Association analysis of single nucleotide polymorphisms of proinflammatory cytokine and their receptors genes with rheumatoid arthritis in northwest Chinese Han population

Cytokine 61 (2013) 133–138

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Association analysis of single nucleotide polymorphisms of proinflammatory cytokine and their receptors genes with rheumatoid arthritis in northwest Chinese Han population Chong-ge You a,b,1, Xiao-jun Li b,1, Yu-min Li a,⇑, Li-ping Wang c, Fei-fei Li a, Xin-ling Guo a, Li-na Gao a a b c

Central Laboratory, Gansu Province Key Laboratory of Digestive System Tumors, Lanzhou University Second Hospital, 730030 Lanzhou, China Department of Clinical Central Laboratory Science, Institute of Clinical Laboratory Medicine, Nanjing General Hospital of Nanjing Military Area, Nanjing, China Department of Rheumatology, Lanzhou University Second Hospital, Lanzhou, China

a r t i c l e

i n f o

Article history: Received 5 April 2012 Received in revised form 4 September 2012 Accepted 17 September 2012 Available online 11 November 2012 Keywords: High resolution melting (HRM) Single nucleotide polymorphism (SNP) Rheumatoid arthritis (RA) Gene–gene interaction

a b s t r a c t Objective: To analyze the relationship of genetic polymorphisms in IL1b, IL6, TNF-a genes and their receptors genes with rheumatoid arthritis (RA) for northwest Han Chinese. This study also explores whether there are gene–gene interactions among these genetic polymorphisms. Methods: A total of 452 patients with RA and 373 matched healthy controls were enrolled to carry out a case-control study for 16 SNPs of IL1B-511 C > T, IL1B-31 T > C, IL1B+3954 C > T, IL1RN T > C, IL6-597 G > A, IL6-572 G > C, IL6-174 G > C, IL6R-183 G > A, IL6R exon2 T > A, IL6R exon1 A > C, TNFA-863 C > A, TNFA-857 C > T, TNFA-308 G > A, TNFA-238 G > A, TNFR1-383 A > C and TNFR2 T676G T > G from seven genes. Genotyping for the SNPs was conducted on the RotorGene 6000 PCR platform using in-house high resolution melting (HRM) approaches. Detection correctness was validated through direct sequencing. Generalized multifactor dimensionality reduction (GMDR) analysis was applied to discover likely gene–gene interaction model among the SNPs. Results: The results showed that the genotype distributions of TNFA-308, TNFA-857 and TNFA-863 are significantly different between case and control groups (P = 0.016, P = 0.048 and P = 0.016, respectively). Carriers of TNFA-857 mutant allele conferred risk to RA (OR = 1.525, 95% CI = 1.157–2.009) while those of TNFA-308 and TNFA-863 mutant alleles conferred protection to RA (OR = 0.459, 95% CI = 0.286– 0.739; OR = 0.490, 95% CI = 0.329–0.732). GMDR analysis for the SNPs indicated that gene–gene interaction existed among IL1B-31, IL1RN, IL6-572, IL6R-183, IL6R-exon1 and TNFA-857. Thirteen of all genotypes of the six SNPs combination were discovered to have significant distribution difference between RA group and the control. Conclusions: This study demonstrated that PCR-HRM assay is a highly efficient SNP genotyping method especially for the detection of large-scale samples. The SNPs of TNFA-308 and TNFA-863 are closely associated with RA susceptibility and that gene–gene interactions may occur among the six SNPs of IL1B-31, IL1RN, IL6-572, IL6R-183, IL6R-exon1 and TNFA-857 in RA patients from northwest Chinese Han population, especially these SNPs’ combination genotypes CT/TT/CC/GG/AC/CC, CT/TT/GC/AA/AC/CT and CT/CT/ CC/GA/AC/CC to show high risk of RA susceptibility in our study. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction Rheumatoid arthritis (RA) is a chronic autoimmune disease closely related to proinflammatory cytokines such as IL1b, IL6 and TNF-a, which play fundamental roles in the occurrence and development of RA. The levels of proinflammatory cytokines are found to be higher in synovial and peripheral blood of RA patients. Further studies confirmed that they are involved in the pathogenic ⇑ Corresponding author. 1

E-mail address: [email protected] (Y.-m. Li). These authors contributed equally to this article.

