Association between KIR gene polymorphisms and rheumatoid arthritis susceptibility: A meta-analysis

Human Immunology xxx (2015) xxx–xxx

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Association between KIR gene polymorphisms and rheumatoid arthritis susceptibility: A meta-analysis Xiaona Li a,1, Qing Xia a,1, Dazhi Fan a, Guoqi Cai a, Xiao Yang a, Li Wang a, Lihong Xin a, Ning Ding a, Yanting Hu a, Li Liu a, Shengqian Xu b, Jianhua Xu b, Kang Wang b, Faming Pan a,⇑ a b

Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui 230032, China Department of Rheumatism and Immunity, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China

a r t i c l e

i n f o

Article history: Received 29 January 2015 Revised 26 June 2015 Accepted 30 June 2015 Available online xxxx Keywords: Killer cell immunoglobulin-like receptors Meta-analysis Polymorphisms Rheumatoid arthritis

a b s t r a c t Objectives: The results of studies on association between KIR (killer cell immunoglobulin-like receptors) polymorphisms and susceptibility to RA (rheumatoid arthritis) are inconsistent. To comprehensively evaluate the effect of KIR polymorphisms on the risk of RA, a meta-analysis was carried out. Methods: The Web of Science, PubMed, the Chinese Biomedical Database (CBM) and Chinese National Knowledge Infrastructure (CNKI) databases were systematically searched to select studies on the association between KIR polymorphisms and RA. The odds ratio (OR) with 95% confidence interval (95%CI) was obtained. Results: Nine qualified case–control studies were included in this meta-analysis. The results showed there were two positive associations of 2DL1, 2DS1 (OR2DL1 = 2.20, 95%CI = 1.20–4.01, Praw = 0.01, PFDR = 0.03; OR2DS1 = 1.84, 95%CI = 1.19–2.85, Praw = 0.006, PFDR = 0.018) and one negative association of 2DL3 (OR2DL3 = 0.42, 95%CI = 0.22–0.79, Praw = 0.006, PFDR = 0.018) with susceptibility to RA in East Asians, but not in Caucasians. Conclusion: The current meta-analysis provides evidence that 2DL3 might be a potential protective factor and 2DL1, 2DS1 might be risk factors for RA in East Asians but not in Caucasians. Ó 2015 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.

1. Introduction Rheumatoid arthritis (RA) is an inflammatory, systemic and autoimmune disease which is characterized by chronic synovial inflammation, leading to joint destruction, affecting up to 1% of the general adult population worldwide [1–3]. Its main symptoms are pain, swelling and stiffness, ultimately resulting in loss of joint function [4]. Although the exact pathogenesis of RA remains

Abbreviations: AS, ankylosing spondylitis; BH, Ben-jamini–Hochberg; 95%CI, 95% confidence interval; CBM, Chinese Biomedical Database; CNKI, Chinese National Knowledge Infrastructure; CTLA4, cytotoxic T-lymphocyte antigen 4; FDR, false discovery rate; HLA-DRB1, human leukocyte antigen-DRB1; HWE, Hardy–Weinberg equilibrium; KIR, human killer cell immunoglobulin-like receptors; NOS, The Newcastle–Ottawa Scale; OR, odds ratio; PTPN22, protein tyrosine phosphatase non-receptor 22; RA, rheumatoid arthritis; SLE, systemic lupus erythematosus; STAT4, signal transducer and activator of transcription 4; TRAF1/C5, the tumor necrosis factor receptor-associated factor 1/complement 5. ⇑ Corresponding author. E-mail address: [email protected] (F. Pan). 1 Contributed equally to this work and should be considered co-first.

