Free
Genetics  |   August 2013
Lack of Association Between Primary Angle-Closure Glaucoma Susceptibility Loci and the Ocular Biometric Parameters Anterior Chamber Depth and Axial Length
Author Affiliations & Notes
  • Monisha E. Nongpiur
    Singapore Eye Research Institute, Singapore
  • Xin Wei
    Singapore Eye Research Institute, Singapore
    Duke University-National University of Singapore Graduate Medical School, Singapore
  • Liang Xu
    Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital University of Medical Science, Beijing, China
  • Shamira A. Perera
    Singapore Eye Research Institute, Singapore
  • Ren-Yi Wu
    Singapore Eye Research Institute, Singapore
  • Yingfeng Zheng
    Singapore Eye Research Institute, Singapore
  • Yang Li
    Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital University of Medical Science, Beijing, China
  • Ya-Xing Wang
    Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital University of Medical Science, Beijing, China
  • Ching-Yu Cheng
    Singapore Eye Research Institute, Singapore
    Department of Ophthalmology, Yong Loo Lin School of Medicine, National University Health System, National University of Singapore (NUS), Singapore
    Saw Swee Hock School of Public Health, National University of Singapore, Singapore
  • Jost B. Jonas
    Department of Ophthalmology, Medical Faculty Mannheim of the Ruprecht-Karls-University Heidelberg, Heidelberg, Germany
  • Tien-Yin Wong
    Singapore Eye Research Institute, Singapore
    Department of Ophthalmology, Yong Loo Lin School of Medicine, National University Health System, National University of Singapore (NUS), Singapore
    Saw Swee Hock School of Public Health, National University of Singapore, Singapore
  • Eranga N. Vithana
    Singapore Eye Research Institute, Singapore
    Department of Ophthalmology, Yong Loo Lin School of Medicine, National University Health System, National University of Singapore (NUS), Singapore
  • Tin Aung
    Singapore Eye Research Institute, Singapore
    Department of Ophthalmology, Yong Loo Lin School of Medicine, National University Health System, National University of Singapore (NUS), Singapore
  • Chiea-Chuen Khor
    Singapore Eye Research Institute, Singapore
    Department of Ophthalmology, Yong Loo Lin School of Medicine, National University Health System, National University of Singapore (NUS), Singapore
    Saw Swee Hock School of Public Health, National University of Singapore, Singapore
    Human Genetics, Genome Institute of Singapore, Singapore
    Department of Paediatrics, Yong Loo Lin School of Medicine, National University Health System, National University of Singapore, Singapore
  • Correspondence: Chiea-Chuen Khor, 60 Biopolis Street, #02-01 Genome Building, Singapore 138672; khorcc@gis.a-star.edu.sg
Investigative Ophthalmology & Visual Science August 2013, Vol.54, 5824-5828. doi:10.1167/iovs.13-11901
  • Views
  • PDF
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Monisha E. Nongpiur, Xin Wei, Liang Xu, Shamira A. Perera, Ren-Yi Wu, Yingfeng Zheng, Yang Li, Ya-Xing Wang, Ching-Yu Cheng, Jost B. Jonas, Tien-Yin Wong, Eranga N. Vithana, Tin Aung, Chiea-Chuen Khor; Lack of Association Between Primary Angle-Closure Glaucoma Susceptibility Loci and the Ocular Biometric Parameters Anterior Chamber Depth and Axial Length. Invest. Ophthalmol. Vis. Sci. 2013;54(8):5824-5828. doi: 10.1167/iovs.13-11901.

      Download citation file:


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

      ×
  • Supplements
Abstract

Purpose.: Three susceptibility loci for primary angle-closure glaucoma (PACG) were recently identified: PLEKHA7 rs11024102, COL11A1 rs3753841, and rs1015213 located in the intergenic region between PCMTD1 and ST18. The purpose of this study was to investigate the associations of these loci with the ocular biometric parameters anterior chamber depth (ACD) and axial length (AL).

