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Genetics  |   June 2014
Extended Association Study of PLEKHA7 and COL11A1 With Primary Angle Closure Glaucoma in a Han Chinese Population
Author Affiliations & Notes
  • Yuhong Chen
    Department of Ophthalmology and Vision Science, Shanghai Medical College, Eye and Ear Nose Throat Hospital, Fudan University, Shanghai, China
  • Xueli Chen
    Department of Ophthalmology and Vision Science, Shanghai Medical College, Eye and Ear Nose Throat Hospital, Fudan University, Shanghai, China
  • Li Wang
    Department of Ophthalmology and Vision Science, Shanghai Medical College, Eye and Ear Nose Throat Hospital, Fudan University, Shanghai, China
  • Guy Hughes
    University of California, Irvine School of Medicine, Irvine, California, United States
  • Shaohong Qian
    Department of Ophthalmology and Vision Science, Shanghai Medical College, Eye and Ear Nose Throat Hospital, Fudan University, Shanghai, China
  • Xinghuai Sun
    Department of Ophthalmology and Vision Science, Shanghai Medical College, Eye and Ear Nose Throat Hospital, Fudan University, Shanghai, China
    State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China
  • Correspondence: Xinghuai Sun, Department of Ophthalmology and Vision Science, Shanghai Medical College, Eye and Ear Nose Throat Hospital, Fudan University, 83 Fenyang Road, Shanghai, China, 200031; xinghuaisun@gmail.com
Investigative Ophthalmology & Visual Science June 2014, Vol.55, 3797-3802. doi:10.1167/iovs.14-14370
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      Yuhong Chen, Xueli Chen, Li Wang, Guy Hughes, Shaohong Qian, Xinghuai Sun; Extended Association Study of PLEKHA7 and COL11A1 With Primary Angle Closure Glaucoma in a Han Chinese Population. Invest. Ophthalmol. Vis. Sci. 2014;55(6):3797-3802. doi: 10.1167/iovs.14-14370.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose.: To investigate the association of PLEKHA7 and COL11A1 with primary angle closure glaucoma, as well as acute and chronic subphenotype, in a Han Chinese population.

Methods.: A total of 984 cases, including 606 primary angle closure glaucoma (PACG) and 378 primary angle closure (PAC), and 922 normal controls were recruited. Twelve single nucleotide polymorphisms (SNPs) (rs1676486, rs3753841, rs12138977, rs2126642, rs2622848, rs216489, rs1027617, rs366590, rs11024060, rs6486330, rs11024097, and rs11024102) in the PLEKHA7 gene and COL11A12 gene were genotyped. Distributions of allele frequencies were compared between cases and controls as well as in patient subgroups with or without acute attacks.

Results.: Four of the 12 SNPs, including rs1676486 (P = 0.0060) and rs12138977 (P = 0.028) in COL11A1, as well as rs216489 (P = 0.0074) and rs11024102 (P = 0.038) in PLEKHA7, were found to have a statistically significant association with PAC/PACG. In the subgroup analysis, 6 out of 12 SNPs (rs1676486, rs3753841, rs12138977, rs216489, rs11024060, and rs11024102) showed statistically significant differences between acute PAC/PACG cases and controls. However, none of them showed statistically significant differences between chronic PAC/PACG cases and controls.

Conclusions.: Our study suggests that rs1676486 and rs12138977 in COL11A1 as well as rs216489 and rs11024102 in PLEKHA7 are associated with an increased risk of PAC/PACG in the Han Chinese population, supporting prior reports of the association of COL11A1 and PLEKH7 with angle closure glaucoma. Both COL11A1 and PLEKHA7 were shown to confer significant risk for acute PAC/PACG. Further work is necessary to confirm the importance of COL11A1 and PLEKHA7 in the pathogenesis of glaucoma.

