The present invention relates to the field of molecular biology, in particular in prediction and/or diagnosis of a condition and/or disease and/or determining susceptibility to the condition and/or disease.

Background of the invention

Glaucoma is known to be a leading cause of irreversible blindness worldwide, characterised by progressive loss of axons in the optic nerve and corresponding visual field damage. Categorized according to the anatomy of the anterior chamber angle, there are two main forms of glaucoma, primary open angle glaucoma (POAG) and primary angle closure glaucoma (PACG). PACG results from elevated intraocular pressure as a consequence of (or due to) iris- trabecular meshwork contact in the angle of the eye hindering aqueous outflow. While POAG is the more predominant form of glaucoma amongst Europeans and Africans, 80% of the estimated 15 million people afflicted with PACG live in Asia. PACG is responsible for substantial blindness in many Asian countries, and in fact it has been estimated that PACG blinds proportionately more people than POAG globally. The pathogenesis of PACG likely involves multiple anatomical and physiological factors, and thus shows many indications of being a complex disease, with both genetic and environmental etiological factors.

Summary of the invention

According to a first aspect, the present invention provides a method for predicting and/or determining susceptibility to primary angle closure glaucoma in a subject, comprising determining the genotype of the subject for at least one variation in at least one single nucleotide polymorphism associated with primary angle closure glaucoma; wherein the single nucleotide polymorphism is selected from the group consisting of PLEKHA7 rs1 1024102, COL11A 1 rs3753941 and rs1015213.

The present invention also provides a probe for use in predicting and/or determining susceptibility to primary angle closure glaucoma in a subject, wherein the probe is for use in determining the genotype of the subject for at least one single nucleotide polymorphism associated with primary angle closure glaucoma, wherein the single nucleotide polymorphism is selected from the group consisting of PLEKHA7 rs11024102, COL11A1 rs3753941 and rs101.5213. Brief description of the figures

Figure 1 shows the principal component analysis between PACG cases and healthy controls for: (A) the Singaporean Chinese PACG collection, (B) the Hong Kong collection; (C) the Indian PACG collection; (D) the Malay PACG collection and (E), the Vietnam PACG collection; where the vast majority of cases have genetically matched controls and additional statistical correction for the top principal components using logistic regression was conducted to minimise residual population stratification.

Figure 2 shows the Manhattan plot of all Stage 1 data involving 1854 PACG cases and 9609 controls. Single nucleotide polymorphism (SNP) markers are plotted according to chromosomal location on the horizontal axis, with -Log-ioP- values on the vertical axis derived from the 1 -degree-of-freedom score test; the lower horizontal line (P < 1 x 10 ^"5 ) denotes the threshold used for bringing genetic loci forward for further testing in Stage 2. The upper horizontal line shows the formal threshold for genome-wide significance (P < 5 x 10 ^"8 ). PLEKHA7 rs11024102 showed evidence of association with PACG surpassing genome-wide significance on Stage 1 data alone. Figure 3 pertains to Forest plots showing evidence of association between genome-wide significant SNPs; (a) PLEKHA 7 rs1 1024102, (b) COL11A1 rs3753841 , and (c) PCMTD1 - ST18 rs1015213. The vertical line represents a per-allele odds ratio of 1 .00. The oblongs represent point estimates (referring to the per-allele odds ratio), with the height of the oblongs inversely proportional to the standard error of the point estimates. Horizontal lines indicate the 95% confidence interval for each point estimate. Meta-analysis of stages 1 and 2 are reflected by blue diamonds, and meta-analysis from all sample collections reflected by purple diamonds. The width of the diamonds indicates their 95% confidence intervals. The number in brackets before each sample collection denotes (minor allele frequency in PACG cases / minor allele frequency in controls).

Figure 4 shows Stage 1 data showing regional association (upper panel) and linkage disequilibrium (LD; lower panel) plots of PLEKHA7 locus around rs1 1024102, (B) COL11A1 locus around rs3753841 , and (c) Chromosome 8q locus around rs1015213. Gene annotations are superimposed. The vertical axis represents -Log-ioP-vlues for association with PACG, and the horizontal axis represents base-pair positions along the chromosome.

Figure 5 shows the expression analysis of PLEKHA7, COL11A 1, PCMTD1 and ST18 in human ocular tissues. Gene specific RT-PCR products, confirmed to be so through re-sequencing, were observed differentially in anterior sclera (AS), posterior sclera (PS), cornea (corneal epithelium, CE; stroma, CS; and endothelium, CEn), iris (I), trabecular meshwork (TM), lens (L), lens capsule (LC), retina and retinal pigment epithelium (R), choroid (CRD), optic nerve head (ONH) and optic nerve (ON). The ubiquitously expressed gene, ACTB was used as the normalizing control. A no template sample acted as the negative control (NC) to ensure non-contamination of the RT-PCR reaction mix. M denotes molecular-weight marker. Figure 6 shows the genotyping cluster plot for (A) rs11024102; (B) rs1015213 and (C) rs3753841.

Definitions

"Determine" or "determining" means to find out or come to a decision about by investigation, reasoning, or calculation, or to settle or decide from alternatives or possibilities; "determine" or "determining" encompasses the meaning of "predicting" or "assessing".

