Genome-wide association meta-analysis highlights light-induced signaling as a driver for refractive error

Abstract

Refractive errors, including myopia, are the most frequent eye disorders worldwide and an increasingly common cause of blindness. This genome-wide association meta-analysis in 160,420 participants and replication in 95,505 participants increased the number of established independent signals from 37 to 161 and showed high genetic correlation between Europeans and Asians (>0.78). Expression experiments and comprehensive in silico analyses identified retinal cell physiology and light processing as prominent mechanisms, and also identified functional contributions to refractive-error development in all cell types of the neurosensory retina, retinal pigment epithelium, vascular endothelium and extracellular matrix. Newly identified genes implicate novel mechanisms such as rod-and-cone bipolar synaptic neurotransmission, anterior-segment morphology and angiogenesis. Thirty-one loci resided in or near regions transcribing small RNAs, thus suggesting a role for post-transcriptional regulation. Our results support the notion that refractive errors are caused by a light-dependent retina-to-sclera signaling cascade and delineate potential pathobiological molecular drivers.

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Fig. 1: GWAS meta-analysis identifies 140 loci for refractive error (stage 3).
Fig. 2: Correlation of statistical significance and effect size of SNPs on the basis of SphE in diopters and AODM in years.
Fig. 3: Risk of refractive error per decile of polygenic risk score (Rotterdam Study I–III, n = 10,792).
Fig. 4: Visualization of the DEPICT gene set enrichment analysis based on loci associated with refractive error and the correlation between the (meta)gene sets.
Fig. 5: Genes ranked according to biological and statistical evidence.
Fig. 6: Schematic representation of the human eye, retinal cell types and functional sites of associated genes.

References

  1. 1.

    Pan, C. W., Ramamurthy, D. & Saw, S. M. Worldwide prevalence and risk factors for myopia. Ophthalmic Physiol. Opt. 32, 3–16 (2012).

    PubMed  Article  Google Scholar 

  2. 2.

    Morgan, I. G. What public policies should be developed to deal with the epidemic of myopia? Optom. Vis. Sci. 93, 1058–1060 (2016).

    PubMed  Article  Google Scholar 

  3. 3.

    Morgan, I. & Rose, K. How genetic is school myopia? Prog. Retin. Eye Res. 24, 1–38 (2005).

    PubMed  Article  Google Scholar 

  4. 4.

    Morgan, I. G., Ohno-Matsui, K. & Saw, S. M. Myopia. Lancet 379, 1739–1748 (2012).

    PubMed  Article  Google Scholar 

  5. 5.

    Williams, K. M. et al. Increasing prevalence of myopia in Europe and the impact of education. Ophthalmology 122, 1489–1497 (2015).

  6. 6.

    Williams, K. M. et al. Prevalence of refractive error in Europe: the European Eye Epidemiology (E(3)) Consortium. Eur. J. Epidemiol. 30, 305–315 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Vongphanit, J., Mitchell, P. & Wang, J. J. Prevalence and progression of myopic retinopathy in an older population. Ophthalmology 109, 704–711 (2002).

    PubMed  Article  Google Scholar 

  8. 8.

    Seet, B. et al. Myopia in Singapore: taking a public health approach. Br. J. Ophthalmol. 85, 521–526 (2001).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  9. 9.

    Smith, T. S., Frick, K. D., Holden, B. A., Fricke, T. R. & Naidoo, K. S. Potential lost productivity resulting from the global burden of uncorrected refractive error. Bull. World Health Organ. 87, 431–437 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  10. 10.

    Verhoeven, V. J. et al. Visual consequences of refractive errors in the general population. Ophthalmology 122, 101–109 (2015).

    PubMed  Article  Google Scholar 

  11. 11.

    Tideman, J. W. et al. Association of axial length with risk of uncorrectable visual impairment for Europeans with myopia. JAMA Ophthalmol. 134, 1355–1363 (2016).

    PubMed  Article  Google Scholar 

  12. 12.

    Flitcroft, D. I. The complex interactions of retinal, optical and environmental factors in myopia aetiology. Prog. Retin. Eye Res. 31, 622–660 (2012).

    PubMed  Article  CAS  Google Scholar 

  13. 13.

    Nakanishi, H. et al. A genome-wide association analysis identified a novel susceptible locus for pathological myopia at 11q24.1. PLoS Genet. 5, e1000660 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  14. 14.

