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. 2016 Aug 19;6:31617. doi: 10.1038/srep31617

Comprehensive Assessment of the Association between FCGRs polymorphisms and the risk of systemic lupus erythematosus: Evidence from a Meta-Analysis

Xiao-Wei Zhu 1,*, Yong Wang 2,*, Yi-Hua Wei 3,*, Pian-Pian Zhao 1, Xiao-Bo Wang 1, Jing-Jing Rong 1, Wen-Ying Zhong 1, Xing-Wei Zhang 1, Li Wang 1, Hou-Feng Zheng 1,a
PMCID: PMC4990922  PMID: 27538381

Abstract

We performed a meta analysis to assess the relationship of FCGRs polymorphisms with the risk of SLE. Thirty-five articles (including up to 5741 cases and 6530 controls) were recruited for meta-analysis. The strongest association was observed between FCGR2B rs1050501 and SLE under the recessive genotypic model of C allele in the overall population (CC vs CT/TT, OR = 1.754, 95%CI: 1.422–2.165, P = 1.61 × 10−7) and in Asian population (CC vs CT/TT, OR = 1.784, 95%CI; 1.408–2.261, P = 1.67 × 10−6). We also found that FCGR3A rs396991 were significant association with the susceptibility to SLE in overall population in recessive model of T allele (TT vs TG/GG, OR = 1.263, 95%CI: 1.123–1.421, P = 9.62 × 10−5). The results also showed that significant association between FCGR2A rs1801274 and SLE under the allelic model in the overall population (OR = 0.879 per A allele, 95%CI: 0.819–0.943, P = 3.31 × 10−4). The meta-analysis indicated that FCGR3B copy number polymorphism NA1·NA2 was modestly associated with SLE in overall population (OR = 0.851 per NA1, 95%CI: 0.772–0.938, P = 1.2 × 10−3). We concluded that FCGR2B rs1050501 C allele and FCGR3A rs396991 T allele might contribute to susceptibility and development of SLE, and were under recessive association model. While, FCGR2A rs1801274 A allele and FCGR3B NA1 were associated with SLE and reduced the risk of SLE.

Systemic lupus erythematosus (SLE) is a kind of autoimmune disease with a strong genetic predisposition caused by complicated factors, it is also considered as an inflammatory disease caused by the mediation and deposition of immune complexes (ICs), leading to damage of multiple organs1. In different races or regions, the morbidity rate of SLE is quite different2,3, it is about 31-70/100,000 across China4, while it is 7-71/100,000 in Europeans5 and it increases to 200/100,000 in African population5. The etiology and pathogenesis of SLE is unclear yet, it is generally accepted that both genetic and environmental factors are involved in the development of this complex disease6. Since the end of last century, scientists were trying to use genetic linkage analysis to investigate the mechanism of SLE, a number of susceptibility area in SLE had been found such as 1q237, 1q418, 4p169, 11q1410, 12q2411. Linkage analysis for SLE had made some achievements, but it is not easy to find real susceptibility genes because of large positioning areas. Then, candidate gene association studies (CGASs), in which single-nucleotide polymorphisms (SNPs) were assayed in cases and controls, were widely used and found some valuable susceptibility genes such as IL-612, TLR213, VDR14, CTLA-415, FCGR2A16, FCGR2B17, PELI118, IKZF319. More recently, genome-wide association studies (GWAS) have been the powerful approach and found a lot of susceptibility genes and SNPs for SLE20,21,22,23,24,25,26,27.

Among these genes/proteins, FC gamma Receptor (FCγR) is a member of immunoglobulin superfamily, and it is very important to bind FCγR with the Fc protein of Immunoglobulin G (IgG), because FCγR binding may activate biological reaction, such as phagocytosis28. The human 1q21-23 locus contains 5 FCGR genes (FCGR2A, 2B, 2C, 3A and 3B) encoding the FCγRIIand FCγRIII receptor·families29. FCγRs mediate clearance of immune complexes and have been strongly implicated in the pathogenesis of SLE and lupus nephritis30. Thus the genes that encode these receptors have been the focus of many genetic studies in SLE31.

FCGRs were not genome-wide significantly identified by any GWAS above, and the results were not always consistent by candidate gene association study. The inconsistency of findings is related to many factors, such as the selecting of the sample, the size of sample and the dealing of the statistics, etc. Therefore, in order to reduce the limitations of single study and to overcome the possible random errors, we performed a large-scale meta-analysis involving different ethnics. Among all the studies, there were 5082 cases and 4951 controls to evaluate the relationship between FCGR2A rs1801274 and SLE and there were 2970 cases and 4197 controls for FCGR2B rs1050501. For FCGR3A rs396991 and FCGR3B NA1·NA2, there were 5694 cases and 6450 controls, 1692 cases and 1899 controls, respectively. The purpose of this study is to analyze whether the polymorphisms of FCGRs are susceptibility to SLE. We also made efforts to find the best-fit association model among the additive, recessive and dominant models for the polymorphisms.

Results

Studies included in the meta-analysis

In this meta-analysis, totally 436 relevant articles were found from PubMed, of which 337 were excluded because they were unrelated articles. Studies investigating other FCGR gene polymorphisms were also excluded17,32,33,34,35,36. One more article was also excluded because there was no detail genotyping data37. After filtering, 35 eligible articles were finally included16,33,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69. The flow chart of selecting articles process is presented in Fig. 1. Therefore, there were 34 studies with 5082 cases and 4951 controls to evaluate the relationship between FCGR2A rs1801274 polymorphism and SLE. For FCGR2B rs1050501 polymorphism, there were 13 studies involving a total of 2970 cases and 4197 controls. For FCGR3A rs396991 polymorphism and FCGR3B NA1·NA2 polymorphism, 26 studies (5694 cases and 6450 controls) and 11 studies (1692 cases and 1899 controls) were available, respectively. The basic information of these included studies genotype distributions and the allele frequencies are showed in Table 1.

