Meta-analysis of genome-wide association studies identifies novel loci that influence cupping and the glaucomatous process

Abstract

Glaucoma is characterized by irreversible optic nerve degeneration and is the most frequent cause of irreversible blindness worldwide. Here, the International Glaucoma Genetics Consortium conducts a meta-analysis of genome-wide association studies of vertical cup-disc ratio (VCDR), an important disease-related optic nerve parameter. In 21,094 individuals of European ancestry and 6,784 individuals of Asian ancestry, we identify 10 new loci associated with variation in VCDR. In a separate risk-score analysis of five case-control studies, Caucasians in the highest quintile have a 2.5-fold increased risk of primary open-angle glaucoma as compared with those in the lowest quintile. This study has more than doubled the known loci associated with optic disc cupping and will allow greater understanding of mechanisms involved in this common blinding condition.

ARTICLE

Received 4 Dec 2013 | Accepted 4 Aug 2014 | Published 22 Sep 2014

Henrit Springelkamp1,2,*, Ren Hhn3,*, Aniket Mishra4,*, Pirro G. Hysi5,*, Chiea-Chuen Khor6,7,*, Stephanie J. Loomis8,*,Jessica N. Cooke Bailey9,10, Jane Gibson11, Gudmar Thorleifsson12, Sarah F. Janssen13, Xiaoyan Luo14, Wishal D. Ramdas1, Eranga Vithana6,15,16,Monisha E. Nongpiur6,15, Grant W. Montgomery17, Liang Xu18,19, Jenny E. Mountain20, Puya Gharahkhani4, Yi Lu4, Najaf Amin2, Lennart C. Karssen2, Kar-Seng Sim7, Elisabeth M. van Leeuwen2, Adriana I. Iglesias2, Virginie J.M. Verhoeven1,2, Michael A. Hauser21, Seng-Chee Loon6, Dominiek D.G. Despriet1, Abhishek Nag5, Cristina Venturini5,22, Paul G. Sanlippo23, Arne Schillert24, Jae H. Kang25, John Landers26, Fridbert Jonasson27,28, Angela J. Cree29,Leonieke M.E. van Koolwijk2, Fernando Rivadeneira2,30,31, Emmanuelle Souzeau26, Vesteinn Jonsson28, Geeta Menon32, Blue Mountains Eye StudyGWAS groupw, Robert N. Weinreb33, Paulus T.V.M. de Jong2,34,35,36, Ben A. Oostra37, Andr G. Uitterlinden2,30,31, Albert Hofman2,31, Sarah Ennis38, Unnur Thorsteinsdottir12,27,

Kathryn P. Burdon26, NEIGHBORHOOD Consortiumz, Wellcome Trust Case Control Consortium 2 (WTCCC2)y, Timothy D. Spector5, Alireza Mirshahi3, Seang-Mei Saw6,15,16,39, Johannes R. Vingerling1,2, Yik-Ying Teo39,40, Jonathan L. Haines9,10, Roger C.W. Wolfs1, Hans G. Lemij41, E-Shyong Tai16,39,42,

Nomdo M. Jansonius43, Jost B. Jonas18,44, Ching-Yu Cheng6,15,16, Tin Aung6,15, Ananth C. Viswanathan45, Caroline C.W. Klaver1,2, Jamie E. Craig26,Stuart Macgregor4, David A. Mackey23,46, Andrew J. Lotery29, Kari Stefansson12,27, Arthur A.B. Bergen13,35,47, Terri L. Young14, Janey L. Wiggs8, Norbert Pfeiffer3,||, Tien-Yin Wong6,15,16,||, Louis R. Pasquale8,25,||, Alex W. Hewitt23,||, Cornelia M. van Duijn2,|| & Christopher J. Hammond5,||

Glaucoma is characterized by irreversible optic nerve degeneration and is the most frequent cause of irreversible blindness worldwide. Here, the International Glaucoma Genetics Consortium conducts a meta-analysis of genome-wide association studies of vertical cup-disc ratio (VCDR), an important disease-related optic nerve parameter. In 21,094 individuals of European ancestry and 6,784 individuals of Asian ancestry, we identify 10 new loci associated with variation in VCDR. In a separate risk-score analysis of ve case-control studies, Caucasians in the highest quintile have a 2.5-fold increased risk of primary open-angle glaucoma as compared with those in the lowest quintile. This study has more than doubled the known loci associated with optic disc cupping and will allow greater understanding of mechanisms involved in this common blinding condition.

1 Department of Ophthalmology, Erasmus Medical Center, Rotterdam 3000 CA, The Netherlands. 2 Department of Epidemiology, Erasmus Medical Center, Rotterdam 3000 CA, The Netherlands.

3 Department of Ophthalmology, University Medical Center Mainz, Mainz 55131, Germany. 4 Department of Genetics and Computational Biology, Statistical Genetics, QIMR Berghofer Medical Research Institute, Royal Brisbane Hospital, Brisbane, Queensland 4006, Australia. 5 Department of Twin Research and Genetic Epidemiology, Kings College London, London WC2R 2LS, UK. 6 Department of Ophthalmology, National University of Singapore and National University Health System, Singapore 119077, Singapore. 7 Division of Human Genetics, Genome Institute of Singapore, Singapore 138672, Singapore. 8 Department of Ophthalmology, Harvard Medical School and Massachusetts Eye and Ear Inrmary, Boston, Massachusetts 02114, USA. 9 Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA. 10 Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio 44106, USA. 11 Centre for Biological Sciences, Faculty of Natural and Environmental Sciences, University of Southampton, Southampton SO17 1BJ, UK. 12 deCODE/Amgen, Reykjavik 101, Iceland. 13 Department of Clinical and Molecular Ophthalmogenetics, The Netherlands Institute for Neuroscience (NIN), Royal Netherlands Academy of Arts and Sciences (KNAW), Amsterdam 1105 BA, the Netherlands. 14 Department of Ophthalmology, Duke University Eye Center, Durham, North Carolina 27710, USA. 15 Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore. 16 Duke-National University of Singapore, Graduate Medical School, Singapore 169857, Singapore. 17 Department of Genetics and Computational Biology, Molecular Epidemiology Laboratory, QIMR Berghofer Medical Research Institute, Royal Brisbane Hospital, Brisbane, Queensland 4006, Australia. 18 Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China. 19 Beijing Ophthalmology and Visual Science Key Lab, Beijing 100730, China. 20 Telethon Institute for Child Health Research, Subiaco, Western Australia 6008, Australia. 21 Departments of Medicine and Ophthalmology, Duke University Medical Center, Durham, North Carolina 27710, USA. 22UCL Institute of Ophthalmology, London EC1V 9EL, UK.