Introduction
Primary angle closure glaucoma (PACG) is a subtype of glaucoma characterized by obstruction of the iridocorneal angle, an increase in intraocular pressure, and slow progressive destruction of the optic nerve with corresponding loss of the peripheral visual field. Epidemiological studies have revealed that in populations of Asian ancestry, as in China, PACG accounts for more than 50% of all cases of glaucoma and is responsible for substantial visual loss. 1,2 Primary angle closure glaucoma is a complex heterogeneous disease; the molecular mechanisms leading to PACG are poorly understood and the genetic susceptibility is still under investigation. Recently, Vithana et al. 3 conducted a genome-wide association study (GWAS) to determine the genetic variants underlying susceptibility to PACG. They found a genome-wide significant association between PACG and three genetic markers: rs11024102 in PLEKHA7 on chromosome 11, rs3753841 in COL11A1 on chromosome 1, and rs1015213 located between PCMTD1 and ST18 on chromosome 8q. 
PLEKHA7 (pleckstrin homology domain-containing family A member 7) is an adherens junction protein. 4 In the ciliary body, iris, aqueous humor outflow system, choroid, and other structures particularly relevant to glaucoma, tight junctions and adherens junctions play an essential role by providing a barrier to fluid leakage. 5,6 Therefore, PLEKHA7 was speculated to be involved in the pathophysiology of angle closure with relation to aberrant fluid dynamics. 3 COL11A1 encodes one of the two alpha chains of type XI collagen. Pathogenic mutations in COL11A1 cause Marshall syndrome 7 and type 2 Stickler syndrome, 8 which both have features of nonprogressive axial myopia. The causal variants may alter expression of COL11A1 and have the opposite effect of that observed in myopic eyes by showing a predisposition to PACG. 3  
The contribution of the top two associated single nucleotide polymorphisms (SNPs) in PLEKHA7 and COL11A1 was confirmed in patients from Australia and Nepal. 9 However, these did not have a statistically significant association with primary angle closure (PAC) in populations from South India and China. 10,11 Validation in additional independent cohorts and analysis of subphenotype classifications of PACG will help to clarify the contributions of these genes in the development of PACG. In this study, we sought to replicate the top-ranked SNPs from a recent GWAS of PACG and to explore additional variants in PLEKHA7 and COL11A1 in a large sample size. Analyses were also conducted to determine the association of markers in PLEKHA7 and COL11A1 with PAC or PACG patients subgrouped as either acute or chronic. 
Materials and Methods
Patients
The study was approved by the ethical committee of Eye and Ear Nose Throat Hospital, Fudan University, and all procedures were conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from each individual. 
The samples used in the study were collected in Eye and Ear Nose Throat Hospital. Each participant underwent a complete eye examination including best-corrected visual acuity, slit-lamp examination of the anterior chamber, measurement of intraocular pressure (IOP), and fundus photo assessment. Anterior chamber depth (ACD), axial length (AL), gonioscopy, and visual field were examined if PAC or PACG was suspected. Anterior chamber depth and AL were measured by A-scan ultrasound pachymetry. The biometric measurements of ACD and AL were excluded when the eye was pseudophakic or aphakic. The analysis of central corneal thickness (CCT), ACD, and AL was performed using the average data from both eyes. As there was high asymmetry between the two eyes for acute angle closure patients, analysis of IOP and cup-to-disc ratio was performed using the greater values. 
The case group included PAC and PACG patients. Primary angle closure glaucoma was diagnosed using the definition from the International Society of Geographical and Epidemiological Ophthalmology (ISGEO). 12 Diagnosis was based on the presence of glaucomatous optic neuropathy with a cup-to-disc ratio ≥ 0.7, peripheral visual loss, an elevated IOP > 21 mm Hg, and the presence of at least two quadrants of iridotrabecular contact (ITC) in which the trabecular meshwork was not visible on gonioscopy. Primary angle closure eyes had features indicating that trabecular obstruction by the peripheral iris had occurred (peripheral anterior synechiae [PAS] or IOP > 21 mm Hg), but without glaucomatous optic neuropathy. Acute PAC/PACG was defined as an episode with (1) at least two of a list of symptoms (namely, ocular or periocular pain, nausea, vomiting, or an antecedent history of intermittent blurring of vision); (2) a presenting IOP > 28 mm Hg on Goldmann applanation tonometry; (3) at least three of a list of signs (namely, conjunctival injection, corneal epithelial edema, mid-dilated nonreactive pupil, and shallow anterior chamber [AC]). 13  
A total of 984 PAC or PACG cases (PAC/PACG) and 922 normal controls were recruited. Among cases, there were 606 PACG and 378 PAC. The control group included ethnically matched individuals who were required to have none of the above-described characteristics, no family history of glaucoma, no previous surgeries for glaucoma, and no other ophthalmic diseases besides cataracts. Participants with secondary angle closure glaucoma due to uveitis, trauma, neovascularization, or any other optic nerve injury affecting either eye were excluded. 
Methods
Genomic DNA was extracted from leukocytes of the peripheral blood for each participant. It was purified by the Qiagen QIAmp Blood Kit (Qiagen, Hilden, Germany). Twelve SNPs (rs1676486, rs3753841, rs12138977, rs2126642, and rs2622848 in COL11A1 as well as rs216489, rs1027617, rs366590, rs11024060, rs6486330, rs11024097, and rs11024102 in PLEKHA7), including the two SNPs previously reported as being significantly associated with PACG through GWAS (rs3753841 and rs11024102), were genotyped. The SNPs chosen in this study were either tagging SNPs or coding SNPs with minor allele frequencies greater than 10% in Chinese Han populations referenced to the HapMap database (http://hapmap.ncbi.nlm.nih.gov/, in the public domain). 
Single nucleotide polymorphisn genotyping was performed using iPLEX Gold chemistry on the MassARRAY system (Sequenom, Inc., San Diego, CA, USA) by means of matrix-assisted laser desorption ionization–time of flight mass spectrometry method (MALDI-TOF) according to the manufacturer's instructions. Genotype calling was performed in real time with MassARRAY RT software version 3.0.0.4 and analyzed using the MassARRAY Typer software version 3.4 (Sequenom, Inc.). Each SNP with call rate greater than 95% was analyzed in the next step. 
Association analyses were conducted using PLINK (version 1.07; http://pngu.mgh.harvard.edu/~purcell/plink/, in the public domain). For each SNP, genotype and allele frequencies were calculated. The allelic association was analyzed using a χ2 test. Logistic regression was used to calculate odds ratios (OR) with 95% confidence intervals (CI) and adjust for age and sex. A linear regression model was used for association testing between genotypes and ACD or AL as quantitative traits. All genotyping results were screened for deviations from Hardy–Weinberg equilibrium, and SNPs with significant deviation (P < 0.05) were excluded. Continuous variables were expressed as mean ± SD and compared across groups using Student's t-test. Bonferroni correction was used to adjust P values for multiple comparisons. Linkage disequilibrium (LD) patterns and haplotype blocks were deduced using Haploview (version 4.2; http://www.broadinstitute.org/scientific-community/science/programs/medical-and-population-genetics/haploview/haploview, in the public domain). A power calculation was performed with the statistical program QUANTO (version 1.2.4, http://biostats.usc.edu/Quanto.html, in the public domain) 14 using the disease trait model. The following assumptions were applied for the power calculation: an additive genetic model, risk allele frequencies (0.2–0.4) and per allele ORs (1.2–1.6) equal to those from Vithana et al., 3 1% population risk, sample size ranging from 400 to 1000, and two-sided P value threshold of 0.05. 
Results
The study included a total of 984 PAC/PACG cases (339 males and 645 females) with a mean age of 60.99 ± 10.53 years and 922 normal controls (248 males and 674 females) with a mean age of 58.44 ± 11.89 years (P < 0.05). The IOP was 42.29 ± 14.40 and 14.89 ± 2.94 mm Hg and the cup-to-disc ratio was 0.69 ± 0.24 and 0.35 ± 0.12 for cases and controls, respectively. Among cases, there were 606 PACG and 378 PAC patients (Table 1). 
Table 1
 