Methods.: Genotype and ocular biometric data were available for four population-based studies, including three from Singapore (Singapore Chinese Eye Study, Singapore Malay Eye Study, and Singapore Indian Eye Study) and one from China (Beijing Eye Study), exceeding 7000 participants. ACD and AL were measured using the IOLMaster for the Singapore cohorts and optical low-coherence reflectometry (Lenstar 900 Optical Biometer) for the Beijing cohort. Five readings were obtained for each participant and the average was computed. Analysis excluded any eye that was pseudophakic or aphakic.

Results.: ACD measurements and genotype data of the three loci were available for 7245, 7243, and 7239 subjects, respectively. We noted nominal evidence of association between single nucleotide polymorphism (SNP) rs1015213 (PCMTD1-ST18) and a shallower ACD when all data were meta-analyzed (β = −0.033, P = 0.021). When multiple testing was considered, the observation was nonsignificant. There was no association between ACD and rs11024102 (PLEKHA7) or rs3753841 (COL11A1). We did not observe significant associations between AL and any of the three SNPs.

Conclusions.: The lack of association between the PACG susceptibility loci with ACD or AL suggests that predilection to PACG may be mediated by factors other than shallow anterior chamber or short eyeball length.

Introduction
Primary angle-closure glaucoma (PACG) is responsible for substantial visual loss in many Asian countries, such as Singapore, 1 China, 2,3 Mongolia, 4 and India. 5,6 PACG has long been suspected to have a substantial hereditable component. This was proven recently in a genome-wide association study (GWAS) of PACG conducted in five sample collections across Asia with validation in a further six sample collections worldwide. 7 Genome-wide significant association (P < 5 × 10−8) with PACG was found in three new susceptibility loci (rs3753841 in COL11A1, rs1015213 located between PCMTD1 and ST18, and rs11024102 in PLEKHA7). However, it is not clear how these three loci contribute to the pathogenesis of PACG or whether these genetic variants act via any known anatomical risk factors for PACG. 
Previous studies have demonstrated that ocular biometric parameters such as a shallow anterior chamber depth (ACD) and short axial length (AL) are strong risk factors for PACG. 811 In a community-based study of older Singaporeans, eyes with ACD less than 2.80 mm were 42.5 times more likely to have angle closure than eyes with ACD of at least 3.00 mm. 10 Likewise, a shorter AL is also associated with an increased risk for angle closure. Reports have shown that eyes of Chinese patients who had acute angle closure had shorter AL than those affected by chronic asymptomatic angle closure, and that both groups had shorter AL than the control group. 12,13  
The aim of this study was to investigate the association of these three PACG susceptibility loci with the anatomical risk factors ACD and AL. To this end, we sought to test this association in large population-based samples of Asian descent. 
Methods
Descriptions of Study Populations
Ethical approval was obtained from the Singapore Eye Research Institute Institutional Review Board and the Medical Ethics Committee of the Beijing Tongren Hospital for the Singapore and Beijing cohorts, respectively. Written informed consent was obtained from all study participants, and the study adhered to the Declaration of Helsinki. 
Singapore Cohorts.
Data from the Singapore Malay Eye Study (SiMES), Singapore Indian Eye Study (SINDI), and Singapore Chinese Eye Study (SCES) were analyzed. SiMES, SINDI, and SCES were population-based, cross-sectional studies of ethnic Malay (aged 40–79 years), Indian (aged from 40–80+ years), and Chinese (aged from 40–80+ years) adults residing in the southwestern part of Singapore. Details of the study design, sampling plan, and methods have been previously reported for these studies. 14,15 In brief, these three studies were designed to determine the prevalence and impact of major eye diseases in Singaporeans of different ethnicities. An age-stratified (by 10-year age group) random sampling strategy was employed for subject selection from a computer-generated list provided by the Ministry of Home Affairs, Singapore. SiMES was conducted from August 2004 to June 2006 and recruited 3280 participants (78.7% response rate). SINDI was conducted from March 2007 to December 2009 and recruited 3400 participants (75% response rate). SCES was conducted from February 2009 to December 2011 and recruited 3353 participants. The final number of subjects who were genotyped was 3072, 2953, and 1952 for SiMES, SINDI, and SCES, respectively. 
