Abstract

Central corneal thickness (CCT) is a highly heritable trait. Genes that significantly influence CCT can be candidate genes for common disorders in which CCT has been implicated, such as primary open-angle glaucoma (POAG) and keratoconus. Because the genetic factors controlling CCT in different Asian populations are unclear, we have built on previous work conducted on Singaporean Indians and Malays and extended our hypothesis to individuals of Chinese descent. We have followed up on all suggestive signals of association with CCT (P < 10−4) from the previously reported meta-analysis comprising Indians and Malays in a sample of Chinese individuals (n= 2681). In the combined sample (n= 7711), strong evidence of association was observed at four novel loci: IBTK on chromosome 6q14.1; CHSY1 on chromosome 15q26.3; and intergenic regions on chromosomes 7q11.2 and 9p23 (8.01 × 10−11 < λGC corrected Pmeta < 8.72 × 10−8). These four new loci explain an additional 4.3% of the total CCT variance across the sample cohorts over and above that of previously identified loci. We also extend on a previous finding at a fifth locus (AKAP13) where a new single-nucleotide polymorphism (rs1821481, Pmeta = 9.99 × 10−9) was found to be significantly more informative compared with the previously reported rs6496932 (Pmeta = 3.64 × 10−5). Performing association analysis in Asians may lead to the discovery of ethnic-specific genes that control CCT, offering further mechanistic insights into the regulation of CCT. In addition, it may also provide several candidate genes for interrogation for POAG, keratoconus and possible racial/ethnic variations.

INTRODUCTION

Based on twin and family studies, central corneal thickness (CCT) displays high heritability with a coefficient as high as 0.95 (1–3). It is a normally distributed quantitative trait within the general population. However, despite the strong evidence of its genetic aetiology, the genetic determinants of CCT remained largely unknown until recent genome-wide association studies (GWASs) led to the identification of several genes (4–6). It was also unknown whether the genes identified in mostly European populations are relevant to Asians in which the disease pattern (prevalence and clinical characteristics) of glaucoma and keratoconus as well as CCT distribution differs from individuals of Caucasian ancestry. For example, normal tension glaucoma is more common in individuals of Asian ancestry than in Caucasians (7,8), and keratoconus is more common in Indians than in Chinese or other ethnic groups (9,10). However, whether these racial/ethnic patterns are due to differences in genetic factors controlling CCT remains poorly described.

We recently conducted a GWAS of CCT in Singaporean Indians and Malays derived from two population-based cohorts in Singapore [the Singapore Indian Eye Study (SINDI) and the Singapore Malay Eye Study (SiMES), respectively (6)], which confirmed the European findings at ZNF469 and further revealed two additional strongly associated genetic regions contributing to CCT variation in Asians (COL8A2 and RXRA/COL5A1). However, as with any quantitative trait, CCT is likely to be determined by multiple genes of various effect sizes, and larger sample sizes are required to identify such genes. Therefore, the aim of this current study was to: (i) identify additional novel genetic variants influencing CCT in Asians, and (ii) determine whether there were racial/ethnic differences in genetic variants associated with CCT between the three major Asian ethnic groups: Chinese, Malays and Indians. To this end, we sought direct replication of the most significant CCT-associated single-nucleotide polymorphisms (SNPs) (P < 10−4) from the SINDI and SiMES meta-analysis in a sample of Singapore Chinese individuals [Singapore Chinese Eye Study (SCES), n = 1883] as well as another sample of Beijing Chinese [Beijing Eye Study (BES), n = 798] (11).

RESULTS

Table 1 summarizes the descriptive statistics for all cohorts used in analysis: SiMES, SINDI, SCES and BES after genotyping and sample quality control (QC).

Table 1.

Characteristics of the study populations

 SiMES SINDI SCES BES 
na 2514 2516 1883 798 
Ageb 59.06 (11.03); 40–80 58.00 (10.00); 43–84 58.86 (9.63); 44–86 58.20 (9.43) 45–86 
Gender (% female) 50.36 48.91 49.01 61.23 
CCT (μm)b,c 540.74 (33.52); 421.00–681.00 539.76 (36.21); 442.00–1015.00 552.63 (33.39); 397.00–689.00 553.79 (33.10) 429.00–659.00 
 SiMES SINDI SCES BES 
na 2514 2516 1883 798 
Ageb 59.06 (11.03); 40–80 58.00 (10.00); 43–84 58.86 (9.63); 44–86 58.20 (9.43) 45–86 
Gender (% female) 50.36 48.91 49.01 61.23 
CCT (μm)b,c 540.74 (33.52); 421.00–681.00 539.76 (36.21); 442.00–1015.00 552.63 (33.39); 397.00–689.00 553.79 (33.10) 429.00–659.00 

aIndividuals who have complete phenotype (including covariates) and genotype data.

bMean (standard deviation); range.

cRight eye.

Discovery analysis

The removal of previously identified CCT loci (ZNF469, COL8A2 and RXRA/COL5A1) still resulted in a significant excess of extreme P-values within the quantile–quantile (QQ) distribution of the SINDI and SiMES meta-analysis (Fig. 1). As this was observed within a background of low genomic inflation (λGC = 1.066), we hypothesized that additional novel CCT loci remained to be discovered. We thus followed up all 88 SNPs exceeding P < 10−4 from the meta-analysis in a replication cohort of Chinese individuals (n = 2681 in aggregate).

