Genome-wide association study of intraocular pressure uncovers new pathways to glaucoma


Intraocular pressure (IOP) is currently the sole modifiable risk factor for primary open-angle glaucoma (POAG), one of the leading causes of blindness worldwide1. Both IOP and POAG are highly heritable2. We report a combined analysis of participants from the UK Biobank (n = 103,914) and previously published data from the International Glaucoma Genetic Consortium (n = 29,578)3,4 that identified 101 statistically independent genome-wide-significant SNPs for IOP, 85 of which have not been previously reported4,5,6,7,8,9,10,11,12. We examined these SNPs in 11,018 glaucoma cases and 126,069 controls, and 53 SNPs showed evidence of association. Gene-based tests implicated an additional 22 independent genes associated with IOP. We derived an allele score based on the IOP loci and loci influencing optic nerve head morphology. In 1,734 people with advanced glaucoma and 2,938 controls, participants in the top decile of the allele score were at increased risk (odds ratio (OR) = 5.6; 95% confidence interval (CI): 4.1–7.6) of glaucoma relative to the bottom decile.

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Fig. 1: Manhattan plot displaying associations with intraocular pressure in people of Northern European descent.
Fig. 2: Regression coefficients or effect size for the top associated SNPs at each locus associated with intraocular pressure at the genome-wide-significant level.


  1. 1.

    Weinreb, R. N. et al. Primary open-angle glaucoma. Nat. Rev. Dis. Prim. 2, 16067 (2016).

    Article  Google Scholar 

  2. 2.

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

    Article  Google Scholar 

  3. 3.

    Bycroft, C. et al. Genome-wide genetic data on ~500,000 UK Biobank participants. Preprint at (2017).

  4. 4.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

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

    Article  Google Scholar 

  6. 6.

    Springelkamp, H. et al. ARHGEF12 influences the risk of glaucoma by increasing intraocular pressure. Hum. Mol. Genet. 24, 2689–2699 (2015).

    CAS  Article  Google Scholar 

  7. 7.

    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).

    Article  Google Scholar 

  8. 8.

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

    CAS  Article  Google Scholar 

  9. 9.

    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).

    CAS  Article  Google Scholar 

  10. 10.

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

    CAS  Article  Google Scholar 

  11. 11.

    Burdon, K. P. et al. Genome-wide association study identifies susceptibility loci for open angle glaucoma at TMCO1 and CDKN2B-AS1. Nat. Genet. 43, 574–578 (2011).

    CAS  Article  Google Scholar 

  12. 12.

    Thorleifsson, G. et al. Common variants near CAV1 and CAV2 are associated with primary open-angle glaucoma. Nat. Genet. 42, 906–909 (2010).

    CAS  Article  Google Scholar 

  13. 13.

    Vithana, E. N. et al. Genome-wide association analyses identify three new susceptibility loci for primary angle closure glaucoma. Nat. Genet. 44, 1142–1146 (2012).

    CAS  Article  Google Scholar 

  14. 14.

    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).

    CAS  Article  Google Scholar 

  15. 15.

    Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–75 (2012).

    CAS  Article  Google Scholar 

  16. 16.

    Lu, Y. et al. Genome-wide association analyses identify multiple loci associated with central corneal thickness and keratoconus. Nat. Genet. 45, 155–163 (2013).

    CAS  Article  Google Scholar 

  17. 17.

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

    CAS  Article  Google Scholar 

  18. 18.

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

    CAS  Article  Google Scholar 

  19. 19.

    Ramdas, W. D. et al. A genome-wide association study of optic disc parameters. PLoS Genet. 6, e1000978 (2010).

    Article  Google Scholar 

  20. 20.

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

    CAS  Article  Google Scholar 

  21. 21.

    Bakshi, A. et al. Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits. Sci. Rep. 6, 32894 (2016).

    CAS  Article  Google Scholar 

  22. 22.

    de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLOS Comput. Biol. 11, e1004219 (2015).

    Article  Google Scholar 

  23. 23.

    Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).

    CAS  Article  Google Scholar 

  24. 24.

    Reis, L. M. & Semina, E. V. Genetics of anterior segment dysgenesis disorders. Curr. Opin. Ophthalmol. 22, 314–324 (2011).

    Article  Google Scholar 

  25. 25.

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

    CAS  Article  Google Scholar 

  26. 26.

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

    Article  Google Scholar 

  27. 27.

    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).

    CAS  Article  Google Scholar 

  28. 28.

    Sundin, O. H. et al. Extreme hyperopia is the result of null mutations in MFRP, which encodes a Frizzled-related protein. Proc. Natl. Acad. Sci. USA 102, 9553–9558 (2005).

    CAS  Article  Google Scholar 

  29. 29.

    Khan, A. O. Microcornea with myopic chorioretinal atrophy, telecanthus and posteriorly-rotated ears: a distinct clinical syndrome. Ophthalmic Genet. 33, 196–199 (2012).

    Article  Google Scholar 

  30. 30.

    Vollrath, D. et al. Loss-of-function mutations in the LIM-homeodomain gene, LMX1B, in nail-patella syndrome. Hum. Mol. Genet. 7, 1091–1098 (1998).

    CAS  Article  Google Scholar 

  31. 31.

    Sweeney, E., Fryer, A., Mountford, R., Green, A. & McIntosh, I. Nail patella syndrome: a review of the phenotype aided by developmental biology. J. Med. Genet. 40, 153–162 (2003).