Beijing Cohort.
The Beijing Eye Study (BES) was a population-based, cross-sectional study of Chinese adults (aged 40+ years) in urban communities in the Haidian district in the north of central Beijing and in rural communities in the village area of Yufa of the Daxing district, south of Beijing. 16 In the year 2001 when the first survey was carried out, 4439 participants were recruited (83.4% response rate). The BES was repeated in 2006 and 2011. In 2011, when ACD and AL were measured, the study included all participants from the previous two surveys and added subjects who fulfilled the eligibility criteria of age of 50+ years, who lived in the study region, and who had not participated in the previous surveys. A total of 3468 participants (78.8% response rate) were recruited in the 2011. 17 The final number of subjects who were genotyped was 988. Measurement of ACD and AL was performed using optical low-coherence reflectometry (Lenstar 900 Optical Biometer; Haag-Streit, Koeniz, Switzerland). The examination was performed by experienced clinical technicians. Five measurements were performed, and the mean value was taken for further statistical analysis. 
Measurements of Ocular Parameters
Singapore Cohorts.
ACD and AL were measured by noncontact partial-coherence laser interferometry (IOLMaster, Carl Zeiss Meditec, Dublin, CA). Five readings were obtained, and the mean value was used for analysis. The biometric measurements were excluded from any eye that was pseudophakic or aphakic. As there was good correlation between biometric data for the two eyes, analysis was performed using only data for the right eyes. 
Beijing Cohort.
ACD and AL were measured by optical low-coherence reflectometry (Lenstar 900 Optical Biometer; Haag-Streit) for the right eyes of study participants in the survey in 2011. Five measurements were performed, and the mean value was used for analysis. 17 Good correlation between IOLMaster and Lenstar measurements for ACD/AL has been demonstrated, and no statistical differences in ACD/AL measurements were observed in a previous study. 18 Therefore, the use of different measurement devices is unlikely to affect the result of analysis. 
Genotyping and Data QC
Methods of genotyping and data quality control (QC) for SiMES, SINDI, SCES, and BES have been described previously. 1922 In brief, participants of all four studies were genotyped using the Illumina Human610-Quad BeadChip (Illumina, Inc., San Diego, CA) with the following QC criteria: Samples were excluded if they had a per sample call rate <95% or showed evidence of admixture, cryptic relatedness, high heterogeneity, or sex discrepancy. Final number of subjects passing quality checks was 2542, 2538, 1949, and 927 for SiMES, SINDI, SCES, and BES, respectively. Genotype at the three loci of interest (rs3753841, rs1015213, and rs11024102) was analyzed using PLINK (version 1.07 23 ). 
Statistical Analysis
Linear regression was performed for primary association testing using a commercially available statistical software package (SPSS for Windows, version 20.0; IBM-SPSS, Chicago, IL). Individual SNP genotypes were coded according to the number of copies of the variant allele present: 0 for the wild-type genotype, 1 for heterozygous, and 2 for homozygous variants. A trend test incorporated within a linear regression model was used for primary association testing between genotypes and ACD/AL as quantitative traits, adjusting for age, sex, and population admixture (reflected by principal components). 
Meta-analysis was performed with PLINK (version 1.07 23 ) using the fixed effects model on primary analysis and was verified with the random effects model when significant heterogeneity was observed. For the meta-analysis, a combined point estimate of the overall effect size (β coefficient) and its corresponding P value were obtained. The Cochran's Q and accompanying I 2 statistic were used to assess intercohort heterogeneity. For the final analysis, a Bonferroni correction factor of 3 was applied to correct for number of loci studied, resulting in a P value threshold of 0.017 to be considered statistically significant experiment-wide. 
Results
Table 1 summarizes the descriptive statistics of all cohorts after genotyping and sample QC. 
Table 1. 
 
Characteristics of the Study Population
Table 1. 
 