Figure 1.

QQ plot for direct genotyped SNPs in the meta-analysis of SiMES and SINDI cohorts. The red dots represent the plot with all analysed SNPs. The blue dots represent the plot after the removal of genome-wide significant SNPs from ZNF469, COL8A2 and RXRA/COL5A1. The diagonal black line represents the null hypothesis, and the formal threshold for genome-wide significance (P= 5.00 × 10−8) is represented by the horizontal black line.

Figure 1.

QQ plot for direct genotyped SNPs in the meta-analysis of SiMES and SINDI cohorts. The red dots represent the plot with all analysed SNPs. The blue dots represent the plot after the removal of genome-wide significant SNPs from ZNF469, COL8A2 and RXRA/COL5A1. The diagonal black line represents the null hypothesis, and the formal threshold for genome-wide significance (P= 5.00 × 10−8) is represented by the horizontal black line.

Replication analysis

Two independent sample sets comprising individuals of Chinese descent (from Singapore, n = 1883, and Beijing, China, n = 798) with genome-wide genotyping data were used as the replication cohort for looking up these 88 SNPs showing P < 10−4 in the meta-analysis of SINDI and SiMES. The genomic inflation factor was 1.015 and 1.047 for SCES and Beijing, respectively.

We observed replication of four novel loci from the original 88 discovery signals: at rs1538138 on chromosome 6q14.1 near IBTK (Preplication = 9.49 × 10−5, Pmeta = 2.10 × 10−11), rs4965359 (corroborated by rs930847, Table 2) on chromosome 15q26.3 (Preplication = 1.24 × 10−4, Pmeta = 7.63 × 10−11), rs1324183 on chromosome 9p23 (Preplication = 2.92 × 10−4, Pmeta = 3.49 × 10−8) and rs4718428 on chromosome 7q11.21 (Preplication = 1.80 × 10−2, Pmeta = 8.82 × 10−9) (Table 2). Given we had full GWA information for BES and SCES, we also performed a full meta-analysis between SiMES, SINDI, SCES and BES. The Manhattan plot of this meta-analysis is shown in Figure 2. No new loci surfaced apart from the four loci when the formal meta-analysis was conducted. At each of the four loci, we did not observe significant heterogeneity in the strength of the association either between the discovery samples, between the replication samples, or in the meta-analysis of all samples (Pheterogeneity> 0.05 for all comparisons). However, even after correcting for genetic ancestry using principal components (PCs) where applicable, low residual genomic inflation for the final meta-analysis was still present (λGC = 1.062). In the light of this, P-values obtained from the final ‘discovery’ and ‘replication’ meta-analysis have been corrected for this residual inflation (Table 2). Even after adjustment, genome-wide significant overall joint evidence of association (P < 5 × 10−8) was observed at three out of the four loci, with the fourth locus (rs1324183 on chromosome 9p) still showing highly suggestive evidence of association (P = 8.72 × 10−8) (Table 2).

Table 2.

Association results between genome-wide significant SNPs in the four novel regions and CCT, as well as in the two new SNPs showing genome-wide significance at the previously reported AKAP13 locus

