Objectives: FAS plays a critical role in the extrinsic apoptosis pathway in autoimmune diseases. Previous studies investigating the association between FAS gene −670 A/G and −1377 G/A polymorphisms and the risk of autoimmune diseases reported controversial results. We performed the meta-analysis to evaluate the possible association. Methods: Relevant studies were identified by searching the PubMed, Embase, CNKI, and Wanfang databases up to December 2018. Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were calculated to determine the association. Results: A total of 43 articles including 67 studies (52 studies for FAS −670 A/G and 15 studies for −1377 G/A) were included in the meta-analysis. Our meta-analysis showed that the FAS −670 A/G polymorphism was associated with the risk of autoimmune diseases (GG vs. GA: OR = 1.079, 95% CI = 1.004–1.160, P=0.038), especially in Caucasians (GG vs. GA: OR = 1.12, 95% CI = 1.03–1.23, P=0.012), Asians (G vs. A: OR = 0.89, 95% CI = 0.83–0.96, P=0.002), systemic lupus erythematosus (SLE) (G vs. A: OR = 0.85, 95% CI = 0.77–0.94, P=0.001), multiple sclerosis (MS) (GG+GA vs. AA: OR = 0.83, 95% CI = 0.70–0.99, P=0.043), systemic sclerosis (SSc) (GG vs. GA: OR = 1.20, 95% CI = 1.07–1.36, P=0.003) and Hashimoto’s thyroiditis (HT) (G vs. A: OR = 1.45, 95% CI = 1.10–1.90, P=0.008); the FAS −1377 G/A polymorphism was associated with the risk of autoimmune diseases (A vs. G: OR = 1.11, 95% CI = 1.03–1.20, P=0.008), especially in Asians (A vs. G: OR = 1.15, 95% CI = 1.05–1.25, P=0.002) and high quality studies (A vs. G: OR = 1.14, 95% CI = 1.05–1.24, P=0.002). Conclusion: This meta-analysis demonstrated that the FAS –670A/G and –1377 G/A polymorphisms were associated with the risk of autoimmune diseases.

Keywords:
Autoimmune Diseases, FAS, Meta-analysis, polymorphisms
Subjects:
Epigenetics

Autoimmune diseases are chronic disorders characterized by the loss of immune tolerance to self-antigens, leading to immune-mediated tissue destruction. They affect 4–5% of adults, the majority of whom are women [1]. Co-occurrence of distinct autoimmune diseases within a single family and genome-wide association studies (GWASs) support the hypothesis that these diseases share common genetic risk factors [2–6]. The etiology of autoimmune diseases is attributed to complex interactions of genetics, epigenetics, and environmental factors that remain to be elucidated [7–12].

FAS (also known as APO-1, CD95, or TNFSF6) is a cell surface receptor that belongs to the tumor necrosis factor (TNF) receptor superfamily [13]. FAS is widely expressed in normal human tissues. To maintain self-tolerance, the binding of FAS-ligand (FASL) to FAS on the cell surface initiates the extrinsic apoptosis pathway [14]; thus, autoreactive lymphocytes are normally eliminated. However, abnormal apoptosis may lead to a failure to eliminate autoreactive lymphocytes, which can induce the appearance and development of autoimmune diseases [15]. The FAS gene is located on chromosome 10q24.1 in humans and is highly polymorphic [16]. In some individuals, there is an A to G substitution at position 670 and a G to A substitution at position 1377 in the FAS promoter region [17]. The FAS −670 A/G and −1377 G/A polymorphisms may destroy signal transducer and activator of transcription protein 1 (STAT1) and stimulatory protein 1 (SP1) transcription factor binding sites, resulting in reduced promoter activity and FAS expression [18]. Abnormal apoptosis mediated by the FASL interaction with the FAS receptor is involved in the pathogenesis of several autoimmune diseases and cancers [19].

Many studies have investigated the relationship between the FAS −670 A/G rs1800682 and −1377 G/A rs2234767 polymorphisms and the risk of autoimmune diseases [15,17,20–60], including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), multiple sclerosis (MS), autoimmune hepatitis (AIH), alopecia areata (AA), lupus nephritis (LN), systemic sclerosis (SSc), primary Sjögren’s syndrome (pSS), Hashimoto’s thyroiditis (HT), Guillain–Barré syndrome (GBS), primary biliary cirrhosis (PBC), vitiligo, Graves’ disease (GD), type 1 diabetes mellitus (T1D), idiopathic aplastic anemia (IAA), juvenile idiopathic arthritis (JIA), and spondyloarthropathies (SPA). However, previous results have been controversial, perhaps due to small sample sizes and low statistical power. Meta-analysis could provide more reliable results, enabling the inclusion of a larger sample size and enhanced statistical power by combining the results of independent eligible studies. Seven previous meta-analyses [43,61–66] have analyzed the association between the FAS −670 A/G or −1377 G/A polymorphisms and some autoimmune diseases. However, these studies only analyzed SLE, RA, LN, SSc, pSS, JIA, SPA, and AIH and did not include all autoimmune diseases. Furthermore, previous meta-analyses [63,65] including several studies [25,30,31,40] contained some errors when extracting the data. Thus, in the present study, we aimed to perform a meta-analysis to investigate whether the FAS −670 A/G or −1377 G/A polymorphisms is associated with autoimmune diseases risk by including 23 new articles, consisting of 33 studies [15,17,22,27–30,32–35,37,41,43–45,50,52–55,59,60] on SLE, MS, pSS, AA, PBC, HT, GBS, LN, vitiligo, T1D, IAA, and GD and correcting the errors in the previous meta-analyses. To our knowledge, this is the most comprehensive meta-analysis to assess the association of an FAS polymorphisms with the risk of autoimmune diseases, including SLE, RA, MS, AIH, LN, SSc, AA, pSS, HT, GBS, PBC, vitiligo, GD, T1D, IAA, JIA, and SPA.

This meta-analysis was conducted and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 checklist [67].

Literature search

Literature published in English and Chinese was retrieved from the PubMed, Embase, CNKI, and Wanfang databases up to December 2018. The search strategy used the following medical subject heading (MeSH) terms combined with text words: ‘FAS or TNFRSF6 or CD95 or APO-1 or rs1800682 or rs2234767’, ‘polymorphism, genetic or polymorphisms or polymorphism or variant or mutation’ and ‘autoimmune diseases or autoimmune disease or autoimmunity’. A manual search of the reference lists was also performed to identify additional articles.

Inclusion and exclusion criteria

Studies meeting all the following criteria were included in the analysis: (1) evaluation of the association between the FAS −670 A/G or −1377 G/A polymorphisms and autoimmune diseases risk; (2) available and sufficient genotype data to calculate the odds ratio (OR) with 95% confidence interval (CI); and (3) a case–control study design.

Studies were excluded if they met the following criteria: (1) containing overlapping data; (2) not containing genotype data from the cases and controls; and (3) reviews, case reports, abstracts, letters, animal experiments and meta-analyses.

Data extraction

Two investigators independently assessed and extracted data from all included studies. Discrepancies were resolved by discussion. The following data were collected from each study: disease type, first author, year of publication, country, ethnicity, genotyping method, sample sizes of cases and controls, genotype frequencies in cases and controls, and P-value of test for Hardy–Weinberg equilibrium (HWE) in controls.

Quality evaluation

The methodological quality of the included studies was assessed independently by two investigators using the Newcastle–Ottawa scale (NOS) score [68]. The NOS score ranges from 0 to 9 and encompasses three components, including selection, comparability, and exposure. A study with score greater than or equal to 6 was considered of high methodological quality. Discrepancies were resolved by discussion.

Statistical analysis

The chi-square test was applied to examine whether the observed genotype frequencies in controls conformed to HWE, and P<0.05 was considered to deviate from HWE. The ORs with their 95% CIs were used to assess the strength of associations between the FAS −670 A/G and −1377 G/A polymorphisms and autoimmune diseases. The statistical significance of the pooled ORs was determined by the Z test. The allelic (FAS −670 A/G: G vs. A; FAS −1377 G/A: A vs. G), homozygous (FAS −670 A/G: GG vs. AA; FAS −1377 G/A: AA vs. GG), heterozygous (FAS −670 A/G: GG vs. GA; FAS −1377 G/A: AA vs. AG), dominant (FAS −670 A/G: GG + GA vs. AA; FAS −1377 G/A: AA+AG vs. GG), and recessive (FAS −670 A/G: GG vs. GA+ AA; FAS −1377 G/A: AA vs. AG+GG) models were examined. The between-studies heterogeneity was assessed by Q test and quantified by I2 test [69]. When P≥0.1 or I2 < 50%, there was no heterogeneity, and pooled OR estimates were combined using the fixed-effects model (Mantel–Haenszel method); otherwise, the random-effects model (Mantel–Haenszel method) was used to combine summary data [70]. To detect the main sources of heterogeneity, subgroup analyses were performed by ethnicity, disease type and quality score. Sensitivity analysis was carried out by excluding studies deviating from HWE to assess the stability of the meta-analysis. Egger’s test was used to assess publication bias [71]. If there was publication bias, we recalculated the adjusted ORs using the trim-and-fill method [72] to evaluate the possible impact of publication bias. The trim-and-fill method was used to impute hypothetical missing studies. For significant results observed in the current meta-analysis, the false-positive report probability (FPRP) test was utilized to examine positive associations. An FPRP threshold of 0.5 and a prior probability of 0.1 were set to detect an OR of 0.67/1.50 (protective/risk effects) for an association with the tested genotypes. FPRP values less than 0.5 were considered as noteworthy associations [73]. All statistical analyses were conducted using Stata 15 software (Stata Corporation, College Station, TX, U.S.A.). Results with P<0.05 were considered significant.

Trial sequential analysis

Traditional meta-analysis may yield type I errors due to dispersed data or repetitive significance testing when new studies are added to it [74,75]. Trial sequential analysis (TSA) was used to minimize the risk of type I errors by calculating required information size (RIS) (meta-analysis sample size) and adjusted threshold for statistical significance [76]. TSA was performed by using TSA software 0.9.5.10 Beta (http://www.ctu.dk/tsa/) in the allelic model with the overall included studies by setting an overall type I error of 5%, power of 80%, relative risk reduction (RRR) of 20%, and control event proportion [77]. If the cumulative Z-curve crosses the trial sequential monitoring boundary or the RIS line, a reliable and conclusive evidence has been reached and further studies are not needed. Otherwise, more studies are needed to reach a firm conclusion.

Characteristics of the included studies

A flowchart of the selection of eligible articles is presented in Figure 1. The initial search identified 2552 articles through the search strategy, and a total of 43 articles [15,17,20–60], consisting of 67 studies comprising 13340 patients and 14547 controls, were finally included in the meta-analysis according to the inclusion and exclusion criteria. Fifty-two studies examined the FAS −670 A/G polymorphism, and 15 studies examined the FAS −1377 G/A polymorphism. The characteristics of the articles included in the meta-analysis are summarized in Table 1.