Figure 1. The process of the articles selected in this meta-analysis.

Figure 1

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Table 1. The basic information of every studies included in this meta-analysis.

Polymorphismsand study Journal Year Ethnicity Sample size
 Genotypes
 Allele frequencies (%)
Cases Controls Cases Controls Cases Controls
rs1801274(FCGR2A)           AA AG GG AA AG GG A G A G
Vigato-Ferreira IC Autoimmunity 2014 Caucasian 157 160 23 59 75 35 43 82 0.334 0.666 0.353 0.647
Dijstelbloem HM Arthritis Rheum 2000 Caucasian 230 154 54 108 68 42 80 32 0.470 0.530 0.532 0.468
Zuñiga R Arthritis Rheum 2001 Caucasian 67 53 5 39 23 11 28 14 0.366 0.634 0.472 0.528
Seligman VA Arthritis Rheum 2001 Caucasian 76 186 10 49 17 28 114 44 0.454 0.546 0.457 0.543
Seligman VA Arthritis Rheum 2001 Caucasian 48 55 7 29 12 10 24 21 0.448 0.552 0.400 0.600
Manger K Ann Rheum Dis 2002 Caucasian 140 187 46 55 39 53 84 50 0.525 0.475 0.508 0.492
Botto M Clin Exp Immunol 1996 Caucasian 215 259 46 97 72 57 120 82 0.440 0.560 0.452 0.548
Duits A Arthritis Rheum 1995 Caucasian 95 69 18 50 27 22 36 11 0.453 0.547 0.580 0.420
Norsworthy P Arthritis Rheum 1999 Caucasian 195 283 32 96 67 62 131 90 0.410 0.590 0.451 0.549
Smyth LJ Ann Rheum Dis 1997 Caucasian 81 66 10 49 22 12 38 16 0.426 0.574 0.470 0.530
Smyth LJ Ann Rheum Dis 1997 Caucasian 42 52 14 16 12 20 24 8 0.524 0.476 0.615 0.385
González-Escribano MF Eur J Immunogenet 2002 Caucasian 276 194 64 137 75 59 86 49 0.480 0.520 0.526 0.474
Zhou XJ Lupus 2011 Asian 589 477 238 269 82 209 220 48 0.632 0.368 0.669 0.331
Kobavashi T J Periodontol 2007 Asian 71 44 34 31 6 28 16 0 0.697 0.303 0.818 0.182
Chu ZT Tissue Antigens 2004 Asian 163 129 72 70 21 53 58 18 0.656 0.344 0.636 0.364
Kyogoku C Arthritis Rheum 2002 Asian 193 303 113 72 8 197 95 11 0.772 0.228 0.807 0.193
Siriboonrit U Tissue Antigens 2003 Asian 87 187 37 40 10 93 76 18 0.655 0.345 0.701 0.299
Seligman VA Arthritis Rheum 2001 Asian 57 40 11 37 9 6 27 7 0.518 0.482 0.488 0.513
Salmon JE Arthritis Rheum 1999 Asian 148 97 70 66 12 41 47 9 0.696 0.304 0.665 0.335
Hatta Y Genes Immun 1999 Asian 81 217 49 30 2 139 71 7 0.790 0.210 0.804 0.196
Hatta Y Genes Immun 1999 Asian 69 93 42 26 1 62 28 3 0.797 0.203 0.817 0.183
Lee HS Rheumatology 2003 Asian 299 144 131 114 54 67 66 11 0.629 0.371 0.694 0.306
Botto M Clin Exp Immunol 1996 Asian 46 49 18 23 5 24 20 5 0.641 0.359 0.694 0.306
Yun HR Lupus 2001 Asian 300 197 132 114 54 82 99 16 0.630 0.370 0.668 0.332
Yap S Lupus 1999 Asian 175 108 59 91 25 28 63 17 0.597 0.403 0.551 0.449
Yap S Lupus 1999 Asian 50 50 20 26 4 21 21 8 0.660 0.340 0.630 0.370
Chen JY Ann Rheum Dis 2004 Asian 329 311 125 155 49 130 144 37 0.616 0.384 0.650 0.350
Zidan HE Mol Biol Rep 2014 African 90 90 20 45 25 22 50 18 0.472 0.528 0.522 0.478
Seligman VA Arthritis Rheum 2001 African 30 31 9 12 9 6 15 10 0.500 0.500 0.435 0.565
Botto M Clin Exp Immunol 1996 African 70 77 8 37 25 17 35 25 0.379 0.621 0.448 0.552
Seligman VA Arthritis Rheum 2001 mixed population 216 318 38 131 47 50 185 83 0.479 0.521 0.448 0.552
Seligman VA Arthritis Rheum 2001 Non-Caucasian 140 132 28 82 30 22 71 39 0.493 0.507 0.436 0.564
Salmon J J Clin Invest 1996 African Americans 43 39 4 23 16 14 15 10 0.360 0.640 0.551 0.449
Salmon J J Clin Invest 1996 African Americans 214 100 37 97 80 27 50 23 0.400 0.600 0.520 0.480
rs1050501(FCGR2B)           CC CT TT CC CT TT C T C T
Pradhan V Indian J Med Res 2011 Asian 80 80 16 49 15 10 52 18 0.506 0.494 0.450 0.550
Koga M J Hum Genet 2011 Asian 282 222 29 103 150 9 85 128 0.285 0.715 0.232 0.768
Willcocks LC PNAS 2010 Asian 819 1026 60 284 475 57 404 565 0.247 0.753 0.252 0.748
Kobavashi T J Periodontol 2007 Asian 71 44 4 26 41 0 6 38 0.239 0.761 0.068 0.932
Ji-Yih Chen Arthritis Rheum 2006 Asian 351 372 39 123 189 22 144 206 0.286 0.714 0.253 0.747
Chu ZT Tissue Antigens 2004 Asian 108 85 11 48 49 4 30 51 0.324 0.676 0.224 0.776
Kyogoku C Arthritis Rheum 2002 Asian 193 303 21 66 106 16 104 183 0.280 0.720 0.224 0.776