Chr SNP BP Effect allele Cohort EAF β (μm) SE P Phet 
Rs1538138 82 851 313 SINDI 0.218 −4.68 1.12 3.04 × 10−5  
    SiMES 0.183 −4.25 1.20 4.17 × 10−4  
    Discovery  −4.48 0.82 4.62 × 10−8 0.79 
    SCES 0.281 −2.32 1.22 0.0570  
    BES 0.295 −6.90 1.70 5.60 × 10−5  
    Replication  −3.87 0.99 9.49 × 10−5 0.06 
    All  −4.23 0.63 2.10 × 10−11 0.21 
    All (λGC corrected)    8.01 × 10−11   
Rs4718428 66 058 881 SINDI 0.459 −4.02 0.93 1.53 × 10−5  
    SiMES 0.611 −3.12 0.97 0.0013  
    Discovery  −3.59 0.67 8.79 × 10−8 0.50 
    SCES 0.687 −1.48 1.16 0.2000  
    BES 0.736 −4.31 1.80 0.0170  
    Replication  −2.31 0.98 0.0180 0.37 
    All  −3.18 0.55 8.82 × 10−9 0.46 
    All (λGC corrected)    2.38 × 10−8   
Rs1324183 13 547 491 SINDI 0.271 −3.29 1.04 0.0016  
    SiMES 0.238 −2.98 1.08 0.0056  
    Discovery  −3.14 0.75 2.75 × 10−5 0.84 
    SCES 0.245 −3.93 1.26 0.0018  
    BES 0.209 −3.62 1.96 0.0065  
    Replication  −3.83 1.06 2.92 × 10−4 0.92 
    All  −3.37 0.61 3.49 × 10−8 0.97 
    All (λGC corrected)    8.72 × 10−8   
15 Rs1828481a 83 641 916 SINDI 0.451 2.43 0.92 0.0084  
    SiMES 0.451 3.28 0.93 4.03 × 10−4  
    Discovery  2.85 0.65 1.26 × 10−5 0.52 
    SCES 0.526 2.87 1.09 0.0084  
    BES 0.558 5.30 1.60 9.81 × 10−4  
    Replication  3.63 0.90 5.43 × 10−5 0.34 
    All  3.12 0.53 3.51 × 10−9 0.55 
    All (λGC corrected)    9.99 × 10−9   
15 Rs7172789a 83 644 521 SINDI 0.451 2.51 0.92 0.0065  
    SiMES 0.458 3.24 0.93 4.74 × 10−4  
    Discovery  2.88 0.65 1.08 × 10−5 0.58 
    SCES 0.528 2.87 1.09 0.0082  
    BES 0.561 5.35 1.61 9.16 × 10−4  
    Replication  3.65 0.90 5.09 × 10−5 0.33 
    All  3.14 0.53 2.84 × 10−9 0.55 
    All (λGC corrected)    8.19 × 10−9   
15 Rs930847 99 376 085 SINDI 0.168 4.49 1.25 3.24 × 10−4  
    SiMES 0.389 3.30 0.97 6.53 × 10−4  
    Discovery  3.74 0.76 9.44 × 10−7 0.45 
    SCES 0.203 3.68 1.34 0.0062  
    BES 0.189 3.62 1.99 0.0690  
    Replication  3.66 1.11 0.0010 0.59 
    All  3.72 0.63 3.60 × 10−9 0.80 
    All (λGC corrected)    1.02 × 10−8   
15 Rs4965359 99 402 859 SINDI 0.404 −3.06 0.93 0.0010  
    SiMES 0.421 −3.94 0.95 3.42 × 10−5  
    Discovery  −3.49 0.66 1.48 × 10−7 0.51 
    SCES 0.642 −2.95 1.11 0.0080  
    BES 0.673 −4.72 1.62 0.0036  
    Replication  −3.52 0.92 1.24 × 10−4 0.59 
    All  −3.50 0.54 7.63 × 10−11 0.83 
    All (λGC corrected)    2.70 × 10−10   
Chr SNP BP Effect allele Cohort EAF β (μm) SE P Phet 
Rs1538138 82 851 313 SINDI 0.218 −4.68 1.12 3.04 × 10−5  
    SiMES 0.183 −4.25 1.20 4.17 × 10−4  
    Discovery  −4.48 0.82 4.62 × 10−8 0.79 
    SCES 0.281 −2.32 1.22 0.0570  
    BES 0.295 −6.90 1.70 5.60 × 10−5  
    Replication  −3.87 0.99 9.49 × 10−5 0.06 
    All  −4.23 0.63 2.10 × 10−11 0.21 
    All (λGC corrected)    8.01 × 10−11   
Rs4718428 66 058 881 SINDI 0.459 −4.02 0.93 1.53 × 10−5  
    SiMES 0.611 −3.12 0.97 0.0013  
    Discovery  −3.59 0.67 8.79 × 10−8 0.50 
    SCES 0.687 −1.48 1.16 0.2000  
    BES 0.736 −4.31 1.80 0.0170  
    Replication  −2.31 0.98 0.0180 0.37 
    All  −3.18 0.55 8.82 × 10−9 0.46 
    All (λGC corrected)    2.38 × 10−8   
Rs1324183 13 547 491 SINDI 0.271 −3.29 1.04 0.0016  
    SiMES 0.238 −2.98 1.08 0.0056  
    Discovery  −3.14 0.75 2.75 × 10−5 0.84 
    SCES 0.245 −3.93 1.26 0.0018  
    BES 0.209 −3.62 1.96 0.0065  
    Replication  −3.83 1.06 2.92 × 10−4 0.92 
    All  −3.37 0.61 3.49 × 10−8 0.97 
    All (λGC corrected)    8.72 × 10−8   
15 Rs1828481a 83 641 916 SINDI 0.451 2.43 0.92 0.0084  
    SiMES 0.451 3.28 0.93 4.03 × 10−4  
    Discovery  2.85 0.65 1.26 × 10−5 0.52 
    SCES 0.526 2.87 1.09 0.0084  
    BES 0.558 5.30 1.60 9.81 × 10−4  
    Replication  3.63 0.90 5.43 × 10−5 0.34 
    All  3.12 0.53 3.51 × 10−9 0.55 
    All (λGC corrected)    9.99 × 10−9   
15 Rs7172789a 83 644 521 SINDI 0.451 2.51 0.92 0.0065  
    SiMES 0.458 3.24 0.93 4.74 × 10−4  
    Discovery  2.88 0.65 1.08 × 10−5 0.58 
    SCES 0.528 2.87 1.09 0.0082  
    BES 0.561 5.35 1.61 9.16 × 10−4  
    Replication  3.65 0.90 5.09 × 10−5 0.33 
    All  3.14 0.53 2.84 × 10−9 0.55 
    All (λGC corrected)    8.19 × 10−9   
15 Rs930847 99 376 085 SINDI 0.168 4.49 1.25 3.24 × 10−4  
    SiMES 0.389 3.30 0.97 6.53 × 10−4  
    Discovery  3.74 0.76 9.44 × 10−7 0.45 
    SCES 0.203 3.68 1.34 0.0062  
    BES 0.189 3.62 1.99 0.0690  
    Replication  3.66 1.11 0.0010 0.59 
    All  3.72 0.63 3.60 × 10−9 0.80 
    All (λGC corrected)    1.02 × 10−8   
15 Rs4965359 99 402 859 SINDI 0.404 −3.06 0.93 0.0010  
    SiMES 0.421 −3.94 0.95 3.42 × 10−5  
    Discovery  −3.49 0.66 1.48 × 10−7 0.51 
    SCES 0.642 −2.95 1.11 0.0080  
    BES 0.673 −4.72 1.62 0.0036  
    Replication  −3.52 0.92 1.24 × 10−4 0.59 
    All  −3.50 0.54 7.63 × 10−11 0.83 
    All (λGC corrected)    2.70 × 10−10   

