February 2014
Volume 55, Issue 2
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Genetics  |   February 2014
Genotype–Phenotype Correlation Analysis for Three Primary Angle Closure Glaucoma-Associated Genetic Polymorphisms
Author Affiliations & Notes
  • Xin Wei
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
    Duke-NUS Graduate Medical School, Singapore
  • Monisha E. Nongpiur
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
    Duke-NUS Graduate Medical School, Singapore
  • Mark S. de Leon
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
  • Mani Baskaran
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
  • Shamira A. Perera
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
  • Alicia C. How
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
    Duke-NUS Graduate Medical School, Singapore
  • Eranga N. Vithana
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
  • Chiea-Chuen Khor
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
    Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
  • Tin Aung
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
  • Correspondence: Tin Aung, Singapore National Eye Centre, 11 Third Hospital Avenue, Singapore 168751; aung.tin@snec.com.sg
Investigative Ophthalmology & Visual Science February 2014, Vol.55, 1143-1148. doi:10.1167/iovs.13-13552
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      Xin Wei, Monisha E. Nongpiur, Mark S. de Leon, Mani Baskaran, Shamira A. Perera, Alicia C. How, Eranga N. Vithana, Chiea-Chuen Khor, Tin Aung; Genotype–Phenotype Correlation Analysis for Three Primary Angle Closure Glaucoma-Associated Genetic Polymorphisms. Invest. Ophthalmol. Vis. Sci. 2014;55(2):1143-1148. doi: 10.1167/iovs.13-13552.

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Abstract

Purpose.: Recently, three genetic susceptibility loci for primary angle closure glaucoma (PACG) were identified: COL11A1 rs3753841, PCMTD1-ST18 rs1015213, and PLEKHA7 rs11024102. The purpose of this study was to investigate whether these single nucleotide polymorphisms (SNPs) affect the phenotype of PACG patients.

Methods.: A retrospective analysis was performed for 700 Singaporean Chinese PACG patients who had been genotyped. The associations between the three SNPs and clinical features related to severity of glaucoma were studied. For a subgroup of patients who had ≥5 years of follow-up and ≥5 reliable visual field (VF) tests, differences in glaucoma progression, as measured by the proportion of VF progression and blindness, were compared among groups with different genotypes.

Results.: The minor allele frequencies at COL11A1 rs3753841, PCMTD1-ST18 rs1015213, and PLEKHA7 rs11024102 were 36%, 2.1%, and 41.5%, respectively. There were no significant differences in sex, diagnosis (acute primary angle closure [APAC] versus non-APAC), age at diagnosis, laterality of glaucoma, or need for filtration surgery among patients with different genotypes (all P > 0.05). We also found no significant difference between genotypes and the IOP at presentation, and other clinical characteristics at DNA collection (vertical cup-to-disc ratio, best corrected visual acuity, baseline VF mean deviation, or pattern standard deviation). For the subgroup analysis, we did not observe significant associations between VF progression and the proportion of blindness with any of the PACG susceptibility loci.

Conclusions.: The three genetic susceptibility loci for PACG did not underlie any major phenotypic diversity in terms of disease severity or progression.

