Genetic associations in CHAT and COL11A1 with primary angle-closure glaucoma susceptibility: A systematic review and meta-analysis : Indian Journal of Ophthalmology

Secondary Logo

Journal Logo

Review Article

Genetic associations in CHAT and COL11A1 with primary angle-closure glaucoma susceptibility: A systematic review and meta-analysis

Wang, Shaowen; Zhang, Guowei; Lu, Hong

Author Information
Indian Journal of Ophthalmology 71(2):p 343-349, February 2023. | DOI: 10.4103/ijo.IJO_1226_22
  • Open


Glaucoma is the leading cause of irreversible blindness worldwide.[1] The likelihood of severe bilateral visual impairment is three times higher in primary angle-closure glaucoma (PACG) than in primary open-angle glaucoma (POAG), especially among Asians.[2,3]

CHAT has been found to encode an enzyme choline O-acetyltransferase (CHAT) synthesizing the neurotransmitter acetylcholine (ACh), which plays a vital role in pupillary constriction and so on.[4]COL11A1 in the trabecular meshwork (TM) is responsible for regulating the aqueous outflow pathway resulting in PACG susceptibility.[5]

Therefore, we performed a meta-analysis of these two SNPs to inquire into their influence on PACG susceptibility by including the nearest studies.


Searching strategy

We systematically searched PubMed, EMBASE, OVID, EBSCO, Elsevier Science Direct, Web of Science, VIP Chinese SCI-Tech Journal full-text Database, Wanfang, and China Biomedical Literature Database (CBM) during October 2010 to March 2022 using Chinese subject words “primary angle-closure glaucoma” and “gene polymorphism (s),” while English keywords were “PACG,” “genetic,” “COL11A1,”CHAT,” “rs1258267,” and “rs3753841.” The search date ends on March 2022. We also screened the references cited in the publications matching the subject words listed above to obtain additional publications. The meta-analysis is guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 checklist.

Inclusion and exclusion criteria

The inclusion criteria are: (1) Original research literature is a case-control study, (2) Using human patients diagnosed with PACG as cases, (3) Referring to the associations of COL11A1 rs3753841 and CHAT rs1258267 with PACG, (4) Providing sufficient genotype data to estimate the odds ratio (OR) with 95% confidence interval (CI), (5) Newcastle-Ottawa Scale (NOS) ≥6. Abstracts from conferences, animal studies, full texts without raw data available for retrieval, non-case-control study, republished data, duplicate studies, and reviews were excluded.

Data extraction and quality assessment

According to the above criteria, the data were retrieved and screened independently by two investigators. If these two investigators could not reach a consensus, disparities were settled by discussion. We extracted the following items from each qualified research: The surname of the first author, year of publication, study location, ethnicity, the total number of cases and normal controls, frequencies of COL11A1 rs3753841 and CHAT rs1258267 polymorphism in cases and controls, and so on. These two investigators also used the NOS to evaluate the quality of the selected studies to ensure the NOS ≥6. The PRISMA 2020 checklist as additional file 1 guided this meta-analysis.[6]

Statistical analysis

The χ2 test was used to evaluate whether the genotype distribution in controls was inconsistent with Hardy-Weinberg equilibrium (HWE). Under five genetic models, G vs. A, GG + GA vs. AA, GG vs. GA + AA, GA vs. AA, and GG vs. AA, a pooled OR and 95% CI was calculated to estimate the association between COL11A1 rs3753841 or CHAT rs1258267 and PACG susceptibility. Heterogeneity among studies was measured with a Chi-square-based Q statistic test and the I2 statistic. With P > 0.10 indicating an absence of heterogeneity, we used the Mantel–Haenszel method to calculate the pooled ORs in a fixed-effect model. On the contrary, the DerSimonian and Laird method was performed to calculate the pooled ORs in a random-effect model if the P ≤ 0.10. I2 statistic was used to estimate heterogeneity quantitatively (I2 > 50% was considered as high-level heterogeneity, 25%–50% medium-level, and I2 < 25% high-level). By the Z test, we analyzed the overall effect of OR and P < 0.05 showed that the results were statistically significant. We conducted subgroup analyses with respect to ethnicity or HWE deviation. By excluding each study, we conducted the leave-one-out sensitivity analysis to examine whether summary ORs were affected by one specific study. Funnel plots and Begg’s funnel plot (P ≤ 0.05 and Z ≥ 1.96 indicated significant publication bias) were utilized to analyze publication bias qualitatively and quantitatively. All statistical analyses were performed by STATA (version 15.1, StataCorp LLC, College Station, Texas 77845, USA).