Characteristics of the Study Population
SiMES SINDI SCES BES
n* 2542 2538 1949 927
Age† 59.09 (11.04), 40–80 58.04 (10.01), 43–80 58.99 (9.98), 44–111 53.11 (9.35), 40–81
Sex‡ 0.51 0.49 0.49 0.63
ACD† 3.10 (0.38), 1.78–4.25 3.22 (0.46), 1.74–5.39 2.95 (0.37), 1.99–5.09 2.41 (0.32), 1.29–3.61
AL† 23.57 (1.05), 20.61–30.53 23.38 (1.11), 19.14–32.76 23.61 (1.37), 20.39–33.27 23.03 (1.11), 19.90–30.36
A total of 7245, 7243, and 7239 subjects who had complete genotype and ACD (including covariates) measurement data were tested for association between the three PACG susceptibility loci (rs3753841, rs1015213, and rs11024102) and ACD, respectively (Table 2). We found nominal evidence of association between SNP rs1015213 (PCMTD1-ST18) and a shallower ACD when all data were meta-analyzed (β = −0.033, P = 0.021). There was no significant heterogeneity of the effect size across all sample collections (P heterogeneity = 0.87, I 2 = 0). The observation was statistically nonsignificant when multiple testing was considered. No significant association was found between the other two loci (rs3753841 and rs11024102) and ACD in the meta-analysis (Table 2). The regional association analysis for the three loci and ACD is illustrated in Supplementary Figures S1, S2, and S3
Table 2. 
 
Association Results Between Three PACG Susceptibility Loci and ACD
Table 2. 
 
Association Results Between Three PACG Susceptibility Loci and ACD
Chr SNP BP A1 Cohort n EAF β SE P P het I 2
1 rs3753841 103152506 G SCES 1690 0.32 0.006 0.014 0.67
SiMES 2209 0.2889 0.019 0.011 0.094
SINDI 2520 0.4377 −0.003 0.013 0.802
BES 826 0.2983 0.014 0.016 0.392
All 7245 0.0093 0.16 0.76 0
8 rs1015213 53050094 A SCES 1690 0.011 −0.041 0.064 0.52
SiMES 2207 0.04055 −0.021 0.025 0.404
SINDI 2520 0.1222 −0.033 0.02 0.089
BES 826 0.02427 −0.068 0.046 0.139
All 7243 −0.033 0.021 0.87 0
11 rs11024102 16965181 G SCES 1690 0.37 −0.0007 0.013 0.96
SiMES 2205 0.37 −0.014 0.011 0.19
SINDI 2518 0.303 0.002 0.014 0.893
BES 826 0.4326 −0.023 0.015 0.119
All 7239 −0.009 0.148 0.65 0
In the association testing between the three PACG susceptibility loci (rs3753841, rs1015213, and rs11024102) and AL, a total of 6902, 6900, and 6896 subjects who had complete genotype and AL (including covariates) measurement data were included, respectively (Table 3). We found that locus rs11024102 (PLEKHA7) was significantly associated with longer AL in the meta-analysis of all collections using the fixed effects model (β = 0.051, P = 0.009). However, significant heterogeneity of effect sizes was observed (P heterogeneity = 0.01, I 2 = 69.19%). Therefore meta-analysis using the random effects model was conducted; the results did not show significant association between rs11024102 (PLEKHA7) and AL (P randomeffects = 0.38). No significant association was found between the other two loci (rs3753841 and rs1015213) and AL in individual cohorts or in the meta-analysis of all cohorts (Table 3). The regional association analysis for the three loci and AL is illustrated in Supplementary Figures S4, S5, and S6
Table 3. 
 
Association Results Between Three PACG Susceptibility Loci and AL
Table 3. 
 