Flow diagram of the study selection process

Figure 1
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Flow diagram of the study selection process
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Flow diagram of the study selection process
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Table 1
Characteristics of the case–control studies association of FAS −670A/G and −1377G/A polymorphisms and autoimmune diseases
Disease typePolymorphismAuthorYearCountryEthnicityGenotyping methodSample size (case/control)Case (GG/GA/AA)Control (GG/GA/AA)HWENOS
P-valuescore
SLE FAS −670A/G Bollain et al. 2014 Mexico Mestizos PCR-RFLP 43/54 16 13 14 14 12 28 <0.001 5 
  Moudi et al. 2013 Iran Caucasian PCR-RFLP 106/149 17 55 34 39 73 37 0.808 7 
  Molin et al. 2012 Germany Caucasian PCR 46/96 8 21 17 34 51 11 0.213 4 
  Lu et al. 2012 China Asian PCR 552/718 96 237 219 138 326 254 0.070 6 
  Pradhan et al. 2012 India Indian PCR-RFLP 70/70 11 37 22 21 42 7 0.036 6 
  Arasteh et al. 2010 Iran Caucasian ASO-PCR 249/212 74 93 82 58 98 56 0.273 7 
  Xu et al. 2004 China Asian PCR-RFLP 103/110 15 59 29 23 61 26 0.249 5 
  Kanemitsu et al. 2002 Japan Asian AS-PCR, PCR-SSCP 109/140 25 49 35 50 64 26 0.492 5 
  Lee et al. 2001 Korea Asian PCR-RFLP 87/87 13 47 27 13 48 26 0.230 5 
  Huang et al. 1999 Australia Caucasian PCR-RFLP 79/86 20 21 38 20 22 44 <0.001 4 
MS FAS −670A/G Mohammadzadeh et al. 2012 Iran Caucasian PCR-RFLP 107/112 22 37 48 18 50 44 0.551 8 
  Kantarci et al. 2004 U.S.A. Caucasian PCR-RFLP 218/441 37 108 73 86 234 121 0.154 8 
  Lucas et al. 2004 Spain Caucasian PCR 320/218 68 177 75 44 113 61 0.525 7 
  Niino et al. 2002 Japan Asian PCR-RFLP 114/121 23 65 26 25 63 33 0.614 7 
  van Veen et al. 2002 Netherlands Caucasian PCR 383/206 80 185 118 42 118 46 0.036 6 
  Huang et al. 2000 Australia Caucasian PCR-RFLP 124/183 22 58 44 40 97 46 0.407 7 
RA FAS −670A/G Yıldır et al. 2013 Turkey Caucasian TaqMan 100/101 20 45 35 22 40 39 0.063 7 
  Kobak et al. 2012 Turkey Caucasian PCR-RFLP 101/105 24 50 27 14 52 39 0.608 5 
  Mohammadzadeh et al. 2011 Iran Caucasian PCR 120/112 17 64 39 18 50 44 0.551 4 
  Lee et al. 2001 Korea Asian PCR-RFLP 87/87 16 38 33 13 48 26 0.230 5 
  Huang et al. 1999 Australia Caucasian PCR-RFLP 185/86 32 105 48 22 44 20 0.825 4 
  Coakley et al. 1999 U.S.A. Caucasian PCR 18/128 4 8 6 31 61 36 0.607 4 
AIH FAS −670A/G Ngu et al. 2013 New Zealand Caucasian PCR 77/455 19 35 23 107 214 134 0.232 5 
  Su et al. 2012 China Asian PCR-RFLP 48/68 5 24 19 20 30 18 0.335 6 
  Agarwal et al. 2007 U.S.A. Caucasian PCR 149/172 35 75 39 32 84 56 0.960 4 
  Hiraide et al. 2005 Japan Asian PCR 72/130 14 31 27 40 63 27 0.811 4 
LN FAS −670A/G Bollain et al. 2014 Mexico Mestizos PCR-RFLP 24/54 8 9 7 14 12 28 <0.001 5 
  Pradhan et al. 2012 India Indian PCR-RFLP 35/70 7 16 12 21 42 7 0.036 6 
  Xu et al. 2004 China Asian PCR-RFLP 62/110 9 34 19 23 61 26 0.249 5 
  Lee et al. 2001 Korea Asian PCR-RFLP 26/87 4 12 10 13 48 26 0.230 5 
SSc FAS −670A/G Liakouli et al. 2013 Italy Caucasian PCR 350/232 65 158 127 60 120 52 0.586 8 
  Broen et al. 2009 Europe, U.S.A. Caucasian TaqMan 2565/2855 616 1205 744 586 1455 814 0.168 7 
  Broen et al. 2009 U.S.A. Hispanic TaqMan 159/137 46 80 33 41 71 25 0.552 7 
  Broen et al. 2009 U.S.A. African TaqMan 176/194 93 68 15 96 83 15 0.613 7 
AA FAS −670A/G Seleit et al. 2018 Egypt Caucasian PCR 60/40 14 37 9 4 23 13 0.181 8 
  Kalkan et al. 2013 Turkey Caucasian PCR-RFLP 118/118 0 81 37 13 65 40 0.077 7 
  Fan et al. 2010 China Asian PCR 84/84 13 35 36 13 49 22 0.099 6 
pSS FAS −670A/G Treviño-Talavera et al. 2014 Mexico Amerindian PCR-RFLP 77/84 25 32 20 22 42 20 0.996 4 
  Mullighan et al. 2004 Australia Caucasian PCR 101/108 17 54 30 21 54 33 0.897 4 
  Bolstad et al. 2000 Norway Caucasian PCR 70/72 26 26 18 12 39 21 0.394 5 
HT FAS −670A/G Erdogan et al. 2016 Turkey Caucasian PCR-RFLP 112/112 31 57 24 15 56 41 0.547 8 
  Inoue et al. 2016 Japan Asian PCR-RFLP 117/80 33 53 31 20 37 23 0.510 6 
GBS FAS −670A/G Islam et al. 2018 Japan Asian PCR 300/300 51 114 135 45 126 129 0.125 7 
  Geleijns et al. 2005 Netherlands Caucasian PCR 272/212 67 129 76 42 114 56 0.243 5 
PBC FAS −670A/G Su et al. 2012 China Asian PCR-RFLP 19/68 5 7 7 20 30 18 0.335 6 
  Hiraide et al. 2005 Japan Asian PCR 96/130 30 37 29 40 63 27 0.811 4 
vitiligo FAS −670A/G Li et al. 2008 China Asian PCR 750/756 101 364 285 108 363 285 0.660 7 
GD FAS −670A/G Inoue et al. 2016 Japan Asian PCR-RFLP 146/80 41 61 44 20 37 23 0.510 6 
T1D FAS −670A/G Sahin et al. 2012 Turkey Caucasian PCR 85/80 13 46 26 10 40 30 0.551 7 
IAA FAS −670A/G Rehman et al. 2018 Pakistan Caucasian PCR 170/222 13 105 52 26 47 149 <0.001 7 
JIA FAS −670A/G Donn et al. 2002 U.K. Caucasian PCR-RFLP 342/255 79 177 86 48 139 68 0.122 4 
SPA FAS −670A/G Lee et al. 2001 Korea Asian PCR 54/84 11 27 16 13 46 25 0.279 5 
        Case (AA/AG/GG) Control (AA/AG/GG)   
SLE FAS −1377A/G Arasteh et al. 2010 Iran Caucasian ASO-PCR 249/212 3 43 203 6 54 152 0.652 7 
  Kanemitsu et al. 2002 Japan Asian AS-PCR, PCR-SSCP 109/140 25 42 42 33 62 45 0.202 5 
  Huang et al. 2000 Australia Caucasian PCR 86/90 3 21 62 2 22 66 0.917 7 
RA FAS −1377A/G Zhu et al. 2016 China Asian MALDI-TOFMS 615/839 68 284 246 85 357 389 0.817 7 
  Yıldır et al. 2013 Turkey Caucasian TaqMan 100/101 0 26 74 2 18 81 0.411 7 
pSS FAS −1377A/G Mullighan et al. 2004 Australia Caucasian PCR 101/108 4 14 83 1 19 88 0.982 4 
  Bolstad et al. 2000 Norway Caucasian PCR 70/72 2 18 50 1 18 53 0.702 5 
GBS FAS −1377A/G Islam et al. 2018 Japan Asian PCR 300/300 12 105 183 9 93 198 0.627 7 
  Geleijns et al. 2005 Netherlands Caucasian PCR 272/212 3 61 208 1 40 171 0.406 5 
Vitiligo FAS −1377A/G Li et al. 2008 China Asian PCR 750/756 100 378 272 82 346 328 0.514 7 
IAA FAS −1377A/G Rehman et al. 2018 Pakistan Caucasian PCR 170/222 26 23 121 31 39 152 <0.001 7 
HT FAS −1377A/G Inoue et al. 2016 Japan Asian PCR-RFLP 123/87 26 61 36 13 40 34 0.826 6 
GD FAS −1377A/G Inoue et al. 2016 Japan Asian PCR-RFLP 160/87 27 78 55 13 40 34 0.826 6 
AIH FAS −1377A/G Hiraide et al. 2005 Japan Asian PCR 74/98 13 28 33 25 39 34 0.051 4 
AA FAS −1377A/G Fan et al. 2010 China Asian PCR 84/84 12 32 40 7 42 35 0.252 6 
Disease typePolymorphismAuthorYearCountryEthnicityGenotyping methodSample size (case/control)Case (GG/GA/AA)Control (GG/GA/AA)HWENOS
P-valuescore
SLE FAS −670A/G Bollain et al. 2014 Mexico Mestizos PCR-RFLP 43/54 16 13 14 14 12 28 <0.001 5 
  Moudi et al. 2013 Iran Caucasian PCR-RFLP 106/149 17 55 34 39 73 37 0.808 7 
  Molin et al. 2012 Germany Caucasian PCR 46/96 8 21 17 34 51 11 0.213 4 
  Lu et al. 2012 China Asian PCR 552/718 96 237 219 138 326 254 0.070 6 
  Pradhan et al. 2012 India Indian PCR-RFLP 70/70 11 37 22 21 42 7 0.036 6 
  Arasteh et al. 2010 Iran Caucasian ASO-PCR 249/212 74 93 82 58 98 56 0.273 7 
  Xu et al. 2004 China Asian PCR-RFLP 103/110 15 59 29 23 61 26 0.249 5 
  Kanemitsu et al. 2002 Japan Asian AS-PCR, PCR-SSCP 109/140 25 49 35 50 64 26 0.492 5 
  Lee et al. 2001 Korea Asian PCR-RFLP 87/87 13 47 27 13 48 26 0.230 5 
  Huang et al. 1999 Australia Caucasian PCR-RFLP 79/86 20 21 38 20 22 44 <0.001 4 
MS FAS −670A/G Mohammadzadeh et al. 2012 Iran Caucasian PCR-RFLP 107/112 22 37 48 18 50 44 0.551 8 
  Kantarci et al. 2004 U.S.A. Caucasian PCR-RFLP 218/441 37 108 73 86 234 121 0.154 8 
  Lucas et al. 2004 Spain Caucasian PCR 320/218 68 177 75 44 113 61 0.525 7 
  Niino et al. 2002 Japan Asian PCR-RFLP 114/121 23 65 26 25 63 33 0.614 7 
  van Veen et al. 2002 Netherlands Caucasian PCR 383/206 80 185 118 42 118 46 0.036 6 
  Huang et al. 2000 Australia Caucasian PCR-RFLP 124/183 22 58 44 40 97 46 0.407 7 
RA FAS −670A/G Yıldır et al. 2013 Turkey Caucasian TaqMan 100/101 20 45 35 22 40 39 0.063 7 
  Kobak et al. 2012 Turkey Caucasian PCR-RFLP 101/105 24 50 27 14 52 39 0.608 5 
  Mohammadzadeh et al. 2011 Iran Caucasian PCR 120/112 17 64 39 18 50 44 0.551 4 
  Lee et al. 2001 Korea Asian PCR-RFLP 87/87 16 38 33 13 48 26 0.230 5 
  Huang et al. 1999 Australia Caucasian PCR-RFLP 185/86 32 105 48 22 44 20 0.825 4 
  Coakley et al. 1999 U.S.A. Caucasian PCR 18/128 4 8 6 31 61 36 0.607 4 
AIH FAS −670A/G Ngu et al. 2013 New Zealand Caucasian PCR 77/455 19 35 23 107 214 134 0.232 5 
  Su et al. 2012 China Asian PCR-RFLP 48/68 5 24 19 20 30 18 0.335 6 
  Agarwal et al. 2007 U.S.A. Caucasian PCR 149/172 35 75 39 32 84 56 0.960 4 
  Hiraide et al. 2005 Japan Asian PCR 72/130 14 31 27 40 63 27 0.811 4 
LN FAS −670A/G Bollain et al. 2014 Mexico Mestizos PCR-RFLP 24/54 8 9 7 14 12 28 <0.001 5 
  Pradhan et al. 2012 India Indian PCR-RFLP 35/70 7 16 12 21 42 7 0.036 6 
  Xu et al. 2004 China Asian PCR-RFLP 62/110 9 34 19 23 61 26 0.249 5 
  Lee et al. 2001 Korea Asian PCR-RFLP 26/87 4 12 10 13 48 26 0.230 5 
SSc FAS −670A/G Liakouli et al. 2013 Italy Caucasian PCR 350/232 65 158 127 60 120 52 0.586 8 
  Broen et al. 2009 Europe, U.S.A. Caucasian TaqMan 2565/2855 616 1205 744 586 1455 814 0.168 7 
  Broen et al. 2009 U.S.A. Hispanic TaqMan 159/137 46 80 33 41 71 25 0.552 7 
  Broen et al. 2009 U.S.A. African TaqMan 176/194 93 68 15 96 83 15 0.613 7 
AA FAS −670A/G Seleit et al. 2018 Egypt Caucasian PCR 60/40 14 37 9 4 23 13 0.181 8 
  Kalkan et al. 2013 Turkey Caucasian PCR-RFLP 118/118 0 81 37 13 65 40 0.077 7 
  Fan et al. 2010 China Asian PCR 84/84 13 35 36 13 49 22 0.099 6 
pSS FAS −670A/G Treviño-Talavera et al. 2014 Mexico Amerindian PCR-RFLP 77/84 25 32 20 22 42 20 0.996 4 
  Mullighan et al. 2004 Australia Caucasian PCR 101/108 17 54 30 21 54 33 0.897 4 
  Bolstad et al. 2000 Norway Caucasian PCR 70/72 26 26 18 12 39 21 0.394 5 
HT FAS −670A/G Erdogan et al. 2016 Turkey Caucasian PCR-RFLP 112/112 31 57 24 15 56 41 0.547 8 
  Inoue et al. 2016 Japan Asian PCR-RFLP 117/80 33 53 31 20 37 23 0.510 6 
GBS FAS −670A/G Islam et al. 2018 Japan Asian PCR 300/300 51 114 135 45 126 129 0.125 7 
  Geleijns et al. 2005 Netherlands Caucasian PCR 272/212 67 129 76 42 114 56 0.243 5 
PBC FAS −670A/G Su et al. 2012 China Asian PCR-RFLP 19/68 5 7 7 20 30 18 0.335 6 
  Hiraide et al. 2005 Japan Asian PCR 96/130 30 37 29 40 63 27 0.811 4 
vitiligo FAS −670A/G Li et al. 2008 China Asian PCR 750/756 101 364 285 108 363 285 0.660 7 
GD FAS −670A/G Inoue et al. 2016 Japan Asian PCR-RFLP 146/80 41 61 44 20 37 23 0.510 6 
T1D FAS −670A/G Sahin et al. 2012 Turkey Caucasian PCR 85/80 13 46 26 10 40 30 0.551 7 
IAA FAS −670A/G Rehman et al. 2018 Pakistan Caucasian PCR 170/222 13 105 52 26 47 149 <0.001 7 
JIA FAS −670A/G Donn et al. 2002 U.K. Caucasian PCR-RFLP 342/255 79 177 86 48 139 68 0.122 4 
SPA FAS −670A/G Lee et al. 2001 Korea Asian PCR 54/84 11 27 16 13 46 25 0.279 5 
        Case (AA/AG/GG) Control (AA/AG/GG)   
SLE FAS −1377A/G Arasteh et al. 2010 Iran Caucasian ASO-PCR 249/212 3 43 203 6 54 152 0.652 7 
  Kanemitsu et al. 2002 Japan Asian AS-PCR, PCR-SSCP 109/140 25 42 42 33 62 45 0.202 5 
  Huang et al. 2000 Australia Caucasian PCR 86/90 3 21 62 2 22 66 0.917 7 
RA FAS −1377A/G Zhu et al. 2016 China Asian MALDI-TOFMS 615/839 68 284 246 85 357 389 0.817 7 
  Yıldır et al. 2013 Turkey Caucasian TaqMan 100/101 0 26 74 2 18 81 0.411 7 
pSS FAS −1377A/G Mullighan et al. 2004 Australia Caucasian PCR 101/108 4 14 83 1 19 88 0.982 4 
  Bolstad et al. 2000 Norway Caucasian PCR 70/72 2 18 50 1 18 53 0.702 5 
GBS FAS −1377A/G Islam et al. 2018 Japan Asian PCR 300/300 12 105 183 9 93 198 0.627 7 
  Geleijns et al. 2005 Netherlands Caucasian PCR 272/212 3 61 208 1 40 171 0.406 5 
Vitiligo FAS −1377A/G Li et al. 2008 China Asian PCR 750/756 100 378 272 82 346 328 0.514 7 
IAA FAS −1377A/G Rehman et al. 2018 Pakistan Caucasian PCR 170/222 26 23 121 31 39 152 <0.001 7 
HT FAS −1377A/G Inoue et al. 2016 Japan Asian PCR-RFLP 123/87 26 61 36 13 40 34 0.826 6 
GD FAS −1377A/G Inoue et al. 2016 Japan Asian PCR-RFLP 160/87 27 78 55 13 40 34 0.826 6 
AIH FAS −1377A/G Hiraide et al. 2005 Japan Asian PCR 74/98 13 28 33 25 39 34 0.051 4 
AA FAS −1377A/G Fan et al. 2010 China Asian PCR 84/84 12 32 40 7 42 35 0.252 6 
View Large