BP, basepair position; EAF, effect allele frequency; β, per-allele change in CCT; SE, standard error for ascertainment of β; SINDI, Singapore Indian Eye Study; SiMES, Singapore Malay Eye Study; SCES, Singapore Chinese Eye Study; BES, Beijing Eye Study; P, P-value for association; Phet, P-value for heterogeneity between cohorts. λGC, corrections are based on a final meta-analysis inflation factor of 1.062.

aSNPs at the AKAP13 locus.

Figure 2.

Manhattan plot of the meta-analysis between SiMES, SINDI, SCES and BES, highlighting genome-wide significant loci influencing CCT. All SNPs are plotted according to statistical significance (−log 10 P-value on the Y-axis) and position along the individual chromosomes (X-axis). The horizontal dotted line denotes P < 10−7. SNPs in red dots denote SNPs exceeding the formal threshold for genome-wide significance (P < 5 × 10−8). Indicated on the figure are the four loci already published [(6); ZNF469, COL8A2, AKAP13 and RXRA/COL5A1] as well as the novel loci identified: IBTK, C7orf42, the intergenic region on Chr. 9p and the locus near LRRK1, ALDH1A3 and CHSY1.

Figure 2.

Manhattan plot of the meta-analysis between SiMES, SINDI, SCES and BES, highlighting genome-wide significant loci influencing CCT. All SNPs are plotted according to statistical significance (−log 10 P-value on the Y-axis) and position along the individual chromosomes (X-axis). The horizontal dotted line denotes P < 10−7. SNPs in red dots denote SNPs exceeding the formal threshold for genome-wide significance (P < 5 × 10−8). Indicated on the figure are the four loci already published [(6); ZNF469, COL8A2, AKAP13 and RXRA/COL5A1] as well as the novel loci identified: IBTK, C7orf42, the intergenic region on Chr. 9p and the locus near LRRK1, ALDH1A3 and CHSY1.

Genome-wide significant evidence of association was also observed at SNPs rs1828481 and rs7172789 near the AKAP13 locus, on chromosome 15q24-q25, when data from all four sample cohorts were combined (Table 2). Both SNPs are located 15 345 and 17 950 base-pairs telomeric from rs6496932 (Pmeta = 3.64 × 10−5, Supplementary Material, Fig. S7), which was previously reported by Vitart et al. (5). Conditional regression analysis between rs1828481 and rs6496932 in all four sample cohorts showed the former to be significantly more informative in terms of association evidence with CCT (Supplementary Material, Table S2).

We also reassessed the strength of previously reported loci which showed very strong evidence of association with CCT in our Chinese replication cohorts. The index SNPs at ZNF469 (rs12447690) and COL8A2 (rs96067) showed consistent direction of effect sizes with CCT across both replication cohorts, and were significant in the overall Chinese replication cohort (Supplementary Material, Figs S5 and S6). However, the magnitude of the effect size was slightly smaller in individuals of Chinese descent for both rs12447690 (ZNF469) and rs96067 (COL8A2), with significant heterogeneity of the effect sizes observed for rs96067 (COL8A2). At the RXRA/COL5A1 locus on chromosome 9q34.3, we note strong evidence of association with CCT in the Chinese at multiple SNPs (Supplementary Material, Table S1). We also observed additional support for the AKAP13 rs6496932 SNP (5,6) in the Chinese cohorts (Supplementary Material, Fig. S7, Table S2). However, we did not observe evidence of association at the intragenic COL5A1 SNP (rs7044529; Preplication = 0.76). Regional association plots for the five loci associated with CCT in the meta-analysis of Asian sample collections are displayed in Supplementary Material, Figure S10, showing the location of the SNP, physical structure of the genes as well as the linkage disequilibrium (LD) pattern of the region.

The four novel loci collectively explained 4.3% of the overall CCT variance across SINDI, SiMES, SCES and BES, with each copy of the CCT-decreasing allele from the four loci resulting in a −3.128 μm decrease in CCT (P = 1.77 × 10−27; Table 3). Inclusion of the four previously reported CCT loci (ZNF469, COL8A2, AKAP13 and RXRA/COL5A1) resulted in a slightly higher overall proportion of variance explained (5.7%, P = 1.18 × 10−53; Table 3).

Table 3.