"Determining the genotype of a subject for a nucleic acid variation" refers to determining the genetic constitution or makeup of the subject for that nucleic acid variation and includes determining the absence or presence of the nucleic acid variation and; where the nucleic acid variation comprises several alternatives, determining the alternative in the subject.

A "single nucleotide polymorphism (SNP)" refers to a DNA and/or RNA sequence variation occurring when a single nucleotide in an organism's DNA sequence differs between members of the species (or between paired chromosomes in the organism).

A "primer" refers to an oligonucleotide to which deoxyribonucleotides may be added by a DNA polymerase. A single primer may be used to amplify a DNA or RNA region, for example, for sequencing. A "primer pair" usually comprises a first primer complementary to one strand of a DNA or RNA molecule and a second primer complementary to a second strand of a DNA or RNA molecule, with both primers flanking a target DNA or RNA region, to be amplified by a DNA polymerase. Detailed description of the invention

The present invention provides a method for predicting and/or determining susceptibility to primary angle closure glaucoma in a subject, comprising determining the genotype of the subject for at least one variation in at least one single nucleotide polymorphism associated with primary angle closure glaucoma; wherein the single nucleotide polymorphism is selected from the group consisting of PLEKHA7 rs11024102, COL11A1 rs3753941 and rs 015213.

Further, the present invention also provides a probe for use in predicting and/or determining susceptibility to primary angle closure glaucoma in a subject, wherein the probe is for use in determining the genotype of the subject for at least one single nucleotide polymorphism associated with primary angle closure glaucoma, wherein the single nucleotide polymorphism is selected from the group consisting of PLEKHA7 rs11024102, COL11A1 rs3753941 and rs1015213.

The method comprises determining the genotype, nucleic acid variation and/or the allele present in the genomic sequence. The method may detect the nucleic acid variation from genomic DNA,. In particular, the method is performed on an isolated sample from the subject. The method comprises determining at least one nucleic acid variation and/or allele present for at least one of the single polynucleotide polymorphism in the subject.

The method may comprise determining the genotype, nucleic acid variation and/or allele present for one, two or all three of the single nucleotide polymorphisms. As a first example, the single nucleotide polymorphism comprises PLEKHA7 rs1 1024102. In another example, the single polymorphism comprises COL11A1 rs3753941. In yet another example, the single polynucleotide polymorphism comprises rs1015213. For examples, the probe is for use in detecting the PLEKHA7 rs1 1024102, COL11A1 rs3753941 or rs1015213 single nucleotide polymorphism. The nucleic acid variation may comprise different alleles of the single nucleotide polymorphism. Accordingly, the probe may be used to identify different alleles. The probe is for use in determining the genotype, nucleic acid variation and/or the allele present in the genomic sequence. The probe may comprise at least one nucleic acid molecule. For example, different nucleic acid molecules hybridizing to different variants of the single nucleotide polymorphism may be used to identify the nucleic acid variation. Different hybridisation probes may be differentially labelled. Detection of hybridized probes may be by any conventional method, including . but not limited to fluorescence-based hybridization assays; chemiluminescence-based hybridization assays; and capture hybridization microtitre assays.

The nucleic acid molecules comprising the probes may also be used as polymerase chain reaction (PCR) primers. PCR may be performed on genomic DNA with PCR primer pairs. Reverse-transcriptase PCR may also be performed. Conventional PCR may be performed or real-time PCR may be performed. Detection of PCR products may be by conventional methods, including but not limited to agarose gel electrophoresis, polyacrylamide gel electrophoresis and capillary electrophoresis. The PCR primers may also be labelled for detection.

A combination of PCR and hybridisation techniques may also be used to identify the nucleic acid variation. Genotyping methods as described in Ragoussis (2009) may also be used for identifying the nucleic acid variation. Alternatively, DNA sequencing may also be used to identify the nucleic acid variation. Having now generally described the invention, the same will be more readily understood through reference to the following examples which are provided by way of illustration, and are not intended to be limiting of the present invention.

EXAMPLES Standard molecular biology techniques known in the art and not specifically described were generally followed as described in Sambrook and Russel, Molecular Cloning: A Laboratory Manual, Cold Springs Harbor Laboratory, New York (2001 ).

Example 1 A two-staged genome-wide association analysis (GWAS) was conducted with replication involving 3771 cases and 18551 controls to identify sequence variants that confer susceptibility to PACG. The discovery staged (Stage 1 ) included 1854 PACG cases and 9608 controls recruited across 5 independent collections (Singapore, Hong Kong, India, Malaysia and Vietnam; Table 1 ). The replication stage (Stage 2 ) included a further 1917 PACG cases and 8943 controls across 6 independent collections (two sites in China, and one site each in Singapore, India, Saudi Arabia and the UK; Table 1 ).

Table 1 : Sample collections of PACG cases and controls for Stages 1 (GWAS discovery) and 2 (replication).

