26 June 2026: Meta-Analysis
Association Between Polymorphisms of 4 Common Genes and High Myopia Risk: A Comprehensive Analysis
Qianqian Yu BC 1*, Chao Sun DE 1, Tianhua Xie FG 1, Ningzhi Wangyang DE 1, Jun Shao BE 1, Yong Yao DF 1
DOI: 10.12659/MSM.952771
Med Sci Monit 2026; 32:e952771
Abstract
BACKGROUND: Genome-wide association studies have been suggested single-nucleotide polymorphisms (SNPs) can influence susceptibility to high myopia (HM). To investigate the associations of multiple SNPs of 4 common genes and HM, we collected all related articles about these 4 common SNPs and risk of HM.
MATERIAL AND METHODS: PubMed and Wanfang databases were searched for articles published until Dec 10, 2025 using the keywords ‘GJD2’ or ‘ZC3H11B’ or ‘MMP1’ or ‘MMP9’, ‘polymorphism’ and ‘myopia’ or ‘shortsightedness’. Odds ratios and 95% confidence intervals were used to examine the association between above 4 genes’ SNPs and HM risk using Stata software.
RESULTS: We performed a meta-analysis of data from 15 published articles. There were 2 SNPs in the GJD2 gene, 4 SNPs in the ZC3H11B gene, 1 in SNP in the MMP1 gene, and 1 SNP in the MMP9 gene. After analyses using Stata, significant results were detected: rs3743123 in the GJD2 gene was associated with a decreased overall HM risk. Additionally, similar trends were detected in all 4 SNPs in the ZC3H11B gene: rs4373767, rs4428898, rs10779363and rs7544369.
CONCLUSIONS: Our results suggest that the GJD2 gene rs3743123 and ZC3H11B gene 4 SNPs (rs4373767, rs4428898, rs10779363, rs7544369) polymorphisms are associated with risk of HM. Our results need to be confirmed by larger studies and mechanism research, which may aid in the early identification and prognostic evaluation of HM.
Keywords: Myopia, Degenerative, Polymorphism, Genetic
Introduction
Myopia prevalence has increased dramatically over recent decades. Globally, this upward trend is particularly pronounced in East Asia, making myopia a major public health concern [1–3]. As the most common cause of visual impairment, myopia results in an estimated global productivity loss of approximately US$250 billion annually [4–6]. To address the burden of eye diseases, it is imperative to develop comprehensive prevention and management strategies by elucidating the risk factors and pathophysiological mechanisms underlying them [7].
Emerging evidence underscores the substantial impact of genetic susceptibility on myopia and high myopia (HM). Polymorphisms in key genetic loci have been shown to significantly elevate susceptibility to this sight-threatening complication [8–10].
Genome-wide association studies (GWAS), when aggregated via meta-analysis, have identified numerous genetic loci significantly associated with both myopia and refractive error; however, the mechanisms by which genotypic identity confers myopia susceptibility remain unclear [11,12].
A previous meta-analysis systematically reviewed 76 studies (89 cohorts) encompassing 77 single-nucleotide polymorphisms (SNP) in 34 genes related to HM, identifying 22 SNPs in 13 genes (eg, ACAN, COL1A1, and CRYBA4) that are significantly associated with HM risk [13]. Additionally, 4 other common genes – GJD2, ZC3H11B, MMP1, and MMP9 – have also been implicated as risk factors for HM susceptibility.
Previous meta-analyses have been small, with insufficiently comprehensive subgroupings. The present study incorporated a larger number of relevant studies and provides a more specific and comprehensive analysis, enabling the derivation of conclusions with greater practical reference value. It is expected to provide essential molecular markers for the early detection of HM. Herein, we conducted pooled analyses of all case-control studies focusing on polymorphisms (rs634990, rs3743123, rs4373767, rs4428898, rs10779363, rs7544369, rs1799750, and rs17576) in these 4 common genes to assess their association with HM risk [14–24]. The aim was to generate stronger evidence regarding the existence of significant associations, which may be help in developing effective strategies to prevent HM.