Clinical Information on Cases and Controls
Table 1
 
Clinical Information on Cases and Controls
Cases Controls
Number 984 922
Female, %* 65.45 73.10
Age, y* 60.99 ± 10.53 58.44 ± 11.89
Max IOP, mm Hg* 42.29 ± 14.40 14.89 ± 2.94
Max cup-to-disc ratio* 0.69 ± 0.24 0.35 ± 0.12
Genotype frequencies of all SNPs were consistent with Hardy-Weinberg equilibrium among both cases and controls (P > 0.05). Among the 12 SNPs, significant genetic association was identified for rs1676486 (P = 0.0060) and rs12138977 (P = 0.028) in COL11A1 as well as rs216489 (P = 0.0074) and rs11024102 (P = 0.038) in PLEKHA7, with higher minor allele frequencies in cases than in controls. None of the SNPs surpassed the Bonferroni correction. Single nucleotide polymorphisms rs1676486 in COL11A1 and rs216489 in PLEKHA7 approached significance (0.05/12) after Bonferroni correction. The allele frequencies and associations with and without adjustment for age and sex for each SNP were evaluated and are shown in Table 2 and Supplementary Table S1
Table 2
 
Single Nucleotide Polymorphism Allele Frequencies and Associations for 12 SNPs Between Cases and Controls
Table 2
 
Single Nucleotide Polymorphism Allele Frequencies and Associations for 12 SNPs Between Cases and Controls
Gene SNP CHR BP MA MAF Case MAF Control P Values OR (95% CI)
COL11A1 rs1676486 1 103354138 A 0.27 0.23 0.0060 1.23 (1.06–1.42)
COL11A1 rs3753841 1 103379918 C 0.33 0.30 0.062 1.14 (0.99–1.31)
COL11A1 rs12138977 1 103393457 C 0.26 0.23 0.028 1.18 (1.02–1.38)
COL11A1 rs2126642 1 103405793 T 0.14 0.14 0.84 1.02 (0.85–1.23)
COL11A1 rs2622848 1 103421003 C 0.10 0.10 0.79 1.03 (0.83–1.27)
PLEKHA7 rs216489 11 16823736 G 0.36 0.32 0.0074 1.21 (1.05–1.38)
PLEKHA7 rs1027617 11 16842787 A 0.37 0.37 0.96 1.00 (0.88–1.14)
PLEKHA7 rs366590 11 16872440 A 0.29 0.28 0.75 1.02 (0.89–1.18)
PLEKHA7 rs11024060 11 16881961 T 0.35 0.38 0.081 0.89 (0.78–1.02)
PLEKHA7 rs6486330 11 16990290 T 0.15 0.17 0.18 0.89 (0.75–1.06)
PLEKHA7 rs11024097 11 16999029 C 0.36 0.38 0.45 0.95 (0.83–1.09)
PLEKHA7 rs11024102 11 17008605 C 0.46 0.42 0.038 1.15 (1.01–1.30)
Single nucleotide polymorphism rs1676486 in COL11A1 showed statistically significant evidence of genetic association in analyses comparing both PAC versus controls (P = 0.018) and PACG versus controls (P = 0.015). Single nucleotide polymorphisms rs3753841 and rs12138977 in COL11A1 showed marginal P values (P = 0.054 and 0.043, respectively) in comparison of PACG versus controls. Single nucleotide polymorphism rs216489 in PLEKHA7 showed statistically significant association (P = 0.0026) and survived Bonferroni correction in analyses consisting of PAC and controls, but not in PACG and controls. Single nucleotide polymorphisms rs11024102 in PLEKHA7 showed statistically significant association (P = 0.0024) and survived Bonferroni correction in analyses consisting of PACG and controls, but not in PAC and controls (Table 3). 
Table 3
 