    Lam, C. Y. et al. A genome-wide scan maps a novel high myopia locus to 5p15. Invest. Ophthalmol. Vis. Sci. 49, 3768–3778 (2008).

    PubMed  Article  Google Scholar 

  15. 15.

    Stambolian, D. et al. Meta-analysis of genome-wide association studies in five cohorts reveals common variants in RBFOX1, a regulator of tissue-specific splicing, associated with refractive error. Hum. Mol. Genet. 22, 2754–2764 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  16. 16.

    Fan, Q. et al. Genetic variants on chromosome 1q41 influence ocular axial length and high myopia. PLoS Genet. 8, e1002753 (2012).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  17. 17.

    Fan, Q. et al. Meta-analysis of gene-environment-wide association scans accounting for education level identifies additional loci for refractive error. Nat. Commun. 7, 11008 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  18. 18.

    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).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  19. 19.

    Shi, Y. et al. Exome sequencing identifies ZNF644 mutations in high myopia. PLoS Genet. 7, e1002084 (2011).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  20. 20.

    Shi, Y. et al. Genetic variants at 13q12.12 are associated with high myopia in the Han Chinese population. Am. J. Hum. Genet. 88, 805–813 (2011).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  21. 21.

    Li, Y. J. et al. Genome-wide association studies reveal genetic variants in CTNND2 for high myopia in Singapore Chinese. Ophthalmology 118, 368–375 (2011).

    PubMed  Article  Google Scholar 

  22. 22.

    Li, Z. et al. A genome-wide association study reveals association between common variants in an intergenic region of 4q25 and high-grade myopia in the Chinese Han population. Hum. Mol. Genet. 20, 2861–2868 (2011).

    PubMed  Article  CAS  Google Scholar 

  23. 23.

    Liu, J. & Zhang, H. X. Polymorphism in the 11q24.1 genomic region is associated with myopia: a comprehensive genetic study in Chinese and Japanese populations. Mol. Vis. 20, 352–358 (2014).

    PubMed  PubMed Central  CAS  Google Scholar 

  24. 24.

    Tran-Viet, K. N. et al. Mutations in SCO2 are associated with autosomal-dominant high-grade myopia. Am. J. Hum. Genet. 92, 820–826 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  25. 25.

    Aldahmesh, M. A. et al. Mutations in LRPAP1 are associated with severe myopia in humans. Am. J. Hum. Genet. 93, 313–320 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  26. 26.

    Verhoeven, V. J. et al. Genome-wide meta-analyses of multiancestry cohorts identify multiple new susceptibility loci for refractive error and myopia. Nat. Genet. 45, 314–318 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  27. 27.

    Kiefer, A. K. et al. Genome-wide analysis points to roles for extracellular matrix remodeling, the visual cycle, and neuronal development in myopia. PLoS Genet. 9, e1003299 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  28. 28.

    Wojciechowski, R. & Hysi, P. G. Focusing in on the complex genetics of myopia. PLoS Genet. 9, e1003442 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  29. 29.

    1000 Genomes Project Consortium. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  CAS  Google Scholar 

  30. 30.

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  31. 31.

    Yang, J. et al. Genomic inflation factors under polygenic inheritance. Eur. J. Hum. Genet. 19, 807–812 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  33. 33.

    Plotnikov, D., Guggenheim, J. & The UK Biobank Eye and Vision Consortium. Is a large eye size a risk factor for myopia? A Mendelian randomization study. https://www.biorxiv.org/content/early/2017/12/29/240283/ (2017).

  34. 34.

    Hsu, F. et al. The UCSC Known Genes. Bioinformatics 22, 1036–1046 (2006).

    PubMed  Article  CAS  Google Scholar 

  35. 35.

    Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

  36. 36.

    Ng, P. C. & Henikoff, S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003).

  37. 37.

    Kelly, M. P. Does phosphodiesterase 11A (PDE11A) hold promise as a future therapeutic target? Curr. Pharm. Des. 21, 389–416 (2015).

  38. 38.

    Kumar, P., Henikoff, S. & Ng, P. C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4, 1073–1081 (2009).

    PubMed  Article  CAS  Google Scholar 

  39. 39.

    Mathe, E. et al. Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods. Nucleic Acids Res. 34, 1317–1325 (2006).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  40. 40.