Material and Methods
STUDY SELECTION AND DATA EXTRACTION:
The PubMed (https://pubmed.ncbi.nlm.nih.gov/) and Wanfang (https://www.wanfangdata.com.cn/) databases were searched for all articles published as of Dec 10, 2025, using the keywords: ‘GJD2 or gap junction protein delta 2’ or ‘ZC3H11B or zinc finger CCCH-type containing 11B’ or ‘MMP1 or matrix metallopeptidase 1’ or ‘MMP9’, ‘polymorphism’ and ‘myopia’ or ‘shortsightedness’. No language or publication year restrictions were imposed on the search. Additionally, the references of retrieved articles and reviews were manually searched to identify relevant studies. This study followed the established Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 reporting standards (Table 1) and included a detailed flow diagram [25]. This study was registered at PROSPERO (number 1365571; https://www.crd.york.ac.uk/prospero/). Finally, 2 independent reviewers from our research team screened the titles, abstracts, and full texts of retrieved articles to confirm eligibility for inclusion.
Eligible studies met the following criteria: (a) evaluated the correlation between HM risk and 1 or more of the selected polymorphisms; (b) were case-control studies; (c) included age- and sex-matched control groups; and (d) had an available full-text manuscript. Studies were excluded if they: (a) lacked a control population; (b) did not provide genotype frequencies; (c) were duplicate studies; or (d) there was clear evidence of bias. Data collected from eligible studies included the first author, publication year, country, ethnicity, different genes, different SNPs, genotypes in the case and control groups, source of controls, Hardy-Weinberg equilibrium (HWE) analyses of controls, and genotyping methods (eg, real-time PCR, TaqMan, MassARRAY).
ASSESSMENT OF STUDY QUALITY:
Study quality was assessed using the Newcastle-Ottawa Scale (NOS), which employs a star scoring system (range: 0–9 stars). A score of 9 stars indicates the highest methodological quality; scores of 5 to 9 are generally considered indicative of high quality, while scores of 0 to 4 suggest lower quality [26].
STATISTICAL ANALYSES:
Minor allele frequencies (MAF) for these 8 polymorphisms were assessed in 6 global populations using the 1000 Genomes Browser (
Odds ratios (ORs) and 95% confidence intervals (CIs) were used to examine the association between polymorphisms in the 4 common genes and HM risk, based on genotypic frequencies in cases and control subjects. Subgroup analyses were initially conducted based on the source of control subjects, separately assessing population-based (PB) and hospital-based (HB) studies.
The significance of pooled ORs was assessed using the Z test [27]. For evaluating the consistency of the included studies, the Cochrane’s Q test was first applied: if the P value of the Q test was >0.10, a fixed-effects model was used; otherwise, a random-effects model was used [28,29]. This was further validated using the I2 test: if I2 ≤50%, a fixed-effects model was used for all OR calculations; otherwise, significant heterogeneity was indicated, and a random-effects model was applied, followed by additional sensitivity and subgroup analyses to identify sources of heterogeneity [30]. For the 8 polymorphisms (rs634990, rs3743123, rs4373767, rs4428898, rs10779363, rs7544369, rs1799750, and rs17576) in the 4 target genes, associations between genotype and HM risk were assessed using 5 genetic models: dominant, heterozygote comparison, allelic contrast, homozygote comparison, and recessive.
To adjust for multiple comparisons, the stepdown Bonferroni method was applied to reduce the type I error (false positive) [31]. Begg’s and Egger’s tests were used to evaluate funnel plot asymmetry to detect publication bias [32], with P<0.05 considered statistically significant. The Pearson chi-square goodness-of-fit test was used to detect departures from HWE for the 8 polymorphisms, with P<0.05 as the significance cut-off. Stata v11.0 (StataCorp LP, TX, USA) was used to calculate the ORs, CIs, and to conduct Begg’s and Egger’s tests.