Association Results Between Three PACG Susceptibility Loci and AL
Chr SNP BP A1 Cohort n EAF β SE P P het I 2
1 rs3753841 103152506 G SCES 1613 0.32 −0.096 0.051 0.06
SiMES 2210 0.2889 0.07 0.034 0.039
SINDI 2488 0.4377 0.043 0.03 0.151
BES 591 0.2983 −0.019 0.069 0.783
All 6902 0.0265 0.18 0.081 51.79
8 rs1015213 53050094 A SCES 1613 0.011 −0.19 0.24 0.41
SiMES 2208 0.04055 −0.112 0.075 0.137
SINDI 2488 0.1222 −0.048 0.046 0.301
BES 591 0.02427 −0.1 0.203 0.622
All 6900 −0.07 0.067 0.766 0
11 rs11024102 16965181 G SCES 1613 0.37 −0.015 0.052 0.77
SiMES 2206 0.37 0 0.032 0.99
SINDI 2486 0.303 0.138 0.032 <0.001
BES 591 0.4326 0.016 0.064 0.801
All 6896 0.051 0.009 0.01 69.19
All (random effects) 0.035 0.384
Discussion
We conducted a candidate gene association study between three established PACG susceptibility loci and anatomical quantitative trait risk factors for angle closure (ACD and AL) using four population-based samples totaling approximately 7000 individuals of Asian descent. Overall, none of the novel PACG loci showed significant association with ACD or AL, which suggests disease mechanisms that act independently of shallower anterior chamber and shorter eyeball length. A notable strength of this study is that it directly examined association between the PACG loci and the anatomical quantitative trait risk factors in sample collections of the same ethnicity (Chinese, Malays, and Indians) as in the initial PACG case–control GWAS. Such an approach minimizes ethnic heterogeneity as a reason for our nonsignificant observations. 
Indeed, several explanations can be given for this result. Angle closure is a heterogeneous condition that can result from one or a combination of anatomical and/or physiological changes of the anterior and posterior segment structures. 24 In addition to ACD and AL, there are several novel imaging-based anatomical risk factors for angle closure, including smaller anterior chamber width 25 ; smaller anterior chamber area and volume 26 ; greater iris thickness, area, and curvature 27 ; and larger lens vault. 28 Although not correlated with ACD or AL, these PACG-associated loci may be associated with these other anatomical parameters. Recent evidence also suggests that dynamic physiological factors such as changes in iris volume with dilation 29,30 and choroidal expansion/effusion 31 may have a role in angle-closure pathogenesis. We speculate that at least one of the PACG-associated variants confers risk via these dynamic (as opposed to static) mechanisms. One of the susceptibility genes, PLEKHA7, encodes a plekstrin homology domain containing protein, proposed to regulate apical junctional complexes (AJCs). 32,33 As AJCs control epithelial and endothelial paracellular permeability, PLEKHA7 may be involved in the pathophysiology of angle closure related to aberrant fluid dynamics. With the identification of newer ocular biometric and dynamic risk factors, there is a better understanding of the mechanisms involved in the disease process. Therefore, the association between PACG susceptibility loci and other anatomical and physiological risk factors of angle closure is worthy of further investigation. Functional characterization of these genes in tissues and cells relevant to PACG is also likely to provide further insights into associated disease mechanisms. 
We have utilized well-characterized population-based studies that encompass three major ethnic groups of Asia in which PACG is a predominant blinding condition. Our study is further strengthened by a large sample size. For SNPs rs3753841 (COL11A1) and rs11024102 (PLEKHA7), which have an effect allele frequency of more than 0.3, we have more than 95% power for detecting differences among the wild-type, heterozygous, and homozygous groups assuming an underlying true effect size of 0.2 (delta/sigma) or larger. For SNP rs1015213 (PCMTD1-ST18), which has a lower effect allele frequency, we still have approximately 80% power for detecting differences if we assume an underlying