Glaucoma is the leading cause of irreversible blindness globally1. Despite its gravity, the disease is frequently undiagnosed in the community2. Raised intraocular pressure (IOP) is the most important risk factor for primary open-angle glaucoma (POAG)3,4. Here we present a meta-analysis of 139,555 European participants, which identified 112 genomic loci associated with IOP, 68 of which are novel. These loci suggest a strong role for angiopoietin-receptor tyrosine kinase signaling, lipid metabolism, mitochondrial function and developmental processes underlying risk for elevated IOP. In addition, 48 of these loci were nominally associated with glaucoma in an independent cohort, 14 of which were significant at a Bonferroni-corrected threshold. Regression-based glaucoma-prediction models had an area under the receiver operating characteristic curve (AUROC) of 0.76 in US NEIGHBORHOOD study participants and 0.74 in independent glaucoma cases from the UK Biobank. Genetic-prediction models for POAG offer an opportunity to target screening and timely therapy to individuals most at risk.

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  1. 1.

    Pascolini, D. & Mariotti, S. P. Global estimates of visual impairment: 2010. Br. J. Ophthalmol. 96, 614–618 (2012).

  2. 2.

    Topouzis, F. et al. Prevalence of open-angle glaucoma in Greece: the Thessaloniki Eye Study. Am. J. Ophthalmol. 144, 511–519 (2007).

  3. 3.

    de Voogd, S. et al. Incidence of open-angle glaucoma in a general elderly population: the Rotterdam Study. Ophthalmology 112, 1487–1493 (2005).

  4. 4.

    Leske, M. C. et al. Predictors of long-term progression in the early manifest glaucoma trial. Ophthalmology 114, 1965–1972 (2007).

  5. 5.

    Garway-Heath, D. F. et al. Latanoprost for open-angle glaucoma (UKGTS): a randomised, multicentre, placebo-controlled trial. Lancet 385, 1295–1304 (2015).

  6. 6.

    Sanfilippo, P. G., Hewitt, A. W., Hammond, C. J. & Mackey, D. A. The heritability of ocular traits. Surv. Ophthalmol. 55, 561–583 (2010).

  7. 7.

    Hysi, P. G. et al. Genome-wide analysis of multi-ancestry cohorts identifies new loci influencing intraocular pressure and susceptibility to glaucoma. Nat. Genet. 46, 1126–1130 (2014).

  8. 8.

    Springelkamp, H. et al. New insights into the genetics of primary open-angle glaucoma based on meta-analyses of intraocular pressure and optic disc characteristics. Hum. Mol. Genet. 26, 438–453 (2017).

  9. 9.

    Choquet, H. et al. A large multi-ethnic genome-wide association study identifies novel genetic loci for intraocular pressure. Nat. Commun. 8, 2108 (2017).

  10. 10.

    Bailey, J. N. C. et al. Genome-wide association analysis identifies TXNRD2, ATXN2 and FOXC1 as susceptibility loci for primary open-angle glaucoma. Nat. Genet. 48, 189–194 (2016).

  11. 11.

    Gharahkhani, P. et al. Common variants near ABCA1, AFAP1 and GMDS confer risk of primary open-angle glaucoma. Nat. Genet. 46, 1120–1125 (2014).

  12. 12.

    Chen, Y. et al. Common variants near ABCA1 and in PMM2 are associated with primary open-angle glaucoma. Nat. Genet. 46, 1115–1119 (2014).

  13. 13.

    Chan, M. P. Y. et al. Associations with intraocular pressure in a large cohort: results from the UK Biobank. Ophthalmology 123, 771–782 (2016).

  14. 14.

    Khawaja, A. P. et al. The EPIC-Norfolk Eye Study: rationale, methods and a cross-sectional analysis of visual impairment in a population-based cohort. BMJ Open 3, 1–10 (2013).

  15. 15.

    Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

  16. 16.

    Jiang, Z. et al. Hepatocyte growth factor genetic variations and primary angle-closure glaucoma in the Han Chinese population. PLoS One 8, e60950 (2013).

  17. 17.

    Khor, C. C. et al. Genome-wide association study identifies five new susceptibility loci for primary angle closure glaucoma. Nat. Genet. 48, 556–562 (2016).

  18. 18.