Uniform quality control (QC) filters were applied for both individual samples and SNP markers across all 5 PACG case-control collections for Stage 1. From a starting number of 1925 PACG cases and 9630 controls, genotype data on 493501 SNPs were available for 1 ,854 PACG cases and 9608 controls after stringent QC were applied on SNPs and samples. Within each sample collection, it was ensured that each PACG case had genetically matched controls as visualized spatially using principal component analysis (Figure 1 ). The genotypes between PACG cases and healthy controls were contrasted via single-SNP analysis using unconditional logistic regression fitted for genotype trend effects (a 1 -degree-of-freedom score test). As the genetic matching between cases and controls was not perfect, the association tests were subjected to adjustments on significant axes of population ancestry to remove any residual population stratification which may be present within each collection following procedures as described in The Australia and New Zealand Multiple Sclerosis Genetics Consortium (2009), Hoglinger et al., (2011 ), Khor et al., (2011a), Kooner ef al., (2011 ) and Reveille et al., (2010). This was followed by random-effects meta-analysis using inverse-variance weights (Nails ef al., 2011 ). No evidence of genomic inflation (Age≤ 1.00) was observed, thereby excluding the likelihood of significant cryptic population substructure between cases and controls. From Stage 1 data alone, genome-wide significance at PLEKHA7 rs11024102 on Chromosome 11 was observed (Table 2, Figures 2 and 3; per-allele OR = 1.27, P = 1.43 x 10 ^-8 ).

A total of 15 SNPs at 13 independent loci showing evidence of association with PACG exceeding P < 1 x 10 ^"5 with no evidence of heterogeneity (I ² index = 0.0%) across the 5 sample collections in Stage 1 were brought forward for replication genotyping in Stage 2, where a further 1 ,973 PACG cases and 9,066 controls were enrolled. Similar QC filters were applied to SNPs and samples in this stage, and a total of 1 ,917 PACG cases and 8,943 controls passing QC were brought forward for association analysis. Three SNP markers {PLEKHA7 rs1 1024102, COL11A1 rs3753841 , and rs1015213) showed significant evidence of replication in Stage 2 (3.72 x 10 ^"5 < P≤ 1 .77 x 10 ^"4 ), and surpassed genome-wide significance on meta-analysis of all data from both stages (5.33 x 10 ^"12 < P≤ 3.29 x 10 ^"9 ; Table 2 and Figure 3). Regional association analysis for these three sequence variants clearly identified PLEKHA7 and COL11A1 as the likeliest PACG candidate susceptibility genes at the Chromosome 1 1 and Chromosome 1 hit regions respectively, whilst rs1015213 was located in an intergenic region between PCMTD1 and ST18 (Figure 4). Evidence of association at loci previously associated with POAG was not observed (Table 3). Conditional logistic regression did not reveal secondary signals of association at each of the 3 genome-wide significant loci, suggesting that the most significant SNP reported largely accounts for the observed.disease associations (Tables 4, 5, 6). As there is a paucity of information on these genes in the eye, the expression of PLEKHA7, COL11A1 , PCMTD1 and ST18 was tested in several eye tissues. Significant expression of PLEKHA7, COL11A1 , and PCMTD1 was observed in the iris and trabecular meshwork, the ocular tissues intimately involved in the pathogenesis of PACG. Expression of ST18 was not observed in either tissue (Figure 5). Table 2 Hits from the GWAS on primary angle closure glaucoma

Top tier SNP markers exceed P< 5 x 10 on meta-analysis.

TXNRD2 rs3788317 showed Stage 2 P < 0.05, and will need further studies for definite verification. A1 : effect allele

OR: per-allele odds ratio of the effect alfele

P: P-value for association

Phet: P-value for heterogeneity

I ² : {-squared index quantiJying heterogeneity (see Methods)

Table 3 Stage 1 association tests (1.854 PACG cases vs 9,608 controls)

Glaucoma (POAG) Chr. SNP Gene A1 Collection P OR PheX I ²

7 rs4236601 CAV1-CAV2 A Singapore 0.088 1.70

Hong Kong 0.31 0.60

India 0.16 1.16

Vietnam 0.63 0.77

Malays 0.73 0.87

All Stage 1 0.26 1.13 0.37 6.14%

7 rs1052990 CAV1-CAV2 C Singapore 0.63 0.96

Hong Kong 0.56 0.92

India 0.26 1.11

Vietnam 0.090 1.31

Malays 0.79 1.07

All Stage 1 0.42 1.05 0.3875 3.37%

9 rs4977756 CDKN2BAS G Singapore 0.079 0.87

Hong Kong 0.20 0.86

India 0.013 1.26

Vietnam 0.77 0.96

Malays 0.65 1.09

All Stage 1 0.93 0.99 0.0253 64.03%

9 rs10120688 CDKN2BAS G Singapore 0.30 0.93

Hong Kong 0.33 0.90

India 0.052 1.18

Vietnam 0.40 1.14

Malays 0.77 0.95

All Stage 1 0.86 1.01 0.1624 38.82%

1 rs10800150 TMC01 A Singapore 0.53 0.96

Hong Kong 0.017 1.26

India 0.19 1.18

Vietnam 0.017 1.35

Malays 0.11 1.31

All Stage 1 0.039 1.17 0.0385 60.46% rs2790051 TMC01 Singapore 0.41 0.93

Hong Kong 0.12 1.20

India 0.12 1.14

Vietnam 0.0011 1.56

Malays 0.97 1.01

All Stage 1 0.12 1.14 0.0225 64.87%

14 rs10483727 SIX1 G Singapore 0.12 1.13

Hong Kong 0.076 1.23

India 0.042 0.84

Vietnam 0.034 0.72

Malays 0.36 0.82

All Stage 1 0.62 0.95 0.0047 73.34%

P: P-value for association with PACG.