Single Nucleotide Polymorphism Allele Frequencies and Associations for 12 SNPs in Subgroup Analysis of PAC Versus Controls and PACG Versus Controls
Table 3
 
Single Nucleotide Polymorphism Allele Frequencies and Associations for 12 SNPs in Subgroup Analysis of PAC Versus Controls and PACG Versus Controls
SNP CHR BP MA MAF Control PAC PACG
MAF P OR (95% CI) MAF P OR (95% CI)
rs1676486 1 103354138 A 0.23 0.28 0.018 1.26 (1.04–1.53) 0.27 0.015 1.23 (1.04–1.45)
rs3753841 1 103379918 C 0.30 0.33 0.23 1.12 (0.93–1.34) 0.34 0.054 1.17 (1.00–1.36)
rs12138977 1 103393457 C 0.23 0.26 0.077 1.19 (0.98–1.45) 0.26 0.043 1.19 (1.01–1.41)
rs2126642 1 103405793 T 0.14 0.14 0.84 1.03 (0.80–1.31) 0.14 0.98 1.00 (0.81–1.23)
rs2622848 1 103421003 C 0.10 0.09 0.59 0.93 (0.69–1.23) 0.11 0.43 1.10 (0.87–1.39)
rs216489 11 16823736 G 0.32 0.38 0.0026 1.31 (1.10–1.56) 0.35 0.11 1.13 (0.97–1.32)
rs1027617 11 16842787 A 0.37 0.37 0.92 0.99 (0.83–1.18) 0.37 0.93 1.01 (0.87–1.17)
rs366590 11 16872440 A 0.28 0.28 0.69 0.96 (0.80–1.16) 0.30 0.44 1.06 (0.91–1.25)
rs11024060 11 16881961 T 0.38 0.36 0.22 0.90 (0.75–1.07) 0.35 0.069 0.87 (0.75–1.01)
rs6486330 11 16990290 T 0.17 0.16 0.74 0.96 (0.77–1.21) 0.14 0.079 0.84 (0.68–1.02)
rs11024097 11 16999029 C 0.38 0.38 0.85 1.02 (0.85–1.21) 0.35 0.19 0.91 (0.78–1.05)
rs11024102 11 17008605 C 0.42 0.43 0.95 1.01 (0.85–1.19) 0.48 0.0024 1.25 (1.08–1.45)
Among cases, the 481 patients with acute attacks were defined as acute PAC/PACG, and the 443 patients with no history of acute attacks were defined as chronic PAC/PACG. There were 60 patients not able to be grouped as either acute or chronic subtype because of clinic information deficiency. The clinical characteristics of acute PAC/PACG and chronic PAC/PACG are shown in Table 4. There were more females in the acute PAC/PACG group than in the chronic PAC/PACG group (P < 0.001). The maximum IOP before treatment of acute PAC/PACG (47.70 ± 14.60 mm Hg) was higher than that of chronic PAC/PACG (38.23 ± 11.84 mm Hg) (P < 0.001), and the AL of acute PAC/PACG (22.02 ± 0.91 mm) was shorter than that of chronic PAC/PACG (22.41 ± 1.01 mm) (P < 0.001). 
Table 4
 
Clinical Characteristics of Acute PAC/PACG and Chronic PAC/PACG
Table 4
 
Clinical Characteristics of Acute PAC/PACG and Chronic PAC/PACG
Acute PAC/PACG, n = 481 Chronic PAC/PACG, n = 443
Female, %* 75.47 53.05
Age at diagnosis 61.44 ± 10.42 60.78 ± 10.77
Max IOP before treatment* 47.70 ± 14.60 38.23 ± 11.84
Center cornea thickness* 549.40 ± 37.68 538.57 ± 33.68
Anterior chamber depth 2.31 ± 0.71 2.35 ± 0.38
Axial length* 22.02 ± 0.91 22.41 ± 1.01
Max cup-to-disc ratio* 0.61 ± 0.24 0.79 ± 0.20
In subgroup analysis of acute PAC/PACG versus controls, six SNPs including rs1676486 (P = 4.71 × 10−4), rs3753841 (P = 0.041), and rs12138977 (P = 0.012) in COL11A1, as well as rs216489 (P = 0.029), rs11024060 (P = 0.0053), and rs11024102 (P = 0.042) in PLEKHA7, showed statistically significant association. However, when the chronic PAC/PACG group was compared to controls, there was no statistically significant association (Table 5). 
Table 5
 