Introduction
Primary angle closure glaucoma (PACG) long has been thought to have a significant genetic basis. 1,2 Recently three novel genetic susceptibility loci for PACG were identified through a genome-wide association study (GWAS) conducted in 5 sample collections across Asia, with validation in a further 6 sample collections worldwide. 3 The single nucleotide polymorphisms (SNPs) found to be associated with PACG were rs3753841 in COL11A1 (per-allele odds ratio [OR] = 1.20, P = 9.22 × 10−10), rs1015213 located between PCMTD1 and ST18 (per-allele OR = 1.50, P = 3.29 × 10−9), and rs11024102 in PLEKHA7 (per-allele OR = 1.22, P = 5.33 × 10−12). However, it is not known whether these genetic variations are associated with specific clinical features in PACG, a disorder with variable clinical presentation and disease course. Knowledge of phenotypic manifestations associated with specific genetic polymorphisms could be helpful in disease management by providing information on the likely course and prognosis for patients. 
The purpose of this study was to investigate whether the three PACG susceptibility loci impart a characteristic clinical phenotype in patients with PACG of Singaporean Chinese descent who were included in the original GWAS. 3 In particular, we were interested in the severity of glaucoma and disease progression as measured by the proportion of visual field (VF) progression and blindness. 
Methods
Study Population and Case Definition
This was a retrospective case-only analysis of Singaporean Chinese PACG or previous acute primary angle closure (APAC) patients attending glaucoma clinics at a Singapore hospital who were genotyped previously. Written informed consent was obtained from all study subjects at the time of biological specimen collection to access their medical records. Ethical approval was obtained from the Singapore Eye Research Institute (SERI) Institutional Review Board (IRB) and the study adhered to the tenets of the Declaration of Helsinki. 
The condition of PACG was defined as the presence of: (1) angle closure (defined as eyes in which at least 180° of the posterior pigmented trabecular meshwork was not visible on gonioscopy in the primary position of gaze without indentation) with peripheral anterior synechiae (PAS) and/or raised IOP (defined as an IOP > 21 mm Hg); and (2) glaucomatous optic neuropathy (GON) defined as loss of neuroretinal rim with a vertical cup-to-disc ratio (CDR) of ≥0.7 or vertical CDR asymmetry of >0.2 between eyes, and/or notching attributable to glaucoma) with compatible VF loss. 4 The APAC was defined as a symptomatic episode with the presence of: (1) at least 2 of the following symptoms: ocular or periorbital pain, nausea, and/or vomiting, and an antecedent history of intermittent blurring of vision with haloes; (2) a presenting IOP of >28 mm Hg on Goldmann applanation tonometry; and (3) at least 3 of the following signs: conjunctival injection, corneal edema, mid-dilated unreactive pupil, and shallow anterior chamber. 5 Patients with secondary causes of angle closure were excluded. 
Collection of Phenotype and Genotype Data
Chart review was performed in a total of 820 PACG patients who were recruited from a single center in Singapore and the following data were collected: sex, age at diagnosis, duration of follow-up, diagnosis for the study eye and fellow eye, history of filtration surgery, IOP at presentation, and clinical characteristics at DNA collection, which included vertical CDR, best corrected visual acuity (BCVA), VF mean deviation (MD), and pattern standard deviation (PSD). 
The IOP was measured by Goldmann applanation tonometry. The BCVA was measured by Snellen chart and converted to the log of the minimal angle of resolution (logMAR) by previously published algorithm. 6 The VF tests were done with static automated white-on-white threshold perimetry (program 24-2; Carl Zeiss Meditec, Dublin, CA). The VF test reliability was defined as fixation losses < 20%, false positives and false negatives < 33%. 
For a subgroup of patients who had at least 5 years of follow-up and at least 5 reliable VF tests in at least one eye (n = 246), data on blindness and VF progression additionally were collected. 
Blindness was defined 7,8 as visual acuity on Snellen chart of 6/60 or worse, or on VF testing, a threshold value of 10 dB or less with a size III stimulus at or within 20° of fixation, such that a continuous line could be drawn between all such points, allowing one point in each quadrant greater than 10 dB. 
The VF progression analysis was done using PROGRESSOR software (Medisoft, Ltd., Leeds, England), which investigates the change in sensitivity of individual points in the VF. The principle of progression analysis has been described previously. 9 Briefly, PROGRESSOR uses point-wise linear regression, providing slope of progression in units of decibels per year (dB/y) globally and locally for each testing point as well as its significance. A testing point was deemed progressing if the rate of sensitivity loss was greater than 1 dB/y (2 dB/y for edge points), with P < 0.01. This criterion was used by a majority of studies that had flagged progression using the PROGRESSOR software. 9,10 In our study, an eye was deemed as progressing if at least 2 adjacent testing points located in the same hemifield met these progression criteria. This definition, according to de Moraes et al., 11 takes into account the topologic orientation of the damaged nerve fiber layer and increased the specificity of detecting progression. The mean number of progressing points, mean slope of sensitivity loss in the whole VF, and among the progressing points also were recorded. 
Methods of genotyping and data quality control were described previously. 3 Briefly, patients were genotyped using the Illumina Human610-Quad BeadChip (Illumina, Inc., San Diego, CA) with the following QC criteria: samples were excluded if they had per sample call rate < 95% or showed evidence of admixture, cryptic relatedness, excess heterozygosity, or sex discrepancy. Genotype at the three loci of interest (rs3753841, rs1015213, and rs11024102) were analyzed using PLINK (version 1.07 12 ). 
Statistical Analysis
Only one eye from each patient was analyzed. The worse eye (the right eye if both eyes had similar severity of disease) was selected for bilateral cases. The worse eye was defined as the eye with an earlier diagnosis of PACG, worse GON, or with a previous APAC. In the subgroup analysis of VF progression, of a total of 492 eyes from 246 patients, 262 eyes were excluded for the following reasons: eyes with less than 5 reliable VF tests (n = 98), eyes with blindness at presentation or earliest reliable VF test (n = 39), the better eye in bilateral cases (n = 122), and eyes whose progression was due to reasons other than glaucoma (n = 3). In the subgroup analysis of blindness, of a total of 246 patients, 36 were excluded for the following reasons: blindness due to reasons other than glaucoma (n = 14) and patients who had a previous APAC, but did not have GON during follow-up (n = 22) were excluded. Therefore, the total numbers for analysis were 230 eyes for VF progression and 210 patients for blindness. 
Statistical analysis was performed 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 carriers of the minor allele, and 2 for individuals homozygous for the minor allele. Association testing was done using a 1-degree-of-freedom score-based test using logistic regression for categorical variables or linear regression for continuous variables. This model assumes a trend-per-copy effect of the variant allele and has the best statistical power for detecting association for complex traits. 3 Kaplan-Meier survival analysis was performed using MedCalc software (version 12.3.0; MedCalc, Ostend, Belgium) to compare the risk of blindness in patients with different genotypes during the years of follow-up. A P value of <0.001 was considered statistically significant after a Bonferroni correction factor of 54 (3 × 18) was applied to correct for the number of loci and number of outcome variables. 
Results
Complete phenotype and genotype data were available for 700 (85.4%) patients. A description of their demographic features and clinical characteristics is shown in Table 1. The minor allele frequency (MAF) in the three loci, rs3753841 (COL11A1), rs1015213 (PCMTD1-ST18), and rs11024102 (PLEKHA7), was 36%, 2.1%, and 41.5%, respectively. 
Table 1
 
