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Background:
Systematic Review

Association of Gene Polymorphisms with Normal Tension Glaucoma: A Systematic Review and Meta-Analysis

by
Lijie Pan
1,
Jian Wu
2,3,* and
Ningli Wang
1,*
1
Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Key Laboratory of Ophthalmology and Visual Sciences, No. 1 Dong Jiao Min Xiang Street, Dongcheng District, Beijing 100730, China
2
School of Life Sciences, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, China
3
Henan Academy of Innovations in Medical Science, No. 2 Biotechnology Street, Hangkonggang District, Zhengzhou 450000, China
*
Authors to whom correspondence should be addressed.
Genes 2024, 15(4), 491; https://doi.org/10.3390/genes15040491
Submission received: 8 March 2024 / Revised: 10 April 2024 / Accepted: 12 April 2024 / Published: 14 April 2024
(This article belongs to the Special Issue Molecular Diagnosis and Disease Mechanisms in Eye Disorders)
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Versions Notes

Abstract

:
Background: Normal tension glaucoma (NTG) is becoming a more and more serious problem, especially in Asia. But the pathological mechanisms are still not illustrated clearly. We carried out this research to uncover the gene polymorphisms with NTG. Methods: We searched in Web of Science, Embase, Pubmed and Cochrane databases for qualified case-control studies investigating the association between single nucleotide polymorphisms (SNPs) and NTG risk. Odds ratios (ORs) and 95% confidence intervals (CIs) for each SNP were estimated by fixed- or random-effect models. Sensitivity analysis was also performed to strengthen the reliability of the results. Results: Fifty-six studies involving 33 candidate SNPs in 14 genetic loci were verified to be eligible for our meta-analysis. Significant associations were found between 16 SNPs (rs166850 of OPA1; rs10451941 of OPA1; rs735860 of ELOVL5; rs678350 of HK2; c.603T>A/Met98Lys of OPTN; c.412G>A/Thr34Thr of OPTN; rs10759930 of TLR4; rs1927914 of TLR4; rs1927911 of TLR4; c.*70C>G of EDNRA; rs1042522/-Arg72Pro of P53; rs10483727 of SIX1-SIX6; rs33912345 of SIX1-SIX6; rs2033008 of NCK2; rs3213787 of SRBD1 and c.231G>A of EDNRA) with increased or decreased risk of NTG. Conclusions: In this study, we confirmed 16 genetic polymorphisms in 10 genes (OPA1, ELOVL5, HK2, OPTN, TLR4, EDNRA, P53, NCK2, SRBD1 and SIX1-SIX6) were associated with NTG.

1. Introduction

Glaucoma is a disease characterized by optic neuropathy with the symptoms of visual impairment and visual field loss. It is usually associated with an increase in intraocular pressure (IOP) [1]. Normal-tension glaucoma (NTG) is always supposed to be a spectrum of primary open-angle glaucoma (POAG) [2,3] but with an IOP in the normal range [4], featured by normal anterior chamber depth, retinal nerve fiber layer (RNFL) thinning and progressing optic neuropathy [5]. NTG is becoming a more and more serious problem, with especially high prevalence in Asia. The morbidity of POAG in East Asians is from 1–4% [6], of which NTG contributes up to 95% [7]. However, it is reported that European Caucasians suffer less from NTG, which takes up about one-third of POAG patients [6]. It is plausible for us to suggest the incidence of NTG differs among various ethnicities. What is more, it should be noted that with the increased longevity, the incidence of NTG is likely to rise.
The pathological mechanisms of NTG are still not illustrated clearly and may be ascribed to multiple factors. Some hypotheses related to the pathogenesis include cardiovascular and neurovascular diseases, vasospasm, oxidative stress, endothelial dysfunction and abnormal biomechanics of the lamina cribrosa and so on [6,8]. Genetic polymorphism is supposed to play an important role in NTG. For one reason, people could suffer from glaucoma at different ages, and genetic predisposition may mean an earlier onset [9]. For another, gene detection has come into effect in the recognition of allele mutations, especially for young Mendelian glaucoma [10]. Some genes have been found to be associated with NTG, including Optineurin (OPTN), TANK-binding kinase (TBK1) and Myocilin (MYOC) [10].
In recent years, more interest has been attracted to the topic of the association between gene polymorphisms and NTG. Many studies have pointed out the relationship and statistical significance of gene mutations in the disease [11,12]. However, it confuses us that the former research studies differ from each other in involved SNPs and statistical significance influenced by different study areas, population ethnicity and research heterogeneity.
Our meta-analysis aims to collect and summarize all the satisfactory literature, and analyze the effect of allele mutations and gene functions specific to the onset of NTG, so as to provide an extensive exploration and evidence for us to uncover the gene polymorphisms with NTG.

2. Materials and Methods

The research protocol has been registered in PROSPERO with the ID CRD42022326782.

2.1. Search Strategy

We conducted the literature search and selection mainly from the following four databases: Web of Science, Embase, Pubmed and Cochrane. Three groups of MeSH terms were put into the search interface to frame the Boolean search strategy as follows, “(((Genes[MeSH Terms]) OR ((((((((Genes[Title/Abstract]) OR (Gene[Title/Abstract])) OR (Cistron[Title/Abstract])) OR (Cistrons[Title/Abstract])) OR (Genetic Materials[Title/Abstract])) OR (Genetic Material[Title/Abstract])) OR (Material, Genetic[Title/Abstract])) OR (Materials, Genetic[Title/Abstract]))) OR ((Polymorphism, Single Nucleotide[MeSH Terms]) OR (((((((Polymorphism, Single Nucleotide[Title/Abstract]) OR (Nucleotide Polymorphism, Single[Title/Abstract])) OR (Nucleotide Polymorphisms, Single[Title/Abstract])) OR (Polymorphisms, Single Nucleotide[Title/Abstract])) OR (Single Nucleotide Polymorphisms[Title/Abstract])) OR (SNPs[Title/Abstract])) OR (Single Nucleotide Polymorphism[Title/Abstract])))) AND ((“Low Tension Glaucoma”[Mesh]) OR ((((((Low Tension Glaucoma[Title/Abstract]) OR (Glaucoma, Low Tension[Title/Abstract])) OR (Low Tension Glaucomas[Title/Abstract])) OR (Normal Tension Glaucoma[Title/Abstract])) OR (Glaucoma, Normal Tension[Title/Abstract])) OR (Normal Tension Glaucomas[Title/Abstract])))”. In this way, a systematic retrospect of original articles of all types analyzing the association between gene polymorphisms and NTG risk was acquired.

2.1.1. Inclusion Criteria

(1)
The diagnostic standard of NTG should be indicated clearly;
(2)
Cohort studies involving NTG patients and healthy controls which evaluate the potential association of specific gene mutations, SNPs, allele variations related to pathogenesis of the disease;
(3)
Some important information should be included: demographic features such as age and sex, allele or genotype frequencies of SNPs in both case and control groups, index of association strength such as odds ratio (OR) with 95% confidence interval (CI).

2.1.2. Exclusion Criteria

(1)
Studies published in the form of meta-analysis, review, case report, patent, guideline, conference abstract and book chapters;
(2)
Studied objects are animals;
(3)
Studies not written in English;
(4)
Studies which lack OR value, only refers to POAG but not NTG or did not indicate a clear definition of POAG.
The included studies were come to by agreement of all the contributors of this article.

2.2. Data Extraction

Two reviewers independently screened and searched for the needed data from all the eligible literature. Disparities were discussed and solved by all the reviewers until consensus were reached. The following data were extracted and recollected in the table: reference (first author, year of publication), involved ethnicity, sample size of both case and control groups, demographic features including age and sex of two groups and genotyping method. If the basic or allele data of NTG were reported together with high-tension glaucoma (HTG) in POAG, we selected data specifically for NTG to document.