23 Centre for Eye Research Australia (CERA), University of Melbourne, Royal Victorian Eye and Ear Hospital, Melbourne, Victoria 3002, Australia. 24 Institute of Medical Biometry and Statistics, University of Lbeck, Lbeck 23562, Germany. 25 Department of Medicine, Channing Division of Network Medicine, Brigham and Womens Hospital, Boston, Massachusetts 02115, USA. 26Department of Ophthalmology, Flinders University, Adelaide, South Australia 5042, Australia. 27 Faculty of Medicine, University of Iceland, Reykjavik 101, Iceland. 28Department of Ophthalmology, Landspitali National University Hospital, Reykjavik 101, Iceland. 29Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO17 1BJ, UK. 30 Department of Internal Medicine, Erasmus Medical Center, Rotterdam 3000 CA, The Netherlands. 31 Netherlands Consortium for Healthy Ageing, Netherlands Genomics Initiative, The Hague 2593 CE, The Netherlands. 32 Department of Ophthalmology, Frimley Park Hospital NHS Foundation Trust, Frimley GU16 7UJ, UK. 33Department of Ophthalmology and Hamilton Glaucoma Center, University of California, San Diego, California 92093, USA. 34 Department of Retinal Signal Processing, Netherlands Institute for Neuroscience, Amsterdam 1105 BA, The Netherlands. 35Department of Ophthalmology, Academic Medical Center, Amsterdam 1105 AZ, The Netherlands. 36 Department of Ophthalmology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands. 37Department of Clinical Genetics, Erasmus Medical Center, Rotterdam 3000 CA, The Netherlands. 38 Human Development and Health, Faculty of Medicine, University of Southampton, Southampton SO17 1BJ, UK. 39Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117597, Singapore. 40 Department of Statistics and Applied Probability, National University of Singapore, Singapore 119077, Singapore. 41 Glaucoma Service, The Rotterdam Eye Hospital, Rotterdam 3011 BH, The Netherlands. 42 Department of Medicine, National University of Singapore and National University Health System, Singapore 119077, Singapore. 43 Department of Ophthalmology, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands. 44 Department of Ophthalmology, Medical Faculty Mannheim of the Ruprecht-Karls-University of Heidelberg, Seegartenklinik Heidelberg, Heidelberg 69117, Germany. 45 NIHR Biomedical Research Centre, Moorelds Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London EC1V 2PD, UK. 46 Centre for Ophthalmology and Visual Science, Lions Eye Institute, University of Western Australia, Perth, Western Australia 6009, Australia. 47 Department of Clinical Genetics, Academic Medical Center, Amsterdam 1105 AZ, The Netherlands. * These authors contributed equally to this work. w Membership of the Blue

Mountains Eye StudyGWAS group is listed at the end of the paper. z Membership of the NEIGHBORHOOD Consortium is listed at the end of the paper. y Membership of the WTCCC2 is listed at the

end of the paper. || These authors jointly supervised this work. Correspondence and requests for materials should be addressed to C.M.v.D. (email: mailto:[email protected]

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DOI: 10.1038/ncomms5883 OPEN

Meta-analysis of genome-wide association studies identies novel loci that inuence cupping and the glaucomatous process

ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms5883

Optic nerve degeneration caused by glaucoma is the most common cause of irreversible blindness worldwide1. Glaucomatous optic neuropathy is recognized by

changes in the morphology of the optic nerve head, or optic disc, caused by loss of retinal ganglion cells and thinning of the retinal nerve bre layer. In glaucoma, the nerve bre layer typically thins in the superior and inferior regions of the nerve creating a vertically elongated depression (the cup). The ratio of the cup to the overall nerve size (the disc), called the vertical cup-disc ratio (VCDR), is a key factor in the clinical assessment and follow-up of patients with glaucoma. VCDR has been shown to be heritable with h2 scores ranging between 0.48 and 0.6627. At least seven loci have been associated with VCDR in previous genome-wide association studies (GWAS) and three of these were subsequently implicated in primary open-angle glaucoma (POAG)811. So far, the explained variance of open-angle glaucoma by age, sex, intraocular pressure and established POAG genes is still small (46%)12. As with other complex diseases, large sample sizes are needed to ensure sufcient power to fully dene the underlying genetic architecture.

Here, we report the largest genome-wide meta-analysis for VCDR, with data from 14 studies from Europe, the United States, Australia and Asia, as part of the International Glaucoma Genetics Consortium. The aim of the study is to identify loci associated with VCDR, and to determine whether these variants are also associated with glaucoma.

We perform the meta-analysis in four stages. In the rst stage, we meta-analyse summary data from 10 populations of European ancestry comprising 21,094 individuals. In the second stage, we test the cross-ancestry transferability of the statistically genome-wide-signicant associations from the rst stage in 6,784 individuals from four Asian cohorts. In the third stage, we examine whether the associations are independent of disc area and/or spherical equivalent. We also combine the genome-wide-signicant effects into a genetic risk score and associate this score with the POAG risk in ve populations. Finally, we perform gene-based tests and pathway analysis.