OR: per-allele odds ratio for the effect allele (A1 ).

Phe _t : P-value for heterogeneity in Stage 1 meta-analysis.

I ² : l-squared index for heterogeneity.

Table 4 Conditional logistic regression analysis at the PLEKHA7 locus on Chromosome 11 ; the conditioning SNP is rs11024102

Before conditioning After conditioning

SNP BP A1 P OR P OR rs11024102 16965181 G 7.97 x 10 ^'9 1.28 - - rs6486334 16972133 A 9.07 x lC ⁸ 1.25 0.027 0.84 rs3950680 17095242 A 9.67 x 10 ^'5 1.18 0.63 0.97 rs12577525 17216692 A 1.04 x 10 ^'4 1.18 0.73 0.98 rs10832757 17292907 G 2.27 x 10- ⁴ 1.17 0.22 1.06 rs2354867 17327277 A 5.50 x 10 ^"4 1.16 0.19 1.06 rs10766383 17286374 C 6.39 x 10 ^"4 1.16 0.33 1.05 rs4756887 17351173 A 8.96 X 10 ^"4 1.15 0.34 1.05 rs214105 17270097 G 9.10 X 10 ^"4 1.15 0.96 1.00 rs7127347 17257420 C 0.0011 0.87 0.30 0.95 rs1330 17272605 A 0.0030 1.13 0.87 0.99 rs366590 16829016 A 0.0055 1.14 0.020 1.12 rs1557765 17360215 A 0.021 1.10 0.64 0.98 rs757110 17375053 C 0.035 1.09 0.89 0.99 rs2285676 17364601 A 0.036 1.09 0.95 1.00 Table 5 Conditional logistic regression analysis at the COL11A1 locus on Chromosome 1 ; the conditioning SNP is rs3753841.

Before conditioning After conditioning

SNP BP A1 P OR P OR rs3753841 103152506 G 3.85 x 10 ^"6 1.22 - - rs1676486 103126726 A 1.09 x 10 ^"5 1.22 0.11 1.11 rs12138977 103166045 G 2.34 x 10 ^"5 1.21 0.76 1.03 rs7550513 102994366 A 3.04 x 10 ^"5 1.21 0.06 1.11 rs2229783 103125039 A 3.50 x 10 ^"5 1.19 0.95 1.01 rs12074523 103040347 A 4.99 x 10 ^"5 1.20 0.14 1.08 rs713T62 102989469 A 6.89 x 10 ^"5 1.18 0.52 1.05 rs1020918 103103937 A 1.25 X 10 ^" 1.18 0.65 1.03 rs1241163 103128353 A - 1.47 X 0 ^"4 1.18 0.51 1.04 rs1085 103128756 A 1.86 x 10 ⁴ 1.18 0.33 1.07 rs921416 103001167 G 2.76 x 10 ^"4 1.16 0.76 1.02 rs8 79334 103139683 A 4.36 x 10 ^"4 1.18 0.79 1.02 rs12030173 102949180 C 9.44 x 10 ^"4 1.17 0.16 1.08 rs9659030 103114980 G 0.0011 1.16 0.59 1.03 rs1241202 103077609 C 0.0012 1.15 0.98 1.00 rs12022173 102874238 G 0.0016 1.17 0.17 1.08 rs11164556 102852775 A 0.0051 1.14 0.47 1.04 rs 10874639 102906497 G 0.0083 1.13 0.43 1.04 rs11164585 102944531 A 0.010 1.13 0.34 1.05 rs1376359 102975695 A 0.012 1.11 0.88 1.01 rs4907975 102802807 G 0.021 1.10 0.99 1.00 rs7539441 102793412 G 0.033 1.09 0.92 1.00 rs2222748 103530366 G 0.036 1.16 0.22 1.09 rs967554 102836762 G 0.039 1.09 0.69 0.98 rs11164544 102785098 G 0.041 0.92 0.77 0.99 rs7517663 102781216 G 0.044 0.92 0.78 0.99 Table 6 Conditional logistic regression analysis at the PCMTD1-ST18 locus on Chromosome 8; the conditioning SNP is rs10 52 3.

Before conditioning After conditioning

SNP BP A1 P OR P OR rs1015213 53050094 A 9.38 x 10 ^"6 1.53 - - rs7846408 52523096 A 3.80 x 10- ⁴ 0.84 0.0013 0.85 rs9792278 52593356 G 0.0016 0.88 0.0023 0.88 rs13271514 52561732 G 0.0040 0.88 0.0094 0.89 rs2017341 52516457 A 0.0040 1.23. 0.0041 1.23 rs10111115 52778748 G 0.0054 1.15 0.13 1.08 rs 69 6394 52555316 A 0.0074 0.87 0.013 0.88 rs16916425 52582435 G 0.010 0.75 0.013 0.76 rs2304365 53292766 A 0.013 0.85 0.013 0.85 rs12155844 53292222 G 0.015 0.85 0.013 0.85 rs7830945 53295719 G 0.027 0.85 0.045 0.86 rs 1786371 52598500 G 0.029 0.90 0.047 0.90 rs7000275 52602042 A 0.047 0.90 0.072 0.91

Methods

Detailed information on all PACG sample collections is provided below. All patients were enrolled into the study following informed consent and ethical approval from the relevant authorities. DNA was extracted from patient blood samples using standard laboratory procedures.