2.3. Quality Assessment

The methodological quality of all the eligible articles were assessed according to the Newcastle–Ottawa scale (NOS) [13]. There are three evaluation criteria in consideration: case selection, comparability and exposure. The quality of studies was recorded in the form of stars and the maximum star was 9. Studies acquired 6 stars or greater were considered up to our analyzing standard and qualified for further assessment.

2.4. Meta-Analysis

SNPs and gene mutations were qualified for meta-analysis if they were investigated by at least two studies. The statistical significance was recorded as OR [95% CI]. Allele frequency in eligible studies was calculated and screened after data organization, and minor allele for specific SNP was determined if it was consistent in all ethnic groups. Meta-analysis was processed by pooling OR values from eligible studies for the allele model (B versus A), dominant model (BB+ AB versus AA), recessive model (BB versus AA+ AB), heterozygote model (AB versus AA) and homozygote model (BB versus AA), respectively. Stata version 15.1 software (Stata Corporation, College Station, TX, USA) was used to perform statistical analyses. The difference was considered to be of statistical significance if the p value was less than 0.05.
The heterogeneity tests for independent studies orienting the same SNPs were conducted by means of Q test and I2 test. p value was used as testing statistics for Q test, and heterogeneity existed if it was below 0.05. Similarly, if that I2 value was greater than 50% it suggested a possibility of heterogeneity [14]. Then we chose fixed-effect model for studies without obvious heterogeneity to analyze the OR value for each gene polymorphism. On the contrary, random-effect model was chosen. What is more, Begg’s Test was used to evaluate the publication bias among included articles [15].
Subjects with NTG were further classified into different ethnicities and stratified meta-analysis was conducted for them. Sensitivity analysis was alco carried out.

3. Results

3.1. Selection of Qualified Literature

The procedure of our selection strategy can be acquired from Figure 1.
A total of 1377 studies could be searched through the four databases, of which 925 were from Web of Science, 219 from Embase, 230 from Pubmed and the remaining 3 from Cochrane Database. Among them, 493 were duplicated articles which should be excluded. We then screened for the title as well as abstract of the other 621 studies and removed a large part of the literature, for there were 272 unrelated articles, 140 meta-analyses and reviews, 65 conference abstracts, 59 animal studies, 12 case reports, 7 non-English articles and 1 guideline. Two hundred studies were left for us to be read through and the articles were to be excluded if important information was absent such as if there was no calculation of the OR value, no NTG group but only POAG group, no control group or POAG was not defined clearly. Finally, 56 articles were verified to be eligible for our meta-analysis [16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71].

3.2. Characteristics of Qualified Studies

The basic information of the included articles is summarized in Table 1. The qualified studies were published between November 2001 and January 2024. Among these studies, 55 were case control studies conducted in 11 countries and regions: 10 in China [22,26,37,40,53,54,57,66,69,70], 13 in Korea [20,25,30,39,47,58,59,60,62,63,64,67,71], 18 in Japan [18,19,23,24,27,31,32,33,34,38,41,42,46,48,49,51,52,56], 3 in Poland [55,61,65], 2 in the U.S [36,50], 3 in England [16,17,45], 2 in Australia [21,35] (one involving ethnicities of Caucasian and Asian with the other only Caucasian) and 1 each in four other countries or regions [28,29,43,68]. These studies involved 10,804 cases with NTG and 217,540 controls in all. Data from one GWAS were available whose cohort consisted of 305 Japanese NTG patients and 355 healthy controls [44]. The NOS scores of all the studies were above 6 stars (thus qualifying for the meta-analysis). Genotype frequency and minor allele frequency are shown in Table S1.

3.3. Meta-Analysis Results

Among all the SNPs extracted from the candidate gene literature, only 33 in 14 genetic loci were reported by at least two studies and met the criteria of this study. The association analysis and heterogeneity test in different genetic models are shown in Table 2 (since minor allele was opposite for SNP c.*1222C>T of EDNRA in the two studies incorporated, further analysis was not carried out in view of the heterogeneity. The related information is exhibited in Table S1). Of the 33 SNPs, 16 SNPs exhibited significant association with NTG, in which 11 variations (rs166850 of OPA1; rs10451941 of OPA1; rs735860 of ELOVL5; rs678350 of HK2; c.603T>A/Met98Lys of OPTN; c.412G>A/Thr34Thr of OPTN; rs10759930 of TLR4; rs1927914 of TLR4; rs1927911 of TLR4; c.*70C>G of EDNRA and rs1042522/-Arg72Pro of P53) showed positive NTG risk, whereas 5 others (rs2033008 of NCK2; rs3213787 of SRBD1; c.231G>A of EDNRA; rs10483727 of SIX1-SIX6 and rs33912345 of SIX1-SIX6) showed negative correlation with the onset of NTG.

3.3.1. Gene Polymorphisms Associated with NTG

The source articles and sample size for analysis of each SNP were summarized in Table 2.

EDNRA Polymorphisms

SNP c.-231G>A was associated with a decreased risk of NTG in the homozygote model (OR 0.61, 95%CI: 0.39–0.97, p = 0.035), but not in other models (Figure S1A).
SNP c.*70C>G was significantly associated with NTG in the dominant model (OR 1.67, 95%CI: 1.08–2.56, p = 0.020), but not in other models (Figure S1B).

ELOVL5 Polymorphism

A significant association between rs735860 of ELOVL5 gene and NTG was found in the heterozygote model (OR 1.51, 95%CI: 1.11–2.05, p = 0.009) (Figure S2A), but not in the other models (Figure S2B).

HK2 Polymorphism

A significant association between rs678350 and NTG could be seen in all genetic models (allele: OR 1.54, 95%CI: 1.23–1.91, p < 0.001; dominant: OR 1.75, 95%CI: 1.32–2.31, p < 0.001; recessive: OR 1.75, 95%CI: 1.09–2.80, p = 0.020; heterozygote: OR 1.65, 95%CI: 1.22–2.23, p = 0.001 and homozygote: OR 2.14, 95%CI: 1.31–3.48, p = 0.002) (Figure S3).

NCK2 Polymorphism

A significant association between rs2033008 and NTG could be seen in the allele (OR 0.70, 95%CI: 0.57–0.87, p = 0.001), recessive (OR 0.44, 95%CI: 0.27–0.70, p = 0.001) and homozygote models (OR 0.41, 95%CI: 0.25–0.67, p < 0.001) (Figure S4).

OPA1 Polymorphisms

A significant association between rs166850 and NTG was found in three genetic models (allele: OR 1.49, 95%CI: 1.03–2.15, p = 0.034; dominant: OR 1.93, 95%CI: 1.09–3.45, p = 0.025 and heterozygote: OR 1.82, 95%CI: 1.04–3.19, p = 0.038) (Figure S5A), but no evidence of an association was found in other models (Figure S5C).
A significant association between rs10451941 and NTG was found in all genetic models (allele: OR 1.49, 95%CI: 1.30–1.71, p < 0.001; dominant: OR 1.55, 95%CI: 1.29–1.87, p < 0.001; recessive: OR 1.87, 95%CI: 1.43–2.45, p < 0.001; heterozygote: OR 1.41, 95%CI: 1.16–1.71, p = 0.001 and homozygote: OR 2.16, 95%CI: 1.59–2.95, p < 0.001) (Figure S5B).