Single Nucleotide Polymorphism Allele Frequencies and Associations for the Top 6 SNPs in Subgroup Analysis of Acute PAC/PACG Versus Controls and Chronic PAC/PACG Versus Controls
Table 5
 
Single Nucleotide Polymorphism Allele Frequencies and Associations for the Top 6 SNPs in Subgroup Analysis of Acute PAC/PACG Versus Controls and Chronic PAC/PACG Versus Controls
SNP CHR MA MAF Control Acute PAC/PACG Chronic PAC/PACG
MAF P OR (95% CI) MAF P OR (95% CI)
rs1676486 1 A 0.23 0.29 4.71 × 10−4 1.37 (1.15–1.63) 0.26 0.13 1.15 (0.96–1.39)
rs3753841 1 C 0.30 0.34 0.041 1.19 (1.01–1.40) 0.33 0.14 1.14 (0.96–1.35)
rs12138977 1 C 0.23 0.27 0.012 1.26 (1.05–1.51) 0.25 0.12 1.16 (0.96–1.40)
rs216489 11 G 0.32 0.36 0.029 1.20 (1.02–1.42) 0.35 0.079 1.16 (0.98–1.38)
rs11024060 11 T 0.38 0.33 0.0053 0.79 (0.67–0.93) 0.37 0.42 0.93 (0.79–1.10)
rs11024102 11 C 0.42 0.46 0.042 1.18 (1.01–1.38) 0.46 0.075 1.16 (0.99–1.36)
In linear regression model testing of PAC/PACG case genotypes with AL and ACD, no significant association was observed between any SNP and AL or ACD. The association results for the 12 SNPs are illustrated in Table 6
Table 6
 
Association Results of 12 SNPs With Axial Length and Anterior Chamber Depth
Table 6
 
Association Results of 12 SNPs With Axial Length and Anterior Chamber Depth
SNP CHR MA ACD AL
Beta P Beta P
rs1676486 1 A −0.02 0.60 0.01 0.91
rs3753841 1 C −0.01 0.75 0.00 0.92
rs12138977 1 C −0.01 0.74 0.01 0.77
rs2126642 1 T 0.07 0.22 −0.03 0.62
rs2622848 1 C 0.02 0.73 −0.04 0.60
rs216489 11 G 0.04 0.31 −0.10 0.04
rs1027617 11 A 0.02 0.70 0.01 0.88
rs366590 11 A −0.06 0.17 0.03 0.51
rs11024060 11 T 0.03 0.50 −0.02 0.74
rs6486330 11 T 0.06 0.29 −0.05 0.42
rs11024097 11 C 0.04 0.32 −0.01 0.91
rs11024102 11 C −0.05 0.19 0.08 0.06
Analysis of the LD structure demonstrated relatively lower LD among polymorphic markers in the region of PLEKHA7 than COL11A1 in the Chinese population (Figs. 1, 2). The LD structures are mostly consistent with those of the CHB HapMap samples (Supplementary Figs. S1, S2). The haplotype analysis results showed that for SNPs rs1676486, rs3753841, and rs12138977 in COL11A1, the predominant haplotype, GTT, was significantly decreased in the patients with PAC/PACG (61.9%) compared with the controls (66.1%) (P = 0.0072). Additionally, the haplotypes of ACC and ATT were slightly increased in the patients with PAC/PACG compared with the controls (P = 0.034 and 0.047, respectively). For the SNPs rs216489, rs11024060, and rs11024102 in PLEKHA7, the GCC haplotype was significantly increased in the cases when compared with the controls (P = 0.0015). No difference was detected between cases and controls with regard to the other haplotypes. 
Figure 1
 
Linkage disequilibrium (LD) plot of five single nucleotide polymorphisms (SNPs) in COL11A1. The physical position of each SNP is shown above the plot. Darker shades of red indicate higher values of the LD coefficient (D′). The numbers listed in each square represent the D′ value for pairwise analysis.
Figure 1
 
Linkage disequilibrium (LD) plot of five single nucleotide polymorphisms (SNPs) in COL11A1. The physical position of each SNP is shown above the plot. Darker shades of red indicate higher values of the LD coefficient (D′). The numbers listed in each square represent the D′ value for pairwise analysis.
Figure 2
 
Linkage disequilibrium (LD) plot of seven single nucleotide polymorphisms (SNPs) in PLEKHA7. The physical position of each SNP is shown above the plot. Darker shades of red indicate higher values of the LD coefficient (D′). The numbers listed in each square represent the D′ value for pairwise analysis.
Figure 2
 