true effect size of 1.1 (delta/sigma) or larger. While we evaluated the correlation between PACG susceptibility loci and only two ocular anatomical features, the association with other PACG-related quantitative traits such as lens, iris, and anterior chamber characteristics should also be investigated in future analyses. Another limitation of our study was that we were able to control only for age, sex, and population stratification in the analysis. A previous study reported that lens vault and posterior corneal arc length were responsible for approximately 75% of the variation of ACD in a Singaporean Chinese cohort. 34 It is likely that such factors could confound the results of the study. However, in the current study, the confounding effect of these determinant factors could not be investigated due to the nonavailability of data. 
In summary, this candidate gene study investigated the possibility that three PACG susceptibility loci could contribute to the pathogenesis of PACG by looking at their association with the most obvious anatomical risk factors of angle closure: ACD and AL. The lack of association with both phenotypes suggests that the pathogenesis of PACG involves risk factors other than ACD and AL. Future research investigating the association between PACG susceptibility loci and other risk factors for angle closure and progression in angle-closure disease should now be performed. 
Supplementary Materials
Acknowledgments
Supported by grants from the National Medical Research Council, Singapore (NMRC 0796/2003, STaR/0003/2008) and the Biomedical Research Council (BMRC 09/1/35/19/616 and 08/1/35/19/550). 
Disclosure: M.E. Nongpiur, None; X. Wei, None; L. Xu, None; S.A. Perera, None; R.-Y. Wu, None; Y. Zheng, None; Y. Li, None; Y.-X. Wang, None; C.-Y. Cheng, None; J.B. Jonas, None; T.-Y. Wong, None; E.N. Vithana, None; T. Aung, None; C.-C. Khor, None 
References
Foster PJ Oen FT Machin D The prevalence of glaucoma in Chinese residents of Singapore: a cross-sectional population survey of the Tanjong Pagar district. Arch Ophthalmol . 2000; 118: 1105–1111. [CrossRef] [PubMed]
Hu CN. An epidemiologic study of glaucoma in Shunyi County, Beijing [in Chinese]. Zhonghua Yan Ke Za Zhi . 1989; 25: 115–119. [PubMed]
Foster PJ Johnson GJ. Glaucoma in China: how big is the problem? Br J Ophthalmol . 2001; 85: 1277–1282. [CrossRef] [PubMed]
Foster PJ Baasanhu J Alsbirk PH Munkhbayar D Uranchimeg D Johnson GJ. Glaucoma in Mongolia: a population-based survey in Hövsgöl Province, northern Mongolia. Arch Ophthalmol . 1996; 114: 1235–1241. [CrossRef] [PubMed]
Dandona L Dandona R Mandal P Angle-closure glaucoma in an urban population in southern India. The Andhra Pradesh eye disease study. Ophthalmology . 2000; 107: 1710–1716. [CrossRef] [PubMed]
Vijaya L George R Arvind H Prevalence and causes of blindness in the rural population of the Chennai Glaucoma Study. Br J Ophthalmol . 2006; 90: 407–410. [CrossRef] [PubMed]
Vithana EN Khor CC Qiao C Genome-wide association analyses identify three new susceptibility loci for primary angle closure glaucoma. Nat Genet . 2012; 44: 1142–1146. [CrossRef] [PubMed]
Alsbirk PH. Primary angle-closure glaucoma. Oculometry, epidemiology, and genetics in a high risk population. Acta Ophthalmol Suppl . 1976; (127): 5–31.
Lowe RF. Aetiology of the anatomical basis for primary angle-closure glaucoma. Biometrical comparisons between normal eyes and eyes with primary angle-closure glaucoma. Br J Ophthalmol . 1970; 54: 161–169. [CrossRef] [PubMed]
Lavanya R Wong TY Friedman DS Determinants of angle closure in older Singaporeans. Arch Ophthalmol . 2008; 126: 686–691. [CrossRef] [PubMed]
Yip JL Foster PJ. Ethnic differences in primary angle-closure glaucoma. Curr Opin Ophthalmol . 2006; 17: 175–180. [CrossRef] [PubMed]
Sun X Ji X Zheng Y Guo B. Primary chronic angle-closure glaucoma in Chinese--a clinical exploration of its pathogenesis and natural course. Yan Ke Xue Bao . 1994; 10: 176–185. [PubMed]