OPTN Polymorphisms

For SNP c.603T>A/Met98Lys, random effects showed a significant association between it and NTG in the allele, dominant and heterozygote models (allele: OR 1.51, 95%CI: 1.14–2.02, p = 0.005; dominant: OR 1.55, 95%CI: 1.12–2.14, p = 0.007; heterozygote: OR 1.49, 95%CI: 1.07–2.07, p = 0.018), but no evidence of association was found in other models (Figure S6A).
Referring to SNP c.412G>A/Thr34Thr, a significant association was found in all genetic models (allele: OR 1.66, 95%CI: 1.29–2.13, p < 0.001; dominant: OR 1.69, 95%CI: 1.27–2.25, p < 0.001; recessive: OR 3.72, 95%CI: 1.41–9.79, p = 0.008; heterozygote: OR 1.58, 95%CI: 1.17–2.12, p = 0.002 and homozygote: OR 4.22, 95%CI: 1.59–11.18, p = 0.004) (Figure S6B).
The other three SNPs (IVS6-5T>C, IVS6-10G>A, IVS7+24G>A) exhibited no statistical significance with NTG (Figure S6C–G).

P53 Polymorphism

A significant correlation of rs1042522/-Arg72Pro with NTG risk was revealed in the dominant model (OR 2.32, 95%CI: 1.02–5.28, p = 0.045), but not in the other four models (Figure S7).

SRBD1 Polymorphism

A negative correlation of rs3213787 and NTG risk could be seen in allele (OR 0.40, 95%CI: 0.30–0.52, p = 0.001), dominant (OR 0.38, 95%CI: 0.26–0.51, p = 0.001) and heterozygote (OR 0.41, 95%CI: 0.30–0.56, p = 0.002) models but not in other models (Figure S8).

TLR4 Polymorphisms

For rs10759930, results showed a significant association between it and NTG in heterozygote (OR 1.27, 95%CI: 1.02–1.59, p = 0.031) and homozygote models (OR 1.43, 95%CI: 1.06–1.94, p = 0.001) (Figure S9A).
For rs1927914, there was a significant association between it and NTG risk in the homozygote model (OR 1.43, 95%CI: 1.06–1.94, p = 0.020) (Figure S9B).
For rs1927911, a significant association between it and NTG risk was found in the heterozygote model (OR 1.29, 95%CI: 1.04–1.61, p = 0.021) (Figure S9C).
Rs12377632, rs2149356, rs11536889, rs7037117, rs7045953 revealed no significant association with NTG (Figure S9).

SIX1–SIX6 Polymorphism

Significant associations between rs10483727 and rs33912345 with a decreased risk of NTG could be seen in all models except for the heterozygote model (Figure S10A,B).

3.3.2. Gene Polymorphisms Not Associated with NTG

Among all the genetic polymorphisms analyzed, 17 SNPs in 7 genes were found not to be statistically significant with NTG (see Table 2).

3.3.3. Stratified Analysis in Different Ethnicities

In the stratification analysis by ethnicity, four SNPs were further investigated, including MTHFR rs397507444, OPA1 rs166850 and rs10451941 as well as p53 rs1042522. These SNPs showed no significant association with NTG in Asians. However, OPA1 rs166850, OPA1 rs10451941 and p53 rs1042522 were significantly associated with NTG in Caucasians (Table S2).

3.4. Measurement of Publication Biases and Sensitivity Analysis

Begg’s Test did not reveal publication bias among the overall analysis for candidate SNPs and corresponding genes (z < 1.96, p > 0.05, Table 2), which strengthened the credibility of our results. In the sensitivity analysis, Suh’s study [47] was excluded for rs7037117 in the TLR4 gene; this followed with a different conclusion that this SNP was significantly associated with NTG risk in the allele model (OR 1.46, 95%CI: 1.19–1.81, p < 0.001; I2 = 0.0%; Figure S11). Other alterations were not detected.

4. Discussion

Results showed that 16 SNPs in 10 genes were significantly associated with NTG in at least one genetic model. Related functions and pathogenic mechanisms of these associated alleles are summarized in Table 3 and Figure 2.

4.1. Oxidative Stress-Related Genes

The OPA1 gene encodes a kind of protein located in the inner membrane of mitochondria and plays an important role in cellular metabolism and activities, including stabilizing the mitochondrial construction, regulating mitochondrial fusion and fissure, taking part in oxidative phosphorylation and inhibiting chromosome c oxidase leaking, thus preventing cell apoptosis [72,73,74,75]. Aung [16] first conducted a study in Britain demonstrating that SNP rs166850 was significantly associated with NTG in 2002. We incorporated nine studies in our analysis with Caucasian, Asian and African-Caribbean populations, and finally elucidated that mutations in rs166850 and rs10451941 took effect in NTG in overall populations. This discovery reached the same conclusion as Guo’s meta-analysis in 2012 [76]. Compared with Guo, two more new studies were searched by us, thus confirming the reliability of the conclusion with a larger sample size. The interactions of the two polymorphisms with other genes may be a possible mechanism for NTG risk [65]. Interestingly, some scientists also found that TC/TC or CT/TT rs166850/rs10451941 combined genotype were more common in the Caucasian NTG population [16,45,65], which possibly indicated the overlapping pathogenetic effect of the two SNPs.
The P53 gene lies on the chromosome 17p13.1, encoding transcription factor p53 which regulates the cell circle, cell metabolism and senescence as well as DNA repair [77,78,79]. It is also related to cell apoptosis by stimulating the transcriptional activity of redox-related genes and producing reactive oxygen species (ROS) which damage the physiological function of mitochondria [80]. SNP rs1042522 has been reported to be located in the proline-rich region of p53 which would induce cell apoptosis by initiating the release of cytochrome c in the mitochondria into the cytosol [81]. Controversy exists about whether G allele or the mutant C allele would increase the susceptibility of POAG, with only different conclusions drawn in different ethnicities.

4.2. Neurodegeneration and Apoptosis-Related Genes

ELOVL5 is a member of the ELOVL gene family encoding a kind of elongase in the production of long-chain fatty acids [82], especially the polyunsaturated omega-3 and omega-6 fatty acids. The polyunsaturated fatty acids’ (PUFAs) metabolites play an important part in neurogenesis, neuronal survival and synaptic transmission [83,84,85]. What is more, ω-3 PUFAs could inhibit the damage of ischemia, inflammation, light, oxygen and age to retina [86]. Others showed that lack of eicosapentaenoic, docosahexaenoic acid and total ω-3 PUFAs were correlated to POAG risk [87]. The evidence above implies that alteration of rs735860 in the ELOVL5 gene may increase NTG susceptibility by affecting the neurons’ metabolism and inducing apoptosis of retinal ganglion cells (RGCs). Overexpression of ELOVL5 was also seen in prostate and gastric cancer cells for its incapability to regulate redox and mitochondrial homeostasis, and maintain appropriate production of reactive oxygen species (ROS) [88,89], which pointed out a new possible pathogenetic mechanism to be studied further.
NCK2 encodes proteins that regulate the cellular actin dynamics and polarity by interacting with tyrosine-phosphorylated growth factor receptors [90,91]. NCK2 is demonstrated to exist in the ganglion cell layer, inner nuclear layer and outer plexiform layer, which are highest in the ganglion cell layer [51]. D2S176, which is located in the locus GLC1B and is only 24 kb from the gene NCK2, was found to be associated with a genetic heterogeneity of adult-onset POAG, and recently was considered to increase NTG risk in the Japanese population [92,93], which indicated the possible correlation of NCK2 and NTG. In our study, the A allele in rs2033008 was negatively related to NTG onset in Korean and Japanese populations; we speculate that this variation changed the interaction of NCK2 with other genes resulting in a defensive effect of RGCs. Shi et al. [51] found that this SNP was associated with NTG but not POAG and supposed that the mechanisms of NTG were focused on optic nerve damage, but for POAG, changes in the anterior chamber weighed more heavily.
The HK2 gene is located in the outer membrane of mitochondria and catalyzes the first step of glycolysis [51]. It is expressed widely in photoreceptors (PRs) and plays a role in the aerobic glycolysis metabolizing glucose entering the cells [94]. HK2 inhibits the release of cytochrome c to prevent apoptosis through the Bax/Bak pathway [95]. Zhou et al. [96] found that the decreased expression of HK2 would lead to irreversible rod degeneration in animal models. Given the importance of HK2-encoding proteins in mitochondria, it is reasonable to believe that the variant phenotypes could induce metabolic dysfunction and, furthermore, optic neuropathy.
OPTN is a 67 kDa protein which is expressed in many cells and tissues, especially in retina, brain, heart and skeleton muscle [97]. It acts as an adaptor protein and participates in many physiological activities such as signal transduction, cell division, cell survival, exocytosis, autophagy, protein trafficking and so on [97]. Mutations of OPTN have been widely considered a pathogenesis of POAG [98] as well as NTG [99,100], of which E50K (c.148G>A) is the most common to be associated with POAG, and another mutation H486R (c.1457A>G) is correlated with juvenile open-angle glaucoma (JOAG). In our study, we drew a conclusion that c.603T>A and c.412G>A in OPTN were significantly associated with NTG, but another POAG meta-analysis [101] only found the association between the former with NTG in the stratified analysis. The reason may lie in the difference in studies included: the POAG meta-analysis included four studies, while we included three studies for one of the four failed to define NTG clearly and was thus excluded.
SIX1-SIX6 belong to the SIX gene family containing two protein domains, which could encode homeobox domain transcription factors and may play a role in regulating the development of the visual system [102]. Studies have shown that a missense variant in rs33912345 of SIX6 was associated with RNFL thinning [103,104], suggesting its function in RGC development or degeneration. The possible mechanism lies in its interaction with CDNK2A/CDNK2B and subsequently triggering RGC loss [105,106]. Our results, finding that the risk allele mutations of both rs10483727 and rs33912345 were associated with NTG, were consistent with the findings of previous studies [104,107], which confirmed the results of this research.