Per-cohort and overall variance analysis for the cumulative effect of reported CCT loci

 All eight reported loci
 
Four novel loci reported here
 
 β (μm) L95 U95 P-value Variance β (μm) L95 U95 P-value Variance 
SINDI −4.006 −4.670 −3.342 3.18 × 10−32 0.053 −3.665 −4.659 −2.672 4.87 × 10−13 0.020 
SiMES −3.907 −4.599 −2.314 1.79 × 10−28 0.047 −3.601 −4.647 −2.555 1.42 × 10−11 0.018 
SCES −1.596 −2.394 −0.798 8.80 × 10−5 0.008 −2.265 −3.380 −1.149 6.87 × 10−5 0.008 
BES −2.368 −3.498 −1.238 3.94 × 10−5 0.018 −4.527 −6.134 −2.920 3.25 × 10−8 0.032 
All (meta) −3.038 −3.424 −2.652 1.18 × 10−53 0.057 −3.128 −3.693 −2.564 1.77 × 10−27 0.043 
 All eight reported loci
 
Four novel loci reported here
 
 β (μm) L95 U95 P-value Variance β (μm) L95 U95 P-value Variance 
SINDI −4.006 −4.670 −3.342 3.18 × 10−32 0.053 −3.665 −4.659 −2.672 4.87 × 10−13 0.020 
SiMES −3.907 −4.599 −2.314 1.79 × 10−28 0.047 −3.601 −4.647 −2.555 1.42 × 10−11 0.018 
SCES −1.596 −2.394 −0.798 8.80 × 10−5 0.008 −2.265 −3.380 −1.149 6.87 × 10−5 0.008 
BES −2.368 −3.498 −1.238 3.94 × 10−5 0.018 −4.527 −6.134 −2.920 3.25 × 10−8 0.032 
All (meta) −3.038 −3.424 −2.652 1.18 × 10−53 0.057 −3.128 −3.693 −2.564 1.77 × 10−27 0.043 

β, per allele change in CCT; L95, lower bounds of the 95% confidence interval for β; U95, upper bounds of the 95%  confidence interval for β; Variance, proportion of CCT variance explained. The SNPs used for all eight reported loci are: rs1536478 (RXRA/COL5A1), rs12447690 (ZNF469), rs96067 (COL8A2), rs1828481 (near AKAP13), rs1538138, rs4718428, rs1324183, rs4965359. The SNPs used for the four novel loci are: rs1538138, rs4718428, rs1324183, rs4965359.

DISCUSSION

In this study, we reassessed all SNPs surpassing a nominal significance level of P < 10−4 from the meta-analysis of both SINDI and SiMES (6) in 2681 Chinese individuals from Singapore and China. By doing so, we identified four novel loci associated with CCT in chromosomal regions 6q14.1, 7q11.21, 9p23 and 15q26.3.

Among the newly identified loci, the most significant result was observed at rs1538138 on chromosome 6q14.1 near the IBTK gene (Pmeta = 2.1 × 10−11). This gene encodes, IBTK, the inhibitor of Bruton's tyrosine kinase (BTK), which binds to the PH domain of BTK and downregulates BTK kinase activity and BTK-mediated calcium mobilization. IBTK also negatively regulates the activation of nuclear factor-kappa-B (NF-κB)-driven transcription (12). No direct link between BTK, IBTK and the cornea has yet been noted in previous studies. Nevertheless, we hypothesize that IBTK modulates corneal development through its negative regulation of BTK activity and that this occurs via the Wnt-β-catenin signalling pathway (13). The canonical Wnt-β-catenin signalling pathway mediated by multiple Wnt ligands and receptors is one pathway among others that has been shown to be involved in the development of neural crest cell-derived tissues, including cornea (14,15). It plays an integral role during embryogenesis and adult tissue homeostasis, including cell proliferation and cell fate determination (16). Regulation of this pathway can occur by binding either to the Wnt or to its receptors (17,18). BTK has been shown to act as a negative regulator of the Wnt/β-catenin signalling pathway by directly interacting with a nuclear component of the Wnt/β-catenin signalling, CDC73 (13). Despite the lack of experimental evidence, it is therefore plausible that IBTK plays a role in the development of the corneal stroma, and thus CCT by modulating the activity of BTK within the Wnt/β-catenin signalling pathway. In addition, the role of IBTK in interfering with the activation of NF-κB, a well-characterized transcription factor involved in developmental processes, cellular growth and apoptosis may also play a part in corneal development and CCT. Future studies are warranted to understand the mechanisms through which these proteins act to regulate CCT.