1043-4666/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.cyto.2012.09.007

processes of RA, especially after successful use of TNF-a and IL1b blockades as a treatment [1,2]. Several association studies on the genetic polymorphisms of the cytokines with RA susceptibility have been performed, but the conclusions from these reports are conflicting and the differences were explained by the collection of subjects are from different races and regions [3–7]. It is now clear that proinflammatory cytokines, and the complex interaction among them, play a key role in the incidence and development of RA [2]. Whether similar interactions exist on the gene level has not yet been explored in previous studies. In this study, we have developed, based on high resolution melting (HRM) technology, PCR genotyping methods for 16 single nucleotide polymorphisms

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(SNPs) from seven genes, and conducted a case-control study for a northwest Chinese Han population. In addition, A generalized multifactor dimensionality reduction (GMDR) analysis was also performed to explores whether there are gene–gene interactions among the 16 SNPs.

(http://dna.utah.edu/umelt/umelt.html) [9]. The detailed information of these primers was shown in Table 1.

2. Materials and methods

The 16 SNPs were genotyped on the RotorGene 6000 PCR detection platform (Corbett Life Science, Australia) using in-house PCR-HRM curve analysis assay which has been validated by direct sequencing. HRM is a novel, homogeneous, close-tube, post-PCR method which enables genomic researchers to analyze genetic variations in PCR amplicons just by using HRM fluorescent dye (for example, LcGreen, EvaGreen and Syto9) and direct melting after PCR (http://gene-quantification.com/hrm.html). Genotyping by HRM is a simply, fast and reliable method and wildly used (http://www.dna.utah.edu/Hi-Res/TOP_Hi-Res%20Melting.html). In this study, PCR-HRM was conducted in 15 lL reaction volume which includes 20 ng DNA template, 1.5 Fast EvaGreenÒ Master Mix for quantitative and high-resolution melting PCR (Biotium, USA), and each primer set ranging from 0.1 M to 0.3 lM based on different amplicons. PCR amplification was performed with initial denaturing at 96 °C for 3 min, followed by 40 cycles of denaturing at 96 °C for 10 s, touchdown annealing from 62 °C to 56 °C for 15 s, and extending at 72 °C for 15 s. After PCR amplification, the samples were heated to 96 °C for 10 s, and then rapidly cooled to 45 °C for 30 s for melting. The HRM was carried out over the range 72–95 °C rising at 0.05 °C s1, and the genotype of each amplicon was determined using RotorGene 6000 Series Software 1.7. Three samples from each of the 16 SNP genotypes were randomly chosen for sequencing to verify genotyping results.

2.1. Subjects A total of 452 northwest Han Chinese RA patients and 373 northwest Han Chinese controls signed informed consents from Lanzhou University Second Hospital from January 2010 to February 2012 to be enrolled in the case-control study. All patients were diagnosed as RA according to Rheumatoid arthritis criteria 2009 released by ACR/EULAR (http://www.medconnect.com.au/tabid/ 84/ct1/c333948/New-Rheumatoid-Arthritis-Criteria-Released-byACREULAR-Panel/Default.aspx) [8]. The inclusion criteria for case were that it must be more than six in quantitative total score including four items of involved joint number, serum indexes, synovitis duration time and acute phase reactant, while the disease course of RA patient must be more than 6 months. The controls were ethnically and geographically matched healthy subjects without RA family history taken from individual checkups. The study was approved by the Ethics Committee of Lanzhou University Second Hospital. 2.2. Extraction of genomic DNA Genomic DNA was extracted from 200 lL of venous blood anticoagulated with EDTA using QuickGene DNA whole blood kit S (Fujifilm, Japan) according to the manufacturer instruction. All DNA samples were detected using the NanoDrop 2000 spectrophotometer (Thermo Scientific, USA) for evaluation of template quality and quantity, and then adjusted to concentrations of 20 ng/lL and stored at 40 °C. 2.3. Primer design The primer sets for the amplifications of 16 SNPs were designed using the online software Primer 3 (http://primer3plus.com/ cgi-bin/dev/primer3plus.cgi) and Beacon designer 7.91 (PREMIER Biosoft, USA). The specificity of primers were checked on Primer-BLAST (http://www.ncbi.nlm.nih.gov/tools/primer-blast). Two-state melting (http://mfold.rna.albany.edu/?q=DINAMelt/ Two-state-folding) was used to check the secondary structure of primers. uMELT was used to predict the amplicon melting curve