Literature search and characteristics

The search process in our meta-analysis is described in Fig. 1. The initial search of databases identified 451 potentially relevant articles. After removing duplication and excluding substandard articles, a total of forty-eight[5,7–13] published studies provided 11373 cases and 36040 controls for the meta-analysis of rs3753841 and rs1258267 variants. All studies are made up of Asian and Caucasian ancestry, and Asian ancestry accounts for the main part. Furthermore, the HWE had been tested for all polymorphisms in the control groups and four of them show significant deviation. Additional file 2 presents more details of these studies.

Figure 1:
Flow diagram of included studies for this meta-analysis. (*four articles included more than one independent study)

Meta-analysis results

All forty-eight studies were pooled into this meta-analysis. The DerSimonian and Laird method was chosen for the G vs. A model of CHAT rs1258267 and three genetic models (G vs. A, GG vs. GA + AA, and GG vs. AA) of COL11A1 rs3753841 because of medium-level heterogeneity (P < 0.10). The model selected for subgroup analysis was consistent with the pooled analysis. There were eighteen studies supplying results associating CHAT rs1258267 with PACG and we found a 20% decrease in the risk of PACG under the G vs. A model (OR = 0.80, 95%CI = 0.74–0.86, P = 0.000, Fig. 2). Table 1 shows more than 21% decrease under other genetic models, especially GG vs. AA model (OR = 0.71, 95%CI = 0.62–0.82, P = 0.000). Subgroup analyses were carried out based on Asian and Caucasian ethnicities [shown in Table 2]. The results were obvious in Asians (GG vs. AA: OR = 0.69, 95%CI = 0.60–0.80, P = 0.000), while no statistical significance was manifested in Caucasians (G vs. A: OR = 0.95, 95%CI = 0.40–2.27, P = 0.917, Fig. 3).

Figure 2:
Forest plot (random-effect model) of the susceptibility of CHAT rs1258267 associated with PACG (G vs. A)
Table 1:
Overall analysis of the association between two SNPs and primary angle-closure glaucoma risk
Table 2:
Stratified analysis of CHAT rs1258267 based on ethnicity
Figure 3:
The results of CHAT rs1258267 analyzed based on Asian and Caucasian ethnicities (G vs. A)

Thirty studies were included to demonstrate a notable increase in the risk of PACG carrying COL11A1 rs3753841 genotypes. A 42% increased risk could be identified under GG vs. AA model (OR = 1.42, 95%CI = 1.28-1.57, P = 0.000) and a 32% higher risk could be indicated under GG vs. GA + AA model (OR = 1.32, 95%CI = 1.20-1.45, P = 0.000). We also found about 15% growth using other genetic models [shown in Table 1]. In stratified analysis by ethnicity, Fig. 4 indicates evidently increased risk in Asians under GG vs. AA model (OR = 1.46, 95%CI = 1.33-1.61, P = 0.000) and the results of other models were consistent [shown in Table 3]. However, in Caucasians, the results were of no statistical significance (P > 0.05 under five genetic models). Depending on HWE deviation, we performed some other stratified analyses. The results of the two groups were almost the same as the meta-analysis including all studies, besides GA vs. AA with no HWE deviation (OR = 1.06, 95%CI = 0.93-1.20, P = 0.381, Table 4).

Figure 4:
The results of COL11A1 rs3753841 analyzed based on Asian and Caucasian ethnicities (GG vs. AA)
Table 3:
Stratified analysis of COL11A1 rs3753841 based on ethnicity
Table 4:
Stratified analysis of COL11A1 rs3753841 based on HWE deviation

Sensitivity analysis

Sensitivity analysis was conducted by excluding the studies with HWE deviation. We also compared the summary ORs calculated by using fixed-effect models and random-effect models, respectively, and the results were not substantially altered, indicating that our conclusion was reliable.

Publication bias

Publication bias was calculated quantitatively by Begg’s test [shown in Table 5] and qualitatively by the funnel plot. In the overall analyses, publication bias for CHAT rs1258267 and COL11A1 rs3753841 had not been discovered. As shown in Figs. 5 and 6, the studies were symmetrically scattered in the funnel plots.