Regression Analysis for the Association of PACG Susceptibility Loci With Demographic and Clinical Characteristic
Table 1
 
Regression Analysis for the Association of PACG Susceptibility Loci With Demographic and Clinical Characteristic
Patient, n = 700 rs3753841 rs1015213 rs11024102
P β OR P β OR P β OR
Univariate linear regression
 Age at diagnosis, y 64.5 ± 8.84* 0.592 −0.254 0.572 0.916 0.889 0.066
 Duration of follow-up 5.1 ± 4.82* 0.863 0.045 0.918 −0.091 0.557 0.150
Univariate logistic regression
 Sex, % female 58.9% 0.118 1.188 0.136 0.575 0.391 0.912
 Diagnosis†
  PACG, non-APAC 435 (62.1%) 0.216 Ref 0.135 Ref 0.747 Ref
  APAC 265 (37.9%) 1.146 1.736 0.965
 Laterality
  Unilateral 367 (52.4%) 0.686 Ref 0.771 Ref 0.360 Ref
  Bilateral 333 (47.6%) 0.957 0.898 1.102
 Filtration surgery† 280 (40%) 0.115 1.188 0.209 1.589 0.072 1.215
The mean age at diagnosis was 64.5 (±8.84) years and the mean duration of follow-up from the time of diagnosis was 5.1 (±4.82) years. A total of 265 patients (37.9%) had previous APAC. There were no significant differences in the sex, duration of follow-up, diagnosis (APAC versus PACG [non-APAC]), age at diagnosis, laterality of glaucoma or need for filtration surgery among patients with different genotypes at the three loci (Table 1, all P > 0.05). 
Clinical characteristics for the worse eye at presentation or DNA collection are shown in Table 2. We did not find significant associations between any of the three PACG susceptibility loci with either IOP at presentation or clinical characteristics (vertical CDR, BCVA, MD, and PSD, Table 2, P = 0.044 [Bonferroni-adjusted P > 0.05] for association between rs11024102 and PSD at DNA collection, all other P > 0.05). 
Table 2
 
Univariate Linear Regression Analysis for Association of PACG Susceptibility Loci With Clinical Characteristics at Presentation or at DNA Collection
Table 2
 