    CAS  Article  Google Scholar 

  32. 32.

    Souzeau, E. et al. Australian and New Zealand Registry of Advanced Glaucoma: methodology and recruitment. Clin. Exp. Ophthalmol. 40, 569–575 (2012).

    Article  Google Scholar 

  33. 33.

    Quigley, H. A. & Broman, A. T. The number of people with glaucoma worldwide in 2010 and 2020. Br. J. Ophthalmol. 90, 262–267 (2006).

    CAS  Article  Google Scholar 

  34. 34.

    Ehlers, N., Bramsen, T. & Sperling, S. Applanation tonometry and central corneal thickness. Acta Ophthalmol. (Copenh.) 53, 34–43 (1975).

    CAS  Article  Google Scholar 

  35. 35.

    McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

    CAS  Article  Google Scholar 

  36. 36.

    UK10K Consortium. The UK10K project identifies rare variants in health and disease. Nature 526, 82–90 (2015).

    Article  Google Scholar 

  37. 37.

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

    Article  Google Scholar 

  38. 38.

    Kneehole, D. R. et al. Genome-wide association meta-analysis identifies new endometriosis risk loci. Nat. Genet. 44, 1355–1359 (2012).

    Article  Google Scholar 

  39. 39.

    Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  Article  Google Scholar 

  40. 40.

    Delaneau, O., Marchini, J. & Zagury, J.-F. A linear complexity phasing method for thousands of genomes. Nat. Methods 9, 179–181 (2011).

    Article  Google Scholar 

  41. 41.

    Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

    CAS  Article  Google Scholar 

  42. 42.

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

    CAS  Article  Google Scholar 

  43. 43.

    Willer, C. J. et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat. Genet. 40, 161–169 (2008).

    CAS  Article  Google Scholar 

  44. 44.

    Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    CAS  Article  Google Scholar 

  45. 45.

    Griffith, M. et al. DGIdb: mining the druggable genome. Nat. Methods 10, 1209–1210 (2013).

    CAS  Article  Google Scholar 

  46. 46.

    Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    Article  Google Scholar 

  47. 47.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq: a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  Article  Google Scholar 

  48. 48.

    McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).

    CAS  Article  Google Scholar 

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This work was conducted by using the UK Biobank Resource (application number 25331) and publicly available data from the International Glaucoma Genetics Consortium. This work was supported by grants from the National Health and Medical Research Council (NHMRC) of Australia (1107098 (J.E.C.), 1116360 (D.A.M.), 1116495 (J.E.C.) and 1023911 (D.A.M.)), the Ophthalmic Research Institute of Australia and the BrightFocus Foundation. S.M. is supported by an Australian Research Council Future Fellowship. K.P.B., J.E.C. and A.W.H. are supported by NHMRC Fellowships. D.J.L. is supported by an EMBL Australia group leader award. We thank S. Wood and J. Pearson from QIMR Berghofer for IT support.

Author information




S.M., A.W.H., J.E.C., P.G. and D.A.M. designed the study and obtained funding. S.M., J.S.O., J.A., X.H., T.Z., M.H.L., S.S., J.E.P., D.L. and J.B. analyzed the data. S.M., T.Z., O.S., E.S., S.S., B.S., R.A.M., J.L., J.B.R., S.L.G., P.R.H., A.J.R.W., R.J.C., S.B., J.R.G., I.G., D.C.W., G.R.S., N.G.M., G.W.M., K.P.B., D.A.M., J.E.C. and A.W.H. contributed to data collection and contributed to genotyping. S.M., J.S.O., D.A.M., P.G. and A.W.H. wrote the first draft of the paper. All authors contributed to the final version of the paper.

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Correspondence to Stuart MacGregor.

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Supplementary Information

Supplementary Text and Figures

Supplementary Figures 1–8

Reporting Summary

Supplementary Table 1

Statistically independent hits that are associated with IOP at the genome-wide significant level, that show at least P < 0.05 with glaucoma. SNPs which are significant after correction for multiple testing (101 SNPs) are shown in bold. This Table presents the results for IOP and glaucoma meta-analysis as well as for each substudy separately

Supplementary Table 2

Statistically independent hits that are associated with IOP at the genome-wide significant level, but are not associated (P > 0.05) with glaucoma, or were more strongly associated with corneal parameters. rs66724425 in ADAMTS6 is known to be associated with central corneal thickness, and SNPs rs1570204, rs78658973, rs12492846 and rs2797560, were more strongly associated with corneal hysteresis than they were with IOP

Supplementary Table 3

GCTA-fastBAT gene-based tests for IOP and the corresponding gene-based results for glaucoma. Of these 22 genes, 4 were significant at P< 0.05 with glaucoma

Supplementary Table 4

Enriched pathways for genes associated with IOP identified using MAGMA and 5,917 pre-specified Gene Ontology gene sets. The corresponding effect size and P value for each pathway in glaucoma is also displayed

Supplementary Table 5

Enriched pathways for genes associated with IOP identified using DEPICT, which uses 14,462 preconstituted gene sets are significantly enriched for genes in the trait-associated loci. The corresponding P value for each pathway in glaucoma is also displayed

Supplementary Table 6

Cell type implicated by analysis of the FANTOM5 Cap Analysis of Gene Expression dataset

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MacGregor, S., Ong, J., An, J. et al. Genome-wide association study of intraocular pressure uncovers new pathways to glaucoma. Nat Genet 50, 1067–1071 (2018).

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