4.3. Inflammation-Related Genes

SRBD1 encodes proteins which modulate signal transduction via binding with RNA. Its overexpression is considered to promote proinflammatory cytokines accumulation, prevent cell proliferation and accelerate cell apoptosis [108,109,110], which would do harm to RGCs in NTG. Kanemaki et al. stated that SRBD1 polymorphisms were associated with NTG, despite IOP [111], suggesting the different pathogenetic factors of NTG from hyper-tension glaucoma (HTG). Rs3213787 was revealed to be negatively correlated with NTG, which indicates that the G allele may reduce SRBD1 activity and protect RGC from apoptosis.
Toll-like receptors (TLRs) are a kind of pattern recognition receptor (PRR) which play an important role in innate immunity and initiate inflammatory response by recognizing and binding with pathogen-associated molecular patterns (PAMPs) [112]. Among them, TLR4 is expressed in the conjunctiva, cornea, uvea and retina [49]. A study found the overexpression of TLR4 in glaucomatous retina and the optic nerve [113], which indicates that inflammation and chronic stress would have an effect on the microenvironment of RGCs, change the construction of lamina cribrosa and increase the susceptibility of remaining axons, leading to irreversible optic neuropathy. Recently, it was suggested that TLR4 was associated with POAG for its activation generates meshwork fibrosis via the TGF-β pathway, leading to elevation of IOP [114]; in addition, ligands of TLR4 (e.g., LPS and HSP) were considered as candidate antigens of NTG [115]. In our study, rs10759930, rs1927914 and rs1927911 were seen to show a significant association with NTG; we speculate that these polymorphisms change the expression of some important proteins by altering the translated regions or intron regions of mRNA in the translation process.

4.4. Microcirculation Disturbance-Related Gene

EDNRA is the specific receptor of endothelial-1 (ET-1), a 21-amino acid peptide performing as a vasoconstrictor [116], and can mediate ET-1 level in retinal blood flow. ET systems express greatly in most ocular tissues [117,118]. There have been studies which reported higher ET-1 concentration in the plasma of NTG patients compared with that of controls [119,120]. ET-1 system activation causes vasospasm, vascular endothelial injury and microvascular lesion, thus damaging the optic nerve. In addition, ET-1 affects the morphology and physiology of the optic nerve in rabbit models, resulting in optic disc excavation, loss of axons and demyelination of the optic nerve despite the level of IOP [121,122]. It also inhibits the anterograde axonal transport, lowers neural metabolic activity and promotes astrocytes’ proliferation, which is responsible for the optic neuropathy in glaucoma [123].
Concerns regarding the limitation of utilizing duplicated datasets from the same researchers or groups (ex. Study 2, 8, 10 and 11 shown in Table 1) were also taken into account. In some specific scenarios, these overlapping data should be selected for further utilization according to standard, otherwise bias may occur if the same subject is incorporated repeatedly. In view of this, we searched further similar literature for advice [101,124,125]. As a result, we found that those SNP-associated meta-analyses also incorporated studies from the “same dataset”. It seems reasonable because the overlapping data were not really included in the analysis for a specific SNP. Though duplicated in the cohort information in some studies, they were independent from each other because they targeted different genes and SNPs. Hence, a great deal of information would be missed once these data were deleted.
In this study, we summarized the reported genotype polymorphisms and obtained an insight into SNPs’ association with the susceptibility to NTG. We adopted some measures such as Quality assessment, HWE test, Begg’s Test and sensitivity analysis to control possible statistical errors and assure the credibility of our meta-analysis. However, there are some limitations which should not be ignored in the meta-analysis. First, the sample size from different ethnicities should be enlarged. Second, only studies published in English met the inclusion criteria, which might cause a failure to incorporate other non-English articles, resulting in incomplete analysis. Finally, the functions and mechanisms of specific allele variants were not clearly explained, partly due to the different results of included articles and limited experimental evidence. Further studies should be conducted to explain the doubts.

5. Conclusions

In conclusion, the present study summarized the reported genotype polymorphisms and obtained an insight into SNPs’ association with susceptibility to NTG. The mechanisms of these mutations on NTG could possibly be attributed to changing the metabolisms and activities of RGCs via mitochondria functional alteration, inflammation and immunity. Experimental evidence and more large-scale studies are required for a greater understanding of these genes and polymorphisms.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/genes15040491/s1, Figure S1. Associations between SNPs in EDNRA gene with NTG onset; Figure S2. Associations between SNPs in ELOVL5 gene with NTG onset; Figure S3. Associations between SNPs in HK2 gene with NTG onset; Figure S4. Associations between SNPs in NCK2 gene with NTG onset; Figure S5. Associations between SNPs in OPA1 gene with NTG onset; Figure S6. Associations between SNPs in OPTN gene with NTG onset; Figure S7. Associations between SNPs in P53 gene with NTG onset; Figure S8. Associations between SNPs in SRBD1 gene with NTG onset; Figure S9. Associations between SNPs in TLR4 gene with NTG onset; Figure S10. Associations between SNPs in SIX1-SIX6 gene with NTG onset; Figure S11. Sensitivity analysis for rs7037117 in TLR4 gene; Table S1. Genotype frequencies for candidate SNPs in the involved studies; Table S2. Genetic associations of NTG in different ethnicities.

Author Contributions

Conceptualization, L.P. and J.W.; Methodology, L.P. and J.W.; Software, L.P. and J.W.; Formal Analysis, L.P. and J.W.; Writing—Original Draft Preparation, L.P.; Writing—Review and Editing, N.W.; Funding Acquisition, N.W. All authors have read and agreed to the published version of the manuscript.

Funding

The study is supported by National Natural Science Foundation of China (GZR-2012–009); Beijing Traditional Chinese Medicine Technology Development, Fund Project (JJ2018-50).

Institutional Review Board Statement

An ethics statement is not applicable because this study is based exclusively on published literature.

Informed Consent Statement

Not applicable.