Results
SYSTEMATIC LITERATURE REVIEW AND STUDY SELECTION:
Our systematic search strategy retrieved 164 potentially relevant articles from PubMed and Wanfang databases. After rigorous evaluation based on predefined inclusion criteria, 15 high-quality studies were selected for meta-analysis (Figure 1), including 2 case-control studies for rs634990, 3 case-control studies for rs3743123, 4 case-control studies for rs4373767, 3 case-control studies for rs4428898, 3 case-control studies for rs10779363, 3 case-control studies for rs7544369, 2 case-control studies for rs1799750, and 2 case-control studies for rs17576. The detailed characteristics of included case-control studies are listed in Table 2.
MAF reports for these 8 polymorphisms were assessed in the 5 different global populations from the 1000 Genomes Browser, which shows the different ratio in different races for different polymorphisms (Figure 2). For example, the frequency from African in rs4428898 was the highest, however, was the lowest in rs4373767 (Figure 2).
POOLED ANALYSES:
A significant decreased association was detected between GJD2 rs3743123 polymorphism and HM risk: OR=0.78, 95% CI (0.67–0.91), Pheterogeneity=0.740, P=0.002, I2=91.5% for A-allele vs G-allele, Figure 3; OR=0.72, 95% CI (0.60–0.87), Pheterogeneity=0.555, P=0.001, I2=0.0% for AG vs GG, Figure 4; OR=0.73, 95% CI (0.61–0.87), Pheterogeneity=0.814, P=0.000, I2=0.0% for AA+AG vs GG, Figure 5. No significant relationship was found for another polymorphism (rs634990) in the GJD2 gene (Table 3).
In the ZC3H11B gene, 4 polymorphisms were analyzed. For rs4373767, a decreased association was observed in 2 genetic models: OR=0.84, 95% CI (0.72–0.98), Pheterogeneity=0.270, P=0.027, I2=23.2% for TC vs CC (Figure 4); OR=0.85, 95% CI (0.73–0.98), Pheterogeneity=0.239, P=0.026, I2=28.8% for TT+TC vs CC (Figure 5). For rs4428898, this polymorphism was a decreased factor to HM: OR=0.87, 95% CI (0.77–0.98), Pheterogeneity=0.294, P=0.019, I2=18.2% for G-allele vs A-allele (Figure 3); OR=0.82, 95% CI (0.69–0.97), Pheterogeneity=0.210, P=0.019, I2=36.0% for GA vs AA (Figure 4); OR=0.82, 95% CI (0.69–0.96), Pheterogeneity=0.159, P=0.012, I2=45.6% for GG+GA vs AA (Figure 5). For rs10779363, a significant relationship was found: OR=0.80, 95% CI (0.68–0.94), Pheterogeneity=0.2556, P=0.007, I2=26.5% for CT vs TT (Figure 4); OR=0.82, 95% CI (0.71–0.96), Pheterogeneity=0.196, P=0.011, I2=38.6% for CC+CT vs TT (Figure 5). Finally, for rs7544369, OR=0.86, 95% CI (0.76–0.97), Pheterogeneity=0.306, P=0.015, I2=15.5% for C-allele vs T-allele (Figure 3); OR=0.81, 95% CI (0.68–0.96), Pheterogeneity=0.371, P=0.013, I2=0.0% for CT vs TT (Figure 4); OR=0.81, 95% CI (0.69–0.95), Pheterogeneity=0.277, P = 0.009, I2=22.2% for CC+CT vs TT (Figure 5, Table 3). To reduce the type I error, the Bonferroni method was used. Finally, no association was found both for rs4373767 and rs4428898 SNPs.
For the matrix metalloproteinase family (MMP1, MMP9) genes, no significant association was found (Table 3).
PUBLICATION BIAS AND SENSITIVITY ANALYSES:
The potential for publication bias was next evaluated with Begg’s funnel plots and Egger’s test. No publication bias was found in any of the 4 genes’ polymorphisms (Table 4, Figure 6). Furthermore, to estimate the heterogeneity and delete studies that may influence the power/stability of the whole study, we applied sensitivity analysis, showing there were no sensitive case-control studies (Figure 7).