    Agarwal, R. & Agarwal, P. Newer targets for modulation of intraocular pressure: focus on adenosine receptor signaling pathways. Expert Opin. Ther. Targets 18, 527–539 (2014).

  19. 19.

    Wiggs, J. L. & Pasquale, L. R. Genetics of glaucoma. Hum. Mol. Genet. 26, R21–R27 (2017).

  20. 20.

    Souma, T. et al. Angiopoietin receptor TEK mutations underlie primary congenital glaucoma with variable expressivity. J. Clin. Invest. 126, 2575–2587 (2016).

  21. 21.

    Eklund, L., Kangas, J. & Saharinen, P. Angiopoietin-Tie signalling in the cardiovascular and lymphatic systems. Clin. Sci. 131, 87–103 (2017).

  22. 22.

    Kizhatil, K., Ryan, M., Marchant, J. K., Henrich, S. & John, S. W. M. Schlemm’s canal is a unique vessel with a combination of blood vascular and lymphatic phenotypes that forms by a novel developmental process. PLoS Biol. 12, e1001912 (2014).

  23. 23.

    Thomson, B. R. et al. A lymphatic defect causes ocular hypertension and glaucoma in mice. J. Clin. Invest. 124, 4320–4324 (2014).

  24. 24.

    Aspelund, A. et al. The Schlemm’s canal is a VEGF-C/VEGFR-3-responsive lymphatic-like vessel. J. Clin. Invest. 124, 3975–3986 (2014).

  25. 25.

    Romero, P., Sanhueza, F., Lopez, P., Reyes, L. & Herrera, L. c.194 A C (Q65P) mutation in the LMX1B gene in patients with nail-patella syndrome associated with glaucoma. Mol. Vis. 17, 1929–1939 (2011).

  26. 26.

    Ali, M. et al. Null mutations in LTBP2 cause primary congenital glaucoma. Am. J. Hum. Genet. 84, 664–671 (2009).

  27. 27.

    Marcos, S. et al. Meis1 coordinates a network of genes implicated in eye development and microphthalmia. Development 142, 3009–3020 (2015).

  28. 28.

    Hsieh, Y.-W., Zhang, X.-M., Lin, E., Oliver, G. & Yang, X.-J. The homeobox gene Six3 is a potential regulator of anterior segment formation in the chick eye. Dev. Biol. 248, 265–280 (2002).

  29. 29.

    Aldahmesh, M. A. et al. The syndrome of microcornea, myopic chorioretinal atrophy, and telecanthus (MMCAT) is caused by mutations in ADAMTS18. Hum. Mutat. 34, 1195–1199 (2013).

  30. 30.

    Cheng, C. Y. et al. Nine loci for ocular axial length identified through genome-wide association studies, including shared loci with refractive error. Am. J. Hum. Genet. 93, 264–277 (2013).

  31. 31.

    Larsson, M. et al. GWAS findings for human iris patterns: associations with variants in genes that influence normal neuronal pattern development. Am. J. Hum. Genet. 89, 334–343 (2011).

  32. 32.

    Khawaja, A. P. et al. Assessing the association of mitochondrial genetic variation with primary open-angle glaucoma using gene-set analyses. Invest. Ophthalmol. Vis. Sci. 57, 5046–5052 (2016).

  33. 33.

    MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017).

  34. 34.

    Marcus, D. M. et al. Sleep disorders: a risk factor for normal-tension glaucoma? J. Glaucoma 10, 177–183 (2001).

  35. 35.

    Carnes, M. U., Allingham, R. R., Ashley-Koch, A. & Hauser, M. A. Transcriptome analysis of adult and fetal trabecular meshwork, cornea, and ciliary body tissues by RNA sequencing. Exp. Eye Res. 167, 91–99 (2018).

  36. 36.

    Momont, A. C. & Mills, R. P. Glaucoma screening: current perspectives and future directions. Semin. Ophthalmol. 28, 185–190 (2013).

  37. 37.

    Luce, D. A. Determining in vivo biomechanical properties of the cornea with an ocular response analyzer. J. Cataract Refract. Surg. 31, 156–162 (2005).

  38. 38.

    Luce, D. Methodology for corneal compensated IOP and corneal resistance factor for an ocular response analyzer. Invest. Ophthalmol. Vis. Sci. 47, 2266 (2006).