We nd 10 new loci associated with VCDR, which together increase the risk on POAG 2.5 times. Our ndings will help us to unravel the pathogenesis of glaucoma.

ResultsMeta-analysis of GWAS. In stage 1, we analysed B2.5 million

HapMap stage 2 single-nucleotide polymorphisms (SNPs) either directly genotyped or imputed in 21,094 subjects of European ancestry (Supplementary Fig. 1; Supplementary Table 1; Supplementary Methods). The ination factors (l)

varied between 0.98 and 1.12, implying adequate within-study control of population substructure (Supplementary Table 2; Supplementary Figs 2 and 3). The overall l was 1.05. This analysis yielded 440 genome-wide-signicant SNPs (Po5.0 10 8)

located across 15 chromosomal regions (Table 1; Supplementary Fig. 4a). In stage 2, we investigated the SNP with the strongest association at each region in the Asian populations and found that eight were nominally signicant (Po0.05) with an effect in the same direction and generally the same order of magnitude (Table 1; Supplementary Fig. 4b). Five of the seven loci that did not reach nominal signicance in those of Asian descent had a similar effect in the same direction. Supplementary Table 3 shows the most signicant SNPs in Asians within 100,000 base pairs from the most signicant associated SNP in Europeans. Meta-analysis of only the Asian populations did not result in new genome-wide-signicant ndings. The combined analysis of the European and Asian populations resulted in three additional genome-wide-signicant associations

on chromosomes 1, 6 and 22 (Table 1; Fig. 1). The level of heterogeneity across the samples are shown in Table 1. Of the 18 genome-wide-signicant loci, 10 are novel for the VCDR outcome (COL8A1, DUSP1, EXOC2, PLCE1, ADAMTS8, RPAP3, SALL1, BMP2, HSF2 and CARD10) (Supplementary Fig. 5). There were no signicant differences in terms of allele frequencies across the different cohorts (Supplementary Table 4). The effect estimates from the participating cohorts appear not to be inuenced by main demographic characteristics, such as mean age and sex ratio (Supplementary Fig. 6).

Adjustment for disc area and spherical equivalent. Four of the 18 genome-wide-signicant loci have been previously associated with optic disc area (CDC7/TGFBR3, ATOH7, SALL1 and CARD10)10,13. Because the size of the optic nerve varies between individuals and is correlated to the VCDR14, we adjusted the association to VCDR for optic nerve (disc) area. This resulted in a reduced effect size and signicance (P 3.48 10 11 to

P 9.00 10 3) at the CDC7TGFBR3 locus, suggesting the

VCDR association at this locus is explained primarily by its known association with disc area (Supplementary Table 5ac). A similar reduction in effect was seen for ATOH7. However, for this locus there remains a signicant disc-area-independent effect (P 7.28 10 9). There was no change in association

signicance for any of the 10 new loci reported here, suggesting

they do not act primarily on disc area.

It is of interest that two genes (SIX6 and BMP2) overlap with those implicated in myopia15, an important risk factor for POAG16. The correlation between VCDR and spherical equivalent is low (Supplementary Table 6), and adjusting for spherical equivalent did not lead to any major changes in the effects for these or other loci in European populations (Supplementary Table 7a), suggesting a joint genetic aetiology for POAG and myopia. In Asian cohorts, the direction of effect on VCDR at the chromosome 11 locus (MIR612-SSSCA1 region) was not consistent with the European populations (Supplementary Table 7b). However, after adjusting for spherical equivalent the direction of effect on VCDR was similar to both populations. At the BMP2 myopia locus, we observed a large difference in allele frequency between those of European and Asian ancestry (Table 1), which may explain the difference in effect direction.

Risk for POAG. The 18 loci, together with age and sex, explain5.15.9% of the VCDR phenotypic variability in Europeans (measured in the Rotterdam Study I, II and III), of which 1.61.8% is explained by the new loci. The phenotypic variability explained by all common SNPs is 4153% in these cohorts, which is in line with the heritability estimates from family-based studies. In addition to conrming the previously published CDKN2BAS and SIX1/6 POAG risk loci, we found nominally signicant (Po0.05) associations with POAG for six newly identied genetic variants (P 8.1 10 5 from binomial test for chance of seeing

six or more such nominally signicant associations in 16 tests) (Supplementary Table 8), with odds ratios varying between 0.73 and 1.20. In the combined case-control studies, we found that the sum of all effects of these genes increased the risk of POAG2.5-fold (Supplementary Table 9) for those in the highest quintile compared with those in the lowest quintile.

Gene-based test. To identify new loci not previously found through individual SNP-based tests, we performed gene-based tests using VEGAS software17. Because of the smaller number of tests (17,872 genes tested), our gene-based signicance threshold is Pgene-basedo0.05/17,872 2.80 10 6. In addition to the SNPs

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Table 1 | Summary of the results of the meta-analyses of genome-wide association studies.