2.4. PCR amplification and HRM genotyping

2.5. Statistical analysis All data were analyzed using statistical software SPSS 13.0. The relationships of the 16 SNPs with RA susceptibility were assessed using chi-square test for estimation of the odds ratio (OR) and 95% confidence interval for each SNP genotype, and performing binary logistic regression analysis in which healthy condition of subject was defined as dependent variable and their genotypes of 16 SNPs, gender and age were defined as covariates. Gene–gene interaction analysis was performed using DMGR9.0 software [10]. Hardy–Weinberg equilibriums of each SNP in the control group were tested using chi-square test. All the statistics are bilateral probability for inspection, statistical significance was determined as below the conventional level of p = 0.05.

Table 1 Sequences and amplicon lengths of 16 SNP primers. SNP

Allele

Forward primer (50 ? 30 )

Reverse primer (50 ? 30 )

Amplicon

rs16944 rs1143627 rs1143634 rs4251961 rs1800797 rs1800796 rs1800795 rs4845617 rs4845374 rs2228145 rs1800630 rs1799724 rs1800629 rs361525 rs2234649 rs1061622

IL1B-511 C>T IL1B-31 T>C IL1B+3954 C > T IL1RN T > C IL6-597 G > A IL6-572 G > C IL6-174 G > C IL6R-183 G > A IL6R-exon2 T > A IL6R-exon1 A > C TNFA-863 C > A TNFA-857 C > T TNFA-308 G > A TNFA-238 G > A TNFR1–383 A > C TNFR2 T676G T > G

CCCAGCCAAGAAAGGTCAAT TTTCTCAGCCTCCTACTTCTGC GGCCTGCCCTTCTGATTTTA CGTGTCATTCATGCTTCCGGTG TGGCAAAAAGGAGTCACACA

TGAGGGTGTGGGTCTCTACC CTTGTGCCTCGAAGAGGTTT CGTGCACATAAGCCTCGTTA CAGGTCTGCAGCCAACCAGTTGTG TGTGTTCTGGCTCTCCCTGT

100 bp 86 bp 95 bp 140 bp 112 bp

GCCTCAATGACGACCTAAGC CGCTCTGAGTCATGTGCGAGTG TCCTCCTATTCCTTTTTCTCCA

GGGGCTGATTGGAAACCTTA GGCTCTCTACACACACTGCGAG GGAATGTGGGCAGTGGTACT

101 bp 112 bp 103 bp

AGACCTCTGGGGAGATGTGA

CGTCCCCTGTATTCCATACC

159 bp

CCCCAAAAGAAATGGAGGCAATAGG GGGTCCTACACACAAATCAGTCAGT CTTGGTGTTTGGTTGGGAGT CTCCTCCTCCAGCTGTAACG

GTAGGACCCTGGAGGCTGAAC CCCCTCACACTCCCCATCC AGGAAGAGCTGGAGGAGGAG GTGTTGGGATCGTGTGGAC

68 bp 79 bp 153 bp 139 bp

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3. Results

3.2. Genotyping through HRM analysis

3.1. Demographic characteristic

After each amplicon was melted with HRM, a characteristic melting curve was generated and processed by HRM analysis software installed in the platform. For each SNP, heterozygous genotyping results in an altered melting curve shape with wider double peaks that is easily identified. Homozygous genotypes can be distinguished by Tm difference between wild type and the homozygous mutant. In this study, all subjects were clearly genotyped using PCR-HRM methods (Fig. 1) and the genotype distributions

There are 348 females and 104 males in 452 recruited cases (ages range from 23 to 76 years old (47.08 + 15.36)), and 271 females and 102 males in 373 controls (ages range from 35 to 81 years old (47.35 + 14.37)). For gender and age, no statistical significance was found between the trial group and the control group (x2 = 2.052, p = 0.152; t = 1.652, p = 0.105).