Siriboonrit U Tissue Antigens 2003 Asian 79 165 12 29 38 12 56 97 0.335 0.665 0.242 0.758
Magnusson V Arthritis Rheum 2004 Caucasian 263 228 7 67 189 4 53 171 0.154 0.846 0.134 0.866
Willcocks LC PNAS 2010 Caucasian 326 1296 9 48 269 13 232 1051 0.101 0.899 0.100 0.900
Li X Arthritis Rheum 2003 Caucasian 148 137 6 30 112 4 27 106 0.142 0.858 0.128 0.872
Zidan HE Mol Biol Rep 2014 African 90 90 32 39 19 17 44 29 0.572 0.428 0.433 0.567
Li X Arthritis Rheum 2003 African-American 160 149 14 49 97 17 53 79 0.241 0.759 0.292 0.708
rs396991(FCGR3A)           TT TG GG TT TG GG T G T G
Brambila-Tapia AJ Rheumatol Int 2011 Caucasian 94 98 61 5 28 52 8 38 0.676 0.324 0.571 0.429
Dong C Arthritis Rheumatol 2014 Caucasian 834 1185 392 370 72 517 564 104 0.692 0.308 0.674 0.326
Dijstelbloem HM Arthritis Rheum 2000 Caucasian 230 154 92 108 30 66 73 15 0.635 0.365 0.666 0.334
Zuñiga R Arthritis Rheum 2001 Caucasian 67 53 25 38 4 15 26 12 0.657 0.343 0.528 0.472
Seligman VA Arthritis Rheum 2001 Caucasian 78 207 37 30 11 55 102 50 0.667 0.333 0.512 0.488
Seligman VA Arthritis Rheum 2001 Caucasian 55 57 25 15 15 30 21 6 0.591 0.409 0.711 0.289
Manger K Ann Rheum Dis 2002 Caucasian 140 187 55 64 21 62 75 50 0.621 0.379 0.532 0.468
Wu J J Clin Invest 1997 Caucasian 200 113 87 92 21 29 69 15 0.665 0.335 0.562 0.438
González-Escribano MF Eur J Immunogenet 2002 Caucasian 276 194 101 131 44 66 104 24 0.603 0.397 0.608 0.392
Dai M Int J Rheum Dis 2013 Asian 732 886 376 308 48 381 427 78 0.724 0.276 0.671 0.329
Kobavashi T J Periodontol 2007 Asian 71 44 43 22 6 24 15 5 0.761 0.239 0.716 0.284
Chu ZT Tissue Antigens 2004 Asian 163 129 76 74 13 48 63 18 0.693 0.307 0.616 0.384
Kyogoku C Arthritis Rheum 2002 Asian 193 303 110 76 7 145 132 26 0.767 0.233 0.696 0.304
Siriboonrit U Tissue Antigens 2003 Asian 87 187 42 35 10 64 96 27 0.684 0.316 0.599 0.401
Seligman VA Arthritis Rheum 2001 Asian 59 41 22 29 8 12 22 7 0.619 0.381 0.561 0.439
Salmon JE Arthritis Rheum 1999 Asian 148 97 44 81 23 19 64 14 0.571 0.429 0.526 0.474
Hatta Y Genes Immun 1999 Asian 81 217 43 34 4 100 99 18 0.741 0.259 0.689 0.311
Hatta Y Genes Immun 1999 Asian 69 93 37 29 3 46 38 9 0.746 0.254 0.699 0.301
Lee EB Rheum Int 2002 Asian 145 75 89 51 5 40 29 6 0.790 0.210 0.727 0.273
Lee HS Rheumatology 2003 Asian 299 144 90 163 46 52 77 15 0.574 0.426 0.628 0.372
Yun HR Lupus 2001 Asian 300 197 90 164 46 71 104 22 0.573 0.427 0.624 0.376
Chen JY Ann Rheum Dis 2004 Asian 302 311 119 138 45 133 146 32 0.623 0.377 0.662 0.338
Dong C Arthritis Rheumatol 2014 African-American 648 953 289 283 76 413 431 109 0.664 0.336 0.659 0.341
Seligman VA Arthritis Rheum 2001 mixed population 233 348 97 96 40 108 172 68 0.622 0.378 0.557 0.443
Seligman VA Arthritis Rheum 2001 Non-Caucasian 155 141 60 66 29 53 70 18 0.600 0.400 0.624 0.376
Seligman VA Arthritis Rheum 2001 African 35 36 11 19 5 7 25 4 0.586 0.414 0.542 0.458
NA1/NA2           NA1·NA1 NA1·NA2 NA2·NA2 NA1·NA1 NA1·NA2 NA2·NA2 NA1 NA2 NA1 NA2
Kobavashi T J Periodontol 2007 Asian 71 44 20 46 5 20 19 5 0.606 0.394 0.670 0.330
Chu ZT Tissue Antigens 2004 Asian 163 129 46 90 29 41 74 14 0.552 0.448 0.605 0.395
Kyogoku C Arthritis Rheum 2002 Asian 193 303 62 98 33 116 145 42 0.575 0.425 0.622 0.378
Siriboonrit U Tissue Antigens 2003 Asian 87 187 30 39 18 85 82 20 0.569 0.431 0.674 0.326
Pradhan V Int J Rheum Dis 2010 Asian 80 80 20 32 28 18 32 30 0.450 0.550 0.425 0.575
Hatta Y Genes Immun 1999 Asian 81 217 23 38 20 92 100 25 0.519 0.481 0.654 0.346
Hatta Y Genes Immun 1999 Asian 69 93 18 33 18 44 39 10 0.500 0.500 0.683 0.317
Chen JY Ann Rheum Dis 2004 Asian 302 311 117 132 53 119 145 47 0.606 0.394 0.616 0.384
Dijstelbloem HM Arthritis Rheum 2000 Caucasian 230 154 42 101 87 27 66 61 0.402 0.598 0.390 0.610
Manger K Ann Rheum Dis 2002 Caucasian 140 187 13 87 40 20 87 80 0.404 0.596 0.340 0.660
González-Escribano MF Eur J Immunogenet 2002 Caucasian 276 194 30 77 169 20 75 99 0.248 0.752 0.296 0.704
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Meta-analysis results