  39. 39.

    van der Valk, R. et al. Intraocular pressure-lowering effects of all commonly used glaucoma drugs: a meta-analysis of randomized clinical trials. Ophthalmology 112, 1177–1185 (2005).

  40. 40.

    van Koolwijk, L. M. E. et al. Common genetic determinants of intraocular pressure and primary open-angle glaucoma. PLoS Genet. 8, e1002611 (2012).

  41. 41.

    Loh, P. R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

  42. 42.

    Riboli, E. & Kaaks, R. The EPIC Project: rationale and study design. European Prospective Investigation into Cancer and Nutrition. Int. J. Epidemiol. 26, S6–S14 (1997). Suppl 1.

  43. 43.

    Day, N. et al. EPIC-Norfolk: study design and characteristics of the cohort. European Prospective Investigation of Cancer. Br. J. Cancer 80, 95–103 (1999). Suppl 1.

  44. 44.

    Hayat, S. A. et al. Cohort Profile: A prospective cohort study of objective physical and cognitive capability and visual health in an ageing population of men and women in Norfolk (EPIC-Norfolk 3). Int. J. Epidemiol. 43, 1063–1072 (2014).

  45. 45.

    Wiggs, J. L. et al. The NEIGHBOR consortium primary open-angle glaucoma genome-wide association study: rationale, study design, and clinical variables. J. Glaucoma 22, 517–525 (2013).

  46. 46.

    Wiggs, J. L. et al. Common variants at 9p21 and 8q22 are associated with increased susceptibility to optic nerve degeneration in glaucoma. PLoS Genet. 8, e1002654 (2012).

  47. 47.

    Feuer, W. J. et al. The Ocular Hypertension Treatment Study: reproducibility of cup/disk ratio measurements over time at an optic disc reading center. Am. J. Ophthalmol. 133, 19–28 (2002).

  48. 48.

    Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

  49. 49.

    Aulchenko, Y. S., Struchalin, M. V. & van Duijn, C. M. ProbABEL package for genome-wide association analysis of imputed data. BMC Bioinformatics 11, 134 (2010).

  50. 50.

    Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

  51. 51.

    Mägi, R. & Morris, A. P. GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11, 288 (2010).

  52. 52.

    Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

  53. 53.

    Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).

  54. 54.

    Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).

  55. 55.

    Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).

  56. 56.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

  57. 57.

    Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

  58. 58.

    Dayem Ullah, A. Z., Lemoine, N. R. & Chelala, C. SNPnexus: a web server for functional annotation of novel and publicly known genetic variants (2012 update). Nucleic Acids Res. 40, W65–W70 (2012).

  59. 59.

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

  60. 60.

    Barbeira, A. et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. https://www.biorxiv.org/content/early/2017/10/03/045260 (2017).

  61. 61.

    Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).

  62. 62.

    Segrè, A. V., Groop, L., Mootha, V. K., Daly, M. J. & Altshuler, D. Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. 6, e1001058 (2010).

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This research was conducted by using the UK Biobank Resource under application no. 17615. UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and Northwest Regional Development Agency. It also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK.

EPIC-Norfolk infrastructure and core functions are supported by grants from the Medical Research Council (G1000143) and Cancer Research UK (C864/A14136). The clinic for the third health examination was funded by Research into Ageing (262). Genotyping was funded by the Medical Research Council (MC_PC_13048). We thank all staff from the MRC Epidemiology laboratory team for the preparation and quality control of DNA samples. A.P.K. is supported by a Moorfields Eye Charity grant. P.J.F. received additional support from the Richard Desmond Charitable Trust (via Fight for Sight) and the Department for Health through the award made by the National Institute for Health Research to Moorfields Eye Hospital and the UCL Institute of Ophthalmology for a specialist Biomedical Research Centre for Ophthalmology.