unknown, it is widely accepted that the expression and development of this disease are due to the interaction of genetic and environmental factors [5–9]. According to two prior studies, the contribution of genetic factors accounts for 50–65% of the risk of developing RA roughly [10,11]. Moreover, the onset of RA is likely to involve multiple genes, such as HLA-DRB1, KIRs, TRAF1/C5, STAT4, PTPN22 and CTLA4, etc. [12–15]. Human killer cell immunoglobulin-like receptors (KIRs) are immunoglobulin superfamily expressed on both natural killer cells and subsets of T cells [16]. The KIR gene cluster spans about 150– 200 kb in the Leukocyte Receptor Complex located on chromosome 19q13.4 [17]. The KIR family consists of 16 highly homologous and closely linked genes and pseudogenes [18]. Among them, 14 genes encode receptors, which triggering either inhibition (3DL1–3, 2DL1–3, 2DL5) or activation (3DS1, 2DS1–5) of NK cell function and/or both (2DL4), and two pseudogenes (2DP1 and 3DP1) with no known function [18]. The KIR genes have been considered potentially important in susceptibility to infection and autoimmune diseases [19]. The associations between KIR genes and

http://dx.doi.org/10.1016/j.humimm.2015.06.017 0198-8859/Ó 2015 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.

Please cite this article in press as: Li X et al. Association between KIR gene polymorphisms and rheumatoid arthritis susceptibility: A meta-analysis. Hum Immunol (2015), http://dx.doi.org/10.1016/j.humimm.2015.06.017

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autoimmune diseases have been widely reported, such as rheumatoid arthritis (RA) [20–22], ankylosing spondylitis (AS) [23,24], systemic lupus erythematosus (SLE) [25], psoriasis [26,27], pemphigus foliaceus [28], scleroderma [25], multiple sclerosis [29], diabetes [30], etc. Plentiful case–control studies have investigated the association between these KIR gene polymorphisms and RA risk, but findings are not always consistent [14,20–22,31–35]. In addition, until now, nobody performed a meta-analysis to estimate the association of KIR polymorphisms with RA risk. Therefore, it is necessary to recognize the role of KIRs in the pathogenesis of RA. Meta-analysis has been deemed as an effective tool to integrate and further analyze the data from different studies in the same topics in a comprehensive way [36]. This method can increase the statistical power and overcome the problem of small sample sizes. Therefore, we carried out a meta-analysis including the latest data to particularly investigate the association between KIR polymorphisms and susceptibility to RA. 2. Materials and methods 2.1. Literature search strategy The following electronic databases were searched without restrictions: The Web of Science, PubMed, the Chinese Biomedical Database (CBM) and Chinese National Knowledge Infrastructure (CNKI) databases (last search was performed on November 1, 2014), with a combination of the following keywords: ‘‘Rheumatoid arthritis’’ or ‘‘RA’’, ‘‘polymorphism’’ or ‘‘gene’’ or ‘‘genes’’ or ‘‘genotype’’ or ‘‘genotypes’’ or ‘‘genotyping’’, ‘‘killer cell immunoglobulin-like receptors’’ or ‘‘KIR’’ or ‘‘KIRs’’. All searched studies were retrieved, and their references were checked for other relevant publications. Only published studies with full text were included. 2.2. Inclusion and exclusion criteria To be included in the meta-analysis, an article had to meet the following criteria: (1) articles evaluating the association between KIR polymorphisms and RA risk; (2) the study followed a case–control design for human; (3) all patients met the ACR classification criteria for RA; (4) the genotype frequencies in case and control groups must be available; (5) the controls’ ethnic background and geographic area were identical with the cases’; (6) The language was either in English or Chinese; (7) full text was available, not published as an abstract or review. Concerning the exclusion criteria, we identified the following: (1) duplicated studies, (2) studies without data we need, (3) studies that did not meet the inclusion criteria. 2.3. Data extraction Two reviewers (Xiaona Li and Qing Xia) independently extracted the data. Any disagreement was settled by discussion and consultation with the third researcher (Dazhi Fan). The following information was collected from each study if available: first author’s name, publication year, country, ethnicity, number of each genotype in cases and controls, sample size, method of genotyping. The Newcastle–Ottawa Scale (NOS) was used to assess the quality of the included studies. 2.4. Statistical analysis The association between KIR gene polymorphisms and RA was estimated by means of odds ratios (ORs) and its corresponding