FCGR2A rs1801274 polymorphism and SLE risk

Test of heterogeneity in the overall population is not significant (P = 0.109, I2 = 23.70%), suggesting fixed effect model could be used. A strong association was found between rs1801274 and SLE under the allelic model in the overall population (OR = 0.879 per A allele, 95%CI: 0.819–0.943, P = 3.31 × 10−4, Table 2, Fig. 2a). Stratification analysis by ethnicity showed significant association between rs1801274 and SLE in Caucasian (OR = 0.845 per A allele, 95%CI: 0.766–0.932, P = 8.08 × 10−4, Table 2, Fig. 2a). And we also observed association between this polymorphism and SLE in African Americans (OR = 0.575 per A allele, 95%CI; 0.429–0.774, P = 2.73 × 10−4, Table 2, Fig. 2a) and in Asian population (OR = 0.896 per A allele, 95%CI: 0.822–0.977, P = 0.013, Table 2, Fig. 2a). No significant association was found in this meta-analysis between the polymorphism and the risk of SLE in African population (OR = 0.853 per A allele, 95%CI: 0.642–1.132, P = 0.271, Table 2, Fig. 2a). We also tested the dominant and recessive models of A allele in the overall, European, Asian and African populations, these results showed that the association was more significant in the recessive model than the dominant model in the overall population (Table 2, Supplementary Fig. S1a, Fig. S2a).

Table 2. Meta-analysis of the association between FCGR2A rs1801274 polymorphism and SLE risk.
Population N A vs. G(allele model)
 AA vs. AG+GG(recessive model)
 AA+AG vs. GG(dominant model)
OR(95%CI) POR Ph OR(95%CI) POR Ph OR(95%CI) POR Ph
Overall 34 0.879(0.819–0.943) 3.31 × 10−4 0.109 0.867(0.784–0.960) 6.14 × 10−3 0.214 0.843(0.739–0.961) 0.011 0.074
Caucasian 12 0.845(0.766–0.932) 8.08 × 10−4 0.439 0.775(0.655–0.917) 3.08 × 10−3 0.522 0.883(0.756–1.032) 0.117 0.427
Asian 15 0.896(0.822–0.977) 0.013 0.543 0.932(0.830–1.046) 0.232 0.658 0.767(0.604–0.975) 0.030 0.179
African 3 0.853(0.642–1.132) 0.271 0.438 0.836(0.428–1.633) 0.601 0.192 0.802(0.515–1.250) 0.331 0.688
Mixed population 1 1.133(0.887–1.448) 0.318 — 1.144(0.721–1.817) 0.568 — 1.27(0.844–1.911) 0.252 —
Non-Caucasian 1 1.259(0.898–1.765) 0.181 — 1.250(0.674–2.317) 0.479 — 1.538(0.887–2.666) 0.125 —
African Americans 2 0.575(0.427–0.774) 2.73 × 10−4 0.422 0.368(0.126–1.078) 0.068 0.100 0.519(0.324–0.831) 6.33 × 10–3 0.786
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OR odd ratio, 95%CI confidence interval, POR P value for the test of association, Ph P value for heterogeneity analysis.

Figure 2. Forest plot for the meta-analysis of the association between FCGRs polymorphisms and SLE.

Figure 2

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(a) FCGR2A rs1801274 and SLE (A vs G); (b) FCGR2B rs1050501 and SLE (CC vs CT/TT); (c) FCGR3A rs396991 and SLE (TT vs TG /GG); (d) FCGR3B NA1·NA2 and SLE (NA1 vs NA2).