Lin YW Wang TH Hung PT. Biometric study of acute primary angle-closure glaucoma. J Formos Med Assoc . 1997; 96: 908–912. [PubMed]
Foong AW Saw SM Loo JL Rationale and methodology for a population-based study of eye diseases in Malay people: The Singapore Malay eye study (SiMES). Ophthalmic Epidemiol . 2007; 14: 25–35. [CrossRef] [PubMed]
Lavanya R Jeganathan VS Zheng Y Methodology of the Singapore Indian Chinese Cohort (SICC) eye study: quantifying ethnic variations in the epidemiology of eye diseases in Asians. Ophthalmic Epidemiol . 2009; 16: 325–336. [CrossRef] [PubMed]
Xu L Li Y Zheng Y Jonas JB. Associated factors for age related maculopathy in the adult population in China: the Beijing eye study. Br J Ophthalmol . 2006; 90: 1087–1090. [CrossRef] [PubMed]
Yin G Wang YX Zheng ZY Yang H Xu L Jonas JB; Beijing Eye Study Group. Ocular axial length and its associations in Chinese: the Beijing Eye Study. PLoS One . 2012; 7: e43172. [CrossRef] [PubMed]
Rohrer K Frueh BE Wälti R Clemetson IA Tappeiner C Goldblum D. Comparison and evaluation of ocular biometry using a new noncontact optical low-coherence reflectometer. Ophthalmology . 2009; 116: 2087–2092. [CrossRef] [PubMed]
Vithana EN Aung T Khor CC Collagen-related genes influence the glaucoma risk factor, central corneal thickness. Hum Mol Genet . 2011; 20: 649–658. [CrossRef] [PubMed]
Cornes BK Khor CC Nongpiur ME Identification of four novel variants that influence central corneal thickness in multi-ethnic Asian populations. Hum Mol Genet . 2012; 21: 437–445. [CrossRef] [PubMed]
Khor CC Ramdas WD Vithana EN Genome-wide association studies in Asians confirm the involvement of ATOH7 and TGFBR3, and further identify CARD10 as a novel locus influencing optic disc area. Hum Mol Genet . 2011; 20: 1864–1872. [CrossRef] [PubMed]
Lu Y Vitart V Burdon KP Genome-wide association analyses identify multiple loci associated with central corneal thickness and keratoconus. Nat Genet . 2013; 45: 155–163. [CrossRef] [PubMed]
Purcell S Neale B Todd-Brown K PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet . 2007; 81: 559–575. [CrossRef] [PubMed]
Nongpiur ME Ku JY Aung T. Angle closure glaucoma: a mechanistic review. Curr Opin Ophthalmol . 2011; 22: 96–101. [CrossRef] [PubMed]
Nongpiur ME Sakata LM Friedman DS Novel association of smaller anterior chamber width with angle closure in Singaporeans. Ophthalmology . 2010; 117: 1967–1973. [CrossRef] [PubMed]
Wu RY Nongpiur ME He MG Association of narrow angles with anterior chamber area and volume measured with anterior-segment optical coherence tomography. Arch Ophthalmol . 2011; 129: 569–574. [CrossRef] [PubMed]
Wang B Sakata LM Friedman DS Quantitative iris parameters and association with narrow angles. Ophthalmology . 2010; 117: 11–17. [CrossRef] [PubMed]
Nongpiur ME He M Amerasinghe N Lens vault, thickness, and position in Chinese subjects with angle closure. Ophthalmology . 2011; 118: 474–479. [CrossRef] [PubMed]
Quigley HA Silver DM Friedman DS Iris cross-sectional area decreases with pupil dilation and its dynamic behavior is a risk factor in angle closure. J Glaucoma . 2009; 18: 173–179. [CrossRef] [PubMed]
Aptel F Denis P. Optical coherence tomography quantitative analysis of iris volume changes after pharmacologic mydriasis. Ophthalmology . 2010; 117: 3–10. [CrossRef] [PubMed]
Quigley HA Friedman DS Congdon NG. Possible mechanisms of primary angle-closure and malignant glaucoma. J Glaucoma . 2003; 12: 167–180. [CrossRef] [PubMed]
Meng W Mushika Y Ichii T Takeichi M. Anchorage of microtubule minus ends to adherens junctions regulates epithelial cell-cell contacts. Cell . 2008; 135: 948–959. [CrossRef] [PubMed]
Pulimeno P Bauer C Stutz J Citi S. PLEKHA7 is an adherens junction protein with a tissue distribution and subcellular localization distinct from ZO-1 and E-cadherin. PLoS One . 2010; 5: e12207. [CrossRef] [PubMed]
Sng CC Foo LL Cheng CY Determinants of anterior chamber depth: the Singapore Chinese Eye Study. Ophthalmology . 2012; 119: 1143–1450. [CrossRef] [PubMed]
Table 1. 
 