Meta-analysis results of the FAS −670 A/G and −1377 G/A polymorphisms and autoimmune diseases

A summary of the meta-analysis of the association between the FAS −670 A/G and −1377 G/A polymorphisms and autoimmune diseases is shown in Table 2. In the FAS −670 A/G polymorphism, a significant association between FAS −670 A/G and the risk of autoimmune diseases was observed under the heterozygous genetic model (GG vs. GA: OR = 1.079, 95% CI 1.004–1.160, P=0.038). In the FAS −1377 G/A polymorphism, our results indicated that FAS −1377 G/A polymorphism was associated with the risk of autoimmune diseases (A vs. G: OR = 1.11, 95% CI = 1.03–1.20, P=0.008; AA vs. GG: OR = 1.23, 95% CI = 1.03–1.47, P=0.024; AA+AG vs. GG: OR = 1.14, 95% CI = 1.02–1.26, P=0.015).

Table 2
Meta-analysis for the association between FAS −670A/G and −1377G/A polymorphisms and autoimmune diseases stratified by ethnicity, disease type and quality score
PolymorphismCategoriesStudies (n)Test of heterogeneityTest of associationsEgger’s testSensitivity analysis
P-valueI2 (%)OR (95% CI)P-valueP-valueP-value
FAS −670 A/G G vs. A 
 Overall 52 <0.001 65.9 0.99 (0.95, 1.03) 0.493 0.222 0.295 
 Caucasian 27 <0.001 71.5 1.03 (0.98, 1.08) 0.241 0.973 0.418 
 Asian 18 0.225 19.2 0.89 (0.83, 0.96) 0.002 0.147 0.002 
 High quality 28 <0.001 70.9 0.98 (0.94, 1.03) 0.446 0.314 0.427 
 Low quality 24 <0.001 59.3 1.00 (0.93, 1.08) 0.958 0.622 0.328 
 SLE 10 <0.001 73.6 0.85 (0.77, 0.94) 0.001 0.583 <0.001 
 RA 6 0.264 22.6 1.04 (0.88, 1.23) 0.675 0.772 0.675 
 MS 6 0.313 15.7 0.92 (0.82, 1.03) 0.148 0.826 0.348 
 AIH 4 0.003 78.2 0.89 (0.74, 1.08) 0.232 0.089 0.232 
 LN 4 0.041 63.6 0.82 (0.62, 1.08) 0.159 0.531 0.201 
 SSc 4 0.002 80.2 1.01 (0.95, 1.09) 0.707 0.419 0.707 
 AA 3 0.024 73.3 0.93 (0.72, 1.19) 0.553 0.372 0.553 
 pSS 3 0.692 0.0 1.02 (0.76, 1.36) 0.914 0.285 0.914 
 HT 2 0.082 67.0 1.45 (1.10, 1.90) 0.008 NA 0.008 
 GBS 2 0.709 0.0 1.03 (0.87, 1.23) 0.729 NA 0.729 
 PBC 2 0.828 0.0 0.82 (0.59, 1.14) 0.240 NA 0.240 
FAS −670 A/G GG vs. AA 
 Overall 52 <0.001 59.1 0.96 (0.89, 1.04) 0.288 0.104 0.375 
 Caucasian 27 <0.001 63.8 1.03 (0.94, 1.14) 0.524 0.519 0.368 
 Asian 18 0.225 19.2 0.81 (0.70, 0.94) 0.005 0.196 0.005 
 High quality 28 <0.001 62.6 0.95 (0.86, 1.04) 0.244 0.078 0.556 
 Low quality 24 <0.001 56.1 0.99 (0.85, 1.16) 0.905 0.287 0.304 
 SLE 10 <0.001 73.2 0.74 (0.61, 0.89) 0.002 0.230 <0.001 
 RA 6 0.263 22.7 1.05 (0.75, 1.48) 0.762 0.930 0.762 
 MS 6 0.353 9.9 0.87 (0.69, 1.10) 0.239 0.686 0.467 
 AIH 4 0.004 77.2 0.80 (0.56, 1.15) 0.232 0.123 0.232 
 LN 4 0.036 65.0 0.68 (0.39, 1.18) 0.173 0.843 0.226 
 SSc 4 0.002 79.1 1.04 (0.91, 1.19) 0.567 0.342 0.567 
 AA 3 0.003 82.9 0.68 (0.36, 1.28) 0.235 0.805 0.235 
 pSS 3 0.683 0.0 1.00 (0.56, 1.79) 0.998 0.044 0.286 
 HT 2 0.062 71.4 2.05 (1.19, 3.54) 0.010 NA 0.010 
 GBS 2 0.818 0.0 1.12 (0.79, 1.59) 0.510 NA 0.510 
 PBC 2 0.913 0.0 0.69 (0.37, 1.28) 0.234 NA 0.234 
FAS −670 A/G GG vs. GA 
 Overall 52 0.018 31.4 1.079 (1.004, 1.160) 0.038 0.087 0.006 
 Caucasian 27 <0.001 54.4 1.12 (1.03, 1.23) 0.012 0.008 0.001 
 Asian 18 0.835 0.0 0.99 (0.86, 1.14) 0.905 0.991 0.905 
 High quality 28 0.003 47.3 1.07 (0.99, 1.17) 0.096 0.022 0.028 
 Low quality 24 0.454 0.5 1.10 (0.95, 1.27) 0.208 0.364 0.180 
 SLE 10 0.568 0.0 0.92 (0.77, 1.11) 0.398 0.177 0.493 
 RA 6 0.276 20.9 0.96 (0.69, 1.31) 0.781 0.497 0.781 
 MS 6 0.766 0.0 1.05 (0.85, 1.30) 0.674 0.627 0.995 
 AIH 4 0.151 43.5 0.90 (0.64, 1.27) 0.563 0.005 0.563 
 LN 4 0.910 0.0 0.83 (0.49, 1.39) 0.471 0.142 0.621 
 SSc 4 0.243 28.2 1.20 (1.07, 1.36) 0.003 0.201 0.003 
 AA 3 0.009 78.7 0.79 (0.44, 1.41) 0.419 0.355 0.419 
 pSS 3 0.252 23.9 1.10 (0.66, 1.85) 0.715 0.358 0.071 
 HT 2 0.267 18.9 1.52 (0.93, 2.50) 0.098 NA 0.098 
 GBS 2 0.726 0.0 1.33 (0.96, 1.85) 0.089 NA 0.089 
 PBC 2 0.809 0.0 1.23 (0.71, 2.16) 0.461 NA 0.461 
FAS −670 A/G GG+GA vs. AA 
 Overall 52 <0.001 70.6 0.94 (0.89, 1.00) 0.051 0.129 0.004 
 Caucasian 27 <0.001 76.0 1.00 (0.93, 1.08) 0.945 0.662 0.306 
 Asian 18 0.143 26.7 0.83 (0.74, 0.92) 0.001 0.056 0.001 
 High quality 28 <0.001 76.7 0.94 (0.88, 1.01) 0.071 0.614 0.023 
 Low quality 24 <0.001 60.1 0.95 (0.85, 1.08) 0.445 0.500 0.050 
 SLE 10 <0.001 74.3 0.78 (0.67, 0.90) 0.001 0.374 <0.001 
 RA 6 0.388 4.4 1.09 (0.85, 1.40) 0.503 0.388 0.503 
 MS 6 0.080 49.1 0.83 (0.70, 0.99) 0.043 0.752 0.261 
 AIH 4 0.026 67.6 0.87 (0.65, 1.15) 0.330 0.170 0.330 
 LN 4 <0.001 84.2 0.86 (0.57, 1.31) 0.483 0.922 0.196 
 SSc 4 0.014 71.7 0.92 (0.82, 1.02) 0.112 0.424 0.112 
 AA 3 0.009 78.5 0.95 (0.66, 1.39) 0.804 0.666 0.804 
 pSS 3 0.741 0.0 0.98 (0.62, 1.54) 0.921 0.964 0.874 
 HT 2 0.150 51.7 1.58 (1.03, 2.42) 0.037 NA 0.037 
 GBS 2 0.988 0.0 0.92 (0.72, 1.19) 0.536 NA 0.536 
 PBC 2 0.976 0.0 0.61 (0.36, 1.03) 0.066 NA 0.066 
FAS −670 A/G GG vs. GA+AA 
 Overall 52 0.003 38.8 1.04 (0.97, 1.11) 0.294 0.083 0.142 
 Caucasian 27 0.001 52.7 1.10 (1.01, 1.19) 0.035 0.175 0.011 
 Asian 18 0.694 0.0 0.91 (0.80, 1.04) 0.162 0.541 0.162 
 High quality 28 0.005 45.3 1.03 (0.95, 1.12) 0.457 0.019 0.226 
 Low quality 24 0.066 32.2 1.06 (0.93, 1.21) 0.421 0.284 0.638 
 SLE 10 0.065 44.1 0.86 (0.72, 1.01) 0.071 0.292 0.034 
 RA 6 0.237 26.4 0.99 (0.74, 1.34) 0.962 0.627 0.962 
 MS 6 0.824 0.0 0.97 (0.80, 1.19) 0.796 0.714 0.709 
 AIH 4 0.027 67.3 0.86 (0.63, 1.18) 0.345 0.060 0.345 
 LN 4 0.895 0.0 0.71 (0.43, 1.15) 0.162 0.193 0.303 
 SSc 4 0.028 67.2 1.14 (1.02, 1.28) 0.022 0.275 0.022 
 AA 3 0.009 79.0 0.77 (0.44, 1.34) 0.349 0.546 0.349 
 pSS 3 0.338 0.0 1.07 (0.66, 1.75) 0.081 0.426 0.081 
 HT 2 0.122 58.1 1.68 (1.06, 2.68) 0.029 NA 0.029 
 GBS 2 0.678 0.0 1.24 (0.91, 1.69) 0.172 NA 0.172 
 PBC 2 0.787 0.0 0.99 (0.66, 1.75) 0.960 NA 0.960 
FAS −1377 G/A A vs. G 
 Overall 15 0.091 34.6 1.11 (1.03, 1.20) 0.008 0.329 0.006 
 Caucasian 7 0.173 33.4 0.98 (0.82, 1.16) 0.790 0.357 0.863 
 Asian 8 0.198 28.8 1.15 (1.05, 1.25) 0.002 0.167 0.002 
 High quality 10 0.116 36.5 1.14 (1.05, 1.24) 0.002 0.285 0.001 
 Low quality 5 0.293 19.2 0.96 (0.79, 1.18) 0.711 0.588 0.711 
FAS −1377 G/A AA vs. GG 
 Overall 15 0.452 0.0 1.23 (1.03, 1.47) 0.024 0.878 0.020 
 Caucasian 7 0.459 0.0 1.06 (0.67, 1.66) 0.816 0.752 0.881 
 Asian 8 0.353 10.0 1.27 (1.04, 1.54) 0.018 0.511 0.018 
 High quality 10 0.702 0.0 1.31 (1.08, 1.59) 0.007 0.234 0.005 
 Low quality 5 0.325 14.1 0.86 (0.54, 1.38) 0.536 0.072 0.536 
FAS −1377 G/A AA vs. AG 
 Overall 15 0.863 0.0 1.12 (0.93, 1.34) 0.234 0.584 0.323 
 Caucasian 7 0.570 0.0 1.30 (0.76, 2.20) 0.335 0.883 0.680 
 Asian 8 0.858 0.0 1.09 (0.90, 1.33) 0.360 0.444 0.360 