Table 5:
Begg’s test to detect publication bias
Figure 5:
(a) The results of sensitivity analysis from CHAT rs1258267 (G vs. A). (b) The results of sensitivity analysis from COL11A1 rs3753841 (G vs. A)
Figure 6:
(a) Funnel plot from CHAT rs1258267. (b) Funnel plot from COL11A1 rs3753841


This meta-analysis included eight articles supplying forty-eight studies to examine whether CHAT rs1258267 G/A polymorphisms and COL11A1 rs3753841 G/A polymorphisms have a possible impact on PACG susceptibility. From what has been discussed above, we may safely confirm these relationships. We found that CHAT rs1258267 decreased the risk of PACG and COL11A1 rs3753841 increased this risk. These associations remained in Asians, but not in Caucasians.

CHAT had been found to encode an enzyme ChAT synthesizing the neurotransmitter Ach and Yang et al.[14] used anticholinergic agents to regulate pupillary block causing an increased risk of acute PACG. Under the circumstances, we could speculate that the risk of PACG is under the control of natural genetic variation in CHAT by adjusting ACh metabolism.[15] The pooled results showed the relevance between CHAT rs1258267 and PACG, and CHAT rs1258267 could decrease the risk of PACG. In the other words, the GG genotype of CHAT was the protective genotype of PACG. It was reported that ethnicity may have a pivotal role in individual susceptibility to the disease.[16] From the results of the subgroup analysis, this effect was more significant in Asians [shown in Table 2], which has been authenticated by Zhuang et al.[8] and Shi et al.[9] But no significance was found in Caucasians under five genetic models. This distinction could be ascribed to differences in ethnicity and insufficient data. The GG genotype frequencies in studies recruited in non-Asian countries such as Brazil (136 PACG cases and 213 controls), Australia (147 PACG cases and 1119 controls), and Iran (270 PACG cases and 4644 controls) were low.

COL11A1 on 1p21.1 encodes one of the two α-chains of type XI collagen.[16] Because of changes in collagen affecting TM function, alterations in the biomechanical features of the extracellular matrix (ECM) lead to decreased outflow and elevated intraocular pressure (IOP).[17] Finally, COL11A1 in TM is responsible for regulating the aqueous outflow pathway. The COL11A1 rs3753841 as a susceptibility locus for PACG has been identified since 2012.[5] Our meta-analysis brought into more studies in recent years with larger populations and more different ethnicities. The overall results suggested that COL11A1 rs3753841 played a significant role in increasing PACG susceptibility. Stratified analyses showed that COL11A1 rs3753841 had the same effect in Asians, but not in Caucasians. The Asians with the genotype GG vs. (GA + AA) even were more susceptible to PACG than the total population in our meta-analysis. A similar situation appeared when we performed stratified analyses based on HWE. People with the genotype GA vs. AA had a minor higher predisposition when the analysis was limited to the studies with no HWE deviation. According to Alsbirk et al.,[18] higher susceptibility in Asians might be reasonable. And it was noteworthy that the direction of effect for COL11A1 rs3753841 in Caucasians was analogous to that of the overall results.

No significant publication bias ensured the dependability of the final results. However, some potential limitations from the following aspects should be solved. Firstly, because of pre-established standards such as lack of sufficient genotype data, some studies were excluded, particularly, which contained more relevant information about Caucasians. Meanwhile, some countries we included such as Iran have more mixed populations. Due to the rarity of the minor allele for CHAT rs1258267 in Europeans such as in the UK from Khor et al.[7] (no one with genotype GG in cases and controls), the analysis might be less persuasive for Caucasians. In addition, the analysis of the COL11A1 rs3753841 in Caucasians was different from previous studies; hence, the role of COL11A1 rs3753841 remained to be investigated. In addition, more factors should be considered which can influence the changes in PACG. Stratified analyses in our meta-analysis were performed just through two aspects, but sex, age, working environment, and family history may interfere with eventual results.


In summary, we conclude that CHAT rs1258267 G/A polymorphisms could cause a decreased risk of PACG susceptibility, and COL11A1 rs3753841 G/A polymorphisms could cause an increased risk. These associations remain in Asians, but not in Caucasians. Given that there are many other factors to consider, further articles with more worldwide studies are demanded to confirm the relationship of these two genes in PACG.

Data availability statement

All data generated or analyzed during this study are included in this article and its additional files. Further inquiries can be directed to the corresponding author.

Statement of ethics

All analyses were based on previously published studies; thus, no ethical approval and patient consent are required.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.