Univariate Linear Regression Analysis for Association of PACG Susceptibility Loci With Clinical Characteristics at Presentation or at DNA Collection
Worse Eye rs3753841 rs1015213 rs11024102
P β P β P β
IOP, mm Hg* 36.1 ± 17.76† 0.587 0.518 0.499 2.203 0.535 0.587
C/D ratio‡ 0.77 ± 0.19† 0.838 0.002 0.351 −0.032 0.208 0.013
BCVA, logMAR‡ 0.60 ± 0.81† 0.509 −0.029 0.057 −0.282 0.082 0.075
MD, dB‡ −12.98 ± 9.30† 0.685 −0.220 0.539 −1.052 0.504 −0.361
PSD, dB‡ 6.35 ± 3.68† 0.096 0.357 0.980 −0.017 0.044 0.429
PACG Susceptibility Loci in Relation to Visual Field Progression
We included 230 eyes from 230 PACG patients in the subgroup analysis for VF progression. The average number of reliable VF tests was 9.0 (±3.96) for the study eye (Table 3). A total of 27 eyes (11.7%) progressed during the follow-up period. We did not note significant association between VF progression with any of the three PACG susceptibility loci (Table 3, all P > 0.05). There also was no difference in the mean number of progressing points, the mean slope of sensitivity loss in the whole VF or among the progressing points (Table 3, all P > 0.05). We also performed VF analysis by including the fellow eyes (better eye, n = 122) when they were eligible (total eyes analyzed = 349). The results again were nonsignificant (data not shown). 
Table 3
 
Regression Analysis for Association of PACG Susceptibility Loci With Visual Field Progression and Blindness
Table 3
 
Regression Analysis for Association of PACG Susceptibility Loci With Visual Field Progression and Blindness
Overall rs3753841 rs1015213 rs11024102
P OR β P OR β P OR β
Visual field progression by PROGRESSOR software, study eyes, n = 230
 Univariate logistic regression
  Progression, n (%)* 27 (11.7%) 0.217 0.681 0.946 1.077 0.757 0.914
 Univariate linear regression
  Mean slope of sensitivity loss in   whole field −0.081 ± 0.51† 0.895 0.006 0.834 0.038 0.812 −0.011
  Mean slope of sensitivity loss among   progressing points −1.86 ± 0.76† 0.642 0.073 0.42 0.367 0.819 0.033
  Mean n of progressing points 1.2 ± 3.47† 0.721 −0.115 0.737 −0.421 0.517 0.209
Blindness at diagnosis or during follow-up, study patients, n = 210
 Univariate logistic regression
  Blindness, n (%) 77 (36.7%) 0.683 0.913 0.457 0.498 0.812 0.949
PACG Susceptibility Loci in Relation to Blindness
A total of 210 patients was included in the analysis of blindness. Of the patients 77 (36.7% of 210 patients) were blind due to glaucoma in at least one eye either at presentation or during follow-up and 12 patients (5.7% of 210 patients) were bilaterally blind (Table 3). We did not find significant association between any of the three PACG susceptibility loci and the proportion of blindness due to glaucoma in either eye (Table 3, all P > 0.05). 
We further excluded 58 patients who were blind in either eye at diagnosis or earliest reliable VF test, resulting in 152 patients eligible for Kaplan-Meier survival analysis to estimate and compare the risk of developing blindness due to glaucoma among PACG patients with different genotypes. No significant association was noted between the risk of developing blindness and any of the three PACG susceptibility loci (see Figure, all P > 0.05). 
Figure
 
Kaplan-Meier survival curve for development of blindness due to glaucoma after diagnosis (n = 152). Top: rs3753841 (COL11A1). Middle: rs1015213 (PCMTD1-ST18). Bottom: rs11024102 (PLEKHA7). SNP genotype: 0, wild-type; 1, heterozygous carriers of the minor allele; 2, homozygous for the minor allele. P value was obtained from logrank test for trend.
Figure
 