The NEIGHBORHOOD data collection and analysis is supported by NIH/NEI R01EY022305 (J.L.W.) and NIH/NEI P30 EY014104 (J.L.W.). Support for collection of cases and controls and analysis for individual datasets is as follows. Genotyping services for the NEIGHBOR study were provided by the Center for Inherited Disease Research (CIDR) and were supported by the National Eye Institute through grant HG005259-01 (J.L.W.). Genotyping for the MEEI dataset and some NHS and HPFS cases (GLAUGEN) was completed at the Broad Institute and was supported by GENEVA project grant HG004728 (L.R.P.) and U01-HG004424 (Broad Institute). Genotype data cleaning and analysis for the GLAUGEN study was supported by U01HG004446 (C. Laurie). Collecting and processing samples for the NEIGHBOR dataset was supported by the National Eye Institute through ARRA grants 3R01EY015872-05S1 (J.L.W.) and 3R01EY019126-02S1 (M. A. Hauser). Funding for the collection of NEIGHBOR cases and controls was provided by NIH grants EY015543 (R. R. Allingham); EY006827 (D. Gaasterland); HL73042, HL073389 and EY13315 (M. A. Hauser); CA87969, CA49449, UM1 CA186107, UM1 CA 167552 and EY009149 (P. R. Lichter); HG004608 (C. McCarty); EY008208 (F. A. Medeiros); EY015473 (L.R.P.); EY012118 (M. Pericak-Vance); EY015682 (A. Realini); EY011671 and EY09580 (J. E. Richards); EY013178 (J. S. Schuman); RR015574, EY015872, EY010886 and EY009847 (J.L.W.); and EY011008, EY144428, EY144448 and EY18660 (K. Zhang). The collection of Marshfield clinic cases and controls was supported by 1U02HG004608-01, 5U01HG006389-02 and NCATS/NIH grant UL1TR000427. In addition, some NHS/HPFS cases and controls and analysis of GWAS data were supported by R01 CA131332. The Women’s Genomes Health Study (WGHS) is supported by HL043851 and HL080467 from the National Heart, Lung, and Blood Institute and CA047988 from the National Cancer Institute, the Donald W. Reynolds Foundation and the Fondation Leducq, with collaborative scientific support and funding for genotyping provided by Amgen. POAG case identification in the WGHS was supported by 3R01 EY15473-5S1 (L.R.P.). J.L.W. and L.R.P. are supported by the Harvard Glaucoma Center for Excellence and an unrestricted grant from Research to Prevent Blindness. L.R.P. is also supported by a Harvard Medical School Distinguished Scholar award. M.S. is supported by a Fight for Sight PhD studentship. P.G.H. is supported by an FfS ECI fellowship. The statistical analyses were run in King’s College London Rosalind HPC LINUX Clusters and cloud server. C.J.H. and P.G.H. acknowledge the TFC Frost Charitable Trust Support for the KCL Department of Ophthalmology.

Author information

Author notes

  1. These authors contributed equally: Anthony P. Khawaja, Jessica N. Cooke Bailey.

  2. These authors jointly supervised this work: Janey L. Wiggs, Chris J. Hammond, Pirro G. Hysi.

  3. A list of members and affiliations appears in the Supplementary Note.


  1. NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK

    • Anthony P. Khawaja
    • , Peng T. Khaw
    •  & Paul J. Foster
  2. Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge School of Clinical Medicine, Cambridge, UK

    • Anthony P. Khawaja
  3. Department of Population and Quantitative Health Sciences, Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, OH, USA

    • Jessica N. Cooke Bailey
    • , Robert P. Igo Jr
    • , Yeunjoo E. Song
    •  & Jonathan L. Haines
  4. MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK

    • Nicholas J. Wareham
    •  & Robert A. Scott
  5. Department of Ophthalmology, King’s College London, St. Thomas’ Hospital, London, UK

    • Mark Simcoe
    • , Chris J. Hammond
    •  & Pirro G. Hysi
  6. Department of Twin Research & Genetic Epidemiology, King’s College London, St. Thomas’ Hospital, London, UK

    • Mark Simcoe
    •  & Pirro G. Hysi
  7. Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    • Robert Wojciechowski
  8. Johns Hopkins Wilmer Eye Institute, Baltimore, MD, USA

    • Robert Wojciechowski
  9. Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore

    • Ching-Yu Cheng
  10. Department of Ophthalmology, National University of Singapore and National University Health System, Singapore, Singapore

    • Ching-Yu Cheng
  11. Ophthalmology & Visual Sciences Academic Clinical Program (Eye-ACP), Duke-NUS Medical School, Singapore, Singapore

    • Ching-Yu Cheng
  12. Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA, USA