Caucasians (n = 21,094) Asians (n = 6,784) Combined (n = 27,878)

[afii9826] s.e. P value P value

hetero

geneity I

rs4658101 1 91849997 CDC7/TGFBR3 intergenic a/g 0.18 0.015 0.002 8.80E14 9.34E02 0.14 0.016 0.005 3.13E03 4.26E01 0.015 0.002 1.06E15 1.68E01 0.54 rs2623325 3 100614445 COL8A1 intergenic a/c 0.13 0.018 0.003 7.05E09 5.62E02 0.16 0.011 0.005 1.46E02 3.43E01 0.016 0.003 6.61E10 7.01E02 0.42 rs17658229 5 172123657 DUSP1 intergenic c/t 0.05 0.020 0.004 8.06E09 5.95E01 0.00 0.086 0.133 5.17E01 ** 0.020 0.004 8.06E09 5.95E01 0 rs17756712 6 570071 EXOC2 intronic g/a 0.18 0.010 0.002 1.98E08 6.74E01 0.14 0.011 0.005 1.76E02 4.05E01 0.010 0.002 1.13E09 7.23E01 0 rs7865618 9 22021005 CDKN2BAS intronic g/a 0.43 0.013 0.001 2.80E20 8.93E01 0.15 0.021 0.005 8.11E06 3.31E01 0.013 0.001 4.97E24 6.97E01 0 rs1900005 10 69668061 ATOH7 intergenic a/c 0.23 0.019 0.002 7.21E31 2.96E04 0.32 0.010 0.004 2.08E02 1.58E01 0.018 0.002 5.51E31 8.54E05 0.69 rs7072574 10 96026296 PLCE1 intronic a/g 0.33 0.009 0.002 6.17E09 1.09E01 0.38 0.007 0.003 4.80E02 8.18E01 0.009 0.001 1.02E09 2.56E01 0.18 rs1346 11 65093827 SSSCA1 5upstream t/a 0.19 0.014 0.002 2.54E15 7.49E01 0.16 0.003 0.005 5.23E01 7.19E01 0.012 0.002 4.89E13 1.51E01 0.28 rs4936099 11 129785935 ADAMTS8 intronic c/a 0.42 0.009 0.002 6.38E09 8.31E01 0.09 0.007 0.009 4.15E01 1.14E01 0.009 0.002 4.61E09 6.79E01 0 rs11168187 12 46330278 RPAP3 intergenic g/a 0.16 0.009 0.002 2.96E08 1.00E+00 0.18 0.005 0.004 2.80E01 6.19E01 0.009 0.002 2.96E08 9.98E01 0 rs10862688 12 82447043 TMTC2 intergenic g/a 0.45 0.008 0.001 1.24E11 4.80E02 0.56 0.004 0.003 2.48E01 1.20E01 0.008 0.001 1.49E11 2.61E02 0.44 rs4901977 14 59858929 SIX1/6 intergenic t/c 0.31 0.010 0.002 1.98E11 7.86E01 0.53 0.017 0.003 2.64E07 3.82E02 0.011 0.001 2.13E16 2.02E01 0.22 rs1345467 16 50039822 SALL1 intergenic g/a 0.27 0.010 0.002 2.70E12 1.68E01 0.13 0.011 0.006 5.53E02 4.13E01 0.010 0.001 4.19E13 2.48E01 0.18 rs6054374 20 6526556 BMP2 intergenic t/c 0.42 0.009 0.002 1.79E08 1.26E01 0.72 0.001 0.004 8.66E01 5.99E01 0.007 0.001 1.69E07 8.19E02 0.37 rs1547014 22 27430711 CHEK2 intronic t/c 0.30 0.013 0.001 2.98E18 1.93E01 0.17 0.013 0.004 4.26E03 8.11E01 0.013 0.001 4.77E20 3.90E01 0.06

rs301801 1 8418532 RERE intronic c/t 0.33 0.008 0.001 1.61E07 2.46E02 0.13 0.012 0.005 2.59E02 5.38E01 0.008 0.001 1.66E08 5.23E02 0.39 rs868153 6 122431654 HSF2 intergenic g/t 0.36 0.007 0.001 5.08E06 9.27E01 0.39 0.013 0.003 1.44E04 4.96E01 0.007 0.001 1.39E08 7.96E01 0 rs5756813 22 36505423 CARD10 intergenic g/t 0.39 0.006 0.001 1.60E05 8.22E01 0.32 0.017 0.004 1.71E06 1.84E01 0.008 0.001 7.73E09 1.98E01 0.22

Chr., chromosome; MAF, minor allele frequency; SNP, single-nucleotide polymorphism.

Summary of SNPs that showed genome-wide-signicant (Po5 10

SNP Ch

r.

Position Nearest Gene Annotation A1

/

A2

MAF [afii9826][afii9826] s.e. P value P value

hetero

geneity

MAF

*

[afii9826] s.e. P value P value

hetero

geneity

8) association with vertical cup-disc ratio (VCDR) in subjects of European ancestry (stage 1), with results of replication in Asians (stage 2) and the additional SNPs that showed genome-wide-signicant (Po5 10

8) association in the combined analysis (stage 3) (P values were calculated by using the z-statistic). We tested for heterogeneous effects between the Asian and European ancestry samples, for which P values are shown (Cochrans Q-test). Nearest gene, reference NCBI build 37; A1, reference allele; A2, other allele; MAF, average minor allele frequency; b, effect size on VCDR based on allele A1; s.e., s.e. of the effect size. The last three rows indicate the SNPs that reached genome-wide signicance in the combined analysis, but not in stage 1 or stage 2.

*Note that, for the sake of keeping the same reference allele, MAF values may be 40.50 in the Asian populations.**For this SNP, only one Asian study is contributing to the meta-analysis, so the P value for heterogeneity could not be calculated for this SNP in stage 2.