FCGR2B rs1050501 polymorphism and SLE risk

To assess the association of FCGR2B rs1050501 polymorphism with SLE, 13 studies were included in this meta-analysis with 2970 cases and 4197 controls, however, we identified publication bias while the study by Kobavashi T et al.59 was included (Begg’s Test: Z = 2.14, P = 0.033), therefore, this study was removed in the final analysis with 2899 cases and 4153 controls. After exclusion, the Begg’s test showed no deviation (Z = 1.58, P = 0.115) (Supplementary Table S1).

A very significant association was identified between rs1050501 and SLE under the recessive genotypic model of C allele in the overall population (CC vs CT/TT, OR = 1.754, 95%CI: 1.422–2.165, P = 1.61 × 10−7, Fig. 2b, Table 3) and in Asian population (CC vs CT/TT, OR = 1.784, 95%CI; 1.408–2.261, P = 1.67 × 10−6, Table 3, Fig. 2b), these associations were not significant under dominant model, suggesting the recessive association model was fit for rs1050501_C (Table 3). In allelic test model, Significant association between rs1050501 and SLE was identified in the overall population (OR = 1.236 per C allele, 95%CI: 1.069–1.429, P = 6.93 × 10−3, Table 3, Supplementary Fig. S2b), and in the Asian population (OR = 1.326 per C allele, 95%CI: 1.095–1.604, P = 6.14 × 10−3, Table 3, Supplementary Fig. S2b) and in African population (OR = 1.749 per C allele, 95%CI: 1.153–2.655, P = 8.54 × 10−3, Table 3, Supplementary Fig. S2b).

Table 3. Meta-analysis of the association between FCGR2B rs1050501 polymorphism and SLE risk.
Population N C vs. T(allele model)
 CC vs. CT+TT(recessive model)
 CC+CT vs. TT(dominant model)
OR(95%CI) POR Ph OR(95%CI) POR Ph OR(95%CI) POR Ph
Overall 12 1.236(1.069–1.429) 0.007 0.030 1.754(1.422–2.165) 1.61 × 10−7 0.404 1.093(0.952–1.255) 0.205 0.140
Asian 7 1.326(1.095–1.604) 0.006 0.065 1.784(1.408–2.261) 1.67 × 10−6 0.630 1.149(0.957–1.380) 0.137 0.121
Caucasian 3 1.087(0.888–1.331) 0.420 0.812 2.055(1.106–3.817) 0.023 0.587 1.019(0.812–1.279) 0.872 0.592
African 1 1.749(1.153–2.655) 0.009 — 2.369(1.198–4.685) 0.013 — 1.777(0.907–3.479) 0.094 —
African-American 1 0.769(0.537–1.099) 0.149 — 0.745(0.353–1.569) 0.438 — 0.733(0.467–1.152) 0.178 —
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OR odd ratio, 95%CI confidence interval, POR P value for the test of association, Ph P value for heterogeneity analysis.

FCGR3A rs396991 polymorphism and SLE risk

There were 26 studies with 5694 cases and 6450 controls in our meta-analysis to evaluate the relationship between FCGR3A rs396991 polymorphism and SLE. Firstly, we tested the dominant and recessive models to estimate the relation between rs396991 and SLE risk (Table 4). We found that rs396991 were significant association with the susceptibility to SLE in overall population in recessive model of T allele (TT vs TG/GG, OR = 1.263, 95%CI: 1.123–1.421, P = 9.62 × 10−5, Table 4, Fig. 2c), and in Caucasian population (TT vs TG/GG, OR = 1.394, 95%CI: 1.087–1.789, P = 9.05 × 10−3) and in mixed population (TT vs TG/GG, OR = 1.585, 95%CI: 1.122–2.239, P = 9.05 × 10−3). Similarly, recessive model is the best fit for the association of rs396991_T, because we didn’t observe any association under dominant model in any populations (Table 4). We also tested the allelic model to observe the relationship between rs396991 and SLE. The significant association was seen between rs396991 and SLE in the overall population (OR = 1.17 per T allele, 95%CI: 1.059–1.291, P = 1.94 × 10−3, Table 4, Supplementary Fig. S2c). And we also found trend of association between this polymorphism and SLE in the stratified analysis of ethnicity: (Caucasian, OR = 1.259 per T allele, P = 0.039; Asian population, OR = 1.152 per T allele, P = 0.05, Table 4, Fig. 2c).

Table 4. Meta-analysis of the association between FCGR3A rs396991 polymorphism and SLE risk.
Population N T vs. G(allele model)
 TT vs. TG+GG(recessive model)
 TT+TG vs. GG(dominant model)
OR(95%CI) POR Ph OR(95%CI) POR Ph OR(95%CI) POR Ph
Overall 26 1.17(1.059–1.291) 0.002 0.000 1.263(1.123–1.421) 9.62 × 10−5 0.003 1.114(0.933–1.331) 0.232 0.004
Caucasian 9 1.259(1.012–1.566) 0.039 0.000 1.394(1.087–1.789) 9.05 × 10−3 0.008 1.187(0.830–1.699) 0.347 0.004
Asian 13 1.152(0.999–1.328) 0.051 0.004 1.211(1.022–1.434) 0.027 0.036 1.164(0.884–1.533) 0.280 0.049
African-American 1 1.022(0.880–1.186) 0.776 — 1.053(0.861–1.287) 0.617 — 0.972(0.712–1.327) 0.858 —
Mixed population 1 1.308(1.029–1.662) 0.028 — 1.585(1.122–2.239) 9.05 × 10−3 — 1.172(0.761–1.804) 0.471 —
Non-Caucasian 1 0.903(0.649–1.258) 0.548 — 1.049(0.656–1.677) 0.843 — 0.636(0.336–1.204) 0.164 —
African 1 1.196(0.616–2.324) 0.597 — 1.899(0.638–5.654) 0.249 — 0.750(0.184–3.060) 0.688 —
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OR odd ratio, 95%CI confidence interval, POR P value for the test of association, Ph P value for heterogeneity analysis.