Fig. 1. Normalized HRM plots of the 16 SNPs genotyping. Representative normalized HRM plots of the amplicons for genotyping 16 SNPs using the thirteen primer pairs. For each amplicon, the corresponding SNP marker is reported on the left upper of the plot; while the different genotypes of each SNP are marked on the corresponding melting curve and their colors are identical. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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of 16 SNPs were shown (Table 2). The correctness of each assay was verified by sequencing for PCR products of randomly selected samples. Sequencing results were in complete accord with all the corresponding genotypes. The homozygous variants were not observed in four SNPs (IL1B+3954, IL1RN, IL6-174 and TNFR2) in the genotyping. Except for IL1RN, IL6R-183, IL6R-exon1, TNFA-857 and TNFR1-383, allele frequencies of all other SNPs in the control group were within the Hardy–Weinberg equilibrium (p > 0.01). 3.3. SNPs associated with RA susceptibility In order to increase statistical power, homozygous mutant genotypes were merged into heterozygous genotypes to conduct statistical analysis. The results of the case-control study and logistical regression analysis on the 16 SNPs are shown in Table 2. Genotype distributions of four SNPs (IL6-597, TNFA-308, TNFA857 and TNFA-863) were found to be statistically different between

RA patients and the controls (p values are 0.025, 0.001, 0.003 and 0.001, respectively). After Bonferroni correction on the abovementioned four p values (corrected p values are 0.400, 0.016, 0.048 and 0.016, respectively), significant differences between TNFA-308, TNFA-857 and TNFA-863 genotype distributions still remained between the two groups, which is consistent with the result of the logistic regression analysis on the 16 SNPs (p values are 0.001, 0.000 and 0.001, respectively). The carriers of mutant alleles from IL6-597 and TNFA-857 increased RA risk (OR = 2.086, 95% CI = 1.102–3.948; OR = 1.525, 95% CI = 1.157–2.009) while those from TNFA-308 and TNFA-863 decreased RA risk (OR = 0.459, 95% CI = 0.286–0.739; OR = 0.490, 95% CI = 0.329–0.732). 3.4. SNPs shown gene–gene interaction Gene–gene interaction analysis was limited in the range of one to ten SNPs according to the analyzing capability of DMGR9.0

Table 2 Genotypic associations between rheumatoid arthritis and 16 single nucleotide polymorphisms. dbSNP

Genotype

rs16944 rs1143627 rs1143634 rs4251961

a

rs1800796 rs1800797 rs1800795 rs4845617a rs2228145a rs4845374 rs361525 rs1800629 rs1799724a rs1800630 rs2234649a rs1061622 a

CC CT/TT TT CT/CC CC CT TT CT GG GC/CC GG GA/AA GG GC GG GA/AA AA AC/CC TT AT GG GA/AA GG GA CC CT/TT CC AC/AA AA AC/CC TT TG/GG

Case

Control

(n = 452)

(n = 373)

130 237/85 121 235/96 407 45 336 116 38 191/222 418 31/3 431 21 160 208/84 188 194/70 381 71 410 40/2 422 30 207 215/30 406 45/1 384 58/10 310 132/10

100 191/82 106 187/80 331 42 271 102 39 166/168 359 14/0 357 16 114 158/101 137 151/85 308 65 334 39/0 323 50 210 126/37 303 70/0 312 51/10 251 118/4

OR (95% CI)

Chi-square

p Value Fisher0 s exact

1 0.907 1 1.086 1 0.871 1 0.905 1 1.237 1 2.086 1 1.087 1 0.803 1 0.815 1 0.883 1 0.877 1 0.459 1 1.525 1 0.490 1 0.906 1 0.942

Bonferroni correction

Logistic regression

0.387

0.585

0.300

0.278

0.639

0.358

0.369

0.570

0.659

0.399

0.528

0.809

1.014

0.337

0.567

5.297

0.025

0.061

0.867

0.433

2.154

0.158

0.126

2.025

0.174

0.093

0.438

0.511

0.742

0.313

0.639

0.995

10.689

0.001

0.016

0.001

9.020

0.003

0.048

0.000

12.478

0.001

0.016

0.000

0.266

0.631

0.525

0.157

0.708

0.372

(0.668–1.233) (0.799–1.176) (0.558–1.360) (0.664–1.234) (0.775–1.972) 0.400

0.106

(1.102–3.948) (0.559–2.115) (0.599–1.076) (0.615–1.080) (0.611–1.276) (0.554–1.389) (0.286–0.739) (1.157–2.009) (0.329–0.732) (0.622–1.320) (0.703–1.264)

statistical difference for Hardy–Weinberg equilibrium in control group (p < 0.01).