Discussion
Recent studies have identified SNPs associated with HM as significant genetic risk factors [10,33]. GWAS have discovered multiple susceptibility loci, particularly within or near genes involved in scleral remodeling and extracellular matrix organization, such as ARHGAP18, SNTB1, BMP2 and RASGRF1 [34–37]. Notably, advancements in large-scale biobank research and meta-analyses have enhanced the understanding of the polygenic architecture of this condition [33]. These investigations suggest that the cumulative effect of numerous common SNPs, combined with environmental stimuli such as prolonged near-vision work, contributes substantially to HM risk. Furthermore, functional genomic analyses are beginning to elucidate the biological pathways through which these variants influence axial elongation and scleral weakening.
The link to GJD2, which encodes connexin 36, underscores the critical role of gap junction communication in eye growth regulation, potentially affecting retinal signaling and scleral remodeling [38]. Similarly, the association with ZC3H11B, an RNA-binding protein, suggests a novel mechanism involving post-transcriptional gene regulation during eye development [39]. Most notably, the involvement of matrix metalloproteinases MMP1 and MMP9 points directly to extracellular matrix degradation and remodeling [40]. Dysregulation of these enzymes could weaken the structural integrity of the sclera, leading to its pathological elongation – a hallmark of HM. Given that these 4 genes have been implicated as important factors in the development of HM, we selected SNPs within these 4 genes to identify potential susceptibility SNPs for HM.
This meta-analysis is the first investigation to evaluate the association between 8 common SNPs across 4 genes and susceptibility to HM. The pooled data comprised a total of 9642 cases and 7941 controls, with the following distribution per SNP: rs634990 (476 cases, 386 controls), rs3743123 (1022 cases, 1022 controls), rs4373767 (1716 cases, 1572 controls), rs4428898 (1481 cases, 1314 controls), rs10779363 (2043 cases, 1379 controls), rs7544369 (1474 cases, 1329 controls), rs1799750 (1018 cases, 685 controls), and rs17576 (412 cases, 254 controls).
Based on the above data, the current analysis identified significant associations between HM risk and the rs3743123, rs4373767, rs4428898, rs10779363, and rs7544369 polymorphisms. These findings move beyond mere associations by suggesting potential mechanistic targets. The convergence of genes involved in cellular communication, genetic regulation, and structural integrity suggests that HM pathogenesis is multifactorial, resulting from subtle disruptions across interconnected systems. Translating this knowledge, the identified SNPs could serve as components of a polygenic risk score, enabling earlier identification of at-risk individuals. This paves the way for personalized preventive strategies, such as intensified monitoring and lifestyle interventions aimed at modulating environmental risk factors, ultimately contributing to a precision medicine approach for this vision-threatening condition.
The present study is subject to several limitations. First, despite the inclusion of all relevant literature, the overall sample size remains relatively modest. This limitation was further compounded when conducting subgroup analyses stratified by variables such as age, sex, ethnicity, axial length, and myopia severity. Second, the risk of high myopia associated with the identified polymorphisms is likely modulated by complex interactions, including gene-gene and gene-environment effects, as well as influences from other genetic variants. Future research should prioritize collecting detailed phenotypic and environmental data to clarify these relationships. Finally, elucidating the precise biological mechanisms through which these loci confer disease susceptibility would strengthen the rationale for genetic screening. Such mechanistic insights could facilitate the identification of clinically accessible biomarkers and reveal novel targets for therapeutic intervention.
Conclusions
The results of the present meta-analysis support a potential link between the GJD2 rs3743123, ZC3H11B polymorphisms, and an overall decrease in HM risk. Further large-scale studies with larger sample sizes, population stratification, environment factors, and mechanism research are needed to reliably clarify the association between HM susceptibility and these 4 genes’ polymorphisms.