FCGR3B NA1·NA2 copy number polymorphism and SLE risk

Totally, 11 studies included 1692 cases and 1899 controls were in our meta-analysis to assess the relation between FCGR3B NA1·NA2 copy number polymorphism and SLE. The meta-analysis indicated that NA1·NA2 was modestly associated with SLE in overall population (allele genetic model: OR = 0.851 per NA1, 95%CI: 0.772–0.938, P = 1.2 × 10−3, Table 5, Fig. 2d; recessive model of NA1: OR = 0.799, 95%CI: 0.685–0.933, P = 0.005, Table 5, Supplementary Fig. S2d). Analysis by population showed that NA1·NA2 was modestly associated with SLE in Asian by three models (allele genetic model: OR = 0.785, 95%CI: 0.697–0.883, P = 6.07 × 10−5, Table 5, Fig. 2d; dominant model: OR = 0.684, 95%CI: 0.549–0.853, P = 7.2 × 10−4, Table 5, Supplementary Fig. S1d; recessive model: OR = 0.756, 95%CI: 0.635–0.898, P = 0.002, Table 5, Supplementary Fig. S2d).

Table 5. Meta-analysis of the association between FCGR3B copy number polymorphism NA1·NA2 and SLE risk.
Population N NA1 vs. NA2(allele model)
 NA1·NA1 vs. NA1·NA2+NA2·NA2 (recessive model)
 NA1·NA1+NA2·NA2 vs. NA2·NA2 (dominant model)
OR(95%CI) POR Ph OR(95%CI) POR Ph OR(95%CI) POR Ph
Overall 11 0.851(0.772–0.938) 1.2 × 10−3 0.004 0.799(0.685–0.933) 0.005 0.182 0.825(0.702–0.969) 0.019 0.001
Asian 3 0.785(0.697–0.883) 6.07 × 10−5 0.040 0.756(0.635–0.898) 0.002 0.116 0.684(0.549–0.853) 7.2 × 10−4 0.103
Caucasian 8 1.013(0.851–1.205) 0.888 0.060 1.006(0.709–1.426) 0.974 0.885 1.021(0.806–1.292) 0.866 0.003
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OR odd ratio, 95%CI confidence interval, POR P value for the test of association, Ph P value for heterogeneity analysis.

Allele frequency of the 3 SNPs and comparing to the 1000 genome population

In Table 6, we showed the distinct difference of allele frequencies in Asian, Caucasian, African and African American population in the meta-analysis of the 3 SNPs. The allele frequencies of the 3 SNPs in Asian, Caucasian, African and African American population in the meta–analysis were consistent with the allele frequencies in 1000 Genome Project EUR (European ancestry), ASN (Asian ancestry), AFR (African ancestry), ASW (Americans of African Ancestry), respectively.

Table 6. The allele frequency comparison between the meta-analysis and 1000 Genomes Project.

Polymorphism Populations Meta-analysis(alleles frequencies)
  
Cases
 Controls
 1000 Genomes(Alleles frequencies)
A G A G A G
SNP rs1801274 Caucasian 0.445 0.555 0.474 0.526 0.500(EUR) 0.5(EUR)
Asian 0.652 0.348 0.697 0.303 0.722(ASN) 0.278(ASN)
African 0.568 0.432 0.602 0.398 0.512(AFR) 0.488(AFR)
African Americans 0.393 0.607 0.529 0.471 0.525(ASW) 0.475(ASW)
Mixed population 0.479 0.521 0.448 0.552    
Non-Caucasian 0.493 0.507 0.436 0.564    
All 0.563 0.437 0.595 0.405 0.57(ALL) 0.43(ALL)
SNP rs1050501   C T C T C T
Asian 0.280 0.720 0.248 0.752 0.255(ASN) 0.745(ASN)
Caucasian 0.128 0.872 0.107 0.893 0.123(EUR) 0.877(EUR)
African 0.572 0.428 0.433 0.567 0.248(AFR) 0.752(AFR)
African-American 0.241 0.759 0.292 0.708 0.213(ASW) 0.787(ASW)
All 0.249 0.751 0.198 0.802 0.188(ALL) 0.812(ALL)
SNP rs396991   T G T G T G
Caucasian 0.659 0.341 0.629 0.371 0.731(EUR) 0.269(EUR)
Asian 0.673 0.327 0.657 0.343 0.731(ASN) 0.269(ASN)
African-American 0.664 0.336 0.659 0.341 0.713(ASW) 0.287(ASW)
Mixed population 0.622 0.378 0.557 0.443    
Non-Caucasian 0.600 0.400 0.624 0.376    
African 0.586 0.414 0.542 0.458 0.785(AFR) 0.215(AFR)
All 0.663 0.337 0.641 0.359 0.755(ALL) 0.245(ALL)
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EUR European ancestry, ASN Asian ancestry, AFR African ancestry, ASW Americans of African Ancestry, ALL All individuals from phase 1 of the 1000 Genomes Project.