95% confidence interval (95%CI). p Value 60.05 was deemed to be statistically significant. Heterogeneity was measured using the Q statistic, and p value <0.1 was considered statistically significant. In addition, we used I2 statistic to quantify heterogeneity, I2 values of 25%, 50% and 75% were defined as low, moderate and high heterogeneity, respectively. When the p value was >0.10 or I2 value was <25%, the pooled OR was calculated using the fixed-effect model (the Mantel–Haenszel method), based on the assumption that treatment effect is the same in each individual study, this means that the variation between studies is only due to sampling; otherwise, a random-effect model (the DerSimonian and Laird method) was used. Under this model, the true treatment effect in each study may be different from each other. Chi-square test was used to determine whether the genotype distributions in the control group of each study were in Hardy–Weinberg equilibrium (HWE). In consideration of multiple comparisons, Ben-jamini– Hochberg (BH) method was applied to control the false discovery rate (FDR) [37]. Owing to the strong evidence for heterogeneity between several studies, subgroup analyses were conducted by ethnic group. Publication bias was investigated by several different methods. Visual inspection of asymmetry in funnel plots was carried out. An asymmetric plot suggested possible publication bias. Publication bias was also evaluated by Egger weighted regression method, which measure the funnel plot asymmetry on the natural logarithm scale of the OR (p < 0.05 was considered to be statistically significant publication bias). All statistical analysis was completed using the software STATA 11.0 (StataCorp, College Station, TX, USA) and Review Manager Software 5.1 (Cochrane Collaboration, Oxford, UK). 3. Results 3.1. Data source The search strategy screened 115 potentially relevant studies, including 73 from The Web of Science; 30 from PubMed; three from CBM and nine from CNKI. Eventually, nine studies, including 1428 RA cases and 2115 healthy controls, met the inclusion requirements and were enrolled in the present study. The screening process of our study was summarized in Fig. 1. Among the nine articles, four [20,22,32,33] were Caucasians, three [21,31,35] were from East Asian countries, one [34] was from India and one [14] was from Latin America. Because of the inadequate sample populations available for the Indian and Latin American group, we performed ethnicity-specific meta-analysis for Caucasian and East Asian populations. The characteristics of each report are listed in Table 1. Meta-analysis of each gene and RA are listed in Table 2. HWE test was performed on genotype distribution of the control groups, only one [34] of them showed p > 0.05 in HWE, other articles could not be tested because of the lack of raw data. Six [14,20,31,32,34,35] of the nine studies were of high quality (NOS score P 7) and the other three scored six and are shown in Table 1. 3.2. Meta-analysis and subgroup analysis results A summary of meta-analysis outcomes concerning the association between KIR polymorphisms and RA susceptibility was provided in Table 2. Owing to the strong evidence for heterogeneity, we made two subgroup analyses in the East Asian and Caucasian populations, according to the ethnic groups. Our results showed that there were two positive associations of 2DL1, 2DS1 (fixed-effect model: OR2DL1 = 2.20, 95%CI = 1.20–4.01, Praw = 0.01, PFDR = 0.03; fixed-effect model: OR2DS1 = 1.84, 95%CI = 1.19–2.85, Praw = 0.006, PFDR = 0.018) and one negative association of 2DL3 (fixed-effect model: OR2DL3 = 0.42, 95%CI = 0.22–0.79, Praw = 0.006,

Please cite this article in press as: Li X et al. Association between KIR gene polymorphisms and rheumatoid arthritis susceptibility: A meta-analysis. Hum Immunol (2015), http://dx.doi.org/10.1016/j.humimm.2015.06.017

X. Li et al. / Human Immunology xxx (2015) xxx–xxx

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Fig. 1. Flow diagram of the study selection process.