    • Louis R. Pasquale
    •  & Janey L. Wiggs
  13. Division of Genetics and Epidemiology, UCL Institute of Ophthalmology, London, UK

    • Paul J. Foster


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  1. UK Biobank Eye and Vision Consortium

    1. NEIGHBORHOOD Consortium


      A.P.K., J.N.C.B., M.S., R.P.I., Y.E.S. and P.G.H. conducted the analyses. A.P.K., J.N.C.B., P.J.F., J.L.W., C.J.H. and P.G.H. jointly wrote the manuscript. P.T.K. and P.J.F. designed the ophthalmic component of the UK Biobank study. N.J.W. and R.A.S. led genotyping of the EPIC-Norfolk study. R.W., C.-Y.C., L.R.P. and J.L.H. critically appraised the analyses and critically reviewed the manuscript. The UK Eyes and Vision Consortium critically appraised the analyses. The NEIGHBORHOOD Consortium carried out or critically appraised the genome-wide analyses in the NEIGHBORHOOD study.

      Competing interests

      A.P.K. has received a lecturing honorarium from Grafton Optical. P.J.F. reports receiving personal fees from Allergan, Carl Zeiss, Google/DeepMind and Santen, and a grant from Alcon, outside the submitted work.

      Corresponding authors

      Correspondence to Janey L. Wiggs or Chris J. Hammond or Pirro G. Hysi.

      Integrated supplementary information

      1. Supplementary Figure 1 Results of the principal component analysis for participants in the UK Biobank.

        The first plot (top-left) shows subjects of European ancestry that we included in our analyses (in blue) against a backdrop of other subjects of other ancestry that were excluded from our analyses (in yellow). The remaining plots use each pair of the first five principal components among included subjects.

      2. Supplementary Figure 2 Results of the principal component analysis for participants in EPIC-Norfolk.

        The subjects were pre-selected on the basis of British European ancestry. Plots show pairs of the first five principal components.

      3. Supplementary Figure 3 The genomic inflation factors for UK Biobank (n = 103,382), IGGC (n = 29,578), EPIC-Norfolk (n = 6,595) and the meta-analysis (139,555) results.

        Genomic inflation values (λ) were calculated for each cohort and the meta-analysis results.

      4. Supplementary Figure 4 Manhattan plot for the results of the meta-analysis of the UK Biobank, IGGC and EPIC-Norfolk cohort participants (n = 139,555).

        Each point of the plot represents SNPs analyzed; their x-axis coordinates are the location (chromosome and the position within the respective chromosome), the y-axis the -log10(P-value) of their association.

      5. Supplementary Figure 5 Receiver operating characteristic curves for prediction of glaucoma among 1,500 cases and 331,078 controls from the UK Biobank.

        These participants are independent to those contributing to our primary IOP GWAS. The red curve (AUC1) represents a predictive model built using the SNPs significantly associated with IOP after our conditional analysis (Supplementary Table 2) together with age, sex and three known POAG-associated polymorphisms showing no evidence of association with IOP in our meta-analysis (rs74315329 within MYOC, rs2157719 near SIX6, and rs8015152 within CDKN2B-As1). The blue curve (AUC2) represents a predictive model built using the SNPs significantly associated with IOP after our conditional analysis, together with the three previously reported POAG SNPs (but without age and sex). The green curve (AUC3) represents a predictive model built using only the IOP SNPs known by the year 2014 (Hysi, P. G. et al. Nat. Genet. 46, 1126–30, 2014), together with the three previously reported POAG SNPs. Standard errors for the AUC estimates are shown in brackets.