Publication bias and Sensitivity analysis

Begg’s funnel plot and Egger’s test were performed to estimate publication bias. There was no obvious evidence of symmetry from the shapes of the funnel plots (Fig. 3), and showed no evidence of publication bias in rs1801274 polymorphism (P = 0.594), rs396991 polymorphism (P = 0.252), NA1·NA2 polymorphism (P = 0.213), and rs1050501 polymorphism (P = 0.115, after excluded the study by Kobavashi T et al.59) under allele genetic model in our meta-analysis (Fig. 3a–d). We also conducted sensitivity analysis to assess the influence of individual studies on the pooled ORs. We found the pooled OR was not substantially altered, when any one study was deleted (Fig. 4a–d).

Figure 3. Begg’s funnel plot of publication bias in the meta-analysis of the association of FCGRs polymorphisms with SLE risk under allele genetic model.

Figure 3

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(a) FCGR2A rs1801274 and SLE (A vs G); (b) FCGR2B rs1050501 and SLE (C vs T); (c) FCGR3A rs396991 and SLE (T vs G); (d) FCGR3B NA1·NA2 and SLE (NA1 vs NA2).

Figure 4. Sensitivity analysis to assess the stability of the meta-analysis.

Figure 4

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(a) FCGR2A rs1801274 in SLE; (b) FCGR2B rs1050501 in SLE; (c) FCGR3A rs396991 in SLE; (d) FCGR3B NA1·NA2 in SLE).

Discussion

In this study, we conducted a meta-analysis of the association between FCGR2A, 2B, 3A and 3B polymorphisms and SLE susceptibility. We found that C allele of rs1050501 (FCGR2B) and T allele of rs396991 (FCGR3A) strongly increase the risk of SLE. We also found significant association between FCGR2A rs1801274, FCGR3B copy number polymorphism NA1·NA2, and SLE in the overall population.

SNP rs1801274 is a missense mutation in FCGR2A gene on chromosome 1q23.3 (161479745), which encodes substitution of histidine (H) by arginine (R) in the IgG-binding domain of FcgRIIa and it was reported that FcgRIIa-R has a lower binding affinity for IgG than FcgRIIa-H68. In our study, we found FCGR2A rs1801274 contributes to SLE susceptibility in overall population. And in the subgroup analysis, the polymorphism was associated with SLE in Asian, Caucasian, and African Americans but not in African population, however, there were only 3 studies for African population in this meta-analysis, consisting only 190 cases and 198 controls, and the effect direction of A allele in African population is the same as that in the overall population. Previous study such as by Karassa FB et al.70 presented the association between FCGR2A rs1801274 and SLE of Caucasian descent, but it was less clear in subjects of Asian or African descent. Another study71 found a significant association of rs1801274 G allele and increased SLE risk in all groups, and a clear effect of G allele on SLE was shown in European and Asian, these results were consistent with our study. We also confirmed the findings from Zhou XJ65 that investigated the association between rs1801274 and SLE in Chinese population. In many ways, we suggest that rs1801274 was associated with SLE, especially in Caucasian and Asian population. As for other populations, more studies were needed to evaluate association between the polymorphism and SLE. It’s likely that such differences may, at less in part, be attributable to the ethnic difference.

GWAS have found that there were significant associations between FCGR2A rs1801274 and Kawasaki disease72 and Inflammatory bowel disease (P = 2.12 × 10−38, OR = 1.12)73 and there were genome-wide significant associations between the SNP and Ulcerative colitis in European74, and Japanese population75. There was only one genome-wide association study between FCGR2A and SLE, however, SNP rs1801274 was not genome-wide significant27.

FcgRIIb is an inhibitory receptor mediating B-cell function via an immune receptor tyrosine-based inhibitory motif 59. FcgRIIb is the only FcgR that transmits an inhibitory signal and is expressed in B cells and myelomonocytic cells57. FCGR2B rs1050501 (c.695T > C) codes a non-synonymous substitution, Ile232Thr (I232T) on chromosome 1q23.3 (161644048), our meta-analysis showed that C allele significantly increased the risk of SLE under recessive association model and allelic test model in overall population (Table 3, Fig. S2a; Supplementary Fig. S2b). By subgroup analysis, the association was also found under allelic genetic model and recessive model in Asian populations, but not in Caucasians under allelic genetic model. In 2004, Chu ZT et al.57 had found rs1050501 was significant associated with SLE in Chinese population. These results were in agreement with Lee YH et al.76 that indicated the C allele significantly increased the risk of SLE in Asian population. Therefore, it was suggested that the association between FCGR2B rs1050501 and SLE was on the basis of ethnicity, and the C allele is a risk for SLE in Asian.

FcγRIIIa is expressed on the surfaces of natural killer (NK) cells, monocytes and macrophages and binds to IgG1 and IgG3 subclasses66. FCGR3A rs396991 is a missense mutation on chromosome 1q23.3 (161514792), leading to a valine (V) substitution for phenylalanine (F) at amino acid residue 176 (including the leader sequence)66. In our meta-analysis, it suggested that a significant association between FCGR3A rs396991_T and SLE in overall population under recessive association models and allele genetic model (Table 4, Fig. 2c; Supplementary Fig. S2c). Previous study77 had suggested a modest trend of SLE predisposition for FCGR3A rs396991 in 1,261 SLE patients and 1,455 disease-free controls but with significant between-study heterogeneity. In addition, we observed trend of association between this polymorphism and SLE in the stratified analysis of ethnicity in Caucasian and Asian population, which was consistent with the study of Li et al.78. However, the association was not confirmed in the population of African and African American.

