the incidence of FCRL gene polymorphisms MAF and RA between the Chinese Han population and the other populations. Ramírez-Bello et al. explored the relationship between FCRL3 polymorphism and juvenile rheumatoid arthritis (JRA) in the Mexican population and found that FCRL3_3 (rs7528684) and FCRL3_6 (rs3761959) showed significant MAF difference (p = 0.03 and p = 0.01, respectively). The protective effect of FCRL3 gene SNP on JRA disease in Mexican male patients [28]. There is a significant difference in the FCRL3 -69 T > C (rs7528684) in the Chinese population (p = 0.003), and the MAF of 1381G > A (rs3761959) is not significantly different (p = 0.493). The FCRL3-169 T/C variant and the RA in the Chinese Han population is significantly related to an increased risk [29]. In our study, our study confirmed that the allele and genotype frequencies of FCRL1 rs2050568, FCRL3 rs2317230 and FCRL6 rs58240276 were not interacted with RA risk in the Chinese Han population. Subsequently, we performed stratification analysis by age and gender. The results demonstrated that the C/T – T/T genotype of FCRL6 rs58240276 polymorphism was increased the risk of RA in age > 54 years (p = 0.016). During the same time, we also confirmed that FCRL1 rs2050568 T/T genotype enhanced the risk of RA in females (p = 0.014). In contrast, the G/T - T/T genotype of FCRL3 rs2317230 was correlated with a decreased RA risk in males (p = 0.021).
Various limitations need to be considered in our study, such as the information of samples was little. All in all, we demonstrated that FCRL1 rs2050568 T/T genotype, FCRL3 rs2317230 G/T - T/T genotype, and FCRL6 rs58240276 T/T genotype were associated with RA risk in Chinese Han people, when the stratified analysis by age and gender was performed. Our study provided a new insight into the pathogenesis of this disease. In the future, the detailed molecular mechanism by which the above mentioned polymorphisms influencing the occurrence and development of RA was necessary to be investigated.
Conclusions
In a word, we firstly provided new evidence for the relationship between the selected variants and RA risk, which may support for the screening of RA in the Han Chinese population and shed light on the mechanism of RA.
Supplementary Information
Additional file 1: Supplementary Table 1. False-Positive Report Probability Values for Associations Between the Risk of rheumatoid arthritis and the Frequency of Variants and Model of FCRL Gene in the Chinese Han population.
Acknowledgements
We appreciate the Yan liang 630 hospital, which provided the samples used in this study. We also appreciate the reviewers and editors for their efforts and patience.
Abbreviations
- RA
Rheumatoid arthritis
- SNP
Single nucleotide polymorphism
- OR
Odds ratio
- CI
Confidence interval
- MDR
Multifactor Dimensionality Reduction
- GWAS
Genome-wide association study
- MAF
Minor allele frequency
- EDTA
Ethylenediamine tetraacetic acid
- HWE
Hardy–Weinberg equilibrium
- LD
Linkage disequilibrium
Authors’ contributions
TJ and LW conceived and designed the experiments. YY, DL, CH, LP and SX performed the experiments. YY, MB, HR and DY analyzed the data. YH, XH wrote the paper. All authors read and approved the final manuscript.
Funding
This study was funded by the National Natural Science Foundation of China (No. 81960291) and major project training plan of Xizang Minzu University (No. 20MDT01).
Availability of data and materials
All data generated or analysed during this study are included in this published article.
Declarations
Ethics approval and consent to participate
This case-control study was conducted in accordance with the Declaration of Helsinki. And the protocol was approved by the Ethics Committee of the Xizang Minzu University. Written informed consents were obtained from all individuals. The procedures were in accordance with the institutional guidelines.
Consent for publication
All patients agreed to publish the manuscript.
Competing interests
The authors declare that they have no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Additional file 1: Supplementary Table 1. False-Positive Report Probability Values for Associations Between the Risk of rheumatoid arthritis and the Frequency of Variants and Model of FCRL Gene in the Chinese Han population.
Data Availability Statement
All data generated or analysed during this study are included in this published article.