Table 3 Gene–gene interaction model analysis of 16 single nucleotide polymorphisms (number of combination limited to 10 SNPs). Model

Testing balanced Accuracy

Sign test (p value)

Cross-validation Accuracy

TNFA-857 IL6-597/TNFA-857 IL6R-exon/TNFA-857/TNFA-863 IL1B-31/IL6-572/TNFA-857/TNFA-863 IL1B-31/IL6-572/IL6R-183/IL6R-exon1/TNFA-857 IL1B-31/IL1RN/IL6-572/IL6R-183/IL6R-exon1/TNFA-857 IL1B-511/IL1RN/IL6-572/IL6R-183/IL6R-exon1/TNFA-857/TNFR2-exon IL1B-511/IL1RN/IL6-572/IL6R-183/IL6R-exon1/IL6R-exon2/TNFA-857/TNFR2-exon IL1B-511/IL1RN/IL6-572/IL6R-183/IL6R-exon1/IL6R-exon2/TNFA-857/TNFR1-383/TNFR2-exon IL1B-511/IL1RN/IL6-572/IL6R-183/IL6R-exon1/IL6R-exon2/TNFA-857/TNFA-863/TNFR1-383/TNFR2-exon

0.5709 0.5476 0.5455 0.5515 0.5293 0.5980 0.5465 0.5476 0.5443 0.5697

8 8 9 7 8 10 8 7 6 8

10/10 8/10 4/10 4/10 10/10 10/10 5/10 6/10 5/10 5/10

p Value was based on 1000 permutation

(0.0547) (0.0547) (0.0107) (0.1719) (0.0547) (0.0010) (0.0547) (0.1719) (0.3770) (0.05472)

C.-g. You et al. / Cytokine 61 (2013) 133–138 Table 4 Thirteen genotypes distribution of six SNPs combination between RA patients and the controls. IL1-31/IL1RN/IL6-572/IL6R183/IL6R-exon1/TNFA-857 CT/TT/CC/GG/AC/CC CT/TT/GC/AA/AC/CT TT/TT/CC/GG/AA/CT CT/CT/GC/GG/AA/CT CT/CT/CC/GG/AA/CC CT/CT/GC/GA/AC/CT CC/TT/GC/GG/AA/CT TT/TT/GC/GG/AA/CT TT/TT/CC/GA/AC/CT CT/TT/GC/GG/AC/CT CT/CT/CC/GA/AC/CC CC/TT/CC/GA/AC/CT CC/TT/CC/GA/AC/CC

RA (n = 452)

Control (n = 373)

Sample

Percentage

Sample

Percentage

5 5 5 5 6 6 6 6 7 7 10 6 7

1.11 1.11 1.11 1.11 1.33 1.33 1.33 1.33 1.55 1.55 2.21 1.33 1.54

0 0 1 1 1 1 1 1 1 1 1 2 2

0 0 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.54 0.54

software. All constructed modes are presented in Table 3. The modes of IL6R-exon1/TNFA-857/TNFA-863 (p = 0.0107) and IL131/IL1RN/IL6-572/IL6R-183/IL6R-exon1/TNFA-857 (p = 0.0010) are statistically eligible. The second mode is more significant which demonstrates the presence of gene–gene interaction among six SNPs of five genes. Within all genotypes of the six SNPs combination, there are 13 kinds of genotypes with their distribution frequencies greater than 1% in RA patients, which are listed in Table 4. RA patients with these genotypes have 17.94% but the controls have only 3.49% in this table. Therefore, these SNP genotype combinations showed high risk of RA susceptibility especially the genotype combinations of CT/TT/CC/GG/AC/CC, CT/TT/GC/AA/AC/ CT and CT/CT/CC/GA/AC/CC. 4. Discussion Much literature has confirmed HRM techniques, which allow for a rapid, easy and inexpensive genotyping method to be increasingly applied to routine detection of disease-associated SNPs and the study of large-scale samples [11–14]. In this paper, 16 SNPs from proinflammatory cytokines of IL1b, IL6 and TNF-a as well as their receptor genes were successfully detected using in-house PCR-HRM approaches in our laboratory. This demonstrated that PCR-HRM assay is a highly efficient SNP genotyping method in detecting large-scale samples. The study initially found that three SNPs (TNFA-308, TNFA-857 and TNFA-863) were statistically associated with RA susceptibility, but TNFA-857 was finally excluded in debate for its allele frequency does not reach genetic equilibrium. TNFA-308A, as a functional SNP, was previously reported to confer risk to RA and it is consistent with our finding [15,16]. TNFA-863A has previously been reported to make the reduction of plasma levels of TNF-a in RA patients but no mention of RA susceptibility has been reported [17]. This paper is the first to report that TNFA-863A decreases RA risk. This conclusion is coincidently accordant to finding that TNF-a plays a key role in the development of RA [2,18]. However, the two SNPs of TNFA-308 and TNFA-863 identified in this study were not included the latest list of identified RA susceptibility SNPs through GWAS [19]. From the point of view that proinflammatory cytokines take an important role in the occurrence and development of RA, their gene polymorphisms should be believed to be related to RA susceptibility. However, many associated studies based on single gene or SNP site with RA risk have not verified the relationship. The existence of cytokine interactions in course of RA pathogenesis prompted this study to consider gene–gene interaction of the 16 SNPs for the first time. Unexpectedly, six SNPs (IL1B-31, IL1RN, IL6-572, IL6R-183,