Kaplan-Meier survival curve for development of blindness due to glaucoma after diagnosis (n = 152). Top: rs3753841 (COL11A1). Middle: rs1015213 (PCMTD1-ST18). Bottom: rs11024102 (PLEKHA7). SNP genotype: 0, wild-type; 1, heterozygous carriers of the minor allele; 2, homozygous for the minor allele. P value was obtained from logrank test for trend.
Discussion
In this study, we did not detect significant associations between the three PACG susceptibility loci with a range of clinical features, such as age at diagnosis, IOP at presentation, clinical characteristics, VF progression, and proportion of blindness. This suggests the three genetic polymorphisms, although strongly associated with initial PACG susceptibility, do not strongly influence disease severity or progression of disease. 
The mechanisms by which the three PACG susceptibility loci cause disease are not well understood. The SNP rs3753841 is located in COL11A1, which encodes one of the two α chains of type XI collagen. Known pathogenic mutations in COL11A1 are associated with ocular, orofacial, auditory, and skeletal abnormalities, one of which is nonprogressive axial myopia. 13 It is speculated that genetic polymorphism in this gene could predispose to PACG by affecting ocular biometric parameters. The COL11A1 gene also has been implicated in the regulation of the drainage of aqueous humor with evidence of its expression in human trabecular meshwork cells. 14 The SNP rs1015213 is located between PCMTD1 and ST18. The PCMTD1 gene encodes protein-L-isoaspartate O-methyltransferase domain-containing protein 1 and its function is largely unknown. The ST18 gene, which encodes suppression of tumorigenicity 18 protein, is known as a mediator of apoptosis and inflammation. 15 It is shown to be expressed in a variety of human ocular tissues, including retina, retinal pigment epithelium, and optic nerve. 3 It is possible that the genetic polymorphism could be linked to loss of neuronal protection, thereby conferring susceptibility to PACG. Interestingly, this SNP was found to be nominally associated with a shallower anterior chamber depth (ACD) in one study, but not in another. 16,17 The SNP rs11024102 is located in PLEKHA7, which encodes pleckstrin homology domain-containing protein 7. As this protein is part of a protein complex that regulates paracellular permeability, 18,19 it is speculated that genetic polymorphism in PLEKHA7 could lead to aberrant fluid dynamics and onset of angle closure. 
The fact that we did not find any correlation between PACG susceptibility loci and the clinical course of disease suggests that disease-causing genes may be different from disease-modifying genes in PACG. This is not an unusual observation in medicine. A recent study that investigated the genotype-clinical sub-phenotype (e.g., age of onset of disease) correlation in 53 Crohn's disease associated genetic polymorphisms using two fairly large cohorts only found nominal significance in some of the loci, and all these associations lost significance when multiple testing was considered. 20 It is possible that other genetic factors that modulate the clinical features/progression of the disease independent of initial susceptibility may exist. Re-analysis of existing GWA studies by taking into account sub-phenotype classifications of PACG patients hopefully will identify these disease-modifying genes and contribute to development of personalized therapy. 
Statistical power calculations were performed as described previously. 3,21 For SNPs rs3753841 (COL11A1) and rs11024102 (PLEKHA7), which have a minor allele frequency of more than 0.3, our sample size had more than 80% power to detect association with outcome variables with modest prevalence (e.g., VF progression, prevalence set as 15%) if the true per-allele OR was 2, and more than 80% power to detect association with outcome variable with high prevalence (e.g., blindness, prevalence set as 35%) if the true per-allele OR was as low as 1.5. This effect size was in the range of what is expected to have a clinical impact. However, due to a low minor allele frequency (2.1%) at SNP rs1015213 (PCMTD1-ST18), we might be underpowered to detect differences in any outcome variables regardless of prevalence. A much larger cohort would be required to investigate a genotype/phenotype relationship at this locus. 
The strength of this study was the well-characterized patient population. These patients had detailed documentation of their clinical visits from the same tertiary referral center. All clinical information was collected in a uniform manner. For the subgroup analysis concerning glaucoma progression, we used even more stringent selection criteria that included only patients with ≥5 reliable VF tests in ≥5 years of follow-up. This ensures adequate and accurate data were available to perform point-wise linear regression on the VF tests, 22 although it did reduce the number of patients eligible for the subgroup analysis. Several limitations of our study should be noted. Firstly, this was a retrospective study and the patient sample was subject to selection bias. Of the 700 PACG patients with complete phenotype and genotype data, only approximately 35% met the inclusion criteria for analysis of glaucoma progression. It is possible that the conclusions may be different if less stringent criteria were adopted, such as including patients with shorter follow-up duration or less number of VF tests. Associations of small effect sizes (<1.5) may not be detectable with the limited sample size. Secondly, patients in this study were managed by different ophthalmologists. Nonstandardization of treatment protocol could be a significant confounding factor for disease course. However, the inconsistency in glaucoma treatment is likely to be minimized within the same institution. Another likely factor associated with the clinical course of glaucoma is patients' noncompliance to therapy, 8 of which we were unable to ascertain the effect due to nonavailability of data. Thirdly, our study only included subjects of Chinese ethnicity. Ethnicity is a known risk factor for blindness due to glaucoma, 23,24 but whether there are ethnic differences in VF progression due to glaucoma still is controversial. 2527 Therefore, caution should be taken if the results of this study were to be extrapolated to other ethnic groups. 
In summary, this study investigated the genotype-phenotype correlation for three PACG-associated genetic polymorphisms in a large disease cohort. The lack of association with a range of clinical features suggested that the three SNPs do not impart a characteristic disease phenotype in terms of severity of disease or proportion of VF progression and blindness. 
Acknowledgments
Supported by grants from the National Medical Research Council, Singapore. The authors alone are responsible for the content and writing of the paper. 
Disclosure: X. Wei, None; M.E. Nongpiur, None; M.S. de Leon, None; M. Baskaran, None; S.A. Perera, None; A.C. How, None; E.N. Vithana, None; C.-C. Khor, None; T. Aung, None 
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Figure
 