Data Availability Statement

The authors confirm that the data supporting the findings of this study are available within the article and its Supplementary Materials.

Acknowledgments

We would like to express our thanks to all the colleagues who helped us.

Conflicts of Interest

The authors have no conflicts of interest to declare.

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Genes 15 00491 g001
Figure 1. The procedure of literature selection for meta-analysis.
Figure 1. The procedure of literature selection for meta-analysis.
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Genes 15 00491 g002
Figure 2. SNPs significantly associated with the risk of NTG and their possible biological functions.
Figure 2. SNPs significantly associated with the risk of NTG and their possible biological functions.
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Table 1. Characteristics of qualified studies involved in the meta-analysis.
Table 1. Characteristics of qualified studies involved in the meta-analysis.
No.ReferenceCountry/City (Ethnicity)Sample SizeMale/FemaleAge, yQuality AssessmentGenotyping MethodsGenotype Frequency
CasesControls
CasesControlsCasesControlsCasesControlsAAABBBAAABBB
1Lee et al., 2022 [70]China Taiwan (Chinese)222236122/100127/10969 ± 968 ± 108☆allelic126801610810127
2Shin et al., 2022 [71]Korea (Korean)210117NANANANA8☆allelic13076464458
3He et al., 2022 [69]China Hongkong (Chinese)537496278/259184/31263.2 ± 12.870.2 ± 10.89☆allelicNANANANANANA
China Shantou (Chinese)13554379/56283/26061.6 ± 14.6 774.4 ± 6.9
4Liuska et al., 2021 [68]Finland (Finnish)892205,435NANANANA9☆allelic88480204,37810534
5Kim et al., 2021 [67]Korea (Korean)282213127/155120/9354.3 ± 13.354.6 ± 9.79☆allelicNANANANANANA
6Milanowski et al., 2021 [65]Poland (Caucasian)20425848/15680/17871.6 ± 11.170.9 ± 11.68☆allelic121700168796
7Yue et al., 2021 [66]China (Chinese)402425226/176254/17163.8 ± 6.564.5 ± 5.17☆allelic3117912338798
8Jung et al., 2020 [63]Korea (Korean)15910360/9944/5961.14 ± 11.9468.78 ± 9.827☆allelic2604412419618
9Lee et al., 2020 [64]Korea (Korean)435419206/229231/18858.8 ± 13.656.2 ± 10.37☆allelic2881272029011613
10Jung et al., 2019 [60]Korea (Korean)15410158/9642/5961.23 ± 11.9567.29 ±11.377☆allelic70681662318
11Jung et al., 2019 [62]Korea (Korean)15710657/10043/6361.06 ± 12.1667.19 ± 10.537☆allelic148909880
12Kosior-Jarecka et al., 2019 [61]Poland (Caucasian)14316543/100NA74NA8☆allelic7757690686
13Jeoung et al., 2017 [58]Korea (Korean)245231117/128115/11660.2 ± 12.758.6 ± 12.48☆allelic211391212330
14Suh et al., 2017 [59]Korea (Korean)140352NANANANA7☆allelic62611615815133
15Nishisako et al., 2016 [56]Japan (Japanese)292500140/152246/25446.7 ± 8.450.2 ± 10.67☆allelic9313564147248105
16Gao et al., 2016 [54]China (Chinese)555029/2631/1952.5 ± 14.049.1 ± 13.67☆allelic3915138120
17Sang et al., 2016 [57]China (Chinese)181266104/77114/15253.5 ± 16.867.6 ± 11.37☆allelic13145514010323
18Kosior-Jarecka et al., 2016 [55]Poland (Caucasian)16016550/11050/11572.01 ± 11.6172.52 ± 11.066☆allelic83671811569
19Lin et al., 2014 [53]China (Chinese)249262123/117140/12263.2 ± 10.261.3 ± 11.46☆allelic231180241210
20Shi et al., 2013 [52]Japan (Japanese)16318086/7795/8561.8 ± 13.768.0 ± 7.76☆allelic147160168111
21Shi et al., 2013 [51]Japan (Japanese)stage 1 12012161/6061/5954.0 ± 12.270.3 ± 10.26☆allelic1591111613010536
stage 2 286271139/147145/12656.4 ± 13.369.7 ± 9.3allelic
22Wiggs et al., 2012 [50]U.S. (Caucasian)6440023/41179/22161.06 ± 11.666.06 ± 11.37☆allelic36151827213
23TAKANO et al., 2012 [49]Japan (Japanese)365216171/194116/10058.6 ± 13.1 69.7 ± 11.38☆allelic141159651038528
24Suh et al., 2011 [47]Korea (Korean)147380NANANANA9☆allelic52722312619163
25Mabuchi et al., 2011 [46]Japan (Japanese)15819165/9370/12168.6 ± 11.865.7 ± 11.47☆allelic518423718931
26Yasumura et al., 2011 [48]Japan (Japanese)295518142/153NA46.4 ± 8.1NA8☆allelic2415224041104
27Wolf et al., 2010 [43]Germany (German)27328096/177115/16563.9 ± 14.266 ± 137☆allelic74131687513510
28Meguro et al., 2010 [44]Japan (Japanese)305355145/160174/18146.6 ± 8.561.7 ± 8.98☆genomic5113811610016293
29Mabuchi et al., 2010 [42]Japan (Japanese)21319191/12270/121NANA7☆allelic7910034778430
30Mabuchi et al., 2010 [41]Japan (Japanese)21319191/12270/121NANA7☆allelic5910747498458
31Fan et al., 2010 [40]China (Chinese)10020154/46120/8163.2 ± 11.569.8 ± 8.78☆allelic8991173271
32Yu-Wai-Man et al., 2010 [45]England (Caucasian)7075NANANA79.37☆allelic4126359133
33Fan et al., 2009 [37]China (Chinese)427733/958/1966.7 ± 10.172.0 ± 8.58☆allelic2713247273
34Clement et al., 2009 [35]Australia (75 Caucasian, 1 Asian)34429/2516/2672.5 ± 9.470.4 ± 7.89☆allelic2111225143
35Daugherty et al., 2009 [36]U.S. (Caucasian)5216718/3462/10569.8 ± 12.060.3 ± 12.07☆allelic292851095712
36Mabuchi et al., 2009 [38]Japan (Japanese)21318991/12270/11963.9 ± 13.765.5 ± 11.47☆allelic929526838323
37Woo et al., 2009 [39]Korea (Korean)7810032/4647/5346.2 ± 11.749.3 ± 9.28☆allelic253419315019
38Shibuya et al., 2008 [34]Japan (Japanese)250318119/131157/16146.1 ± 7.761.2 ± 8.38☆allelic811274213714140
39Tosaka et al., 2007 [33]Japan (Japanese)290241142/148114/12755.8 ± 13.069.7 ± 11.37☆allelic106130546713044
40Mabuchi et al., 2007 [31]Japan (Japanese)194185NANA63.6 ± 13.365.3 ± 11.58☆allelic1904018230
41Miyazawa et al., 2007 [32]Japan (Japanese)10311853/5062/5661.8 ± 11.768.0 ± 7.77☆allelic7625272415