The region with the second strongest statistical evidence for association was a locus on chromosome 15q26.3. This region includes the leucine-rich repeat kinase 1 (LRRK1) gene, the aldehyde dehydrogenase 1A3 (ALDH1A3) gene and the chondroitin sulphate synthase 1 (CHSY1) gene. Although, the most significant evidence was found in the intergenic region of LRRK1 (7.63 × 10−11 < Pmeta < 3.60 × 10−9), the gene most likely involved in CCT is CHSY1, located downstream of LRRK1. The enzyme encoded by CHSY1 synthesizes chondroitin sulphate, a glycosaminoglycan (GAG) found abundantly in the corneal stromal extracellular matrix (19). The GAGs, chondroitin sulphate and dermatan sulphate are covalently linked to the core protein of corneal proteoglycans, decorin and biglycan (20,21). Incidentally loss of CHSY1 has also been shown to cause syndromic brachydactyly in humans via increased NOTCH signalling (22). The ophthalmic features of the affected probands with homozygous loss of function mutations in CHSY1 included macrophthalmia accompanied by bilateral blue sclera, remnants of pupillary membrane over the crystalline lens, and tilted optic discs in both eyes. There was no mention, in the clinical description, however, of any aberrant corneal thickness, which may have been overlooked. During stromal development, keratocytes in the stroma express both decorin and biglycan to regulate the collagen fibril growth (23). The highly charged GAG chains are known to extend out and regulate interfibrillar spacings of the stromal collagenous matrix (24–26). It is therefore within reason for an enzyme, such as CHSY1, involved in the synthesis of chondroitin sulphate to be also implicated in corneal stromal development and CCT. Next to CHSY1, LRRK1—with no known function in the cornea—can be considered a poor candidate for CCT determination. It is also worth noting that ALDH1A3, identified as one of the corneal crystallins, has moderate expression in the mammalian cornea (27,28). This protein however is most likely involved in protecting the cornea tissues against ultraviolet radiation-induced oxidative damage (29,30). Bioinformatic analysis of the region surrounding SNPs (rs930847 and rs4965359) (ENCODE; http://genome.ucsc.edu/ENCODE/) also indicates that these SNPs are near DNase1 clusters, where the chromatin is hypersensitive to cutting by the DNase enzyme, suggestive of regulatory elements. A CTCF transcription factor-binding site (31) is also located ∼600 bp upstream of rs930647. Further sequencing of the region may be required to identify other functional polymorphisms as the possibility remains that both of these SNPs may simply be in LD with as-yet-unidentified casual variant. Until the causal variant is identified and characterized, it remains to be seen which of the genes within this cluster constitute the true CCT gene.

Other genome-wide significant association signals were located on chromosome 7q11.21 in the intergenic region of C7orf42, encoding a hypothetical protein LOC55069 (Pmeta = 8.82 × 10−9) and on chromosome 9p23 (Pmeta = 3.49 × 10−8). Although the chromosome 9 region concerns a gene desert, the closest gene in this locus associated with CCT is MPDZ encoding a multiple PDZ domain protein. MPDZ is highly similar to Homo sapiens multi-PDZ domain protein, MUPP1, which along with several other proteins such as occludin, ZO-1, AF-6, PAR-3 and cingulin are associated with tight junctions of cells (32). MUPP1 interacts with JAM-1, a membrane adhesion protein expressed in corneal epithelial cells with a role in maintenance of corneal epithelial barrier and regulation of cell shape (33,34). As neither of these regions nor the genes contained within are well characterized, further research is warranted, such as fine-mapping, sequencing and/or molecular functional studies, to fully understand their involvement in the variation of CCT.

The recent GWA studies conducted in individuals from different populations of Caucasian and Asian ancestry have identified several CCT-associated genes with diverse functions and have also provided novel insights into pathways and mechanisms that influence the normal variations of CCT (4–6). As collagen forms an essential component of the corneal stroma, therefore, it is not surprising that a few of these CCT associated genes (i.e. COL8A2, COL5A1, COL1A1 and COL1A2) encode collagens or those involved in collagen fibril assembly (i.e. CHSY1). The fact that mutations in some of the collagen genes are causative of genetic diseases that have extreme/abnormal CCT values such as Ehlers Danlos syndrome types I and II (COL5A1) and osteogenesis imperfecta (COL1A1 and COL1A2) indicates that common, non-pathogenic polymorphisms within these genes may have more subtle effects on CCT than the larger differences caused by more deleterious mutations found in disease conditions. Although CCT-associated genes that encode for transcription factors such as ZNF469 and AVGR8 most likely modify the normal variations of corneal thickness by regulating corneal gene expression, AKAP13 and IBTK, which encode signal transduction molecules, may contribute to CCT variation by modulating the signalling pathways involved in corneal development. Given that the variation in CCT explained by all of the associated SNPs/loci identified thus far is 5.7%, it is very likely that there are more genes yet to be unravelled that are associated with CCT. Whether it is possible to identify these genes via genome association methods however remains to be seen.

For the previously reported CCT loci (6), we were able to confirm association at ZNF469, COL8A2, the interval between RXRA/COL5A1 and AKAP13 in the Chinese replication cohort. Interestingly, the per-allele effect sizes for COL8A2 and ZNF469 SNPs appeared to be smaller in effect size and much less statistically significant compared with that observed in the other ethnic groups. As we did not observe any significant differences in LD patterns at both loci across all the sample cohorts (discovery and replication), this suggests that some degree of genetic heterogeneity could exist between these multi-ethnic Asian cohorts in the case of these loci (Supplementary Material, Figs S8 and S9). Conversely, we were able to observe strong association at AKAP13, where two new SNPs showed genome-wide significant association in our combined Asian cohort alone as well as RXRA/COL5A1 in the Chinese samples. Specifically, we were able to build on and extend previous findings near the AKAP13 locus using conditional regression as our denser GWAS genotyping chip (Illumina 610quad) allowed us to interrogate SNPs at higher resolution compared with the previous report, which uses a combination of 300K–370K arrays (5).

In conclusion, we report the identification of four novel CCT genetic loci in a multi-ethnic Asian sample which explains an additional 4.3% of the total CCT variance across the Chinese, Indian and Malay sample cohorts studied here. We also confirmed association at ZNF469, COL8A2, RXRA/COL5A1 and AKAP13 in the Chinese cohorts. Further detailed association analysis of the genomic regions implicated by these variants in multiple populations will be beneficial to fully elucidate the relationship between the genes currently identified and CCT and/or primary open-angle glaucoma.