Kaplan-Meier survival curve for development of blindness due to glaucoma after diagnosis (n = 152). Top: rs3753841 (COL11A1). Middle: rs1015213 (PCMTD1-ST18). Bottom: rs11024102 (PLEKHA7). SNP genotype: 0, wild-type; 1, heterozygous carriers of the minor allele; 2, homozygous for the minor allele. P value was obtained from logrank test for trend.
Figure
 
Kaplan-Meier survival curve for development of blindness due to glaucoma after diagnosis (n = 152). Top: rs3753841 (COL11A1). Middle: rs1015213 (PCMTD1-ST18). Bottom: rs11024102 (PLEKHA7). SNP genotype: 0, wild-type; 1, heterozygous carriers of the minor allele; 2, homozygous for the minor allele. P value was obtained from logrank test for trend.
Table 1
 
Regression Analysis for the Association of PACG Susceptibility Loci With Demographic and Clinical Characteristic
Table 1
 
Regression Analysis for the Association of PACG Susceptibility Loci With Demographic and Clinical Characteristic
Patient, n = 700 rs3753841 rs1015213 rs11024102
P β OR P β OR P β OR
Univariate linear regression
 Age at diagnosis, y 64.5 ± 8.84* 0.592 −0.254 0.572 0.916 0.889 0.066
 Duration of follow-up 5.1 ± 4.82* 0.863 0.045 0.918 −0.091 0.557 0.150
Univariate logistic regression
 Sex, % female 58.9% 0.118 1.188 0.136 0.575 0.391 0.912
 Diagnosis†
  PACG, non-APAC 435 (62.1%) 0.216 Ref 0.135 Ref 0.747 Ref
  APAC 265 (37.9%) 1.146 1.736 0.965
 Laterality
  Unilateral 367 (52.4%) 0.686 Ref 0.771 Ref 0.360 Ref
  Bilateral 333 (47.6%) 0.957 0.898 1.102
 Filtration surgery† 280 (40%) 0.115 1.188 0.209 1.589 0.072 1.215
Table 2
 
Univariate Linear Regression Analysis for Association of PACG Susceptibility Loci With Clinical Characteristics at Presentation or at DNA Collection
Table 2
 
Univariate Linear Regression Analysis for Association of PACG Susceptibility Loci With Clinical Characteristics at Presentation or at DNA Collection
Worse Eye rs3753841 rs1015213 rs11024102
P β P β P β
IOP, mm Hg* 36.1 ± 17.76† 0.587 0.518 0.499 2.203 0.535 0.587
C/D ratio‡ 0.77 ± 0.19† 0.838 0.002 0.351 −0.032 0.208 0.013
BCVA, logMAR‡ 0.60 ± 0.81† 0.509 −0.029 0.057 −0.282 0.082 0.075
MD, dB‡ −12.98 ± 9.30† 0.685 −0.220 0.539 −1.052 0.504 −0.361
PSD, dB‡ 6.35 ± 3.68† 0.096 0.357 0.980 −0.017 0.044 0.429
Table 3
 
Regression Analysis for Association of PACG Susceptibility Loci With Visual Field Progression and Blindness
Table 3
 
Regression Analysis for Association of PACG Susceptibility Loci With Visual Field Progression and Blindness
Overall rs3753841 rs1015213 rs11024102
P OR β P OR β P OR β
Visual field progression by PROGRESSOR software, study eyes, n = 230
 Univariate logistic regression
  Progression, n (%)* 27 (11.7%) 0.217 0.681 0.946 1.077 0.757 0.914
 Univariate linear regression
  Mean slope of sensitivity loss in   whole field −0.081 ± 0.51† 0.895 0.006 0.834 0.038 0.812 −0.011
  Mean slope of sensitivity loss among   progressing points −1.86 ± 0.76† 0.642 0.073 0.42 0.367 0.819 0.033
  Mean n of progressing points 1.2 ± 3.47† 0.721 −0.115 0.737 −0.421 0.517 0.209
Blindness at diagnosis or during follow-up, study patients, n = 210
 Univariate logistic regression
  Blindness, n (%) 77 (36.7%) 0.683 0.913 0.457 0.498 0.812 0.949
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