 High quality 10 0.820 0.0 1.12 (0.92, 1.36) 0.268 0.958 0.375 
 Low quality 5 0.507 0.0 1.11 (0.70, 1.77) 0.662 0.166 0.662 
FAS −1377 G/A AA+AG vs. GG 
         
 Overall 15 0.055 39.9 1.14 (1.02, 1.26) 0.015 0.113 0.008 
 Caucasian 7 0.190 31.1 0.95 (0.78, 1.16) 0.620 0.366 0.798 
 Asian 8 0.157 34.0 1.21 (1.07, 1.36) 0.002 0.080 0.002 
 High quality 10 0.047 47.5 1.17 (1.05, 1.31) 0.005 0.257 0.002 
 Low quality 5 0.402 0.7 0.96 (0.74, 1.23) 0.727 0.560 0.727 
FAS −1377 G/A AA vs. AG+GG 
 Overall 15 0.741 0.0 1.16 (0.98, 1.37) 0.090 0.888 0.097 
 Caucasian 7 0.490 0.0 1.10 (0.70, 1.72) 0.674 0.823 0.834 
 Asian 8 0.683 0.0 1.17 (0.97, 1.40) 0.098 0.959 0.098 
 High quality 10 0.823 0.0 1.20 (0.99, 1.44) 0.054 0.444 0.056 
 Low quality 5 0.392 2.5 0.96 (0.63, 1.47) 0.848 0.120 0.848 
PolymorphismCategoriesStudies (n)Test of heterogeneityTest of associationsEgger’s testSensitivity analysis
P-valueI2 (%)OR (95% CI)P-valueP-valueP-value
FAS −670 A/G G vs. A 
 Overall 52 <0.001 65.9 0.99 (0.95, 1.03) 0.493 0.222 0.295 
 Caucasian 27 <0.001 71.5 1.03 (0.98, 1.08) 0.241 0.973 0.418 
 Asian 18 0.225 19.2 0.89 (0.83, 0.96) 0.002 0.147 0.002 
 High quality 28 <0.001 70.9 0.98 (0.94, 1.03) 0.446 0.314 0.427 
 Low quality 24 <0.001 59.3 1.00 (0.93, 1.08) 0.958 0.622 0.328 
 SLE 10 <0.001 73.6 0.85 (0.77, 0.94) 0.001 0.583 <0.001 
 RA 6 0.264 22.6 1.04 (0.88, 1.23) 0.675 0.772 0.675 
 MS 6 0.313 15.7 0.92 (0.82, 1.03) 0.148 0.826 0.348 
 AIH 4 0.003 78.2 0.89 (0.74, 1.08) 0.232 0.089 0.232 
 LN 4 0.041 63.6 0.82 (0.62, 1.08) 0.159 0.531 0.201 
 SSc 4 0.002 80.2 1.01 (0.95, 1.09) 0.707 0.419 0.707 
 AA 3 0.024 73.3 0.93 (0.72, 1.19) 0.553 0.372 0.553 
 pSS 3 0.692 0.0 1.02 (0.76, 1.36) 0.914 0.285 0.914 
 HT 2 0.082 67.0 1.45 (1.10, 1.90) 0.008 NA 0.008 
 GBS 2 0.709 0.0 1.03 (0.87, 1.23) 0.729 NA 0.729 
 PBC 2 0.828 0.0 0.82 (0.59, 1.14) 0.240 NA 0.240 
FAS −670 A/G GG vs. AA 
 Overall 52 <0.001 59.1 0.96 (0.89, 1.04) 0.288 0.104 0.375 
 Caucasian 27 <0.001 63.8 1.03 (0.94, 1.14) 0.524 0.519 0.368 
 Asian 18 0.225 19.2 0.81 (0.70, 0.94) 0.005 0.196 0.005 
 High quality 28 <0.001 62.6 0.95 (0.86, 1.04) 0.244 0.078 0.556 
 Low quality 24 <0.001 56.1 0.99 (0.85, 1.16) 0.905 0.287 0.304 
 SLE 10 <0.001 73.2 0.74 (0.61, 0.89) 0.002 0.230 <0.001 
 RA 6 0.263 22.7 1.05 (0.75, 1.48) 0.762 0.930 0.762 
 MS 6 0.353 9.9 0.87 (0.69, 1.10) 0.239 0.686 0.467 
 AIH 4 0.004 77.2 0.80 (0.56, 1.15) 0.232 0.123 0.232 
 LN 4 0.036 65.0 0.68 (0.39, 1.18) 0.173 0.843 0.226 
 SSc 4 0.002 79.1 1.04 (0.91, 1.19) 0.567 0.342 0.567 
 AA 3 0.003 82.9 0.68 (0.36, 1.28) 0.235 0.805 0.235 
 pSS 3 0.683 0.0 1.00 (0.56, 1.79) 0.998 0.044 0.286 
 HT 2 0.062 71.4 2.05 (1.19, 3.54) 0.010 NA 0.010 
 GBS 2 0.818 0.0 1.12 (0.79, 1.59) 0.510 NA 0.510 
 PBC 2 0.913 0.0 0.69 (0.37, 1.28) 0.234 NA 0.234 
FAS −670 A/G GG vs. GA 
 Overall 52 0.018 31.4 1.079 (1.004, 1.160) 0.038 0.087 0.006 
 Caucasian 27 <0.001 54.4 1.12 (1.03, 1.23) 0.012 0.008 0.001 
 Asian 18 0.835 0.0 0.99 (0.86, 1.14) 0.905 0.991 0.905 
 High quality 28 0.003 47.3 1.07 (0.99, 1.17) 0.096 0.022 0.028 
 Low quality 24 0.454 0.5 1.10 (0.95, 1.27) 0.208 0.364 0.180 
 SLE 10 0.568 0.0 0.92 (0.77, 1.11) 0.398 0.177 0.493 
 RA 6 0.276 20.9 0.96 (0.69, 1.31) 0.781 0.497 0.781 
 MS 6 0.766 0.0 1.05 (0.85, 1.30) 0.674 0.627 0.995 
 AIH 4 0.151 43.5 0.90 (0.64, 1.27) 0.563 0.005 0.563 
 LN 4 0.910 0.0 0.83 (0.49, 1.39) 0.471 0.142 0.621 
 SSc 4 0.243 28.2 1.20 (1.07, 1.36) 0.003 0.201 0.003 
 AA 3 0.009 78.7 0.79 (0.44, 1.41) 0.419 0.355 0.419 
 pSS 3 0.252 23.9 1.10 (0.66, 1.85) 0.715 0.358 0.071 
 HT 2 0.267 18.9 1.52 (0.93, 2.50) 0.098 NA 0.098 
 GBS 2 0.726 0.0 1.33 (0.96, 1.85) 0.089 NA 0.089 
 PBC 2 0.809 0.0 1.23 (0.71, 2.16) 0.461 NA 0.461 
FAS −670 A/G GG+GA vs. AA 
 Overall 52 <0.001 70.6 0.94 (0.89, 1.00) 0.051 0.129 0.004 
 Caucasian 27 <0.001 76.0 1.00 (0.93, 1.08) 0.945 0.662 0.306 
 Asian 18 0.143 26.7 0.83 (0.74, 0.92) 0.001 0.056 0.001 
 High quality 28 <0.001 76.7 0.94 (0.88, 1.01) 0.071 0.614 0.023 
 Low quality 24 <0.001 60.1 0.95 (0.85, 1.08) 0.445 0.500 0.050 
 SLE 10 <0.001 74.3 0.78 (0.67, 0.90) 0.001 0.374 <0.001 
 RA 6 0.388 4.4 1.09 (0.85, 1.40) 0.503 0.388 0.503 
 MS 6 0.080 49.1 0.83 (0.70, 0.99) 0.043 0.752 0.261 
 AIH 4 0.026 67.6 0.87 (0.65, 1.15) 0.330 0.170 0.330 
 LN 4 <0.001 84.2 0.86 (0.57, 1.31) 0.483 0.922 0.196 
 SSc 4 0.014 71.7 0.92 (0.82, 1.02) 0.112 0.424 0.112 
 AA 3 0.009 78.5 0.95 (0.66, 1.39) 0.804 0.666 0.804 
 pSS 3 0.741 0.0 0.98 (0.62, 1.54) 0.921 0.964 0.874 
 HT 2 0.150 51.7 1.58 (1.03, 2.42) 0.037 NA 0.037 
 GBS 2 0.988 0.0 0.92 (0.72, 1.19) 0.536 NA 0.536 
 PBC 2 0.976 0.0 0.61 (0.36, 1.03) 0.066 NA 0.066 
FAS −670 A/G GG vs. GA+AA 
 Overall 52 0.003 38.8 1.04 (0.97, 1.11) 0.294 0.083 0.142 
 Caucasian 27 0.001 52.7 1.10 (1.01, 1.19) 0.035 0.175 0.011 
 Asian 18 0.694 0.0 0.91 (0.80, 1.04) 0.162 0.541 0.162 
 High quality 28 0.005 45.3 1.03 (0.95, 1.12) 0.457 0.019 0.226 
 Low quality 24 0.066 32.2 1.06 (0.93, 1.21) 0.421 0.284 0.638 
 SLE 10 0.065 44.1 0.86 (0.72, 1.01) 0.071 0.292 0.034 
 RA 6 0.237 26.4 0.99 (0.74, 1.34) 0.962 0.627 0.962 
 MS 6 0.824 0.0 0.97 (0.80, 1.19) 0.796 0.714 0.709 
 AIH 4 0.027 67.3 0.86 (0.63, 1.18) 0.345 0.060 0.345 
 LN 4 0.895 0.0 0.71 (0.43, 1.15) 0.162 0.193 0.303 
 SSc 4 0.028 67.2 1.14 (1.02, 1.28) 0.022 0.275 0.022 
 AA 3 0.009 79.0 0.77 (0.44, 1.34) 0.349 0.546 0.349 
 pSS 3 0.338 0.0 1.07 (0.66, 1.75) 0.081 0.426 0.081 
 HT 2 0.122 58.1 1.68 (1.06, 2.68) 0.029 NA 0.029 
 GBS 2 0.678 0.0 1.24 (0.91, 1.69) 0.172 NA 0.172 
 PBC 2 0.787 0.0 0.99 (0.66, 1.75) 0.960 NA 0.960 
FAS −1377 G/A A vs. G 
 Overall 15 0.091 34.6 1.11 (1.03, 1.20) 0.008 0.329 0.006 
 Caucasian 7 0.173 33.4 0.98 (0.82, 1.16) 0.790 0.357 0.863 
 Asian 8 0.198 28.8 1.15 (1.05, 1.25) 0.002 0.167 0.002 
 High quality 10 0.116 36.5 1.14 (1.05, 1.24) 0.002 0.285 0.001 
 Low quality 5 0.293 19.2 0.96 (0.79, 1.18) 0.711 0.588 0.711 
FAS −1377 G/A AA vs. GG 
 Overall 15 0.452 0.0 1.23 (1.03, 1.47) 0.024 0.878 0.020 
 Caucasian 7 0.459 0.0 1.06 (0.67, 1.66) 0.816 0.752 0.881 
 Asian 8 0.353 10.0 1.27 (1.04, 1.54) 0.018 0.511 0.018 
 High quality 10 0.702 0.0 1.31 (1.08, 1.59) 0.007 0.234 0.005 
 Low quality 5 0.325 14.1 0.86 (0.54, 1.38) 0.536 0.072 0.536 
FAS −1377 G/A AA vs. AG 
 Overall 15 0.863 0.0 1.12 (0.93, 1.34) 0.234 0.584 0.323 
 Caucasian 7 0.570 0.0 1.30 (0.76, 2.20) 0.335 0.883 0.680 
 Asian 8 0.858 0.0 1.09 (0.90, 1.33) 0.360 0.444 0.360 
 High quality 10 0.820 0.0 1.12 (0.92, 1.36) 0.268 0.958 0.375 
 Low quality 5 0.507 0.0 1.11 (0.70, 1.77) 0.662 0.166 0.662 
FAS −1377 G/A AA+AG vs. GG 
         