Linkage disequilibrium (LD) plot of seven single nucleotide polymorphisms (SNPs) in PLEKHA7. The physical position of each SNP is shown above the plot. Darker shades of red indicate higher values of the LD coefficient (D′). The numbers listed in each square represent the D′ value for pairwise analysis.
Discussion
Previously, rs11024102 in PLEKHA7 and rs3753841 in COL11A1 were identified as genetic risk factors for PACG in a GWAS. 3 The involvement of genetic risk factors in disease pathogenesis can be understood better through study of their association in an independent cohort. We examined the association of five SNPs in COL11A1 and seven SNPs in PLEKHA7, including the two previously identified SNPs (rs3753841 in COL11A1 and rs11024102 in PLEKHA7), with PAC/PACG in a Chinese population. Both rs3753841 and rs11024102 showed minor allele frequencies in cases and controls comparable to previously reported values 3 (Table 2). Additionally, rs3753841 and rs11024102 also had nominal association with PAC/PACG (P = 0.062 and 0.038, respectively) in our results. In the previous GWAS, rs1676486, a coding SNP with acid amino change of serine to proline, was the second-highest associated SNP in the COL11A1 gene. 3 In our study, however, rs1676486 showed the most significant association with PAC/PACG (P = 0.006). In the PLEKHA7 gene, rs216489 showed the most significant association with PAC/PACG (P = 0.0074). 
In contrast to previous studies, 3,9 patients with PAC were included in our study, which could potentially dilute the observations of association with PACG. However, PAC is an early stage of PACG, 11,15 and it is interesting and meaningful to investigate whether this group has an association with SNPs in COL11A1 and PLEKHA7 as well. Clarification of risk factors for different stages of a disease can be helpful in early diagnosis and prognosis in clinical practice. Additionally, our results showed an association effect size for the combined PAC/PACG cohort that was comparable to that of PACG alone seen in the study of Vithana et al. 3 As such, it is less likely that the inclusion of PAC generated significant bias. Furthermore, subgroup analyses were conducted to determine the association of these two genes with PACG of varying severity. However, no consistent predilection to PAC or PACG was seen. Larger sample sizes of PAC patients will be needed to validate the association in this early stage. 
Chinese populations have an incidence of acute primary angle closure (APAC) twice that of other Asian populations, such as Thai, Malay, Indian, and Indonesian. 1618 Although previous studies have shown that biometric factors, including smaller corneal diameters, shallower ACD, thicker lens, and shorter AL, are associated with angle closure, no specific factors are known to predict development of APAC, a disease with devastating consequences. 1921 In general, acute PACG eyes tend to carry more severe anatomical deviations than are found in chronic PACG eyes, and this is consistent with our data set (Table 4). Recently, Quigley et al. 22 postulated that the pathogenesis of angle closure may be linked to dynamic processes related to the iris and the choroid rather than just static anatomical differences. The change in iris volume in response to illumination was found to be predictive for angle closure 23,24 and also differs between acute PAC/PACG and chronic PAC/PACG. 25 One aim of our study was to investigate whether the two susceptibility genes impart a characteristic clinical phenotype and potentially aid in understanding the pathogenesis of PAC with or without acute attacks. 
Thus, we investigated the association of these SNPs separately with acute PAC/PACG and chronic PAC/PACG. None of the SNPs harbored statistically significant differences in minor allele frequencies when acute PAC/PACG was compared with chronic PAC/PACG. However, we found that there were six SNPs (rs1676486, rs3753841, rs12138977, rs216489, rs11024060, and rs11024102) significantly associated with acute PAC/PACG, led by SNP rs1676486 (P value = 4.71 × 10−4), but not with chronic PAC/PACG (Table 5). Wei et al. 26 did not find a statistically significant association of the three GWAS-identified SNPs with any major phenotypic diversity in terms of disease severity or progression. Considering that additional SNPs throughout the two genes were included and genotyped in our study, our concordant results for the six SNPs suggest that these SNPs might confer higher risk for acute PAC/PACG rather than chronic PAC/PACG. However, none of them were identified as associated with ocular biometric parameters such as ACD and AL (Table 6), which is consistent with previous studies. 27,28 It follows that the predilection to PACG with acute attacks may be mediated by factors other than shallow anterior chamber or short AL. 
Statistical power calculations showed that our sample size had roughly 80% power to detect association for SNPs that have a minor allele frequency of more than 0.3 and per allele OR more than 1.2 in published data. 3 But due to the relatively low per allele OR (<1.2) of rs3753841 (COL11A1) and rs11024102 (PLEKHA7) detected in our cohort, we might be slightly underpowered to detect differences in association for these SNPs. However, this should not affect our results, given that consistent allele frequencies and trends were identified. 
Overall, our study validates the findings of a large GWAS implicating COL11A1 and PLEKHA7 in the pathogenesis of PACG. Our results showed mild association of rs3753841 and rs11024102 with PAC/PACG. Additional SNPs, including rs1676486 and rs216489, were genotyped and demonstrated a stronger association with PAC/PACG. Both COL11A1 and PLEKHA7 were shown to confer significant risk for acute PAC/PACG. We also demonstrated the haplotype structure and association patterns of SNPs in the COL11A1 and PLEKHA7 region in a Chinese population. Further studies will be needed to investigate how the candidate genes affect the pathogenicity of angle closure glaucoma. 
Supplementary Materials
Acknowledgments
We thank all the PAC/PACG patients and normal controls for participating in this study. The samples used in this study were all from the Eye and Ear Nose Throat Hospital Biobank. 
Disclosure: Y. Chen, None; X. Chen, None; L. Wang, None; G. Hughes, None; S. Qian, None; X. Sun, None 
Supported by the National Natural Science Foundation of China (81200723) and Special Scientific Research Project of Health Professions of China (201302015). The authors alone are responsible for the content and writing of the paper. 
References
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Figure 1
 