42Jeoung et al., 2007 [30]Korea (Korean)6710028/3947/5348.8 ± 10.249.3 ± 9.28☆allelic5313183161
43How et al., 2007 [29]Singapore (Chinese)947964/3032/4772.967.77☆allelic7117164132
44Kim et al., 2006 [25]Korea (Korean)6710028/3947/5348.8 ± 10.249.3 ± 9.28☆allelic29326443917
45Lam et al., 2006 [26]China (Chinese)106300NA191/109NA70.4 ± 9.37☆allelic10231286131
46Inagaki et al., 2006 [24]Japan (Japanese)294240144/150114/12658.8 ± 13.269.7 ± 11.27☆allelic219723176631
47Mabuchi et al., 2006 [27]Japan (Japanese)131106NANA62.8 ± 13.365.0 ± 10.57☆allelic545819483919
48Yao et al., 2006 [28]Africa (African-Caribbean)6148NANA52.161.39☆allelic58304620
49Hashizume et al., 2005 [23]Japan (Japanese)268240129/139113/12758.8 ± 13.469.7 ± 11.27☆allelic16490141636611
50Dimasi et al., 2005 [21]Australia (Caucasian)62178NANANANA8☆allelic3443223810855
51Fan et al., 2005 [22]China (Chinese)106281NA180/101NA69.8 ± 9.86☆allelic67363200747
52Funayama et al., 2004 [18]Japan (Japanese)21721897/12092/12660.3 ± 12.470.6 ± 10.97☆allelic169435182351
53Fuse et al., 2004 [19]Japan (Japanese)6510027/3862/3861.8 ± 13.768 ± 7.76☆allelic55919550
54Woo et al., 2004 [20]Korea (Korean)6510126/3948/5347.0 ± 10.349.0 ± 9.28☆allelic623010100
55Powell et al., 2003 [17]England (Caucasian)6116826/35109/59NANA6☆allelic41164111534
56Aung et al., 2002 [16]England (Caucasian)163186NANANANA7☆allelic5726086140
NA: not applicable. ☆: The quality of studies was recorded in the form of stars and the maximum star was 9.
Table 2. Significant association analysis of genetic polymorphisms with NTG.
Table 2. Significant association analysis of genetic polymorphisms with NTG.
No.Gene
Symbol		SNPMinor AlleleNo. of CohortsEthnicityPooled Sample SizeGenetic ModelHeterogeneity TestFixed or Random Effect ModelOR95%CIpBegg’s Test
CasesControlsp (Q)I² (%)zp
1APOE-491A>TT2Chinese312581B vs. A0.928 0.0 fixed0.91 0.44–1.880.800 0.000 1.000
BB + AB vs. AA0.933 0.0 fixed0.77 0.35–1.730.531 0.000 1.000
BB vs. AA + AB0.974 0.0 fixed2.76 0.39–19.680.312 0.000 1.000
AB vs. AA0.940 0.0 fixed0.63 0.25–1.540.307 0.000 1.000
BB vs. AA0.973 0.0 fixed2.71 0.38–19.360.321 0.000 1.000
-427T>CC2 312582B vs. A0.941 0.0 fixed0.50 0.11–2.250.365 0.000 1.000
BB + AB vs. AA0.940 0.0 fixed0.50 0.11–2.250.363 0.000 1.000
BB vs. AA + ABexcludedexcludedNANANANANANA
AB vs. AA0.940 0.0 fixed0.50 0.11–2.250.363 0.000 1.000
BB vs. AAexcludedexcludedNANANANANANA
-219T>GG2 312581B vs. A0.885 0.0 fixed0.98 0.78–1.250.899 0.000 1.000
BB + AB vs. AA0.865 0.0 fixed0.80 0.59–1.100.172 0.000 1.000
BB vs. AA + AB0.957 0.0 fixed1.65 1.01–2.710.046 0.000 1.000
AB vs. AA0.874 0.0 fixed0.70 0.50–0.980.039 0.000 1.000
BB vs. AA0.916 0.0 fixed1.39 0.83–2.330.215 0.000 1.000
2EDNRAc.-231G>AA2Caucasian Korean227265B vs. A0.578 0.0 fixed0.95 0.73–1.220.683 0.000 1.000
BB + AB vs. AA0.731 0.0 fixed0.86 0.60–1.220.384 0.000 1.000
BB vs. AA + AB0.128 56.7 random0.95 0.64–1.420.816 0.000 1.000
AB vs. AA0.057 72.4 random0.850.50–1.450.554 0.000 1.000
BB vs. AA0.118 59.0 random0.610.39–0.970.0350.000 1.000
c.*70C>GG2 227265B vs. A0.005 87.4 random1.290.60–2.780.513 0.000 1.000
BB + AB vs. AA0.168 47.4 fixed1.671.08–2.560.0200.000 1.000
BB vs. AA + AB0.008 86.0 random1.14 0.37–3.560.817 0.000 1.000
AB vs. AA0.769 0.0 fixed1.45 0.89–2.350.135 0.000 1.000
BB vs. AA0.026 79.7 random1.44 0.44–4.720.543 0.000 1.000
3ELOVL5rs735860C2Japanese463546B vs. A0.001 91.3 random1.49 0.79–2.800.216 0.000 1.000
BB + AB vs. AA0.015 83.2 random1.81 0.88–3.700.106 0.000 1.000
BB vs. AA + AB0.049 74.2 random1.29 0.67–2.490.447 0.000 1.000
AB vs. AA0.448 0.0 fixed1.511.11–2.050.0090.000 1.000
BB vs. AA0.030 78.7 random1.65 0.71–3.820.246 0.000 1.000
4HK2rs678350G2Korean Japanese440372B vs. A0.580 0.0 fixed1.541.23–1.910.0000.000 1.000
BB + AB vs. AA0.689 0.0 fixed1.751.32–2.310.0000.000 1.000
BB vs. AA + AB0.499 0.0 fixed1.751.09–2.800.0200.000 1.000
AB vs. AA0.482 0.0 fixed1.651.22–2.230.0010.000 1.000
BB vs. AA0.633 0.0 fixed2.141.31–3.480.0020.000 1.000
5NCK2rs2033008A2Korean Japanese440372B vs. A0.799 0.0 fixed0.700.57–0.870.0010.000 1.000
BB + AB vs. AA0.636 0.0 fixed0.77 0.58–1.020.065 0.000 1.000
BB vs. AA + AB0.526 0.0 fixed0.440.27–0.700.0010.000 1.000
AB vs. AA0.968 0.0 fixed0.86 0.64–1.160.321 0.000 1.000
BB vs. AA0.558 0.0 fixed0.410.25–0.670.0000.000 1.000
6MTHFRrs397507444, c.677 C/TT3Caucasian Asian Korean Japanese243248B vs. A0.923 0.0 fixed1.02 0.79–1.330.855 0.000 1.000
BB + AB vs. AA0.828 0.0 fixed1.05 0.73–1.520.778 0.000 1.000
BB vs. AA + AB0.517 0.0 fixed1.00 0.62–1.620.986 0.000 1.000
AB vs. AA0.582 0.0 fixed1.07 0.72–1.600.725 0.000 1.000
BB vs. AA0.813 0.0 fixed1.01 0.59–1.720.969 0.000 1.000
rs1217691063c.1298 A/CC2Korean Japanese209206B vs. A0.604 0.0 fixed0.94 0.65–1.340.720 0.000 1.000
BB + AB vs. AA0.572 0.0 fixed0.95 0.63–1.430.797 0.000 1.000
BB vs. AA + AB0.712 0.0 fixed0.55 0.12–2.650.459 0.000 1.000
AB vs. AA0.574 0.0 fixed0.97 0.64–1.470.873 0.000 1.000
BB vs. AA0.511 0.0 fixed0.63 0.13–2.970.556 0.000 1.000
7NOS3rs1799983, 894 G>TT2Korean Chinese350446B vs. A0.248 25.0 fixed1.03 0.71–1.470.888 0.000 1.000
BB + AB vs. AA0.21933.7fixed1.000.68–1.460.9890.0001.000
BB vs. AA + AB0.865 0.0 fixed2.43 0.30–19.580.404 0.000 1.000
AB vs. AA0.215 35.0 fixed0.97 0.66–1.430.879 0.000 1.000
BB vs. AA0.833 0.0 fixed2.38 0.30–19.030.414 0.000 1.000