 Overall 15 0.055 39.9 1.14 (1.02, 1.26) 0.015 0.113 0.008 
 Caucasian 7 0.190 31.1 0.95 (0.78, 1.16) 0.620 0.366 0.798 
 Asian 8 0.157 34.0 1.21 (1.07, 1.36) 0.002 0.080 0.002 
 High quality 10 0.047 47.5 1.17 (1.05, 1.31) 0.005 0.257 0.002 
 Low quality 5 0.402 0.7 0.96 (0.74, 1.23) 0.727 0.560 0.727 
FAS −1377 G/A AA vs. AG+GG 
 Overall 15 0.741 0.0 1.16 (0.98, 1.37) 0.090 0.888 0.097 
 Caucasian 7 0.490 0.0 1.10 (0.70, 1.72) 0.674 0.823 0.834 
 Asian 8 0.683 0.0 1.17 (0.97, 1.40) 0.098 0.959 0.098 
 High quality 10 0.823 0.0 1.20 (0.99, 1.44) 0.054 0.444 0.056 
 Low quality 5 0.392 2.5 0.96 (0.63, 1.47) 0.848 0.120 0.848 
View Large

Stratification analyses by ethnicity, disease type, and quality score

Based on ethnicity, disease type, and quality score, we performed stratification analyses.

The results of the meta-analysis of the association between the FAS −670 A/G and −1377 G/A polymorphisms and autoimmune diseases risk stratified by ethnicity, disease type, and quality score are shown in Table 2.

On the basis of ethnicity, the stratified meta-analysis showed an association between FAS −670 A/G polymorphism and the risk of autoimmune diseases in Caucasians (GG vs. GA: OR = 1.12, 95% CI 1.03–1.23, P=0.012) and Asians (G vs. A: OR = 0.89, 95% CI 0.83–0.96, P=0.002) but not in other ethnic groups. The association between FAS −1377 G/A polymorphism and the risk of autoimmune diseases was observed in Asians (A vs. G: OR = 1.15, 95% CI 1.05–1.25, P=0.002) but not in Caucasians.

On the basis of disease type, the stratified meta-analysis suggested that the FAS −670 A/G polymorphism might be associated with the risk of SLE (G vs. A: OR = 0.85, 95% CI 0.77–0.94, P=0.001), MS (GG+GA vs. AA: OR = 0.83, 95% CI 0.70–0.99, P=0.043), SSc (GG vs. GA: OR = 1.20, 95% CI 1.07–1.36, P=0.003), and HT (G vs. A: OR = 1.45, 95% CI 1.10–1.90, P=0.008). However, no association was observed between the FAS −670A/G polymorphism and the risk of RA, AIH, AA, pSS, GBS, PBC, or LN. For FAS −1377 G/A polymorphism, subgroup analysis was not performed owing to the limited study number.

On the basis of quality score, the stratified meta-analysis suggested that the FAS −670 A/G polymorphism might not be associated with autoimmune diseases in high- or low-quality studies. However, the association between FAS −1377 G/A polymorphism and the risk of autoimmune diseases was observed in high-quality studies (A vs. G: OR = 1.14, 95% CI 1.05–1.24, P=0.002) but not in low-quality studies.

Stratification analysis showed that ethnicity, disease type, and quality score might be the factors of heterogeneity across all studies of association between FAS −670 A/G polymorphism and autoimmune diseases risk, and quality score may be the factor of heterogeneity across all studies of association between FAS −1377 G/A polymorphism and autoimmune diseases risk.

Stratification analysis by ethnicity for SLE, RA, MS, AIH, LN, SSc, AA, and pSS

The associations between the FAS −670 A/G polymorphism and SLE, RA, MS, AIH, LN, SSc, AA, and pSS are summarized in Table 3 (for FAS −1377 G/A polymorphism, subgroup analysis was not performed owing to the limited study number). An association between the FAS −670 A/G polymorphism and the risk of autoimmune diseases was observed in Asian patients with SLE (G vs. A: OR = 0.84, 95% CI 0.74–0.95, P=0.007) or AIH (G vs. A: OR = 0.55, 95% CI 0.40–0.76, P<0.001) and in Caucasian patients with SLE (G vs. A: OR = 0.80, 95% CI 0.67–0.96, P=0.015), MS (GG+GA vs. AA: OR = 0.80, 95% CI 0.66–0.96, P=0.018), or SSc (GG vs. GA: OR = 1.22, 95% CI 1.07–1.39, P=0.003). However, no significant risk was found in any specific ethnicity for RA, LN, AA, or pSS.