30

25

20

15

10

ATOH7

CDKN2BAS

log 10(p)

SIX6

SSSCA1

PLCE1

SALL1

CARD10CHEK2

COL8A1

TMTC2

RERE

CDC7/TGFBR3

DUSP1

EXOC2

HSF2

ADAMTS8

RPAP3

BMP2

5

0

1

3 4 5 6 7 8 9 10

11

12

13

14

15

16

17

18

19

20

21

22

2

Chromosome

Figure 1 | Manhattan plot of the GWAS meta-analysis for vertical cup-disc ratio in the combined analysis (n 27,878). The plot shows log10-

transformed P values for all SNPs (z-statistic). The red-dotted horizontal line represents the genome-wide signicance threshold of Po5.0 10 8; the

blue-dotted line indicates P value of 1 10 5.

identied as signicant (Po5 10 8) in a SNP-based test,

we also found two new genes signicantly associated with VCDR using the VEGAS gene-based test (Supplementary Table 10). These were REEP5 (P 7.48 10 7) and PITPNB

(P 4.89 10 7). PITPNB is B800 kb from another gene with a

signicant SNP association (CHEK2, rs1547014) (Supplementary Fig. 7). Although the association signal centred over CHEK2

extends a long distance towards PITPNB, a separate association peak over PITPNB can be observed, which is unrelated (no linkage disequilibrium (LD)) to the CHEK2 peak. The results we obtained using the specied denition of the gene unit were substantially the same when alternative cutoff points from the transcription initiation and end sites were used (Supplementary Table 11). The REEP5 gene showed no association with POAG

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(Supplementary Table 12). The PITPNB gene showed evidence for association with POAG in Australian & New Zealand Registry of Advanced Glaucoma (ANZRAG) (P 0.03) in the gene-based

test, with a best single SNP P value of 0.003, but this was not conrmed in two other studies.

Pathway analysis. To test whether gene-based statistics identied were enriched in 4,628 pre-specied Gene Ontology pathways, we performed pathway analysis using Pathway-VEGAS18. We used a pathway-wide signicance threshold to be1.08 10 5 (0.05/4,628). The only pathway exceeding the

pathway-wide signicance level was negative regulation of cyclin-dependent protein kinase activity (Supplementary Table 13). The second top-pathway negative regulation of epithelial cell proliferation is related to the top pathway, both suggesting retardation of cell growth. The negative regulation of cyclin-dependent protein kinase activity nding was driven not only by the result at the CDKN2A locus but also by the result at APC, a gene close to REEP5.

Regulatory elements and expression data. Six of the 18 most associated SNPs are located in DNase I hypersensitivity sites (Supplementary Table 14). The retinal pigment epithelium has the highest signal of all 125 available cell lines in one of these DNase I hypersensitivity sites. Thus, these results are suggesting that some of the SNPs may have their effect on VCDR by altering regulatory functions. We investigated the expression of the genes implicated in VCDR by these analyses in human ocular gene expression databases or the published literature. Most of these genes are expressed in eye tissues, including the optic nerve (Supplementary Tables 15 and 16).

DiscussionThis study reports 10 novel loci associated with VCDR, with an additional two loci identied using gene-based testing. Pathway analysis suggests retardation of cell growth as a major biological mechanism. The results for the most associated pathways negative regulation of cyclin-dependent protein kinase activity and negative regulation of epithelial cell proliferation are primarily driven by the CDKN2A and CDKN2B genes, respectively, but in both pathways the gene-based result at APC (P 7.20 10 5 in

Caucasians and P 8.80 10 3 in Asians) also contributes to the

pathway result. The APC gene has previously been reported to be a critical gene regulating retinal pigment epithelium proliferation and development19. These results add to our earlier ndings on the role of growth and the transforming growth factor beta (TGFB) pathways in VCDR10. Various new genes fall into these pathways. The protein encoded by the BMP2 (bone morphogenetic protein 2) gene on chromosome 20 belongs to the TGFB super-family. Two other new genes regulate apoptosis: RPAP3 (RNA polymerase II-associated protein 3) on chromosome 1220 and CARD10, a gene that was previously found to be associated with disc area13. Another new VCDR association previously associated with disc area is SALL110. This gene is implicated in ocular development.

Our ndings offer new insights in the aetiology of optic nerve degeneration. COL8A1 (collagen, type VIII, alpha 1) is part of a collagen pathway recently implicated in corneal thickness18, an ocular trait also associated with glaucoma risk. Missense mutations in COL8A2 (collagen, type VIII, alpha2) were found in POAG patients with a very thin central corneal thickness (CCT)21. The collagen SNP (rs2623325) was not signicantly associated with CCT (in Caucasians: b 0.044, P 0.19;

in Asians: b 0.007, P 0.89) or intraocular pressure (in

Caucasians and Asians combined: b 0.02, P 0.73) in

largely the same cohorts18,22, suggesting that the collagen involvement in VCDR is not due to the inuence by CCT or intraocular pressure. We also found several genes involved in cellular stress response. DUSP1 (dual specicity phosphatase 1) is the nearest gene to the most strongly associated SNP on chromosome 5. This gene, inducible by oxidative stress and heat shock, may play a role in environmental stress response23, and may also participate in the negative regulation of cellular proliferation. HSF2 (heat shock transcription factor 2), one of the genes at the chromosome 6 locus, also is part of the cellular stress response pathway. Deciency of this factor causes various central nervous system defects in mice24,25. Another pathway emerging in this study is that of exocytosis. The SNP on the other chromosome 6 locus is located in EXOC2 (exocyst complex component 2). The encoded protein is one of the eight proteins of the exocyst complex26. This multi-protein complex is important for directing exocytic vesicles to the plasma membrane, a mechanism that also has been implicated in neuronal degeneration in the brain27. Lipid metabolism emerges as another pathway. The gene on chromosome 10, PLCE1 (phospholipase C, epsilon 1), belongs to the phospholipase C family, which plays a role in the generation of second messengers28. Various processes affecting cell growth, differentiation and gene expression are regulated by these second messengers. From a clinical perspective, the ndings on ADAMTS8 are of interest. ADAMTS enzymes have different functions, including the formation and turnover of the extracellular matrix29. Strikingly, a variant in ADAMTS10 has been linked to a form of glaucoma in dogs30,31.