rs2070744,
-786T>C		C2 350446B vs. A0.315 0.8 fixed1.04 0.75–1.430.816 0.000 1.000
BB + AB vs. AA0.363 0.0 fixed1.00 0.70–1.420.987 0.000 1.000
BB vs. AA + AB0.261 20.7 fixed1.97 0.52–7.390.315 0.000 1.000
AB vs. AA0.427 0.0 fixed0.96 0.67–1.370.814 0.000 1.000
BB vs. AA0.248 25.1 fixed1.92 0.51–7.170.334 0.000 1.000
8OPA1rs166850, IVS8+4C⬎TT9Caucasian Chinese Japanese Korean African-Caribbean9041217B vs. A0.034 52.1 random1.491.03–2.150.0340.940 0.348
BB + AB vs. AA0.000 76.1 random1.931.09–3.450.0250.520 0.602
BB vs. AA + AB0.174 39.6 fixed0.96 0.41–2.240.931 1.020 0.308
AB vs. AA0.000 73.0 random1.821.04–3.190.0380.310 0.754
BB vs. AA0.216 32.6 fixed1.04 0.44–2.430.930 1.020 0.308
rs10451941, IVS8+32T⬎CC99441220B vs. A0.405 3.5 fixed1.491.30–1.710.0000.100 0.917
BB + AB vs. AA0.243 22.5 fixed1.551.29–1.870.0001.150 0.251
BB vs. AA + AB0.603 0.0 fixed1.871.43–2.450.0000.300 0.764
AB vs. AA0.130 36.0 fixed1.411.16–1.710.0010.520 0.602
BB vs. AA0.564 0.0 fixed2.161.59–2.950.0000.000 1.000
9OPTNc.603T>A, Met98LysA3Chinese Japanese388599B vs. A0.239 30.1 fixed1.511.14–2.020.0051.040 0.296
BB + AB vs. AA0.341 7.0 fixed1.551.12–2.140.0071.040 0.296
BB vs. AA + AB0.417 0.0 fixed2.20 0.82–5.950.119 1.040 0.296
AB vs. AA0.401 0.0 fixed1.491.07–2.070.0180.000 1.000
BB vs. AA0.459 0.0 fixed2.41 0.88–6.580.087 0.000 1.000
c.412G>A, Thr34ThrA3 388599B vs. A0.185 40.8 fixed1.661.29–2.130.0000.000 1.000
BB + AB vs. AA0.346 5.7 fixed1.691.27–2.250.0000.000 1.000
BB vs. AA + AB0.272 23.3 fixed3.721.41–9.790.0080.000 1.000
AB vs. AA0.529 0.0 fixed1.581.17–2.120.0020.000 1.000
BB vs. AA0.258 26.3 fixed4.221.59–11.180.0040.000 1.000
IVS6-5T>CC2 171381B vs. A0.048 74.4 random1.26 0.64–2.500.507 0.000 1.000
BB + AB vs. AA0.141 53.8 random1.07 0.59–1.970.817 0.000 1.000
BB vs. AA + AB0.135 55.3 random2.08 0.53–8.180.296 0.000 1.000
AB vs. AA0.058 72.2 random2.03 0.98–4.170.055 0.000 1.000
BB vs. AA0.387 0.0 fixed1.01 0.41–2.500.976 0.000 1.000
IVS6-10G>AA2 171381B vs. A0.532 0.0 fixed1.31 0.79–2.180.296 0.000 1.000
BB + AB vs. AA0.499 0.0 fixed1.33 0.78–2.270.299 0.000 1.000
BB vs. AA + AB..fixed1.55 0.10–25.170.759 NANA
AB vs. AA0.474 0.0 fixed1.32 0.77–2.280.316 0.000 1.000
BB vs. AA..fixed1.56 0.10–25.400.757 NANA
IVS7+24G>AA2 171381B vs. A0.760 0.0 fixed1.58 0.99–2.510.053 0.000 1.000
BB + AB vs. AA0.148 52.3 random1.29 0.60–2.750.517 0.000 1.000
BB vs. AA + AB0.398 0.0 fixed2.75 0.51–14.870.241 0.000 1.000
AB vs. AA0.061 71.6 random1.17 0.42–3.320.761 0.000 1.000
BB vs. AA0.856 0.0 fixed2.71 0.49–14.920.253 0.000 1.000
10p53rs1042522,
-Arg72Pro 		C5Caucasian Chinese Japanese 4901135B vs. A0.000 81.3 random0.97 0.64–1.450.868 0.240 0.806
BB + AB vs. AA0.000 93.3 random2.321.02–5.280.0450.240 0.806
BB vs. AA + AB0.013 68.5 random1.140 0.58–2.250.704 0.240 0.806
AB vs. AA0.003 75.1 random0.880 0.53–1.460.630 -0.240 1.000
BB vs. AA0.001 79.3 random1.020 0.41–2.510.973 -0.240 1.000
11SRBD1rs3213787G3Korean Japanese 622649B vs. A0.050 66.6 random0.400.30–0.520.0010.000 1.000
BB + AB vs. AA0.080 60.4 random0.380.26–0.510.0010.000 1.000
BB vs. AA + AB0.082 60.1 random0.23 0.09–0.590.252 0.000 1.000
AB vs. AA0.067 63.0 random0.410.30–0.560.0020.000 1.000
BB vs. AA0.075 61.4 random0.20 0.08–0.500.201 0.000 1.000
12TLR4rs10759930 C3Korean Japanese762914B vs. A0.087 59.0 random1.21 0.97–1.530.097 1.040 0.296
BB + AB vs. AA0.095 57.6 random1.29 0.94–1.780.114 0.000 1.000
BB vs. AA + AB0.186 40.5 fixed1.20 0.91–1.580.192 0.000 1.000
AB vs. AA0.178 42.1 fixed1.271.02–1.590.0310.000 1.000
BB vs. AA0.151 47.1 fixed1.431.06–1.940.0011.040 0.296
rs1927914 G3 762914B vs. A0.002 84.2 random1.29 0.89–1.870.180 1.040 0.296
BB + AB vs. AA0.074 61.7 random1.32 0.94–1.850.104 0.000 1.000
BB vs. AA + AB0.489 0.0 fixed1.24 0.94–1.640.125 1.040 0.296
AB vs. AA0.129 51.1 random1.30 0.94–1.780.108 1.040 0.296
BB vs. AA0.172 43.1 fixed1.431.06–1.940.0200.000 1.000
rs1927911 A3 762914B vs. A0.001 85.5 random1.33 0.91–1.960.141 0.000 1.000
BB + AB vs. AA0.091 58.2 random1.31 0.95–1.800.102 1.040 0.296
BB vs. AA + AB0.486 0.0 fixed1.25 0.94–1.660.125 0.000 1.000
AB vs. AA0.155 46.3 fixed1.291.04–1.610.0210.000 1.000
BB vs. AA0.013 76.9 random1.48 0.78–2.790.227 1.040 0.296
rs12377632T3 762914B vs. A0.114 54.0 random1.16 0.93–1.440.181 1.040 0.296
BB + AB vs. AA0.060 64.4 random1.27 0.90–1.800.171 0.000 1.000
BB vs. AA + AB0.379 0.0 random1.08 0.81–1.450.589 0.000 1.000
AB vs. AA0.057 65.0 random1.29 0.89–1.870.179 0.000 1.000
BB vs. AA0.016 75.8 random1.46 0.79–2.720.231 0.000 1.000
rs2149356T3 762914B vs. A0.020 74.5 random1.25 0.93–1.680.133 0.000 1.000
BB + AB vs. AA0.057 65.0 random1.29 0.91–1.830.155 1.040 0.296
BB vs. AA + AB0.359 2.3 fixed1.31 0.99–1.740.062 0.000 1.000
AB vs. AA0.120 52.8 random1.24 0.90–1.710.181 0.000 1.000
BB vs. AA0.019 74.9 random1.47 0.80–2.720.219 1.040 0.296
rs11536889 C3 762914B vs. A0.315 13.5 fixed1.05 0.89–1.240.527 0.000 1.000
BB + AB vs. AA0.000 93.8 random0.86 0.37–1.980.720 1.040 0.296
BB vs. AA + AB0.508 0.0 fixed0.88 0.57–1.350.559 1.040 0.296
AB vs. AA0.448 0.0 fixed1.13 0.92–1.400.247 0.000 1.000
BB vs. AA0.017 75.5 random1.34 0.59–3.020.867 0.000 1.000
rs7037117G3 762914B vs. A0.000 89.1 random1.12 0.67–1.890.665 1.040 0.296
BB + AB vs. AA0.051 66.4 random1.34 0.93–1.920.112 1.040 0.296
BB vs. AA + AB0.746 0.0 fixed1.28 0.82–2.010.280 0.000 1.000