Table 1 Characteristics of individual studies included in meta-analysis. Author [Refs.]

Year

Region

Ethnicity

NOS score

Genotyping method

RA cases

Controls

KIR polymorphisms

Majorczyk et al. [20] Kimoto et al. [31] Ramírez et al. [14] Nowak et al. [33] Yen et al. [21] Prakash et al. [34] Middleton et al. [32] Yen et al. [22] Sun et al. [35]

2007

Poland

Caucasian

7

PCR-SSP

177

243

2DL1, 2DL2, 2DL3, 2DS1, 2DS2, 2DS3, 2DS4fl, 2DS4del, 2DS5, 3DL1, 3DS1

2010

Japan

8

PCR-SSP

72

256

2012

Mexico

8

PCR-SSP

100

100

2010

Poland

East Asian Latin American Caucasian

6

?

366

690

2DL1, 3DL1, 2DL1, 3DL1, 2DS5

2006

Taiwan

6

PCR-SSP

122

96

2014

7

PCR-SSP

100

100

Caucasian

7

SSOP

331

354

2001

North India North Ireland America

East Asian Indian

Caucasian

6

PCR-SSP

70

76

2010

China

East Asian

7

PCR-SSP

90

200

2007

2DL2( ), 2DL3, 2DL4, 2DL5, 2DS1(+), 2DS2, 2DS3, 2DS4, 2DS5, 3DL2, 3DL3, 3DS1 2DL2(+), 2DL3( ), 2DL4, 2DL5, 2DS1, 2DS2(+), 2DS3, 2DS4, 2DS5, 3DL2, 3DL3, 3DS1, 2DP1, 3DP1

2DL1(+), 2DL2, 2DL3( ), 2DS1, 2DS2, 2DS3, 2DS4, 3DL1, 3DL2, 3DS1, 2DL1 2DL1, 2DL2( ), 2DL3( ), 2DL4, 2DL5, 2DS1( ), 2DS2(+), 2DS3, 2DS4, 2DS5, 3DL1( ), 3DL2, 3DL3, 3DS1(+), 2DP1 2DL1, 2DL2, 2DL3(+), 2DL5, 3DL1, 3DS1, 2DS1, 2DS2, 2DS3, 2DS4, 2DS5 2DS1, 2DS2 2DL1, 2DL3, 2DL5, 2DS2, 2DS4( ), 2DS5, 3DL1, 3DS1

Refs.: references; NOS: New-castle–Ottawa Scale; PCR-SSP: polymerase chain reaction-sequence specific primer; SSOP: sequence-specific oligonucleotide probe; RA: rheumatoid arthritis; KIR: killer cell immunoglobulin-like receptors; bold: the gene associated with susceptibility to RA in the study; (+): positive association of the gene with susceptibility to RA in the study; ( ): negative association of the gene with susceptibility to RA in the study.

PFDR = 0.018) with susceptibility to RA in East Asians (Fig. 2A–C), but not in Caucasians. No associations had been found between 2DL2, 2DL4, 2DL5, 2DS2, 2DS3, 2DS4, 2DS5, 3DL1, 3DL2, 3DL3, 3DS1 and susceptibility to RA in each race. 3.3. Heterogeneity test, sensitivity analysis and publication bias Differences in the study design, study subject and outcome measure could lead to heterogeneity between all qualified studies included in meta-analysis. In our analyses, significant heterogeneity (p for heterogeneity < 0.10 or I2 > 50%) between studies were

found in 2DL1, 2DL2, 2DL3, 2DS1, 2DS2, 2DS4, 3DL1 and 3DS1, but no heterogeneity was observed in other KIR polymorphisms. Hence, the random effect model and fixed effect model were used to calculate the pooled OR, respectively. Sensitivity analysis was conducted to reckon the influence of single study on the pooled ORs by deleting one study from pooled analysis each time. For KIR2DL3 and RA, after removing the Middleton et al. study [32], p = 0.14 changed to p = 0.004. In addition, for 2DL5 and 2DS5, because of the marginal p value, after removing the Middleton et al. study [32], p = 0.05 changed to p = 0.02. Other corresponding pooled ORs were not materially altered. Given the above results,