Sample collections for Stage 1 Singapore: Cases of primary angle closure glaucoma (PACG) included patients with chronic PACG or those with acute PACG. Chronic PACG was defined as the presence of appositional angle closure for > 180 degrees with (a) peripheral anterior synechiae and/ or (b) raised intraocular pressure associated with glaucomatous optic neuropathy (defined as loss of neuroretinal rim with a vertical cup: disc ratio of > 0.7 or an inter-eye asymmetry of > 0.2, and/or notching attributable to glaucoma). Patients with acute PACG had a previous symptomatic episode defined as the presence of at least two of the following symptoms: ocular or periocular pain, nausea or vomiting or both, and an antecedent history of intermittent blurring of vision with haloes; a presenting intraocular pressure of more than 30 mmHg on Goldmann applanation tonometry; and the presence of at least three of the following signs: conjunctival injection, corneal epithelial edema, mid-dilated unreactive pupil, and shallow anterior chamber. Patients diagnosed with secondary angle closure (such as neovascular or uveitic glaucoma) were excluded. All Singapore PACG cases for Stage 1 were recruited between years 2005 - 2010. Controls were ascertained from an on-going population based study of Chinese persons aged 40 years and older (the Singapore Chinese Eye Study [SCES]). The SCES is a population-based, cross-sectional study of Chinese adults residing in the southwestern part of Singapore. The Ministry of Home Affairs of Singapore provided an initial computer-generated list ethnic Chinese names of adults aged 40-80+ years of age. A final sampling frame of 6,350 ethnic Chinese residents was derived from this list using an age-stratified random sampling strategy. A control was defined as IOP < 21 mm Hg with open angles, healthy optic nerves and normal visual fields, and no previous intraocular surgery.

Hong Kong: Patients with PACG were defined using the same criteria as that in the Singaporean GWAS collection. All subjects of the Hong Kong study population were recruited from the Prince of Wales Hospital and the Hong Kong Eye Hospital, Hong Kong. A total of 303 PACG patients of Han Chinese ancestry were recruited. The controls comprised of 225 Hong Kong Chinese and 976 Guangdong Chinese of Han ancestry, of which 1 ,049 were genotyped (73 from Hong Kong and 976 from Guangdong) and 1 ,044 passed quality checks (73 Hong Kong and 971 from Guangdong). The Hong Kong control subjects were recruited in a hospital-based manner. They were all given complete ocular examinations, and confirmed to have no sign of glaucoma, angle closure or narrow angle, or other major eye diseases except for mild cataract and mild refractive errors. Control subjects were recruited from elderly people aged≥ 60 years to ensure they were at least free of early-onset major eye diseases. They had IOP < 21 mmHg, and had no known family history of glaucoma. Additional healthy controls were recruited from local communities in the Guangdong province of Southern China (Bei et al., 2010). Vietnam: PACG cases were recruited from the Vietnam National Institute of Ophthalmology in Hanoi, Vietnam. They were defined using the same criteria as that in the Singaporean GWAS collection. The controls comprise 2,018 cord bloods, previously described (Khor et al., 2011b).

Malaysia: PACG cases were recruited from the Department of Ophthalmology, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu, Kelantan, Malaysia. They were defined using the same criteria as that in the Singaporean GWAS collection. The controls were a population-based collection of healthy ethnic Malays recruited from Singapore (N = 3065), previously described (Vinthana et al., 2011). India: 337 PACG cases (as defined above) of South Indian ancestry were recruited from a large population based cross sectional study, the Chennai Glaucoma study, and from the outpatient department of the Medical Research Foundation, Sankara Nethralaya, Chennai, India. All PACG cases had peripheral anterior synechiae on gonioscopy, and were identified prior to laser iridotomy. The Chennai Glaucoma study recruited 7851 persons from rural and urban cohorts in the Southern Indian state of Tamil Nadu (Arvind et al., 2003) The controls were a population-based collection of healthy ethnic Indians recruited from Singapore (N = 2538), previously described (Cornes et al., 2012).

Sample collections for Stage 2

Beijing: PACG patients were recruited from the Beijing Tongren Hospital, Xingtai Eye Hospital and Anyang Eye Hospital, China. The diagnostic criteria for PACG were as described above. Controls were recruited from the Handan Eye Study (HES), a population-based study of eye disease in rural Chinese aged 30 years and over. The criteria for selecting controls are also as described above. Singapore: All PACG cases and controls were recruited using the exact same criteria from Stage . They were recruited from year 2010 onwards. India: 80 PACG cases (as defined above for Stage 1 ) were recruited from the outpatient department of the Medical Research Foundation, Sankara Nethralaya, Chehnai, India and 309 unrelated healthy controls were recruited from the Chennai Glaucoma study. A control was defined as IOP < 21 mm Hg with open angles, healthy optic nerves and normal visual fields, and no previous intraocular surgery.