AB vs. AA0.055 65.6 random1.33 0.91–1.930.138 1.040 0.296
BB vs. AA0.079 60.6 random1.48 0.71–3.080.290 1.040 0.296
rs7045953G3 762914B vs. A0.307 15.4 fixed1.12 0.86–1.450.414 1.040 0.296
BB + AB vs. AA0.339 7.5 fixed1.11 0.84–1.470.467 1.040 0.296
BB vs. AA + AB0.506 0.0 fixed1.56 0.51–4.750.436 0.000 1.000
AB vs. AA0.393 0.0 fixed1.09 0.82–1.460.534 1.040 0.296
BB vs. AA0.491 0.0 fixed1.42 0.46–4.350.542 0.000 1.000
13WDR36rs17553936, IVS16–30A>GG2 Chinese Japanese 145195B vs. A0.287 11.6 fixed0.70 0.47–1.040.078 0.000 1.000
BB + AB vs. AA0.365 0.0 fixed0.65 0.41–1.030.068 0.000 1.000
BB vs. AA + AB0.440 0.0 fixed0.68 0.20–2.280.528 0.000 1.000
AB vs. AA0.469 0.0 fixed0.66 0.41–1.060.086 0.000 1.000
BB vs. AA0.378 0.0 fixed0.61 0.18–2.060.426 0.000 1.000
14SIX1-SIX6rs10483727C2Korean Japanese 391383B vs. A0.141 53.8 random0.550.38–0.800.0020.000 1.000
BB + AB vs. AA0.062 71.2 random0.560.32–0.990.0470.000 1.000
BB vs. AA + AB0.543 0.0 fixed0.250.12–0.540.0000.000 1.000
AB vs. AA0.041 76.0 random0.65 0.34–1.240.188 0.000 1.000
BB vs. AA0.748 0.0 fixed0.210.10–0.460.0000.000 1.000
rs33912345A2 391383B vs. A0.069 69.7 random0.560.35–0.890.0130.000 1.000
BB + AB vs. AA0.067 70.3 random0.550.32–0.770.0380.000 1.000
BB vs. AA + AB0.856 0.0 fixed0.240.11–0.540.0010.000 1.000
AB vs. AA0.086 66.1 random0.62 0.36–1.080.089 0.000 1.000
BB vs. AA0.696 0.0 fixed0.200.08–0.450.0000.000 1.000
NTG: normal tension glaucoma; SNP: single nucleotide polymorphism; OR: odds ratio; CI: confidence interval; NA: not applicable. Bold value: OR (95%CI) >1 or <1 with p < 0.05.
Table 3. Possible functions and pathogenic mechanisms of the associated SNPs in the development of NTG.
Table 3. Possible functions and pathogenic mechanisms of the associated SNPs in the development of NTG.
GeneSNPOR (95%CI)p ValueInvolved MechanismsPossible Function in NTG
NameSymbol
optic atrophy 1OPA1rs166850, IVS8+4C⬎T1.49 (1.03–2.15)0.034 Encoding proteins crucial for normal mitochondrial functionDownregulation of OPA1 gene is associated with increased mitochondrial fission in optic nerve, increasing cell death of RGC-5 cells
rs10451941, IVS8+32T⬎C1.49 (1.30–1.71)0.000
elongation of long-chain fatty acids family member 5ELOVL5rs7358601.51 (1.11–2.05)0.009 Encoding elongases of polyunsaturated fatty acidsEnhanced ELOVL5 expression may cause apoptosis and cell growth inhibition in retinal ganglion cells
non-catalytic region of tyrosine kinase adaptorNCK2rs20330080.70 (0.57–0.87)0.001 Regulating the cellular actin dynamics and polarityParticipating in neural regeneration and protection, especially for the transition of glia cells into photoreceptors
hexokinase 2HK2rs6783501.54 (1.23–1.91)0.000 Catalyzing the first step of glycolysisImportant for photoreceptors’ function and preventing cell apoptosis
optineurinOPTNc.603T>A, Met98Lys1.51 (1.14–2.02)0.005 An adaptor protein involved in many cellular functionsInhibition of autophagy and induced cell death of RGCs
c.412G>A, Thr34Thr1.66 (1.29–2.13)0.000
S1 RNA binding domain 1SRBD1rs32137870.40 (0.30–0.52)0.001 Modulating signal transductionPrevent cell proliferation, promote proinflammatory cytokines accumulation and accelerate cell apoptosis of RGCs
toll-like receptor 4TLR4rs10759930 1.27 (1.02–1.59)0.031 Participating in innate immunity and initiating inflammatory responseInflammation and immunity lead to RGC apoptosis and optic nerve damage
rs1927914 1.43 (1.06–1.94)0.020
rs1927911 1.29 (1.04–1.61)0.021
endothelin receptor type AEDNRAc.-231G>A0.61 (0.39–0.97)0.035 Bind with ET-1 to activate vasoconstriction Damaging optic nerve resulted from vascular dysfunction and promoting astrocytes proliferation
c.*70C>G1.67 (1.08–2.56)0.020
tumor protein p53p53rs1042522,
-Arg72Pro 		2.32 (1.02–5.28)0.045 Regulating cell circle, cell metabolism, senescence and DNA repairProducing ROS causing mitochondria damage and inducing cell apoptosis
sine oculis homeobox homolog 1- sine oculis homeobox homolog 6SIX1-SIX6rs104837270.55 (0.38–0.80)0.002 Regulating the development of the visual systemReducing the number of retinal ganglion cells, especially during the aging process
rs339123450.56 (0.35–0.89)0.013
NTG: normal tension glaucoma; SNP: single nucleotide polymorphism; OR: odds ratio; CI: confidence interval; OPA1: optic atrophy 1; ELOVL5: elongation of long-chain fatty acids family member 5; NCK2: non-catalytic region of tyrosine kinase adaptor 2; HK2: hexokinase 2; OPTN: optineurin; SRBD1: S1 RNA binding domain 1; TLR4: toll-like receptor 4; EDNRA: endothelin receptor type A; p53: tumor protein 53.
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Pan, L.; Wu, J.; Wang, N. Association of Gene Polymorphisms with Normal Tension Glaucoma: A Systematic Review and Meta-Analysis. Genes 2024, 15, 491. https://doi.org/10.3390/genes15040491

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Pan L, Wu J, Wang N. Association of Gene Polymorphisms with Normal Tension Glaucoma: A Systematic Review and Meta-Analysis. Genes. 2024; 15(4):491. https://doi.org/10.3390/genes15040491

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Pan, Lijie, Jian Wu, and Ningli Wang. 2024. "Association of Gene Polymorphisms with Normal Tension Glaucoma: A Systematic Review and Meta-Analysis" Genes 15, no. 4: 491. https://doi.org/10.3390/genes15040491

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