Please cite this article in press as: Li X et al. Association between KIR gene polymorphisms and rheumatoid arthritis susceptibility: A meta-analysis. Hum Immunol (2015), http://dx.doi.org/10.1016/j.humimm.2015.06.017

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Table 2 Meta-analysis of the association between KIR polymorphisms and RA. Polymorphisms

Qualified studies

RA cases (n/N)

Control (n/N)

OR (95%CI)

p-Value

FDR

Heterogeneity test 2

Effect model

2DL1 East Asians Caucasians

7 3 2

941/992 260/284 501/508

1082/1141 310/344 581/597

1.25 (0.55, 2.85) 2.21 (1.21, 4.02) 2.01 (0.81, 4.96)

0.60 0.01* 0.13

0.60 0.03* 0.20

p = 0.03, I = 59% p = 0.65, I2 = 0% p = 0.27, I2 = 19%

Random Fixed Fixed

2DL2 East Asians Caucasians

6 2 2

456/902 81/194 259/508

478/941 67/144 298/597

0.94 (0.57, 1.53) 0.67 (0.18, 2.44) 1.06 (0.84, 1.35)

0.79 0.54 0.62

0.79 0.79 0.79

p < 0.0001, I2 = 83% p = 0.01, I2 = 84% p = 0.64, I2 = 0%

Random Random Fixed

2DL3 East Asians Caucasians

7 3 2

867/992 241/284 477/508

1052/1141 327/344 546/597

0.59 (0.30, 1.19) 0.42 (0.22, 0.78) 1.43 (0.90, 2.26)

0.14 0.006** 0.13

0.14 0.018* 0.14

p = 0.0009, I2 = 76% p = 0.81, I2 = 0% p = 0.20, I2 = 40%

Random Fixed Fixed

2DL4 East Asians Caucasians

4 1 1

601/603 70/72 331/331

600/602 46/48 354/354

1.52 (0.21, 11.19) 1.52 (0.21, 11.19) _

0.68 0.68 _

0.68 0.68 _

_ _ _

Random Random _

2DL5 East Asians Caucasians

5 2 1

309/693 56/162 153/331

379/802 89/248 169/354

0.81 (0.66, 1.00) 0.78 (0.50, 1.22) 0.94 (0.70, 1.27)

0.06 0.28 0.69

0.18 0.56 0.69

p = 0.64, I2 = 0% p = 0.73, I2 = 0% _

Fixed Fixed Random

2DS1 East Asians Caucasians

7 2 3

401/972 114/194 218/578

426/1017 63/144 277/673

0.98 (0.73, 1.31) 1.84 (1.19, 2.85) 0.86 (0.68, 1.08)

0.89 0.006* 0.20

0.89 0.018* 0.30

p = 0.04, I2 = 55% p = 063, I2 = 0% p = 0.97, I2 = 0%

Random Fixed Fixed

2DS2 East Asians Caucasians

8 3 3

465/1062 65/284 300/578

492/1217 76/344 348/673

1.10 (0.83, 1.45) 0.78 (0.52, 1.16) 1.02 (0.82, 1.28)

0.52 0.22 0.84

0.78 0.66 0.84

p = 0.03, I2 = 54% p = 0.21, I2 = 36% p = 0.66, I2 = 0%

Random Fixed Fixed

2DS3 East Asians Caucasians

6 2 2

218/902 36/194 140/508

249/941 32/144 166/597

0.91 (0.74, 1.13) 0.88 (0.32, 2.39) 0.99 (0.76, 1.29)