The authors would like to thank the Department of Ophthalmology, Affiliated Hospital of Nantong University and Chiea Chuen Khor (Singapore National Eye Centre and Eye ACP, Duke-National University of Singapore, Singapore) for providing the basic data.


1. Lin S, Zuo C, Liu Y, Xiao H, Fang L, Su Y, et al. Ocular biometry of primary angle-closure disease in younger patients Front Med (Lausanne) 2021;8:772578.
2. Thangavelu L, Che Mat Nor SM, Abd Aziz D, Sulong S, Tin A, Ahmad Tajudin LS. Genetic markers PLEKHA7, ABCC5, and KALRN are not associated with the progression of Primary angle closure glaucoma (PACG) in malays Cureus 2021;13:e18823.
3. Zhang N, Wang J, Chen B, Li Y, Jiang B. Prevalence of primary angle closure glaucoma in the last 20 years:A meta-analysis and systematic review Front Med (Lausanne) 2020;7:624179.
4. Mandak JS, Minerva P, Wilson TW, Smith EK. Angle closure glaucoma complicating systemic atropine use in the cardiac catheterization laboratory Cathet Cardiovasc Diagn 1996;39:262–4.
5. Vithana EN, Khor CC, Qiao C, Nongpiur ME, George R, Chen LJ, et al. Genome-wide association analyses identify three new susceptibility loci for primary angle closure glaucoma Nat Genet 2012;44:1142–6.
6. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement:An updated guideline for reporting systematic reviews BMJ 2021;372:n71.
7. Khor CC, Do T, Jia H, Nakano M, George R, Abu-Amero K, et al. Genome-wide association study identifies five new susceptibility loci for primary angle closure glaucoma Nat Genet 2016;48:556–62.
8. Zhuang W, Wang S, Hao J, Xu M, Chi H, Piao S, et al. Genotype-ocular biometry correlation analysis of eight primary angle closure glaucoma susceptibility loci in a cohort from Northern China PLoS One 2018;13:e0206935.
9. Shi H, Chen Y, Lu H, Zhu R, Zhang J, He M, et al. In-depth analysis of eight susceptibility loci of primary angle closure glaucoma in Han Chinese Exp Eye Res 2020;202:108350.
10. Yousefian A, Shokoohi-Rad S, Abbaszadegan MR, Rad DM, Zargari S, Milanizadeh S, et al. Primary angle closure glaucoma-associated genetic polymorphisms in Northeast Iran J Ophthalmic Vis Res 2020;15:45–52.
11. Wan Y, Li S, Gao Y, Tang L, Cao W, Sun X. COL11A1 polymorphisms are associated with primary angle-closure glaucoma severity J Ophthalmol 2019;2019:2604386.
12. Tan C, Huang L, Yang Z. [Association of PLEKHA7, COL11A1 and PCMTD1-ST18 gene polymorphisms with primary angle closure glaucoma in ethnic Han Chinese from Sichuan Zhonghua Yi Xue Yi Chuan Xue Za Zhi 2016;33:545–9.
13. Jiang R, Wang YX, Wei WB, Xu L, Jonas JB. Peripapillary choroidal thickness in adult Chinese:The Beijing eye study Invest Ophthalmol Vis Sci 2015;56:4045–52.
14. Yang MC, Lin KY. Drug-induced acute angle-closure glaucoma:A review J Curr Glaucoma Pract 2019;13:104–9.
15. Gowtham L, Halder N, Angmo D, Singh SB, Jayasundar R, Dada T, et al. Elevated histamine levels in aqueous humor of patients with glaucoma Mol Vis 2021;27:564–73.
16. Balikov DA, Jacobson A, Prasov L. Glaucoma syndromes:Insights into glaucoma genetics and pathogenesis from monogenic syndromic disorders Genes (Basel) 2021;12:1403.
17. McGrady NR, Pasini S, Baratta RO, Del Buono BJ, Schlumpf E, Calkins DJ. Restoring the extracellular matrix:A neuroprotective role for collagen mimetic peptides in experimental glaucoma Front Pharmacol 2021;12:764709.
18. Alsbirk PH. Primary angle- closure glaucoma. Oculometry, epidemiology, and genetics in a high risk population Acta Ophthalmol Suppl 1976;127:5–31.

CHAT; COL11A1; genetic variant; meta-analysis; PACG

Copyright: © 2023 Indian Journal of Ophthalmology