MATERIALS AND METHODS

Study populations

Ethics statement

All studies adhered to the Declaration of Helsinki. Ethics approval was obtained from the Singapore Eye Research Institute (SERI) Institutional Review Board (IRB) for all Singaporean studies and from the Medical Ethics Committee of the Beijing Tongren Hospital for the Beijing cohort. Written informed consent was obtained from all participants.

Discovery cohort

Singapore Malay Eye Study

SiMES is a population-based, cross-sectional study of 3280 Malay adults aged 40–79 years. Details of the SiMES design, sampling plan and methods have been reported elsewhere (35). In brief, an age-stratified random sampling of all Malay adults, aged 40–80 years, residing in 15 residential districts in the southwestern part of Singapore was drawn from the computer-generated random list of 16 069 Malay names provided by the Ministry of Home Affairs. A total of 1400 names from each decade of age (40–49, 50–59, 60–69 and 70–79 years), or 5600 names, were selected. Of these, 4168 individuals (74.4%) were determined to be eligible to participate. A person was considered ineligible if he or she had moved from the residential address, had not lived there in the past 6 months, was deceased or was terminally ill. Of the 4168 eligible individuals, 3280 participants (78.7%) took part in the study. The study was conducted from August 2004 to June 2006.

Singapore Indian Eye Study

As with SiMES, SINDI is a population-based, cross-sectional epidemiological study, but of ethnic Indian adults aged between 40 and 80+ years residing in Singapore. As with SiMES, the Ministry of Home Affairs provided an initial computer-generated list of Indian names derived from a simple random sampling of all ethnic Indian adults aged 40–80+ years of age residing in 15 residential districts in southwestern Singapore. From this list, a final sampling frame of 6350 ethnic Indian residents was derived using an age-stratified random sampling strategy similar to SiMES. SINDI was conducted from March 2007 to December 2009 and recruited 3400 (75% response rate) participants (36).

Replication cohort

Singapore Chinese Eye Study

Similar to SiMES and SINDI, the SCES is a population-based, cross-sectional study of Chinese adults residing in the southwestern part of Singapore. The Ministry of Home Affairs provided an initial computer-generated list ethnic Chinese names of adults aged 40–80+ years of age. A final sampling frame of 6350 ethnic Chinese residents was derived from this list using an age-stratified random sampling strategy similar to SiMES and SINDI. The ongoing SCES began in February 2009 with an aim to recruit 3300 (75% response rate) participants (36). As of June 2011, we recruited and genotyped 1952 participants, who were used in this study.

Beijing Eye Study

BES is a population-based, cross-sectional study of Chinese adults aged 40+ years and residing in four communities in the urban district of Haidian in the north of Central Beijing and in three communities in the village area of Yufa of the Daxing District, south of Beijing (11). At the time of the first survey in the year 2001, the seven communities had a total population of 5324 individuals aged 40 years or older and eligible to take part in the study. In total, 4439 individuals participated in the eye examination (83.4% response rate). In the year 2006, when CCT was measured, the study was repeated by re-inviting all participants from the survey from 2001 to be re-examined, with 3251 subjects participating (73.3% response rate).

Measurements of CCT

SiMES, SINDI, SCES

Five CCT measurements were obtained from each eye with an ultrasound pachymeter (Advent, Mentor O & O, Norwell, MA, USA) and the median reading was taken (37,38). As there was good correlation between the measurements in both eyes, only the readings from the right eye were used for analysis.

Beijing Eye Study

CCT was measured by slit lamp-mounted optical coherence tomography (Heidelberg Engineering Co., Heidelberg, Germany) for the right eye of the study participants of the survey from 2006.

Good agreement between slit lamp OCT and ultrasound pachymetry measurements for CCT has been demonstrated and no statistical differences on CCT measurements were observed between the two instruments (39). Therefore, results of the analysis are unlikely to be affected by the use of different measurement devices.

Genotyping and data QC

Discovery cohort

SINDI and SiMES

Single-cohort and meta-analysis of SINDI and SiMES has been previously described (6). For SINDI, 2516 samples passing quality checks had complete data for CCT measurements, age, gender and genetic ancestry (as indicated by PCs 1, 2 and 3). For SiMES, 2514 samples passing quality checks had complete data for CCT measurements, age, gender and PCs 1 and 2.

Replication cohort

Singapore Chinese Eye Study

Study participants were genotyped using the Illumina Human610-Quad BeadChips, which assays 620 901 SNPs across the genome, according to the manufacturer's protocols. The same QC methods used for SiMES and SINDI were applied to the SCES genotyping samples: samples were excluded if they showed evidence of admixture, cryptic relatedness, high heterogeneity and gender discrepancies [QC criteria have been explained in detail elsewhere (6,40)]. From a starting number of 1952 individuals, three samples had per-sample call rate of <95% and were removed from analysis. A total of 21 individuals showed evidence of admixture and were consequently excluded. This confirmed that participants were drawn from the same, single population. Biological relationship verification revealed a total of 29 sample pairs with cryptic relatedness. For these, the sample with the lower call rate was removed. In addition, further 14 samples with impossible biological sharing or heterogeneity, probably because of contamination, were removed, as well as two individuals who were removed due to gender discrepancies. PC analysis in EIGENSTRAT (41) of the remaining individuals for SCES against the HapMap CHB (Han Chinese) reference populations did not show the cohort to be dissimilar in ancestry, and therefore no PCs were used to correct for any underlying population substructure in the analysis performed. A final number of 1883 individuals with complete data for CCT measurements, age and gender were available as a replication sample.