Table 3
Meta-analysis for the association between FAS −670 A/G polymorphism and SLE, RA, MS, AIH, LN, SSc, AA, and pSS stratified by ethnicity
DiseasesFAS −670A/G polymorphismPopulationStudies (n)Test of heterogeneityTest of associationsEgger’s test P-valuePower analysis (%)Sensitivity analysis value
P-valueI2 (%)OR (95% CI)P-value
SLE           
 G vs. A Caucasian 4 0.017 70.7 0.80 (0.67, 0.96) 0.015 0.634 73.1 0.015 
  Asian 4 0.296 18.9 0.84 (0.74, 0.95) 0.007 0.634 80.2 0.007 
 GG vs. AA Caucasian 4 0.011 73.2 0.68 (0.49, 0.94) 0.021 0.279 63.9 0.021 
  Asian 4 0.209 33.9 0.71 (0.55, 0.92) 0.010 0.545 72.9 0.010 
 GG vs. GA Caucasian 4 0.130 46.9 0.96 (0.70, 1.31) 0.797 0.288 5.1 0.797 
  Asian 4 0.848 0.0 0.93 (0.72, 1.18) 0.537 0.600 13.7 0.537 
 GG+GA vs. AA Caucasian 4 0.027 67.4 0.70 (0.54, 0.92) 0.011 0.442 81.6 0.011 
  Asian 4 0.091 53.6 0.77 (0.63, 0.93) 0.007 0.581 76.7 0.007 
 GG vs. GA+AA Caucasian 4 0.056 60.4 0.84 (0.63, 1.12) 0.228 0.272 19.2 0.228 
  Asian 4 0.767 0.0 0.83 (066, 1.05) 0.118 0.551 40.4 0.118 
RA           
 G vs. A Caucasian 5 0.197 33.7 1.06 (0.88, 1.27) 0.520 0.838 5.7 0.149 
 GG vs. AA Caucasian 5 0.169 37.8 1.07 (0.74, 1.55) 0.723 0.956 8.6 0.250 
 GG vs. GA Caucasian 5 0.309 16.6 0.88 (0.62, 1.24) 0.471 0.633 21.4 0.941 
 GG+GA vs. AA Caucasian 5 0.568 0.0 1.19 (0.90, 1.56) 0.221 0.376 27.6 0.103 
 GG vs. GA+AA Caucasian 5 0.178 36.5 0.95 (0.69, 1.32) 0.764 0.737 10.1 0.764 
MS           
 G vs. A Caucasian 5 0.282 20.9 0.90 (0.80, 1.02) 0.095 0.863 23.3 0.242 
 GG vs. AA Caucasian 5 0.296 18.6 0.84 (0.66, 1.08) 0.172 0.981 10.4 0.356 
 GG vs. GA Caucasian 5 0.678 0.0 1.07(0.85, 1.34) 0.576 0.410 17.7 0.899 
 GG+GA vs. AA Caucasian 5 0.103 48.1 0.80 (0.66, 0.96) 0.018 0.754 49.9 0.139 
 GG vs. GA+AA Caucasian 5 0.703 0.0 0.97 (0.79, 1.20) 0.809 0.710 5.0 0.716 
LN           
 G vs. A Asian 2 0.796 0.0 0.79 (0.55, 1.14) 0.201 NA 19.7 0.201 
 GG vs. AA Asian 2 0.634 0.0 0.62 (0.28, 1.35) 0.226 NA 18.7 0.226 
 GG vs. GA Asian 2 0.480 0.0 0.83 (0.40, 1.72) 0.621 NA 6.7 0.621 
 GG+GA vs. AA Asian 2 0.964 0.0 0.69 (0.40, 1.21) 0.196 NA 21.3 0.196 
 GG vs. GA+AA Asian 2 0.528 0.0 0.75 (0.37, 1.49) 0.407 NA 10.3 0.407 
SSc           
 G vs. A Caucasian 2 <0.001 93.3 1.01 (0.94, 1.09) 0.688 NA 6.3 0.688 
 GG vs. AA Caucasian 2 <0.001 92.8 1.05 (0.91, 1.22) 0.476 NA 84.1 0.476 
 GG vs. GA Caucasian 2 0.056 72.6 1.22 (1.07, 1.39) 0.003 NA 10.3 0.003 
 GG+GA vs. AA Caucasian 2 0.001 90.5 0.92 (0.82, 1.03) 0.137 NA 33.6 0.137 
 GG vs. GA+AA Caucasian 2 0.003 88.4 1.15 (1.02, 1.30) 0.021 NA 64.5 0.021 
AIH           
 G vs. A Caucasian 2 0.368 0.0 1.14 (0.91, 1.43) 0.265 NA 13.4 0.265 
  Asian 2 0.786 0.0 0.55 (0.40, 0.76) <0.001 NA 95.9 <0.001 
 GG vs. AA Caucasian 2 0.369 0.0 1.29 (0.82, 2.02) 0.276 NA 5.9 0.276 
  Asian 2 0.591 0.0 0.31 (0.16, 0.60) <0.001 NA 51.7 <0.001 
 GG vs. GA Caucasian 2 0.776 0.0 1.16 (0.76, 1.75) 0.489 NA 13.1 0.489 
  Asian 2 0.230 30.7 0.54 (0.29, 1.00) 0.051 NA 95.4 0.051 
 GG+GA vs. AA Caucasian 2 0.369 0.0 1.17 (0.82, 1.68) 0.384 NA 12.6 0.384 
  Asian 2 0.658 0.0 0.48 (0.29, 0.79) 0.004 NA 84.2 0.004 
 GG vs. GA+AA Caucasian 2 0.560 0.0 1.20 (0.82, 1.77) 0.350 NA 8.5 0.350 
  Asian 2 0.303 5.7 0.44 (0.25, 0.78) 0.005 NA 84.3 0.005 
AA           
 G vs. A Caucasian 2 0.021 81.2 1.06 (0.78, 1.45) 0.703 NA 9.7 0.703 
 GG vs. AA Caucasian 2 0.001 91.1 0.75 (0.32, 1.74) 0.496 NA 25.0 0.496 
 GG vs. GA Caucasian 2 0.002 89.3 0.50 (0.22, 1.10) 0.086 NA 5.2 0.086 
 GG+GA vs. AA Caucasian 2 0.118 59.2 1.39 (0.87, 2.23) 0.172 NA 34.0 0.172 
 GG vs. GA+AA Caucasian 2 0.001 90.1 0.61 (0.29, 1.32) 0.211 NA 15.2 0.211 
pSS           
 G vs. A Caucasian 2 0.096 64.0 1.19 (0.88, 1.60) 0.252 NA 20.7 0.252 
 GG vs. AA Caucasian 2 0.097 63.7 1.40 (0.77, 2.55) 0.273 NA 32.5 0.273 
 GG vs. GA Caucasian 2 0.015 83.0 1.49 (0.87, 2.56) 0.144 NA 24.4 0.144 
 GG+GA vs. AA Caucasian 2 0.783 0.0 1.10 (0.69, 1.74) 0.694 NA 6.8 0.694 
 GG vs. GA+AA Caucasian 2 0.020 81.7 1.49 (0.89, 2.47) 0.128 NA 34.0 0.128 
DiseasesFAS −670A/G polymorphismPopulationStudies (n)Test of heterogeneityTest of associationsEgger’s test P-valuePower analysis (%)Sensitivity analysis value
P-valueI2 (%)OR (95% CI)P-value
SLE           
 G vs. A Caucasian 4 0.017 70.7 0.80 (0.67, 0.96) 0.015 0.634 73.1 0.015 
  Asian 4 0.296 18.9 0.84 (0.74, 0.95) 0.007 0.634 80.2 0.007 
 GG vs. AA Caucasian 4 0.011 73.2 0.68 (0.49, 0.94) 0.021 0.279 63.9 0.021 
  Asian 4 0.209 33.9 0.71 (0.55, 0.92) 0.010 0.545 72.9 0.010 
 GG vs. GA Caucasian 4 0.130 46.9 0.96 (0.70, 1.31) 0.797 0.288 5.1 0.797 
  Asian 4 0.848 0.0 0.93 (0.72, 1.18) 0.537 0.600 13.7 0.537 
 GG+GA vs. AA Caucasian 4 0.027 67.4 0.70 (0.54, 0.92) 0.011 0.442 81.6 0.011 
  Asian 4 0.091 53.6 0.77 (0.63, 0.93) 0.007 0.581 76.7 0.007 
 GG vs. GA+AA Caucasian 4 0.056 60.4 0.84 (0.63, 1.12) 0.228 0.272 19.2 0.228 
  Asian 4 0.767 0.0 0.83 (066, 1.05) 0.118 0.551 40.4 0.118 
RA           
 G vs. A Caucasian 5 0.197 33.7 1.06 (0.88, 1.27) 0.520 0.838 5.7 0.149 
 GG vs. AA Caucasian 5 0.169 37.8 1.07 (0.74, 1.55) 0.723 0.956 8.6 0.250 
 GG vs. GA Caucasian 5 0.309 16.6 0.88 (0.62, 1.24) 0.471 0.633 21.4 0.941 
 GG+GA vs. AA Caucasian 5 0.568 0.0 1.19 (0.90, 1.56) 0.221 0.376 27.6 0.103 
 GG vs. GA+AA Caucasian 5 0.178 36.5 0.95 (0.69, 1.32) 0.764 0.737 10.1 0.764 
MS           
 G vs. A Caucasian 5 0.282 20.9 0.90 (0.80, 1.02) 0.095 0.863 23.3 0.242 
 GG vs. AA Caucasian 5 0.296 18.6 0.84 (0.66, 1.08) 0.172 0.981 10.4 0.356 
 GG vs. GA Caucasian 5 0.678 0.0 1.07(0.85, 1.34) 0.576 0.410 17.7 0.899 
 GG+GA vs. AA Caucasian 5 0.103 48.1 0.80 (0.66, 0.96) 0.018 0.754 49.9 0.139 
 GG vs. GA+AA Caucasian 5 0.703 0.0 0.97 (0.79, 1.20) 0.809 0.710 5.0 0.716 
LN           
 G vs. A Asian 2 0.796 0.0 0.79 (0.55, 1.14) 0.201 NA 19.7 0.201 
 GG vs. AA Asian 2 0.634 0.0 0.62 (0.28, 1.35) 0.226 NA 18.7 0.226 
 GG vs. GA Asian 2 0.480 0.0 0.83 (0.40, 1.72) 0.621 NA 6.7 0.621 
 GG+GA vs. AA Asian 2 0.964 0.0 0.69 (0.40, 1.21) 0.196 NA 21.3 0.196 
 GG vs. GA+AA Asian 2 0.528 0.0 0.75 (0.37, 1.49) 0.407 NA 10.3 0.407 
SSc           
 G vs. A Caucasian 2 <0.001 93.3 1.01 (0.94, 1.09) 0.688 NA 6.3 0.688 
 GG vs. AA Caucasian 2 <0.001 92.8 1.05 (0.91, 1.22) 0.476 NA 84.1 0.476 
 GG vs. GA Caucasian 2 0.056 72.6 1.22 (1.07, 1.39) 0.003 NA 10.3 0.003 
 GG+GA vs. AA Caucasian 2 0.001 90.5 0.92 (0.82, 1.03) 0.137 NA 33.6 0.137 
 GG vs. GA+AA Caucasian 2 0.003 88.4 1.15 (1.02, 1.30) 0.021 NA 64.5 0.021 
AIH           
 G vs. A Caucasian 2 0.368 0.0 1.14 (0.91, 1.43) 0.265 NA 13.4 0.265 
  Asian 2 0.786 0.0 0.55 (0.40, 0.76) <0.001 NA 95.9 <0.001 
 GG vs. AA Caucasian 2 0.369 0.0 1.29 (0.82, 2.02) 0.276 NA 5.9 0.276 
  Asian 2 0.591 0.0 0.31 (0.16, 0.60) <0.001 NA 51.7 <0.001 
 GG vs. GA Caucasian 2 0.776 0.0 1.16 (0.76, 1.75) 0.489 NA 13.1 0.489 
  Asian 2 0.230 30.7 0.54 (0.29, 1.00) 0.051 NA 95.4 0.051 
 GG+GA vs. AA Caucasian 2 0.369 0.0 1.17 (0.82, 1.68) 0.384 NA 12.6 0.384 
  Asian 2 0.658 0.0 0.48 (0.29, 0.79) 0.004 NA 84.2 0.004 
 GG vs. GA+AA Caucasian 2 0.560 0.0 1.20 (0.82, 1.77) 0.350 NA 8.5 0.350 
  Asian 2 0.303 5.7 0.44 (0.25, 0.78) 0.005 NA 84.3 0.005 
AA           
 G vs. A Caucasian 2 0.021 81.2 1.06 (0.78, 1.45) 0.703 NA 9.7 0.703 
 GG vs. AA Caucasian 2 0.001 91.1 0.75 (0.32, 1.74) 0.496 NA 25.0 0.496 
 GG vs. GA Caucasian 2 0.002 89.3 0.50 (0.22, 1.10) 0.086 NA 5.2 0.086 
 GG+GA vs. AA Caucasian 2 0.118 59.2 1.39 (0.87, 2.23) 0.172 NA 34.0 0.172 
 GG vs. GA+AA Caucasian 2 0.001 90.1 0.61 (0.29, 1.32) 0.211 NA 15.2 0.211 
pSS           
 G vs. A Caucasian 2 0.096 64.0 1.19 (0.88, 1.60) 0.252 NA 20.7 0.252 
 GG vs. AA Caucasian 2 0.097 63.7 1.40 (0.77, 2.55) 0.273 NA 32.5 0.273 
 GG vs. GA Caucasian 2 0.015 83.0 1.49 (0.87, 2.56) 0.144 NA 24.4 0.144 
 GG+GA vs. AA Caucasian 2 0.783 0.0 1.10 (0.69, 1.74) 0.694 NA 6.8 0.694 
 GG vs. GA+AA Caucasian 2 0.020 81.7 1.49 (0.89, 2.47) 0.128 NA 34.0 0.128 

Abbreviation: NA, not available.