UK: Cases of PACG in the UK were defined using the same criteria as that in the Singaporean GWAS collection. All subjects were of UK European descent and were recruited from Moorfields Eye Hospital, London UK. A total of 132 cases were genotyped and 127 passed quality checks. The controls comprised of 4,703 healthy individuals of UK European descent, recruited and genotyped by the Wellcome Trust Case-Control Consortium 2. Their extensive use for genetics studies (such as ankylosing spondylitis, psoriasis, ulcerative colitis, ischaemic stroke, Meningococcal sepsis, and Kawasaki disease) has been described. Saudi Arabia: PACG cases and controls were collected at the glaucoma clinic at King Abdulaziz University Hospital (KAUH), Department of Ophthalmology, College of Medicine, King Saud University, Riyadh, Saudi Arabia. They were defined using the same criteria as that in the Singaporean GWAS collection. All patients and controls were unrelated Saudi Arabs, all whose known ancestors were of Saudi Arabian origin. As judged by the family names, PACG patients and controls were representative of the five provinces of the Kingdom of Saudi Arabia. Shantou: PACG cases and controls were collected at the glaucoma clinic of the Chinese University of Hong Kong Joint Shantou International Eye Centre, Shantou, China. They were defined using the same criteria as that in the Singaporean GWAS collection, and were of Chinese descent. Genotyping

For Stage 1 , genome-wide genotyping was performed using the lllumina 61 OK Quad beadchips, following manufacturer's instructions. For Stage 2 (replication stage), genotyping was performed using the Sequenom MassArray platform, with the exception of the Shantou collection which was genotyped using Taqman probes (Applied Biosystems).

Statistical analysis

Stringent quality control filters were used to remove poorly performing samples and SNP markers in both the GWAS discovery (Stage 1 ) and replication (Stage 2) phases. SNPs with a call rate of 95 percent, minor allele frequency of less than 1 percent, and showing significant deviation from Hardy-Weinberg Equilibrium (P-value for deviation < 0-6) were removed from further statistical analysis. Likewise, samples with an overall genotyping success rate of less than 95% were removed from further analysis. The remaining samples were then subjected to biological relationship verification by using the principle of variability in allele sharing according to the degree of relationship. Identity-by- state (IBS) information was derived using PLINK (Purcell et a/., 2003). For those pairs of individuals who showed evidence of cryptic relatedness (possibly either due to duplicated or biologically related samples), we removed the sample with the lower call rate before performing principal component (PC) analysis. PC analysis was undertaken to account for spurious associations resulting from ancestral differences of individual SNPs and PC plots were performed using the R statistical program package (www.r-project.org/). For Stage 1 , all cases had genetically matched controls as visualized spatially on PC analysis for each sample collection (Figure 1 ).

For both the GWAS and replication stages, analysis of association with PACG disease status was carried out using a 1 degree of freedom (d.f.) score-based test using logistic regression. This test models for a trend-per-copy effect of the minor allele on disease risk. It has the best statistical power to detect association for complex traits across a wide range of alternative hypotheses, with the exception of those involving rare recessive variants. The threshold for significant independent replication was set at P < 0.05 in the combined Stage 2 datasets. For Stage 1 (GWAS discovery), we incorporated the top four principal components of genetic stratification which were significant on uni-variate analysis into the logistic regression model whilst performing the analysis for association to minimize the effect of residual population stratification. As Stage 2 (Replication) only tested 15 SNP markers, we were unable to adjust for population stratification for the Stage 2 sample collections.

Meta-analysis was conducted using inverse variance weights for each sample collection, which calculates an overall Z-statistic, its corresponding P-value, and accompanying per-allele odds ratios for each SNP analyzed. Genotyping clusters were directly visualized for the 15 SNPs exceeding P < 10-5 and confirmed to be of good quality before inclusion for statistical analysis -Figure 6 shows the cluster plots for the SNPs surpassing the formal threshold for genome-wide significance (P < 5 x 10-8): PLEKHA7 rs11024012, COL11A1 rs3753841 , and Chr. 8q rs1015213. The I ² (l-squared) index is calculated to quantify the extent of heterogeneity between sample collections in the meta- analysis. I ² < 25% reflects low heterogeneity, 25% < I ² < 50% reflects moderate heterogeneity, and I2 >50% reflects high heterogeneity. Analysis of linkage disequilibrium was performed using the R software package. 2Q

Power calculations

All statistical power calculations were performed as previously described . For the Stage 1 discovery analysis, power calculations indicated that there is 90 percent power of detecting loci at P < 1.0 x 10-5 (the threshold for following up sequence variants in Stage 2) at minor allele frequencies as low as 15 percent with per-allele odds ratios of 1.30.

The entire sample set of 3,771 PACG cases and 18,551 controls has 90 percent power to detect loci at the formal threshold for genome-wide significance (P < 5.0 x 10-8) at minor allele frequencies as low as 15 percent with per-allele odds ratios as low as 1.25, in line with the effect sizes we are reporting in this manuscript. Table 7 shows the formal power calculations in the context of the final meta-analysis.

Table 7 Study power as a function of disease allele frequency and per-allele odds ratio.