Beijing Eye Study

Like SCES, study participants for BES were genotyped using the Illumina Human610-Quad BeadChips, with the same QC parameters: samples were excluded if they showed evidence of admixture, cryptic relatedness, high heterogeneity and if there were gender discrepancies [QC criteria explained in detail elsewhere (6,40)]. The initial sample size for BES was 988 individuals. Twelve individuals had per-sample call rates of <95% and 49 individuals showed evidence of admixture and all were consequently excluded. Biological relationship verification revealed a total of 129 sample pairs with cryptic relatedness. For these individuals, the sample with the lower call rate was removed. PC analysis similar to SCES was used to assess population substructure for Beijing. Like SCES, participants in BES did not show deviation from the HapMap Han Chinese reference population and therefore PCs were not included into the final analysis. Final sample size was 798 individuals after QC, which had complete data for CCT measurements, age and gender for replication analysis.

Data from both SCES and BES cohorts were used for independent ‘look-up’ replication for SNPs exceeding a nominal significance threshold of P < 10−4 identified in the meta-analysis of SiMES and SINDI. Genotype cluster plots for each of the SNPs surpassing P < 5 × 10−8 in the overall final analysis are shown in Supplementary Material, Figure S1.

Statistical analysis

Linear regression was performed to test for association between SNP genotypes and CCT as implemented by PLINK [version 1.06; (42)] in primary analysis. Individual SNP genotypes were coded according to the number of copies of the variant allele present: 0 for the wild-type genotype, 1 for heterozygotes and 2 for homozygote variants. A trend test incorporated within a linear regression model was used for primary association testing between genotypes and CCT as a quantitative trait, adjusting for age, gender and population admixture (reflected by PCs—1 and 2 for Malays and 1, 2 and 3 for Indians).

Meta-analysis for the discovery cohort has been previously described elsewhere (6). Although it is clear that the SCES and BES replication cohorts share a common ancestry when compared on the global perspective (Supplementary Material, Figs S2 and S3), detailed analysis revealed mild genetic stratification between both population-based cohorts (Supplementary Material, Fig. S4). We thus decided to obtain summary association statistics for SCES and BES separately prior to meta-analysis. Meta-analysis across SCES and BES was performed in a manner similar to that of the discovery cohort. Briefly, the inverse-variance, fixed effects model was used in order to obtain a combined point estimate of the overall effect size (β) coefficients and its corresponding standard error. Inter-cohort heterogeneity was assessed with the Cochran's Q statistic. For the final analysis, P < 5.0 × 10−8 was considered statistically significant. QQ and Manhattan plots were created using the software R [www.r-project.org; (43)].

To calculate the joint contribution of the four novel loci as a proportion of overall CCT variance, one SNP at each of the four loci (rs1538138 near IBTK, rs4718428 at 7q11, rs1324183 at 9p23 and rs4965359 at 15q26) was chosen. For each SNP, the genotypes were coded as follows: ‘2’ for individuals homozygous for the CCT-decreasing allele, ‘1’ for individuals carrying one copy of the CCT-decreasing allele and ‘0’ for individuals homozygous for the base-line genotype (non-CCT-decreasing). A SNP score was then computed according to the number of CCT-decreasing alleles carried by each individual. A linear trend test testing for association between the SNP score and CCT was then carried out (44) and the following parameters were calculated; per-allele decrease in CCT and its corresponding 95% confidence intervals, as well as the CCT variance explained by the SNP score (as estimated by Nagelkerke's r2 from the linear regression model). This analysis was repeated to calculate the joint contribution for all eight (the four known—ZNF469, COL8A2, AKAP13 and RXRA/COL5A1—with the addition of the four reported here) CCT loci. Only individuals with complete genotyping data at all analysed SNPs were included for this variance analysis.

WEB RESOURCES

EIGENSTRAT software: http://genepath.med.harvard.edu/~reich/EIGENSTRAT.htm.

Genome browser: http://genome.ucsc.edu/http://genome.ucsc.edu/.

International HapMap Project: http://www.hapmap.org/.

NCBI website: http://www.ncbi.nlm.nih.gov/.

Online Mendelian Inheritance in Man (OMIM): http://www.ncbi.nlm.nih.gov/omim.

Software R: http://www.r-project.org.

UniGene: http://www.ncbi.nlm.nih.gov/UniGene/.

SUPPLEMENTARY MATERIAL

Supplementary Material is available at HMG online.

FUNDING

This work was 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).

ACKNOWLEDGEMENTS

The authors would like to thank Wee-Yang Meah, Suo Chen, Jun Liu for technical and genotyping support.

Conflict of Interest statement. The authors declare that they have no competing financial interests.

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Author notes

These authors contributed equally and are joint First Authors.
These authors are joint Last Authors.