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Publication bias

The Egger’s test was performed to assess the publication bias under all genetic models of the meta-analysis and the results are shown in Table 2. For the FAS −670 A/G polymorphism, the results from Egger’s tests indicated evidence for publication bias in the homozygous model for pSS, heterozygous models for Caucasians, AIH and high-quality studies, and recessive models for high-quality studies (P=0.044, 0.008, 0.005, 0.022, and 0.019, respectively). After adjustment by the trim-and-fill method, the ORs corrected for publication bias were not qualitatively different for the five models (OR = 1.30, 95% CI = 0.79–2.13, P=0.298; OR = 1.13, 95% CI = 1.03–1.23, P=0.007; OR = 0.92, 95% CI = 0.65–1.30, P=0.636; OR = 1.09, 95% CI = 1.00–1.18, P=0.052; and OR = 1.04, 95% CI = 0.96–1.13, P=0.323, respectively). No publication bias was found among the studies regarding the association between Fas −1377 G/A polymorphism and autoimmune diseases risk (all P>0.05). Therefore, the presence of publication bias did not influence the stability of the results. In addition, the results concerning association between FAS −670 A/G polymorphism and SLE, RA, MS, AIH, LN, SSc, AA, and pSS stratified by ethnicity did not show any evidence of publication bias (Table 3).

Sensitivity analysis

The genotype frequencies in the controls of five articles [22,31,51,52,55] deviated significantly from the HWE, which could cause potential bias. To check the robustness of our results, sensitivity analysis was performed by excluding these five HWE-deviating studies. The corresponding results of the sensitivity analysis are provided in Tables 2 and 3. The results showed that the overall OR changed only under the dominant model (P=0.051 vs. 0.004) after excluding the HWE-deviating studies, but the association between the FAS −670 A/G polymorphism and autoimmune diseases risk was not qualitatively altered under the heterozygous model (P=0.038 vs. 0.006), illustrating that the meta-analysis results were stable. In the stratification analysis by ethnicity, the results in Caucasians and Asians did not change when the HWE-deviating studies were excluded. In the stratification analysis by disease type, the OR changed only under the recessive model (P=0.071 vs. 0.034) after excluding the HWE-deviating studies from the analysis of SLE, but the association between the FAS −670 A/G polymorphism and SLE risk was not qualitatively altered under the allelic model (P=0.001 vs. <0.001). However, the association between FAS −670 A/G and MS risk was materially altered under the dominant model (P=0.043 vs. 0.261) after excluding the HWE-deviating studies. Similarly, a change was observed in the analysis of Caucasian patients with MS under the dominant model (P=0.018 vs. 0.139). In the stratification analysis by quality score, the association between FAS −670 A/G and high-quality studies was materially altered under the heterozygous and dominant model (P=0.096 vs. 0.028; P=0.071 vs. 0.023) after excluding the HWE-deviating studies. Additionally, the results of the association between FAS −1377 G/A and autoimmune diseases risk did not change when the HWE-deviating studies were excluded in five models.

FPRP analysis results

The FPRP values were calculated for the main significant associations and the results are shown in Table 4. For a prior probability of 0.1, the FPRP values indicated that four genetic models (FAS −670 A/G: GG vs. GA; FAS −1377 G/A: A vs. G; FAS −1377 G/A: AA vs. GG; FAS −1377 G/A: AA+AG vs. GG) of the FAS −670 A/G and −1377 G/A polymorphisms were truly associated with an increased risk of autoimmune diseases (FPRP = 0.262, 0.073, 0.173, and 0.085, respectively). Furthermore, with regard to the FAS −670 A/G polymorphism, noteworthy results were found in Asians, Caucasians, SLE, HT, SSc, and MS. Regarding the FAS −1377 G/A polymorphism, a positive association was observed in Asians and high-quality studies.

Table 4
FPRP values for associations between FAS −670A/G and −1377G/A polymorphisms and autoimmune disease PRISMA 2009 Checklist
GenotypePopulationStudies (n)OR (95% CI)P-value1Statistical power2Prior probability
0.250.10.010.0010.0001
FAS −670 A/G G vs. A 
 Asian 18 0.89 (0.83, 0.96) 0.003 1.000 0.0083 0.0223 0.2023 0.718 0.962 
 SLE 10 0.85 (0.77, 0.94) 0.002 1.000 0.0053 0.0143 0.1333 0.608 0.939 
 HT 2 1.45 (1.10, 1.90) 0.007 0.597 0.0343 0.0963 0.539 0.922 0.992 
FAS −670 A/G GG vs. AA 
 Asian 18 0.81 (0.70, 0.94) 0.006 0.995 0.0163 0.0483 0.3553 0.847 0.982 
 SLE 10 0.74 (0.61, 0.89) 0.001 0.866 0.0053 0.0143 0.1373 0.615 0.941 
 HT 2 2.05 (1.19, 3.54) 0.010 0.131 0.1863 0.4073 0.883 0.987 0.999 
FAS −670 A/G GG vs. GA 
 Overall 52 1.079 (1.004, 1.160) 0.040 1.000 0.1063 0.2623 0.796 0.975 0.997 
 Caucasian 27 1.12 (1.03, 1.23) 0.018 1.000 0.0513 0.1383 0.637 0.947 0.994 
 SSc 4 1.20 (1.07, 1.36) 0.004 1.000 0.0133 0.0373 0.2993 0.811 0.977 
FAS −670 A/G GG+GA vs. AA 
 Asian 18 0.83 (0.74, 0.92) <0.001 1.000 0.0013 0.0033 0.0373 0.2803 0.795 
 SLE 10 0.78 (0.67, 0.90) <0.001 0.984 0.0023 0.0063 0.0633 0.4033 0.871 
 MS 6 0.83 (0.70, 0.99) 0.038 0.993 0.1043 0.2583 0.792 0.975 0.997 
 HT 2 1.58 (1.03, 2.42) 0.035 0.406 0.2083 0.4403 0.896 0.989 0.999 
FAS −670 A/G GG vs. GA+AA 
 Caucasian 27 1.10 (1.01, 1.19) 0.018 1.000 0.0503 0.1363 0.634 0.946 0.994 
 SSc 4 1.14 (1.02, 1.28) 0.027 1.000 0.0743 0.1933 0.725 0.964 0.996 
 HT 2 1.68 (1.06, 2.68) 0.029 0.317 0.2183 0.4553 0.902 0.989 0.999 
FAS −1377 G/A A vs. G 
 Overall 15 1.11 (1.03, 1.20) 0.009 1.000 0.0253 0.0733 0.4643 0.897 0.989 
 Asian 8 1.15 (1.05, 1.25) 0.001 1.000 0.0033 0.0093 0.0923 0.504 0.911 
 High quality 10 1.14 (1.05, 1.24) 0.002 1.000 0.0073 0.0203 0.1833 0.693 0.958 
FAS −1377 G/A AA vs. GG 
 Overall 15 1.23 (1.03, 1.47) 0.023 0.985 0.0653 0.1733 0.696 0.959 0.996 
 Asian 8 1.27 (1.04, 1.54) 0.015 0.955 0.0453 0.1253 0.610 0.940 0.994 
 High quality 10 1.31 (1.08, 1.59) 0.007 0.915 0.0203 0.0583 0.4053 0.873 0.986 
FAS −1377 G/A AA+AG vs. GG 
 Overall 15 1.14 (1.02, 1.26) 0.010 1.000 0.0303 0.0853 0.505 0.911 0.990 
 Asian 8 1.21 (1.07, 1.36) 0.001 1.000 0.0043 0.0123 0.1213 0.581 0.933 
 High quality 10 1.17 (1.05, 1.31) 0.005 1.000 0.0193 0.0553 0.3913 0.866 0.985 
GenotypePopulationStudies (n)OR (95% CI)P-value1Statistical power2Prior probability
0.250.10.010.0010.0001
FAS −670 A/G G vs. A 
 Asian 18 0.89 (0.83, 0.96) 0.003 1.000 0.0083 0.0223 0.2023 0.718 0.962 
 SLE 10 0.85 (0.77, 0.94) 0.002 1.000 0.0053 0.0143 0.1333 0.608 0.939 
 HT 2 1.45 (1.10, 1.90) 0.007 0.597 0.0343 0.0963 0.539 0.922 0.992 
FAS −670 A/G GG vs. AA 
 Asian 18 0.81 (0.70, 0.94) 0.006 0.995 0.0163 0.0483 0.3553 0.847 0.982 
 SLE 10 0.74 (0.61, 0.89) 0.001 0.866 0.0053 0.0143 0.1373 0.615 0.941 
 HT 2 2.05 (1.19, 3.54) 0.010 0.131 0.1863 0.4073 0.883 0.987 0.999 
FAS −670 A/G GG vs. GA 
 Overall 52 1.079 (1.004, 1.160) 0.040 1.000 0.1063 0.2623 0.796 0.975 0.997 
 Caucasian 27 1.12 (1.03, 1.23) 0.018 1.000 0.0513 0.1383 0.637 0.947 0.994 
 SSc 4 1.20 (1.07, 1.36) 0.004 1.000 0.0133 0.0373 0.2993 0.811 0.977 
FAS −670 A/G GG+GA vs. AA 
 Asian 18 0.83 (0.74, 0.92) <0.001 1.000 0.0013 0.0033 0.0373 0.2803 0.795 
 SLE 10 0.78 (0.67, 0.90) <0.001 0.984 0.0023 0.0063 0.0633 0.4033 0.871 
 MS 6 0.83 (0.70, 0.99) 0.038 0.993 0.1043 0.2583 0.792 0.975 0.997 
 HT 2 1.58 (1.03, 2.42) 0.035 0.406 0.2083 0.4403 0.896 0.989 0.999 
FAS −670 A/G GG vs. GA+AA 
 Caucasian 27 1.10 (1.01, 1.19) 0.018 1.000 0.0503 0.1363 0.634 0.946 0.994 
 SSc 4 1.14 (1.02, 1.28) 0.027 1.000 0.0743 0.1933 0.725 0.964 0.996 
 HT 2 1.68 (1.06, 2.68) 0.029 0.317 0.2183 0.4553 0.902 0.989 0.999 
FAS −1377 G/A A vs. G 
 Overall 15 1.11 (1.03, 1.20) 0.009 1.000 0.0253 0.0733 0.4643 0.897 0.989 
 Asian 8 1.15 (1.05, 1.25) 0.001 1.000 0.0033 0.0093 0.0923 0.504 0.911 
 High quality 10 1.14 (1.05, 1.24) 0.002 1.000 0.0073 0.0203 0.1833 0.693 0.958 
FAS −1377 G/A AA vs. GG 
 Overall 15 1.23 (1.03, 1.47) 0.023 0.985 0.0653 0.1733 0.696 0.959 0.996 
 Asian 8 1.27 (1.04, 1.54) 0.015 0.955 0.0453 0.1253 0.610 0.940 0.994 
 High quality 10 1.31 (1.08, 1.59) 0.007 0.915 0.0203 0.0583 0.4053 0.873 0.986 
FAS −1377 G/A AA+AG vs. GG 
 Overall 15 1.14 (1.02, 1.26) 0.010 1.000 0.0303 0.0853 0.505 0.911 0.990 
 Asian 8 1.21 (1.07, 1.36) 0.001 1.000 0.0043 0.0123 0.1213 0.581 0.933 
 High quality 10 1.17 (1.05, 1.31) 0.005 1.000 0.0193 0.0553 0.3913 0.866 0.985 
1

Chi-square test was used to calculate the genotype frequency distributions.

2

Statistical power was calculated using the number of observations in the subgroup and the OR and P-values in this table.