Per-allele odds ratio

Risk allele frequency 1.20 1.25 1.30 1.35 1.40 1.50 1.60

0.10 21.0% 62.2% 92.1% >99.9% >99.9% >99.9% >99.9%

0.15 51.4% 91.0% >99.9% >99.9% >99.9% >99.9% >99.9%

0.20 74.0% 98.1% >99.9% >99.9% >99.9% >99.9% >99.9%

0.25 86.3% >99.9% >99.9% >99.9% >99.9% >99.9% >99.9%

0.30 92.3% >99.9% >99.9% >99.9% >99.9% >99.9% >99.9%

0.35 95.0% >99.9% >99.9% >99.9% >99.9% >99.9% >99.9%

These power calculations are based on a GWAS and replication (double staged) study design whereby SNPs showing P-values < 10 ^"4 in the GWAS is brought forward for replication in 4 independent additional samples. The power calculation is based on final meta-analysis, as previously described (Gharavi et a/., 201 1 and Purcell et al., 2003). Bold areas represent > 90 percent statistical power for detection.

Gene expression analysis

The expression of PLEKHA7, COL11A1, PCMTD1 and ST18 genes was assessed by semi quantitative reverse transcription PCR (RT-PCR) using the following primers (PLEKHA7( ) 5 -TAAAGACAGCCGAGAAGAAG-3' [SEQ ID NO: 1], PLEKHA7(R) 5 -TGTCGGCACTGAAGTAGTAG-3 ' [SEQ ID NO: 2]s; COL11A1{F) 5 -GCAGGGAGAGATGGAGTTCAAGG-3' [SEQ ID NO: 3], COL11A1(R) 5'-TGCCCAAACATCCCCTGCTG-3' [SEQ ID NO: 4]; PCMTD1(F) 5'- ACACAGATTATGCGAACTGG-3' [SEQ ID NO: 5], PCMTD1(R) 5'- TGTAAATACGAGCCAAGTCC-3' [SEQ ID NO: 6]; ST18(F) 5 - AGGGTAGAGAAGGCAGGACGG-3' [SEQ ID NO: 7, ST18(R) 5'- AGGTCTCTTCCACATCGCCCC-3' [SEQ ID NO: 8]). Primers were selected specifically to target the mRNA and not the genomic DNA of the said genes. Total RNA was extracted from a variety of ocular tissues (sclera, cornea, iris, trabecular meshwork, lens, lens capsule, retina and retinal pigment epithelium, choroid, optic nerve head and optic nerve) with TRIzol® Reagent (Invitrogen, Carlsbad, California) in accordance with the manufacturer's protocol. First- strand cDNA synthesis was performed with Superscript First-Strand Synthesis System for RT-PCR (Invitrogen, Carlsbad, California) using random primers. Semi quantitative RT-PCR was performed according to manufacturer's protocol, with the SYBR® Green Master Mix (Invitrogen, Carlsbad, California) using the specified gene primers and equal amount of cDNA template. The resulting PCR products were separated on a 2% agarose gel and visualized by ethidium bromide staining. The ubiquitously expressed beta-actin (ACTB) gene, amplified with primers F 5'- CCAACCGCGAGAAGATGA -3' [SEQ ID NO: 9] and R 5'- CCAGAGGCGTACAGGGATAG-3' [SEQ ID NO: 10], was used as an amplification and normalizing control. All RT-PCR products were re-sequenced to confirm that the correct template was targeted by the primer pair selected for each gene. Discussion

PLEKHA 7 encodes for Pleckstrin-homology-domain-containing protein 7, which is critical for the maintenance and stability of adherens junctions (Pulimeno et al., 2010; Meng et al., 2008). In adult tissues, the adherens junctions maintain tissue homeostasis and along with tight junctions control epithelial and endothelial para-cellular permeability (Harris and Tepass, 2010). In the eye, tight junctions and adherens junctions play an essential role in structures of particular relevance to glaucoma such as the ciliary body, iris, the aqueous humor outflow system and the choroid by providing a barrier to fluid leakage (Tian et al., 2000). Factors, such as an attenuated reduction in iris volume with pupillary dilation, and exaggerated choroidal expansion have been proposed as having a significant role in the spectrum of angle closure pathogenesis. Given its role in maintaining a protein complex that regulates paracellular permeability, it is possible that PLEKHA7 may be involved in the pathophysiology of angle closure related to aberrant fluid dynamics. Recently, a GWAS on blood pressure in excess of 60,000 individuals identified SNPs within this gene to be associated with systolic blood pressure (Levy et al., 2009) a systemic risk factor for glaucoma. The SNP associated with PACG in our study is located 80 - 00 Kb upstream from the SNPs associated with blood pressure. No evidence of association between PLEKHA7 rs11024074, the SNP reported to be strongly associated with systemic hypertension 35 and PACG status was observed in the present Stage 1 meta-analysis (P = 0.13, per-allele OR = 1.11 ). The pair- wise measures of linkage disequilibrium (LD) between rs1 1024074 (SNP for systemic hypertension) and rs11024102 (SNP for PACG) was very weak (D' = 0.154, r2 = 0.009), suggesting that different genetic polymorphisms could underlie the subtly different mechanisms influencing control for distinct phenotypes even though the underlying gene is a common denominator (Davila et al., 2010; Gharavi et al., 2011 ; Klein er a/., 2005).