In summary, we have now identied 10 novel loci associated with cupping of the optic nerve, a key determinant of glaucoma. Together, these genetic risk variants increased the risk of POAG in case-control validation studies. Pathway analysis implicated negative regulation of cell growth and cellular response to environmental stress as key pathological pathways in glaucoma, and that novel therapies targeting these pathways may be neuro-protective in glaucoma.

Methods

Study design. We performed a meta-analysis on directly genotyped and imputed SNPs from individuals of European ancestry in 10 studies, with a total of 21,094 individuals. Subsequently, we evaluated signicantly associated SNPs in 6,784 subjects of Asian origin including four different studies and performed a meta-analysis on all studies combined.

Subjects and phenotyping. All studies included in this meta-analysis are part of the International Glaucoma Genetics Consortium. The ophthalmological examination of each study included an assessment of the optic nerve head to measure the VCDR (Supplementary Table 17a). Unreliable optic nerve datawere excluded.

The meta-analysis of stage 1 was based on 10 studies of European ancestry: Brisbane Adolescent Twin Study, Blue Mountains Eye Study, Erasmus Rucphen Family Study, Gutenberg Health Study (GHS I/GHS II), Glaucoma Genes and Environment (controls only), National Eye Institute Glaucoma Human Genetics Collaboration (NEIGHBOR; controls only), Raine Study, Rotterdam Study (RS-I/RS-II/RS-III), Twins Eye Study in Tasmania and TwinsUK. Stage 2 comprised four Asian studies: Beijing Eye Study, Singapore Chinese Eye Study, Singapore Malay Eye Study and Singapore Indian Eye Study. For each SNP with the strongest association at each locus the association with POAG was tested in ve case-control studies: ANZRAG, deCODE, Massachusetts Eye and Ear Inrmary, NEIGHBOR and Southampton.

Information on general methods, demographics, phenotyping and genotyping methods of the study cohorts can be found in Supplementary Tables 1 and 17 and the Supplementary Note. All studies were performed with the approval of their local medical ethics committee, and written informed consent was obtained from all participants in accordance with the Declaration of Helsinki.

Genotyping and imputation. Information on genotyping in each cohort and the particular platforms used to perform genotyping can be found in more detail in Supplementary Table 17b. To produce consistent data sets and enable a meta-analysis of studies across different genotyping platforms, the studies performed

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Regulatory functions. We used the ENCyclopedia Of DNA Elements42 data in the UCSC Genome Browser43 to look at DNase I hypersensitivity sites and other functional elements.

Gene expression in human eye tissue. We examined the expression of genes that reached signicance in the individual SNP-based test or gene-based test. We used published literature or human ocular gene expression databases (Supplementary Tables 15 and 16).

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genomic imputation on available HapMap Phase 2 genotypes with MACH32 or IMPUTE33, using the appropriate ancestry groups as templates.

Each study applied stringent quality control procedures before imputation, including minor allele frequency cutoffs, HardyWeinberg equilibrium, genotypic success rate, mendelian inconsistencies, exclusion of individuals with 45% shared ancestry (exception made for family-based cohorts in which due adjustment for family relationship was made) and removal of all individuals whose ancestry as determined through genetic analysis did not match the prevailing ancestry group of the corresponding cohort (Supplementary Note). SNPs with low imputation quality were ltered using metrics specic to the imputation method and thresholds used in previous GWAS analyses. For each cohort, only SNPs with imputation quality scores 40.6 (proper-info of IMPUTE) or R240.6 (MACH) were included into the meta-analysis.

Statistical analysis. In subjects drawn from their respective populations in which the prevalence of glaucomatous changes is relatively low, the correlation between left and right eye is high34. Therefore, we used the mean VCDR of both eyes. In cases of missing or unreliable data for one eye, data of the other eye was taken. Each individual study did a linear regression model between the VCDR and the SNPs under the assumption of an additive model for the effect of the risk allele. Analyses were adjusted for age, sex and the rst two principal components (for population-based studies) or family structure (for family-based studies). Secondary analyses were done with adjustments for disc area or spherical equivalent. In the Rotterdam Studies, we calculated the phenotypic variability explained by the new loci, and explained by all common SNPs using the Genome-wide Complex Trait Analysis tool35,36.

We performed an inverse variance weighted xed-effect meta-analysis. This was performed with METAL software37. P values for the association results were calculated by using the z-statistic. P values for heterogeneity were calculated by using the Cochrans Q-test for heterogeneity. In addition to this, I2 values were calculated to assess heterogeneity38. Fst values were calculated to assess the genetic variation due to subdivision of populations. All study effect estimates were corrected using genomic control and were oriented to the positive strand of the NCBI Build 36 reference sequence of the human genome, which was the genomic build on which most available genotyping platforms were based. Coordinates and further annotations for the SNPs were converted into Build 37, the most recent version of the available builds at the time of this study.

In stage 1, a P value o5.0 10 8 (the genome-wide threshold of association)

was considered signicant. In stage 2, a P value o0.05 was considered signicant. Manhattan, regional and forest plots were made using R39, LocusZoom40 and Stata/

SE 12.0 (StataCorp LP, College Station, TX, USA).

Risk-score models. In ve case-control studies, a weighted genetic risk score per individual was calculated. Standardized regression coefcients were used as weighting factor. The weighted risk scores were divided into quintiles. Odds ratios were calculated for each quintile, using the rst quintile as a reference.