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IL6R-exon1 and TNFA-857) have been found to have gene–gene interactions, of which four SNPs (IL1B-31, IL6-572, IL6R-183 and TNFA-857) were located in the promoter or UTR region of corresponding genes that can affect the transcription or expression levels of these genes. Previous studies had confirmed that IL1B-31 T gene can increase IL-1b mRNA expression level, IL1RN-T, IL6-572 C, IL6R183 A and IL6R-exon1C genes can increase their plasma levels of corresponding cytokines [20–24], TNFA-857T can increase the activity of the promoter [25]. After reanalyzing genotype distributions of the six SNPs combination between case and control, we found that there are 13 kinds of genotype combinations to show higher ratio in RA patients group than in the control, especially CT/TT/CC/GG/AC/CC and CT/TT/GC/AA/AC/CT genotypes only existing in RA group. This indicated that interaction of the six SNPs may result in RA occurrence and development although single SNP’s function seems to be conflicting in analyzing each SNP separately. In addition, the result may explain why the same SNP showed conflicting conclusions in different research reports because association analysis based on independent SNP ignores inherent gene interaction in body so to induce micro-effect gene statistically lost. Although the RA susceptibility of the 13 genotypes still needs further verification through prospective study, we believed that the gene–gene interactions exist in RA patients and appear a little complicate. Rafiq’s data [23] show that IL6-572C and IL6R-exon1C genes are related with susceptibility and resistance of RA. It may cause by increasing the level of IL-6 and IL-6R, because increasing the binding of soluble IL-6R and free IL-6 will decrease the binding of membrane IL-6R and IL-6 so that RA resistance was shown. Similarly, IL1RN-T increasing expression of IL-1Ra will also reduce the binding of IL-1b and its membrane receptor. Recent literature [26] has reported that there is gene–gene interaction for seven SNPs from HLA class II locus in RA patients. The result supported that the gene interactions affect RA susceptibility. Just as the TNF-a activated by macrophages enable the production of IL-1b and IL-6 while IL-1b and IL-6 create feedback for TNF-a [2], gene interaction is a probable action model for micro-effect genes in complex diseases. 5. Conclusions In this study, we successfully performed rapid and accurate genotyping for 16 SNPs using PCR-HRM technology in large-scale samples. The results suggested that two SNPs of TNFA-308 and TNFA-863 are closely associated with RA susceptibility in northwestern Han Chinese. Six SNPs of IL1B-31, IL1RN, IL6-572, IL6R183, IL6R-exon1 and TNFA-857 have gene–gene interaction in RA patients, and three genotype combinations of CT/TT/CC/GG/AC/ CC, CT/TT/GC/AA/AC/CT and CT/CT/CC/GA/AC/CC have high risk of RA susceptibility. Acknowledgments This work is supported by Foundation of Key Laboratory of Digestive System Tumors, Gansu Province and Fundamental Research Funds for the Central Universities (lzujbky-2011-t03-01), China’s Postdoctoral Scientific Fund (20080430239) and Springsunshine plan from the Ministry of Education (Z2010085). We thank Dr. Lu-ming Zhou (Department of Pathology, University of Utah School of Medicine, Salt Lake City, USA) for the revision of the manuscript. References [1] Kokkonen H, Soderstrom I, Rocklov J, Hallmans G, Lejon K, Rantapaa Dahlqvist S. Up-regulation of cytokines and chemokines predates the onset of rheumatoid arthritis. Arthritis Rheum 2010;62(2):383–91.

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