COL11A1 encodes for one of the two alpha chains of type XI collagen. Pathogenic mutations in COL11A1 cause Marshall (MIM:154780), Stickler type 2 (STL2) [MIM: 604841], or Stickler-like syndromes (Richards et al., 1996). All are associated with ocular, orofacial, auditory and skeletal manifestations (Snead and Yates 1999). Interestingly, one of the ocular features of these diseases is non-progresssive axial myopia likely caused by an aberrant fibrillar collagen matrix within the sclera. The present data suggest that common variations in COL11A1 are associated with PACG, and eyes predisposed to PACG are generally hyperopic having a shorter axial length and a crowded anterior segment (George et a/., 2003). Therefore the causal variants predisposing towards PACG within COL11A1 may alter its gene expression such as to engineer a reverse effect to that observed in myopic eyes. COL11A 1 is also expressed in the human ocular trabecular meshwork cells (Michael et a/., 2008), which is pivotal for regulating the drainage of the aqueous humor from the eye. Therefore the aberrant action of this gene, albeit mild, could be at multiple sites within the PACG eye.

The third locus, rs1015213 on Chromosome 8q, is located within an intergenic region located 120Kb upstream of PCMTD1 and 130K downstream of ST18 PCMTD1 encodes for protein-L-isoaspartate O-methyltransferase domain- containing protein 1 , whose function remains relatively unknown. ST18 encodes for the protein named Suppression of Tumorigenecity 18, and has been shown to be significantly down-regulated in breast cancer cell lines (Jandrig ef a/., 2004). More recent studies have also shown it to be a mediator for apoptosis and inflammation (Yang et al., 2008). The LD block where rs1015213 was located extends into PCMTD1 (Figure 4C), but not into ST18, suggesting that PCMTD1 was the likelier candidate susceptibility gene for PACG at this locus, in keeping with the gene expression data (Figure 5). The minor allele frequency of rs1015213 appears to be rare (between 1 and 3 percent) in many of the sample collections (Figure 3C), particularly in individuals of Chinese and Vietnamese descent. It is possible that residual population stratification could confound the genetic association in these collections (Mathieson and McVean 2012). It was somewhat reassuring that ten out of the eleven PACG sample collections show the same direction of effect size for rs1015213. Furthermore, the overall meta-analysis for all sample collections revealed only mild heterogeneity (I2 index = 19 percent), which was not statistically significant between the collections (P-value for heterogeneity = 0.19), thus arguing against population stratification as the cause of the observed association. In terms of the biological consequences of rs1015213, we are unable to exclude the possibility that this sequence variant could be tagging the presence of functional variants which are exerting long-range control on distant gene targets in a position- and orientation-independent manner (Pomerantz et al., 2009; Tuupanen et al., 2009).

Sequence information for the three SNPs are provided below. The SNP is indicated in bold. PLEKHA7 rs11024102 (SEQ ID NO: 11 )

CCAGTCTTAC TGCCCAGCCC AACCCAGCCC AGGGAAGGCG GAGAGGAAGC AGTGAGCTGT

TTTACAATCC ATCAAATTAA Y TGCCCCAAAT GGGAAATTTG CAGGCCTCCT ACAGCCTCCT

GTGTATTCCA TAATCTCCCA TGGCCCTAAT CTGCTGAGAA AGAATGTGTG GGCTGTAATC TTTTCAGCCC ACTGTTCACA

Y is pyrimidine

COL11A1 rs3753941(SEQ ID NO: 12)

GGCCTGGAGA AGAGCCACTG TCTACATTTT AACAAACTTC ACAGAGGCTG TAGATAGAAT

TGGTCTATGA TCACAATTCT R AGAAACTTTA AGAACAATAC TGATGAGAAA TGGGAAATAC

TATGTGCTTA AGAGATGCCC AAAGGTTTCA AAAAATTTGA AAAGCCAAAT CATCAACGGA TGCATGGAAT GGGTAATGAG R is purine

rs1015213 (SEQ ID NO: 13)

TTTTAAGCTC CTCAGCTTAT TCTTATTCTC AGAGAGGGTT GAAGGTTGAC AATCCCACAG Y GTTGTCTGGC ACTAATTGAT TTGACCTACT TTTTGAGTAT TCAGACAAAG AAGGAGGAAT

Y is pyrimidine

Information for these SNPs may also be obtained from the dbSNP database (http://www.ncbi.nlm.nih/gov/projects/SNP/). No evidence of association at previously reported POAG loci surpassing genome-wide significance was observed despite sufficient statistical power to detect the previously-reported effect sizes (see Methods). This reinforces clinical and epidemiological data that PACG and POAG are distinct disease entities, with different molecular signatures underlying their pathogenic mechanisms. In summary, three novel loci for PACG, a major blinding disease with largely unresolved causal mechanisms were identified. These findings provide insight into the genetic mechanisms responsible for individual susceptibility to PACG. These observed associations have significant potential for developing novel therapies for the prevention or treatment of the condition. Further elucidation of the genetic architecture of PACG may eventually allow the development of a clinically useful genetic profile for the identification, risk stratification and thus treatment of PACG patients.

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