0.39 0.80 0.95

0.95 0.95 0.95

p = 0.49, I2 = 0% p = 0.08, I2 = 67% p = 0.87, I2 = 0%

Fixed Random Fixed

2DS4 East Asians Caucasians

6 3 1

693/815 217/284 319/331

770/898 270/344 340/354

1.04 (0.58, 1.86) 1.00 (0.33, 3.03) 1.09 (0.50, 2.40)

0.89 0.99 0.82

0.99 0.99 0.99

p = 0.003, I2 = 72% p = 0.0006, I2 = 87% _

Random Random Random

2DS5 East Asians Caucasians

6 2 2

308/1059 37/162 191/697

467/1492 57/248 315/1044

0.84 (0.70, 1.00) 0.95 (0.58, 1.55) 0.85 (0.69, 1.06)

0.051 0.83 0.15

0.15 0.83 0.30

p = 0.82, I2 = 0% p = 0.88, I2 = 0% p = 0.24, I2 = 29%

Fixed Fixed Fixed

3DL1 East Asians Caucasians

7 3 2

912/992 266/284 485/508

1081/1141 331/344 573/597

0.83 (0.42, 1.64) 0.75 (0.35, 1.60) 0.87 (0.48, 1.56)

0.60 0.45 0.64

0.64 0.64 0.64

p = 0.02, I2 = 59% p = 0.11, I2 = 55% p = 0.38, I2 = 0%

Random Fixed Fixed

3DL2 East Asians Caucasians

5 2 1

708/725 177/194 331/331

691/698 138/144 354/354

0.53 (0.22, 1.29) 0.45 (0.17, 1.16) _

0.16 0.10 _

0.16 0.16 _

p = 0.43, I2 = 0% p = 0.51, I2 = 0% _

Fixed Fixed _

3DL3 East Asians Caucasians

4 1 1

579/603 66/72 331/331

601/602 47/48 354/354

0.23 (0.03, 2.01) 0.23 (0.03, 2.01) _

0.19 0.19 _

0.19 0.19 _

_ _ _

Random Random _

3DS1 East Asians Caucasians

7 3 2

360/992 88/284 183/508

403/1141 109/344 222/597

1.02 (0.75, 1.38) 0.90 (0.63, 1.28) 0.94 (0.74, 1.21)

0.89 0.55 0.64

0.89 0.89 0.89

p = 0.02, I2 = 60% p = 0.45, I2 = 0% p = 0.85, I2 = 0%

Random Fixed Fixed

RA, rheumatoid arthritis; OR, odds ratios; 95%CI, 95% confidence interval; n/N: the number of people having a certain KIR gene in each group/the total number of people in each group; FDR, p value of Benjamini–Hochberg method control for false discovery rate. * Statistical significance and positive association. ** Statistical significance and negative association.

suggesting a poor stability of the meta-analysis, the conclusions must be made in caution. Funnel plot and Egger’s test were performed to access the publication bias of those studies, no significant publication bias was observed in our meta-analysis. 4. Discussion In 2001, Yen et al. [22] first reported KIR2DS2 gene was involved in the development of RA vasculitis. Since then, eight case–control studies have been undertaken to identify the association between KIR polymorphisms and RA [14,20,21,31–35], but the results were controversial. Based on cumulated evidence, we performed a meta-analysis with data from nine studies with a total of 1428 RA cases and 2115 healthy controls to reassess the associations. The results showed two positive associations of 2DL1, 2DS1 and one negative association of 2DL3 with susceptibility to RA in East Asians, but not in Caucasians, suggesting a possible influence

of the geographic factors and different genetic backgrounds. As far as we know, 2DL1 and 2DL3 exist together on the same haplotype, but they have different roles in RA. Although allele-typing was not performed in the disease studies, functional differences between the allotypes may explain this finding. There is very strong linkage disequilibrium between 2DL3 and 2DL1, where 2DL3*001– 2DL1*003 (KIR A haplotype) and 2DL2*001–2DL1*004 (KIR B haplotype) are the most common of the haplotypes in Asians and Europeans [38,39]. There are ligand specificity and other functional differences between 2DL2 and 2DL3 [40]. There are significant differences in receptor signaling capacity between 2DL1*003 and 2DL1*004, with 2DL1*003 a much stronger inhibitor than 2DL1*004 [41]. KIR2DL1, which recognizes HLA-C molecules of C2 group, has stronger avidity and narrower HLA class I specificity than KIR2DL3, which recognizes C1group [42]. It was reported that KIR2DL4, 2DL5, 2DS3, 2DS5, 3DL2 and 3DL3 were not associated with RA, which was consistent in previous nine studies