Characteristics of the Study Population
Table 1. 
 
Characteristics of the Study Population
SiMES SINDI SCES BES
n* 2542 2538 1949 927
Age† 59.09 (11.04), 40–80 58.04 (10.01), 43–80 58.99 (9.98), 44–111 53.11 (9.35), 40–81
Sex‡ 0.51 0.49 0.49 0.63
ACD† 3.10 (0.38), 1.78–4.25 3.22 (0.46), 1.74–5.39 2.95 (0.37), 1.99–5.09 2.41 (0.32), 1.29–3.61
AL† 23.57 (1.05), 20.61–30.53 23.38 (1.11), 19.14–32.76 23.61 (1.37), 20.39–33.27 23.03 (1.11), 19.90–30.36
Table 2. 
 
Association Results Between Three PACG Susceptibility Loci and ACD
Table 2. 
 
Association Results Between Three PACG Susceptibility Loci and ACD
Chr SNP BP A1 Cohort n EAF β SE P P het I 2
1 rs3753841 103152506 G SCES 1690 0.32 0.006 0.014 0.67
SiMES 2209 0.2889 0.019 0.011 0.094
SINDI 2520 0.4377 −0.003 0.013 0.802
BES 826 0.2983 0.014 0.016 0.392
All 7245 0.0093 0.16 0.76 0
8 rs1015213 53050094 A SCES 1690 0.011 −0.041 0.064 0.52
SiMES 2207 0.04055 −0.021 0.025 0.404
SINDI 2520 0.1222 −0.033 0.02 0.089
BES 826 0.02427 −0.068 0.046 0.139
All 7243 −0.033 0.021 0.87 0
11 rs11024102 16965181 G SCES 1690 0.37 −0.0007 0.013 0.96
SiMES 2205 0.37 −0.014 0.011 0.19
SINDI 2518 0.303 0.002 0.014 0.893
BES 826 0.4326 −0.023 0.015 0.119
All 7239 −0.009 0.148 0.65 0
Table 3. 
 
Association Results Between Three PACG Susceptibility Loci and AL
Table 3. 
 
Association Results Between Three PACG Susceptibility Loci and AL
Chr SNP BP A1 Cohort n EAF β SE P P het I 2
1 rs3753841 103152506 G SCES 1613 0.32 −0.096 0.051 0.06
SiMES 2210 0.2889 0.07 0.034 0.039
SINDI 2488 0.4377 0.043 0.03 0.151
BES 591 0.2983 −0.019 0.069 0.783
All 6902 0.0265 0.18 0.081 51.79
8 rs1015213 53050094 A SCES 1613 0.011 −0.19 0.24 0.41
SiMES 2208 0.04055 −0.112 0.075 0.137
SINDI 2488 0.1222 −0.048 0.046 0.301
BES 591 0.02427 −0.1 0.203 0.622
All 6900 −0.07 0.067 0.766 0
11 rs11024102 16965181 G SCES 1613 0.37 −0.015 0.052 0.77
SiMES 2206 0.37 0 0.032 0.99
SINDI 2486 0.303 0.138 0.032 <0.001
BES 591 0.4326 0.016 0.064 0.801
All 6896 0.051 0.009 0.01 69.19
All (random effects) 0.035 0.384
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×