Linkage disequilibrium (LD) plot of five single nucleotide polymorphisms (SNPs) in COL11A1. The physical position of each SNP is shown above the plot. Darker shades of red indicate higher values of the LD coefficient (D′). The numbers listed in each square represent the D′ value for pairwise analysis.
Figure 1
 
Linkage disequilibrium (LD) plot of five single nucleotide polymorphisms (SNPs) in COL11A1. The physical position of each SNP is shown above the plot. Darker shades of red indicate higher values of the LD coefficient (D′). The numbers listed in each square represent the D′ value for pairwise analysis.
Figure 2
 
Linkage disequilibrium (LD) plot of seven single nucleotide polymorphisms (SNPs) in PLEKHA7. The physical position of each SNP is shown above the plot. Darker shades of red indicate higher values of the LD coefficient (D′). The numbers listed in each square represent the D′ value for pairwise analysis.
Figure 2
 
Linkage disequilibrium (LD) plot of seven single nucleotide polymorphisms (SNPs) in PLEKHA7. The physical position of each SNP is shown above the plot. Darker shades of red indicate higher values of the LD coefficient (D′). The numbers listed in each square represent the D′ value for pairwise analysis.
Table 1
 
Clinical Information on Cases and Controls
Table 1
 
Clinical Information on Cases and Controls
Cases Controls
Number 984 922
Female, %* 65.45 73.10
Age, y* 60.99 ± 10.53 58.44 ± 11.89
Max IOP, mm Hg* 42.29 ± 14.40 14.89 ± 2.94
Max cup-to-disc ratio* 0.69 ± 0.24 0.35 ± 0.12
Table 2
 
Single Nucleotide Polymorphism Allele Frequencies and Associations for 12 SNPs Between Cases and Controls
Table 2
 
Single Nucleotide Polymorphism Allele Frequencies and Associations for 12 SNPs Between Cases and Controls
Gene SNP CHR BP MA MAF Case MAF Control P Values OR (95% CI)
COL11A1 rs1676486 1 103354138 A 0.27 0.23 0.0060 1.23 (1.06–1.42)
COL11A1 rs3753841 1 103379918 C 0.33 0.30 0.062 1.14 (0.99–1.31)
COL11A1 rs12138977 1 103393457 C 0.26 0.23 0.028 1.18 (1.02–1.38)
COL11A1 rs2126642 1 103405793 T 0.14 0.14 0.84 1.02 (0.85–1.23)
COL11A1 rs2622848 1 103421003 C 0.10 0.10 0.79 1.03 (0.83–1.27)
PLEKHA7 rs216489 11 16823736 G 0.36 0.32 0.0074 1.21 (1.05–1.38)
PLEKHA7 rs1027617 11 16842787 A 0.37 0.37 0.96 1.00 (0.88–1.14)
PLEKHA7 rs366590 11 16872440 A 0.29 0.28 0.75 1.02 (0.89–1.18)
PLEKHA7 rs11024060 11 16881961 T 0.35 0.38 0.081 0.89 (0.78–1.02)
PLEKHA7 rs6486330 11 16990290 T 0.15 0.17 0.18 0.89 (0.75–1.06)
PLEKHA7 rs11024097 11 16999029 C 0.36 0.38 0.45 0.95 (0.83–1.09)
PLEKHA7 rs11024102 11 17008605 C 0.46 0.42 0.038 1.15 (1.01–1.30)
Table 3
 
Single Nucleotide Polymorphism Allele Frequencies and Associations for 12 SNPs in Subgroup Analysis of PAC Versus Controls and PACG Versus Controls
Table 3
 