Gene-based test using VEGAS. There are different gene-based tests of which VEGAS is one of the most powerful tests41. We therefore performed gene-based testing using VEGAS software17, which combines the test statistics of all SNPs present within and 50 kb upstream/downstream of each gene. LD between the markers is accounted for through simulations from the multivariate normal distribution, based on estimates of LD from reference populations. Since Asian and European ancestry populations show different LD patterns, we performed separate gene-based tests for each population. Hapmap 2 CEU population was used as a reference to calculate LD for European ancestry data, whereas Hapmap 2 JPT and CHB combined population was used as a reference for Asian ancestry data. After calculation of gene-based test statistics for Asian and European ancestry populations separately, meta-analysis was conducted using Fishers method for combining P values. VEGAS was applied to the summary data from the full VCDR analysis (as in Table 1) and to three of the POAG data sets; ANZRAG, Massachusetts Eye and Ear Inrmary glaucoma clinic and Glaucoma Genes and Environment (Supplementary Note).

Pathway-analysis using pathway-VEGAS. Pre-specied pathways from the Gene Ontology database with size ranging in 5500 genes were used to perform pathway analysis. Pathway-VEGAS combines VEGAS gene-based test statistics based on prespecied biological pathways18. Pathway P values were computed by summing w2-test statistics derived from VEGAS P values. Empirical VEGAS-pathway P values for each pathway were computed by comparing the real-data-summed w2-test statistics with 500,000 simulations where the relevant number (as per size of pathway) of randomly drawn w2-test statistics was summed. To ensure clusters of genes did not adversely affect results, within each pathway, gene sets were pruned such that each gene was 4500 kb from all other genes in the pathway. Where required, all but one of the clustered genes was dropped at random when genes were clustered. Pathway-VEGAS was performed separately for European and Asian ancestry data sets. Meta-analysis was conducted using Fishers method for combining P values.

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Acknowledgements

We gratefully thank the invaluable contributions of all study participants and staff at the recruitment centers. Complete funding information and acknowledgements for each individual study can be found in the Supplementary Note.

Author contributions

H.S., R.H., A.Mishra, P.G.H., C.-C.K. and S.J.L. contributed equally to this work. N.P.,T.-Y.W., L.R.P., A.W.H., C.M.v.D. and C.J.H. jointly supervised this work. H.S., R.H., P.G.H.,T.-Y.W., L.R.P., A.W.H., C.M.v.D. and C.J.H. performed analyses and drafted the manuscript. J.B.J., A.C.V., C.C.W.K., J.E.C., S.M., D.A.M., A.J.L., J.L.W., N.P., T.-Y.W., L.R.P., A.W.H., C.M.v.D. and C.J.H. jointly conceived the project and supervised the work. W.D.R., E.V., M.E.N., G.W.M., L.X., J.E.M., Y.L., N.A., L.C.K., K.-S.S., E.M.v.L., A.I.I., V.J.M.V., M.A.H., S.-C.L., D.D.G.D., A.N., C.V., P.G.S., A.S., J.H.K., J.L., F.J., A.J.C., L.M.E.v.K., F.R., E.S., V.J., G.M., R.N.W., P.T.V.M.d.J., B.A.O., A.G.U., A.H., S.E., T.D.S., A.Mirshahi, S.-M.S., J.R.V., Y.-Y.T., R.C.W.W., H.G.L., E.-S.T., N.M.J., C.-Y.C., T.A., Blue Mountains Eye Study-GWAS Group, NEIGHBORHOOD Consortium, and Wellcome Trust Case Control Consortium 2 (WTCCC 2) were responsible for study-specic data. H.S., S.J.L., J.N.C.B., J.G., G.T., P.G., U.T., K.P.B., J.L.H., J.E.C., A.J.L., K.S. and J.L.W. were involved in the genetic risk-score analysis. S.F.J., X.L., A.A.B.B. and T.L.Y. performed the data expression experiments.A.Mishra and S.M. were involved in pathway analyses. A.Mishra, C.-C.K., W.D.R., P.T.V.M.d.J., H.G.L., N.M.J., J.B.J., A.C.V., C.C.W.K., J.E.C., S.M., D.A.M., A.J.L. and J.L.W. critically reviewed the manuscript.

Additional information

Supplementary Information accompanies this paper at http://www.nature.com/naturecommunications

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Competing nancial interests: G.T., U.T. and K.S. are employees ofdeCODE genetics/Amgen. The remaining authors declare no competing nancial interests.

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How to cite this article: Springelkamp, H. et al. Meta-analysis of genome-wide association studies identies novel loci that inuence cupping and the glaucomatous process. Nat. Commun. 5:4883 doi: 10.1038/ncomms5883 (2014).

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Blue Mountains Eye StudyGWAS group

Paul Mitchell48, Jie Jin Wang48, Elena Rochtchina48, John Attia49, Rodney Scott49, Elizabeth G. Holliday49, Tien-Yin Wong50, Paul N. Baird50, Jing Xie50, Michael Inouye51, Ananth Viswanathan52, Xueling Sim53.

NEIGHBORHOOD Consortium

R. Rand Allingham54, Murray H. Brilliant55, Donald L. Budenz56, Jessica N. Cooke Bailey57,58, William G. Christen59, John Fingert60,61, David S. Friedman62, Douglas Gaasterland63, Terry Gaasterland64, Jonathan L. Haines57,58, Michael A. Hauser54,65, Jae Hee Kang66, Peter Kraft67, Richard K. Lee68, Paul R. Lichter69, Yutao Liu54,65,

Stephanie J. Loomis70, Sayoko E. Moroi69, Louis R. Pasquale66,70, Margaret A. Pericak-Vance71,Anthony Realini72, Julia E. Richards69, Joel S. Schuman73, William K. Scott71, Kuldev Singh74, Arthur J. Sit75, Douglas Vollrath76, Robert N. Weinreb77, Janey L. Wiggs70, Gadi Wollstein73, Donald J. Zack62, Kang Zhang77.