      Supplementary information

      1. Supplementary Text and Figures and Tables

        Supplementary Figures 1–5, Supplementary Note and Supplementary Tables 1,3 and 5

      2. Reporting Summary

      3. Supplementary Table 2: Association results for the 112 genomic regions identified in the meta-analysis (n=139,555).

        Alternating shades represent different regions. Conditional analyses identified multiple origins for the association signals, which are all shown within the respective regions. Meta-analysis P-values are color-coded to match the colors in Figure 1 (10-10<P<5x10-8 in dark blue, 10-20<P<10-10 in light blue, 10-30<P<10-20 in dark green, 10-40<P<10-30 in light green, 10-50<P<10-40 in light brown, 10-60<P<10-50 in dark brown, P<10-60 in red). SNPs highlighted in purple represent loci that loci in/near genes that have previously been reported as associated with IOP, any type of glaucoma, or glaucoma-related traits. The field “Gene” denotes the nearest gene; Freq refers to the frequency of the effect allele in the UK Biobank cohort; NA values in the ‘Gene’ field mean absence of a transcript within 250kbp, and in the other numerical fields, NA means unavailability of results for that SNP in a specific cohort. For the 74 SNPs significantly (P<5x10-8) associated with IOP in the UK Biobank cohort, the Bonferroni-adjusted level of replication significance is P<6.8x10-04. The False Discovery Rate was 0.05 or less in the IGGC dataset for all SNPs with P<0.03.

      4. Supplementary Table 4: GWAS Catalog entries for the SNPs significantly associated with IOP in the meta-analysis.

        Every trait reported in the GWAS Catalog associated with any trait is given; the number within brackets is the PubMed ID reference for the publication. In case multiple publications reported association between the same SNP and same or closely related phenotypes, only one instance is given in this table.

      5. Supplementary Table 6: Results of the GTEx eQTL database query for pairs of SNPs most associated with IOP in the meta-analysis and their adjacent genes.

        Results are sorted by significance (P-value).

      6. Supplementary Table 7: Gene expression in human trabecular meshwork (TM) and ciliary body (CB) tissue of genes at loci significant in the IOP GWAS (Supplementary Table 2).

        Results are acquired from a published RNA sequencing study (Carnes, M. U. et al, Exp. Eye Res. 167, 91–99, 2018). The expression level for each gene (adjusted for gene length and number of sequencing reads in a given sample) is given in fragments per kilobase of transcript per million mapped reads (FPKM). Based on the overall gene expression distribution, genes with an FPKM≥1, an FPKM≥4.7 (33rd percentile) and an FPKM≥15.9 (67th percentile) were classified as lowly (highlighted in yellow), moderately (highlighted in orange), or highly (highlighted in red) expressed, respectively. Genes with no expression (FPKM<1) are not highlighted. NA – transcriptome analyses did not identify the gene in the referenced study.

      7. Supplementary Table 8: S-PrediXcan analyses results.

        Variance of the gene expression is calculated as W' * G * W (where W is the vector of SNP weights in a gene's model, W' is its transpose, and G is the covariance matrix). Prediction performance parameters are the R2 and P-value of the tissue model's correlation to the gene's measured transcriptome.

      8. Supplementary Table 9: Association with POAG in the NEIGHBORHOOD study of the available SNPs previously associated with IOP in the GWAS meta-analysis (Supplementary Table 2).

        Of the 133 SNPs in the IOP GWAS conditional analysis, 120 were available in the NEIGHBORHOOD study. SNPs in loci that have previously been associated with POAG are in purple font. For the primary analysis with all POAG subjects (3,853 cases and 33,480 controls): P-values less than the Bonferroni-corrected significance threshold of 4.2x10-4 are shown in red, nominally significant at P<0.05 are shown in green, P≥0.05 are shown in blue. HTG= High Tension Glaucoma (N=1868), NTG=Normal Tension Glaucoma (N=725), OR = odds ratio, SE = spherical error.

      9. Supplementary Table 10: Association of IOP-associated SNPs with glaucoma among UK Biobank subjects (ascertained by self-report and hospital episode statistics data).

        Results shown for the same SNPs as in Supplementary Table 2. Included are 1,500 cases and 331,078 controls (see Figure B in Supplementary Note). None of the individuals included in the IOP meta-analysis were included in this glaucoma case-control analysis.

      10. Supplementary Table 11: Association analysis of age of POAG diagnosis with IOP-associated SNPs in 2,606 participants of the NEIGHBOR and MEEI studies (subset of NEIGHBORHOOD, see Online Methods) for the SNPs remaining significantly associated after the conditional analysis (Supplementary Table 2).

        The linear models had age of diagnosis as outcome, allele dosages and sex as predictors and co-variate respectively.

      11. Supplementary Table 12: Association of SNPs significantly associated with IOP with age at glaucoma diagnosis among all the UK Biobank participants self-reporting glaucoma.

        The SNPs are the same as in Supplementary Table 2. All the UK Biobank participants who reported a valid age were tested (n=2,031).

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