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Fig. 2. Meta-analysis for the association between RA risk and the KIR gene polymorphisms: (A) subgroup analysis with a fixed-effect model for the association between RA risk and the KIR2DL1 in East Asians; (B) subgroup analysis with a fixed-effect model for the association between RA risk and the KIR2DS1 in East Asians; (C) subgroup analysis with a fixed-effect model for the association between RA risk and the KIR2DL3 in East Asians. Events: the number of people having a certain KIR gene in each group; total: the total number of people in each group.

[14,20–22,31–35]. However, there were several KIR genes with different results. Differences in the demographic characteristics especially genetic backgrounds, different methods of genotyping, the study design, different inclusion criteria of the controls and lack of statistical power are all potential factors which could lead to the discrepancy. The strength of our analysis was that all of the qualified association studies from the different populations were included with greatly increased sample size, which enhanced the power to assess the association. In contrast, the previous studies have only been carried out in a particular race so that the results cannot be popularized. Similar to other meta-analyses, our study does have some limitations. First, only published studies in English or Chinese were included in the meta-analysis. Therefore, this study may have a publication bias. Although we performed Egger’s regression test, we still could not remove the possibility of bias. Second, most subjects in our meta-analysis were from Asia and Europe, so our findings may only be suitable for these populations. Third, our meta-analysis is only based on 9 studies, with so restricted sample of world populations so that the results must be interpreted with caution. Fourth, we could not carry out further subgroup analysis for the limitation of raw data for each subject. Fifth, KIR2DL2/3 is the inhibitory gene that most vary in frequencies across populations. Part of it may be due to redundant function and relaxation of natural selection and demography, but it also may be due to genotyping inconsistency. Sixth, meta-analysis belongs to observational study that is subject to unmeasured confounders. In the future, more studies should be performed to further validate these results. Although our study has some limitations, this is the first

meta-analysis to investigate the correlation between KIR genes family and susceptibility to RA. In conclusion, this meta-analysis shows 2DL3 might be potential protective factors and 2DL1, 2DS1 might be risk factors for RA in East Asians. As few studies are available in this field and current evidence remains limited, to reach a definitive conclusion, it should be emphasized the necessity to conduct large studies with an adequate methodological quality, properly controlled for possible confounds in order to obtain valid results. Conflict of interest There are no conflict of interests. Acknowledgments We thank all the people who have helped us in this study. This work was supported by grants from the National Natural Science Foundation of China (30771849, 30972530, 81273169). References [1] Brennan F, Beech J. Update on cytokines in rheumatoid arthritis. Curr. Opin. Rheumatol. 2007;19:296. [2] Costenbader KH, Chang SC, Laden F, Puett R, Karlson EW. Geographic variation in rheumatoid arthritis incidence among women in the United States. Arch. Intern. Med. 2008;168:1664. [3] Li RN, Hung YH, Lin CH, Chen YH, Yen JH. Inhibitor IkappaBalpha promoter functional polymorphisms in patients with rheumatoid arthritis. J. Clin. Immunol. 2010;30:676. [4] Klareskog L, Catrina AI, Paget S. Rheumatoid arthritis. Lancet 2009;373:659.

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Please cite this article in press as: Li X et al. Association between KIR gene polymorphisms and rheumatoid arthritis susceptibility: A meta-analysis. Hum Immunol (2015), http://dx.doi.org/10.1016/j.humimm.2015.06.017