Wellcome Trust Case-Control Consortium 2 (WTCCC2)

Management Committee: Peter Donnelly (Chair)78,79, Ines Barroso (Deputy Chair)80, Jenefer M. Blackwell81,82, Elvira Bramon83, Matthew A. Brown84, Juan P. Casas85, Aiden Corvin86, Panos Deloukas80, Audrey Duncanson87, Janusz Jankowski88,89, Hugh S. Markus90, Christopher G. Mathew91, Colin N.A. Palmer92, Robert Plomin93, Anna Rautanen78, Stephen J. Sawcer94, Richard C. Trembath91, Ananth C. Viswanathan52, Nicholas W. Wood95.

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Data and Analysis Group: Chris C.A. Spencer78, Gavin Band78, Cline Bellenguez78, Colin Freeman78,Garrett Hellenthal78, Eleni Giannoulatou78, Matti Pirinen78, Richard Pearson78, Amy Strange78, Zhan Su78, Damjan Vukcevic78, Peter Donnelly78,79. DNA, Genotyping, Data QC and Informatics Group: Cordelia Langford80, Sarah E. Hunt80, Sarah Edkins80, Rhian Gwilliam80, Hannah Blackburn80, Suzannah J. Bumpstead80,Serge Dronov80, Matthew Gillman80, Emma Gray80, Naomi Hammond80, Alagurevathi Jayakumar80,Owen T. McCann80, Jennifer Liddle80, Simon C. Potter80, Radhi Ravindrarajah80, Michelle Ricketts80, Matthew Waller80, Paul Weston80, Sara Widaa80, Pamela Whittaker80, Ines Barroso80, Panos Deloukas80. Publications Committee: Christopher G. Mathew (Chair)92, Jenefer M. Blackwell81,82, Matthew A. Brown84, Aiden Corvin86, Chris C.A. Spencer78.

48Centre for Vision Research, Department of Ophthalmology and Westmead Millennium Institute, University of Sydney, Sydney, New South Wales, Australia.

49University of Newcastle, Newcastle, New South Wales, Australia. 50Department of Ophthalmology, Centre for Eye Research Australia, University of Melbourne, Melbourne, Florida, USA. 51Walter and Elisa Hall Institute of Medical Research, Melbourne, Victoria, Australia. 52NIHR Biomedical Research Centre, Moorelds Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK. 53National University of Singapore, Singapore, Singapore.

54Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina, USA. 55Center for Human Genetics, Marsheld Clinic Research Foundation, Marsheld, Wisconsin, USA. 56Department of Ophthalmology, University of North Carolina, Chapel Hill, North Carolina, USA. 57Center for Human Genetics Research, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. 58Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA. 59Department of Medicine, Brigham and Womens Hospital, Boston, Massachusetts, USA. 60Department of Ophthalmology, College of Medicine, University of Iowa, Iowa City, Iowa, USA. 61Department of Anatomy/Cell Biology, College of Medicine, University of Iowa, Iowa City, Iowa, USA. 62Wilmer Eye Institute, John Hopkins University, Baltimore, Maryland, USA. 63Eye Doctors of Washington, Chevy Chase, Maryland, USA.

64Scripps Genome Center, University of California at San Diego, San Diego, California, USA. 65Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA. 66Channing Division of Network Medicine, Brigham and Womens Hospital, Harvard Medical School, Boston, Massachusetts, USA. 67Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA. 68Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA. 69Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA.

70Department of Ophthalmology, Harvard Medical School and Massachusetts Eye and Ear Inrmary, Boston, Massachusetts, USA. 71Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, USA. 72Department of Ophthalmology, WVU Eye Institute, Morgantown, West Virginia, USA. 73Department of Ophthalmology, UPMC Eye Center, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. 74Department of Ophthalmology, Stanford University, Palo Alto, California, USA. 75Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota, USA. 76Department of Genetics, Stanford University, Palo Alto, California, USA. 77Department of Ophthalmology, Hamilton Eye Center, University of California, San Diego, California, USA. 78Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK. 79Dept Statistics, University of Oxford, Oxford OX1 3TG, UK.

80Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. 81Telethon Institute for Child Health Research, Centre for Child Health Research, University of Western Australia, 100 Roberts Road, Subiaco, Western Australia 6008, Australia. 82Cambridge Institute for Medical Research, University of Cambridge School of Clinical Medicine, Cambridge CB2 0XY, UK. 83Department of Psychosis Studies, NIHR Biomedical Research Centre for Mental Health at the Institute of Psychiatry, Kings College London and The South London and Maudsley NHS Foundation Trust, Denmark Hill, London SE5 8AF, UK. 84University of Queensland Diamantina Institute, Brisbane, Queensland, Australia. 85Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK and Department of Epidemiology and Public Health, University College London, London WC1E 6BT, UK. 86Neuropsychiatric Genetics Research Group, Institute of Molecular Medicine, Trinity College Dublin, Dublin 2, Ireland.

87Molecular and Physiological Sciences, The Wellcome Trust, London NW1 2BE, UK. 88Department of Oncology, Old Road Campus, University of Oxford, Oxford OX3 7DQ, UK, Digestive Diseases Centre, Leicester Royal Inrmary, Leicester LE7 7HH, UK. 89Centre for Digestive Diseases, Queen Mary University of London, London E1 2AD, UK. 90Clinical Neurosciences, St Georges University of London, London SW17 0RE, UK. 91Kings College London Department of Medical and Molecular Genetics, Kings Health Partners, Guys Hospital, London SE1 9RT, UK. 92Biomedical Research Centre, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK. 93Kings College London Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Denmark Hill, London SE5 8AF, UK. 94Department of Clinical Neurosciences, University of Cambridge, Addenbrookes Hospital, Cambridge CB2 0QQ, UK. 95Department of Molecular Neuroscience, Institute of Neurology, Queen Square, London WC1N 3BG, UK.

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