The copy number variation (NA1·NA2) in FCGR3B has shown to influence the interaction between FcγRIIIb and human IgG61. Individuals who are homozygous for NA1 allele has greater phagocytosis of IgG opsonized targets than that of NA2 homozygous individuals. Our meta-analysis illustrated a modest association between this copy number polymorphism and SLE in overall population by allele genetic model and recessive model. Analysis by population showed that NA1·NA2 was associated with SLE in Asians by three models. This association was not observed in a small sample size of 165 Chinese patients with SLE and 129 healthy controls by Chu ZT et al.57. To further explain the differences, we compared frequency between our meta analysis and those from Chu ZT et al.57 in Table 1, From this table, we could tell the frequencies were consistent between the two, the sample size might have been responsible for the different results. Besides, we didn’t find an association between FCGR3B NA1·NA2 polymorphism and SLE in Caucasian.

Though we tried to control the potential bias of publications and populations. There were still have several limitations to be taken into consideration in this meta-analysis. Firstly, although the overall sample size is large, the size of each study is relatively small, with the smallest sample of 30 cases and 31 controls. Secondly, the meta-analysis for ethnicity included data more from population with Caucasian and Asian origin, and the findings are applicable to only these populations, more studies are required in other populations. Furthermore, the mechanism of SLE is considered to be sophisticated, including gene-gene and gene-environment interactions. More studies with enough statistical power are needed for deeply evaluation. Lastly, publication bias might affect the results, because the studies that found any negative results may not have been published.

Despite the limitations, this meta-analysis illustrated that C allele of FCGR2B rs1050501 and T allele of FCGR3A rs396991 might contribute to susceptibility and development of SLE, and were under recessive association model. While, A allele of FCGR2A rs1801274 and FCGR3B NA1 were associated with SLE and reduced the risk of SLE. Considering the limited samples in Africans and African Americans in this meta-analysis, studies with larger sample size including diverse ethnic populations are still required to investigate the association between FCGRs genes polymorphisms and SLE in the future.

Methods

Identification of eligible studies

We aimed to analyze the association between FCGR2A (SNP rs1801274), FCGR2B (SNP rs1050501), FCGR3A (SNP rs396991), FCGR3B copy number polymorphism (NA1/NA2) polymorphisms and SLE. Therefore, all published literatures before December 2015 that investigated the association between these polymorphisms and SLE risk were searched using the PubMed engine (National Center for Biotechnology, National Library of Medicine). We looked for the articles with keywords “FCGR2A”, “FCGR2B”, “FCGR3A”, “FCGR3B”, “FCγRs”, “polymorphism” in combination with “Systemic Lupus erythematosus” or “SLE”. Finally, we extracted data from the published articles, not from conference abstracts or any meetings.

Data extraction

All studies should meet the following conditions: 1) case-control study; 2) with original data to calculate genotype counts and odds ratio (OR); 3) the diagnosis of SLE patients according to the American College of Rheumatology criteria79,80. The following information is shown in our study: first author, year of publication, ethnicity, sample size of cases and controls, allele frequency and genotype frequency.

Statistical analysis

The allele frequencies of polymorphisms from each study were calculated by the allele counting method. Pooled ORs and 95% confidence intervals (CIs) were used to evaluate the strength of association between polymorphisms and SLE risk for every eligible study. Heterogeneity was evaluated using the I2 metric, which ranges between 0 and 100% (25%, low heterogeneity; 50%, moderate; 75%, high heterogeneity)81. If the P value for heterogeneity test was higher than 0.01, the fixed effect model was used to weight of each study. Moreover, the random effect model was also used. In this meta-analysis, P value of less than 0.05 was considered a statistically significant.

In order to get better search results, we evaluated possible publication bias by Egger’s linear regression text82. P value < 0.05 was considered representative of statistical publication bias82. We also used a funnel plot to evaluate the publication bias by Begg’s test83. For sensitivity analysis, removed one study from the total and tested residual studies. Statistical analysis was carried out using the software program STATA10.1 (Stata Corporation, College Station, Texas).

Additional Information

How to cite this article: Zhu, X.-W. et al. Comprehensive Assessment of the Association between FCGRs polymorphisms and the risk of systemic lupus erythematosus: Evidence from a Meta-Analysis. Sci. Rep. 6, 31617; doi: 10.1038/srep31617 (2016).

Supplementary Material

Supplementary Information
srep31617-s1.pdf (1.7MB, pdf)

Acknowledgments

This study was supported by the National Natural Science Foundation of China (81501145), the State Key Development Program for Basic Research of China (973 Program, 2014CB541701), the HZNUARI-Pilot Research Grant, the Medical Science and Technology Program of Shandong Province (2014WS0191) and the Science and Technology Program of Binzhou City (2014ZC0125). The funding agencies had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank the peer reviewers for their thorough and helpful review of this manuscript.

Footnotes

Author Contributions Conceived and designed the study: H.-F.Z. Performed the literature searching: X.-W.Z., Y.W. and Y.-H.W. Contributed crosschecking the literatures: Y.W., Y.-H.W., J.-J.R., P.-P.Z., X.-B.W., W.-Y.Z., X.-W.Z., L.W., X.-W.Z. and H.-F.Z. Analyzed the data: H.-F.Z. Wrote the manuscript: X.-W.Z. and H.-F.Z.

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