Data Availability Statement
Further inquiries can be directed to the corresponding author Qianqian Yu at [email protected].
Figures
Figure 1. Flowchart depicting the systematic search strategy from different databases about the identification of studies investigating 4 genes’ (GJD2, ZC3H11B, MMP1, MMP9) polymorphisms and HM risk. 
Figure 2. Minor (mutant) allele frequencies for 8 polymorphisms based on data from the online 1000 Genomes database. 
Figure 3. Forest plots corresponding to HM risk between the 8 polymorphisms in M-allele vs W-allele in total. The squares and horizontal lines respectively correspond to the study-specific ORs and 95% CIs, with square area being indicative of weight (the inverse of the variance). Diamonds additionally reflect the summary OR and 95% CI. 
Figure 4. Forest plots corresponding to HM risk between the 8 polymorphisms in MW vs WW-allele in total. 
Figure 5. Forest plots corresponding to HM risk between the 8 polymorphisms in MM+MW vs WW in total. 
Figure 6. (A) Begg’s funnel plot for publication bias test (MW vs WW). (B) Egger’s publication bias plot (MW vs WW) for HM. (C) Begg’s funnel plot for publication bias test (MM vs MW+WW). (D) Egger’s publication bias plot (MM vs MW+WW) for HM. 
Figure 7. Sensitivity analysis of 8 polymorphisms and HM risk. (A) M-allele vs W-allele; (B) MW vs WW; (C) MM vs WW; (D) MM+MW vs WW. Tables
Table 1. PRISMA flow checklist (2020).
Table 2. Characteristics of included studies about polymorphisms in 4 different genes and HM risk.
Table 3. Stratified analyses of 4 different genes’ common polymorphisms on myopia risk.
Table 4. Publication bias tests (Begg’s funnel plot and Egger’s test for publication bias test) for 8 polymorphisms and HM risk.
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Figures
Figure 1. Flowchart depicting the systematic search strategy from different databases about the identification of studies investigating 4 genes’ (GJD2, ZC3H11B, MMP1, MMP9) polymorphisms and HM risk.Figure 2. Minor (mutant) allele frequencies for 8 polymorphisms based on data from the online 1000 Genomes database.Figure 3. Forest plots corresponding to HM risk between the 8 polymorphisms in M-allele vs W-allele in total. The squares and horizontal lines respectively correspond to the study-specific ORs and 95% CIs, with square area being indicative of weight (the inverse of the variance). Diamonds additionally reflect the summary OR and 95% CI.Figure 4. Forest plots corresponding to HM risk between the 8 polymorphisms in MW vs WW-allele in total.Figure 5. Forest plots corresponding to HM risk between the 8 polymorphisms in MM+MW vs WW in total.Figure 6. (A) Begg’s funnel plot for publication bias test (MW vs WW). (B) Egger’s publication bias plot (MW vs WW) for HM. (C) Begg’s funnel plot for publication bias test (MM vs MW+WW). (D) Egger’s publication bias plot (MM vs MW+WW) for HM.Figure 7. Sensitivity analysis of 8 polymorphisms and HM risk. (A) M-allele vs W-allele; (B) MW vs WW; (C) MM vs WW; (D) MM+MW vs WW. Tables
Table 1. PRISMA flow checklist (2020).Table 2. Characteristics of included studies about polymorphisms in 4 different genes and HM risk.Table 3. Stratified analyses of 4 different genes’ common polymorphisms on myopia risk.Table 4. Publication bias tests (Begg’s funnel plot and Egger’s test for publication bias test) for 8 polymorphisms and HM risk.Table 1. PRISMA flow checklist (2020).Table 2. Characteristics of included studies about polymorphisms in 4 different genes and HM risk.Table 3. Stratified analyses of 4 different genes’ common polymorphisms on myopia risk.Table 4. Publication bias tests (Begg’s funnel plot and Egger’s test for publication bias test) for 8 polymorphisms and HM risk. In Press
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