3

The level of FPRP threshold was set at 0.5 and noteworthy findings are presented.

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TSA results

In the TSA of association of FAS −670 A/G polymorphism and autoimmune diseases risk, the cumulative Z-curve neither crossed conventional boundary nor trial sequential monitoring boundary, however, the sample size reached RIS (3365) in allelic model (Figure 2A). In the TSA of association of FAS −1377 G/A polymorphism and autoimmune diseases risk, the sample size also reached RIS (4387) and the cumulative Z-curve crossed the conventional boundary, although the cumulative Z-curve did not cross trial sequential monitoring boundary in allelic model (Figure 2B). The TSA results indicated that the cumulative evidence was reliable and sufficient, and no additional studies were required.

Trial sequential analyses of the associations between FAS polymorphisms and autoimmune diseases risk

Figure 2
The RIS was calculated based on a type I error = 5%, power = 80%, and RRR = 20%. (A) FAS −670 A/G polymorphism; (B) FAS −1377G/A polymorphism.
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Trial sequential analyses of the associations between FAS polymorphisms and autoimmune diseases risk

The RIS was calculated based on a type I error = 5%, power = 80%, and RRR = 20%. (A) FAS −670 A/G polymorphism; (B) FAS −1377G/A polymorphism.

Figure 2
The RIS was calculated based on a type I error = 5%, power = 80%, and RRR = 20%. (A) FAS −670 A/G polymorphism; (B) FAS −1377G/A polymorphism.
View largeDownload slide
Trial sequential analyses of the associations between FAS polymorphisms and autoimmune diseases risk

The RIS was calculated based on a type I error = 5%, power = 80%, and RRR = 20%. (A) FAS −670 A/G polymorphism; (B) FAS −1377G/A polymorphism.

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Our results showed that ethnicity, disease type, and quality score may be the factors of heterogeneity across all studies of association between FAS −670 A/G polymorphism and autoimmune diseases, and quality score may be the factor of heterogeneity across all studies of association between FAS −1377 G/A polymorphism and autoimmune diseases. In the ethnicity stratification analysis, the results of our meta-analysis revealed diverse associations between the FAS −670 A/G and −1377 G/A polymorphisms and various autoimmune diseases in different ethnic groups. The findings indicated that the FAS gene polymorphisms might play different roles in different ethnic groups. This suggests that ethnic differences may be involved in the genetic backgrounds of these patients. There are several possible explanations for such an ethnic discrepancy. First, different populations usually have different patterns of linkage disequilibrium. The FAS −670 A/G and −1377 G/A polymorphisms may be in close linkage with different nearby causal variants in different populations. Second, the FAS −670 A/G and −1377 G/A polymorphisms may interact with environmental and genetic factors or combined effects among different ethnicities. Furthermore, lifestyle factors such as alcohol consumption, cigarette smoking, nutritional status, and menopausal status may also explain this discrepancy. Finally, study numbers and sample sizes were relatively small in the stratification analysis by ethnicity, which may have resulted in inadequate statistical power to detect associations between the FAS −670 A/G and −1377 G/A polymorphisms and autoimmune diseases.

In the disease-type stratification analysis, the FAS −670 G allele was associated with an increased risk of SSc and HT and with a decreased risk of SLE, MS, and AIH (in Asians) but was not associated with other autoimmune diseases. These findings may reflect differences in the risks of various autoimmune diseases due to differences in environmental and genetic backgrounds. The present results indicate that the FAS −670 G allele is associated with a decreased risk of SLE, MS, and AIH (in Asians), which conflicts with a previous finding that the FAS −670 G allele in the FAS promoter was associated with an increased risk of autoimmune diseases [22,78]. One possible mechanism by which this allele may reduce the risk of SLE, MS, and AIH (in Asians) is by a reduction in soluble FAS (sFAS). The FAS protein exists in two isoforms, one a transmembrane protein and the other a soluble protein. sFAS expression is highly regulated at the mRNA transcript level [79,80]. Transcription of both FAS and sFAS is driven by the same gene promoter [22], with alternative splicing of the FAS mRNA resulting in a variant that lacks exon 6, which encodes the transmembrane domain of FAS [81]. Plasma sFAS, an antiapoptotic molecule, has been found to block apoptosis in autoreactive lymphocytes by competing with FAS for FASL or soluble FASL binding in SLE, MS, and AIH (in Asians) [79,82–85]. Similarly, this may explain why the FAS −670 G allele was associated with an increased risk of autoimmune diseases in Caucasians and with a decreased risk in Asians. For FAS −1377 G/A polymorphism, subgroup analysis was not performed owing to the limited study number. The FAS −1377 G/A polymorphism occurs at the consensus sequence of transcription factor SP1 binding site in the silencer region [48]. The FAS −1377 A allele may destroy SP1 transcription factor binding sites, resulting in reduced promoter activity and FAS expression [18]. Abnormal apoptosis mediated by the FASL interaction with the FAS receptor is involved in the pathogenesis of several autoimmune diseases [19].

We performed a meta-analysis of data from patients diagnosed with autoimmune diseases (SLE, MS, RA, AIH, LN, SSc, AA, pSS, HT, GBS, PBC, vitiligo, GD, T1D, IAA, JIA, and SPA) and healthy controls. This meta-analysis differs from the seven previous meta-analyses [43,61–66] because the present study included 33 more studies (consisting of new studies with same and different disease types) [15,17,22,27–30,32–35,37,41,43–45,50,52–55,59,60] and yielded several novel and distinct findings. One previous meta-analysis [62] including SLE, RA, SSc, pSS, JIA, and SPA demonstrated that the FAS −670 A/G polymorphism might be associated with the risk of rheumatic disease, especially in Asians, SLE and RA, and the FAS −1377 G/A polymorphism was associated with SLE risk. Compared with this meta-analysis, our meta-analysis focused on overall autoimmune diseases risk and showed that FAS −670 A/G polymorphism was associated with autoimmune diseases risk in Caucasians, MS, SSc and HT; and the FAS −1377 G/A polymorphism was associated with autoimmune diseases risk in Asians and high quality studies, which were different from the previous meta-analyses. One meta-analysis [43] showed that the FAS −670 A/G polymorphism may be associated with SLE risk in the Chinese population. Two meta-analyses [64,66] suggested that the FAS −670 A/G and −1377 G/A polymorphisms was associated with the risk of SLE, stratification by ethnicity indicated an association between the FAS −670 A/G and SLE in Asian populations. Two meta-analyses [61,63] showed that the FAS −670 A/G polymorphism was not associated with the risk of RA. One meta-analysis [65] suggested that the FAS −670 A/G polymorphism was not associated with the risk of AIH. These six meta-analyses focused on the association between FAS polymorphism and a single disease (SLE, RA, or AIH). Compared with these meta-analyses, our meta-analysis covered overall autoimmune diseases, and subgroup analyses were performed by ethnicity, disease type, and quality score, thereby yielding several novel and distinct findings. Furthermore, some previous meta-analyses [63,65] including several studies [25,30,31,40] made some errors when extracting the data. Thus, we here added 33 new studies [15,17,22,27–30,32–35,37,41,43–45,50,52–55,59,60] on SLE, MS, pSS, AA, PBC, HT, GBS, LN, vitiligo, T1D, IAA, and GD and corrected the previous errors, providing more reliable results. In addition, FPRP test was performed to support that the evidence of our results was robust and sufficiently conclusive, and the result of TSA showed that there was sufficient evidence and much larger sample size to support these conclusions, thereby increasing the statistical power. We strongly believe that our findings can help resolve many of the controversies of the association of FAS polymorphism and autoimmune diseases.

Sensitivity analysis are generally performed to assess the robustness of meta-analyses by excluding and including HWE-deviating studies from genetic association studies, which is a recommended approach [86]. Probable explanations for deviation from HWE include nonrandom mating, population stratification, selection bias, genotyping error, inbreeding, genetic drift, chance, differential survival of marker carriers, or combinations of these reasons [87]. However, key empirical evidence does not support a strong association between estimates of genetic effect and deviations from HWE [88]. Nonetheless, the findings of our meta-analysis should be interpreted with caution in the case of material alterations in results after excluding the HWE-deviating studies.

The present study has several limitations that should be considered when interpreting the conclusions. First, only case–control studies were considered for inclusion. Selection bias and unmeasured confounding can occur at both the design and analysis stages of observational studies. Second, this analysis only included articles published in English and Chinese; this may reduce the credibility of the results because of language bias [89]. Third, our study only analyzed a single locus, single nucleotide polymorphism (SNP) −670 A/G and −1377 G/A in the FAS gene and did not investigate associations between genetic haplotypes containing the FAS −670 A/G and −1377 G/A polymorphisms and the risk of autoimmune diseases because of inadequate haplotype data. It is unknown whether other genetic mutations contribute to changes in the expression or function of the FAS gene. For uncovering the genetic causes of disease, haplotypes provide more information and have a greater influence than genotypes and single SNPs. Fourth, most studies included in our analysis were performed in the Caucasian and Asian populations; therefore, our results may apply only to these ethnic groups. Additional studies of other ethnicities are needed. Fifth, autoimmune diseases are multifactorial diseases caused by interactions between genetic and environmental factors, meaning that the FAS −670 A/G and −1377 G/A polymorphisms may only partially influence the pathogenesis of autoimmune diseases; this may lead to bias in the present results. Finally, the findings of our meta-analysis should be interpreted with caution in the case of heterogeneity observed under some genetic models.

Translating information of genetic associations into clinical diagnostics would help with improved understanding of the autoimmune diseases’ etiology. Establishing evidence-based medical evidence of genetic susceptibility to autoimmune diseases risk might facilitate the preventive and therapeutic strategies, which has a beneficial clinical utility for not only clinicians and researchers but also patients.

In summary, our meta-analysis suggested that the FAS −670 A/G polymorphism might be associated with the risk of autoimmune diseases, especially in Caucasians and Asians, SLE, MS, SSc, and HT. Moreover, the FAS −670 A/G polymorphism might be associated with the risk of autoimmune diseases in Asian patients with SLE or AIH and Caucasian patients with SLE, MS, or SSc. The FAS −1377 G/A/ polymorphism might be associated with the risk of autoimmune diseases, specifically for Asians and high quality studies. Stratification analysis showed that ethnicity, disease type and quality score might be the factors of heterogeneity across all studies of association between FAS −670 A/G polymorphism and autoimmune diseases risk, and quality score might be the factor of heterogeneity across all studies of association between FAS −1377 G/A polymorphism and autoimmune diseases risk.

Y.C. conceived and designed the meta-analysis. Y.H. and Y.C. performed the literature search and study selection. H.Y. and Y.H. extracted the data. Y.C. performed the quality evaluation and statistical analysis. H.Y. and Y.C. wrote the paper. Y.C. performed language correction and manuscript revision.

The authors declare that there are no competing interests associated with the manuscript.

The authors declare that there are no sources of funding to be acknowledged.

AA

alopecia areata

AIH

autoimmune hepatitis

CI

confidence interval

FASL

FAS-ligand

FPRP

false-positive report probability

GBS

Guillain–Barré syndrome

GD

Graves’ disease

HT

Hashimoto’s thyroiditis

HWE

Hardy–Weinberg equilibrium

IAA

idiopathic aplastic anemia

JIA

juvenile idiopathic arthritis

LN

lupus nephritis

MS

multiple sclerosis

NOS

Newcastle–Ottawa scale

OR

odds ratio

PBC

primary biliary cirrhosis

pSS

primary Sjögren’s syndrome

RA

rheumatoid arthritis

RIS

required information size

sFAS

soluble FAS

SLE

systemic lupus erythematosus

SNP

single nucleotide polymorphism

SPA

spondyloarthropathy

SP1

stimulatory protein 1

SSc

systemic sclerosis

TNF

tumor necrosis factor

TSA

trial sequential analysis

T1D

type 1 diabetes mellitus

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2020
This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).