Single Nucleotide Polymorphism Allele Frequencies and Associations for 12 SNPs in Subgroup Analysis of PAC Versus Controls and PACG Versus Controls
SNP CHR BP MA MAF Control PAC PACG
MAF P OR (95% CI) MAF P OR (95% CI)
rs1676486 1 103354138 A 0.23 0.28 0.018 1.26 (1.04–1.53) 0.27 0.015 1.23 (1.04–1.45)
rs3753841 1 103379918 C 0.30 0.33 0.23 1.12 (0.93–1.34) 0.34 0.054 1.17 (1.00–1.36)
rs12138977 1 103393457 C 0.23 0.26 0.077 1.19 (0.98–1.45) 0.26 0.043 1.19 (1.01–1.41)
rs2126642 1 103405793 T 0.14 0.14 0.84 1.03 (0.80–1.31) 0.14 0.98 1.00 (0.81–1.23)
rs2622848 1 103421003 C 0.10 0.09 0.59 0.93 (0.69–1.23) 0.11 0.43 1.10 (0.87–1.39)
rs216489 11 16823736 G 0.32 0.38 0.0026 1.31 (1.10–1.56) 0.35 0.11 1.13 (0.97–1.32)
rs1027617 11 16842787 A 0.37 0.37 0.92 0.99 (0.83–1.18) 0.37 0.93 1.01 (0.87–1.17)
rs366590 11 16872440 A 0.28 0.28 0.69 0.96 (0.80–1.16) 0.30 0.44 1.06 (0.91–1.25)
rs11024060 11 16881961 T 0.38 0.36 0.22 0.90 (0.75–1.07) 0.35 0.069 0.87 (0.75–1.01)
rs6486330 11 16990290 T 0.17 0.16 0.74 0.96 (0.77–1.21) 0.14 0.079 0.84 (0.68–1.02)
rs11024097 11 16999029 C 0.38 0.38 0.85 1.02 (0.85–1.21) 0.35 0.19 0.91 (0.78–1.05)
rs11024102 11 17008605 C 0.42 0.43 0.95 1.01 (0.85–1.19) 0.48 0.0024 1.25 (1.08–1.45)
Table 4
 
Clinical Characteristics of Acute PAC/PACG and Chronic PAC/PACG
Table 4
 
Clinical Characteristics of Acute PAC/PACG and Chronic PAC/PACG
Acute PAC/PACG, n = 481 Chronic PAC/PACG, n = 443
Female, %* 75.47 53.05
Age at diagnosis 61.44 ± 10.42 60.78 ± 10.77
Max IOP before treatment* 47.70 ± 14.60 38.23 ± 11.84
Center cornea thickness* 549.40 ± 37.68 538.57 ± 33.68
Anterior chamber depth 2.31 ± 0.71 2.35 ± 0.38
Axial length* 22.02 ± 0.91 22.41 ± 1.01
Max cup-to-disc ratio* 0.61 ± 0.24 0.79 ± 0.20
Table 5
 
Single Nucleotide Polymorphism Allele Frequencies and Associations for the Top 6 SNPs in Subgroup Analysis of Acute PAC/PACG Versus Controls and Chronic PAC/PACG Versus Controls
Table 5
 
Single Nucleotide Polymorphism Allele Frequencies and Associations for the Top 6 SNPs in Subgroup Analysis of Acute PAC/PACG Versus Controls and Chronic PAC/PACG Versus Controls
SNP CHR MA MAF Control Acute PAC/PACG Chronic PAC/PACG
MAF P OR (95% CI) MAF P OR (95% CI)
rs1676486 1 A 0.23 0.29 4.71 × 10−4 1.37 (1.15–1.63) 0.26 0.13 1.15 (0.96–1.39)
rs3753841 1 C 0.30 0.34 0.041 1.19 (1.01–1.40) 0.33 0.14 1.14 (0.96–1.35)
rs12138977 1 C 0.23 0.27 0.012 1.26 (1.05–1.51) 0.25 0.12 1.16 (0.96–1.40)
rs216489 11 G 0.32 0.36 0.029 1.20 (1.02–1.42) 0.35 0.079 1.16 (0.98–1.38)
rs11024060 11 T 0.38 0.33 0.0053 0.79 (0.67–0.93) 0.37 0.42 0.93 (0.79–1.10)
rs11024102 11 C 0.42 0.46 0.042 1.18 (1.01–1.38) 0.46 0.075 1.16 (0.99–1.36)
Table 6
 
Association Results of 12 SNPs With Axial Length and Anterior Chamber Depth
Table 6
 
Association Results of 12 SNPs With Axial Length and Anterior Chamber Depth
SNP CHR MA ACD AL
Beta P Beta P
rs1676486 1 A −0.02 0.60 0.01 0.91
rs3753841 1 C −0.01 0.75 0.00 0.92
rs12138977 1 C −0.01 0.74 0.01 0.77
rs2126642 1 T 0.07 0.22 −0.03 0.62
rs2622848 1 C 0.02 0.73 −0.04 0.60
rs216489 11 G 0.04 0.31 −0.10 0.04
rs1027617 11 A 0.02 0.70 0.01 0.88
rs366590 11 A −0.06 0.17 0.03 0.51
rs11024060 11 T 0.03 0.50 −0.02 0.74
rs6486330 11 T 0.06 0.29 −0.05 0.42
rs11024097 11 C 0.04 0.32 −0.01 0.91
rs11024102 11 C −0.05 0.19 0.08 0.06
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