    Tavtigian, S. V. et al. Comprehensive statistical study of 452 BRCA1 missense substitutions with classification of eight recurrent substitutions as neutral. J. Med. Genet. 43, 295–305 (2006).

    PubMed  Article  CAS  Google Scholar 

  41. 41.

    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).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  42. 42.

    Ferreira, M. A. et al. Gene-based analysis of regulatory variants identifies 4 putative novel asthma risk genes related to nucleotide synthesis and signaling. J. Allergy Clin. Immunol. 139, 1148–1157 (2017).

    PubMed  Article  CAS  Google Scholar 

  43. 43.

    Pickrell, J. K. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559–573 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  44. 44.

    Purcell, S. M. et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).

    PubMed  CAS  Google Scholar 

  45. 45.

    Verhoeven, V. J. et al. Large scale international replication and meta-analysis study confirms association of the 15q14 locus with myopia. The CREAM consortium. Hum. Genet. 131, 1467–1480 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  47. 47.

    Brown, B. C., Asian Genetic Epidemiology Network Type 2 Diabetes Consortium, Ye, C. J., Price, A. L. & Zaitlen, N. Transethnic genetic-correlation estimates from summary statistics. Am. J. Hum. Genet. 99, 76–88 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  48. 48.

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  49. 49.

    Fritsche, L. G. et al. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat. Genet. 48, 134–143 (2016).

    PubMed  Article  CAS  Google Scholar 

  50. 50.

    Ritchey, E. R. et al. Vision-guided ocular growth in a mutant chicken model with diminished visual acuity. Exp. Eye Res. 102, 59–69 (2012).

    PubMed  Article  CAS  Google Scholar 

  51. 51.

    Vincent, A. et al. Biallelic mutations in GNB3 cause a unique form of autosomal-recessive congenital stationary night blindness. Am. J. Hum. Genet. 98, 1011–1019 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  52. 52.

    Blake, J. A. et al. Mouse Genome Database (MGD)-2017: community knowledge resource for the laboratory mouse. Nucleic Acids Res. 45, D723–D729 (2017).

    PubMed  Article  CAS  Google Scholar 

  53. 53.

    Nikonov, S. S. et al. Cones respond to light in the absence of transducin β subunit. J. Neurosci. 33, 5182–5194 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  54. 54.

    Stone, E. M. et al. A single EFEMP1 mutation associated with both Malattia Leventinese and Doyne honeycomb retinal dystrophy. Nat. Genet. 22, 199–202 (1999).

    PubMed  Article  CAS  Google Scholar 

  55. 55.

    Mackay, D. S., Bennett, T. M. & Shiels, A. Exome sequencing identifies a missense variant in EFEMP1 co-segregating in a family with autosomal dominant primary open-angle glaucoma. PLoS One 10, e0132529 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  56. 56.

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

    PubMed  Article  CAS  Google Scholar 

  57. 57.

    Haeseleer, F. et al. Essential role of Ca2+-binding protein 4, a Cav1.4 channel regulator, in photoreceptor synaptic function. Nat. Neurosci. 7, 1079–1087 (2004).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  58. 58.

    Littink, K. W. et al. A novel homozygous nonsense mutation in CABP4 causes congenital cone-rod synaptic disorder. Invest. Ophthalmol. Vis. Sci. 50, 2344–2350 (2009).

    PubMed  Article  Google Scholar 

  59. 59.

    Grimes, W. N., Li, W., Chávez, A. E. & Diamond, J. S. BK channels modulate pre- and postsynaptic signaling at reciprocal synapses in retina. Nat. Neurosci. 12, 585–592 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  60. 60.

    Keckeis, S., Reichhart, N., Roubeix, C. & Strauß, O. Anoctamin2 (TMEM16B) forms the Ca2+-activated Cl channel in the retinal pigment epithelium. Exp. Eye Res. 154, 139–150 (2017).

    PubMed  Article  CAS  Google Scholar 

  61. 61.

    Prasanna, G., Narayan, S., Krishnamoorthy, R. R. & Yorio, T. Eyeing endothelins: a cellular perspective. Mol. Cell. Biochem. 253, 71–88 (2003).

    PubMed  Article  CAS  Google Scholar 

  62. 62.

    Yamashita, T. et al. Essential and synergistic roles of RP1 and RP1L1 in rod photoreceptor axoneme and retinitis pigmentosa. J. Neurosci. 29, 9748–9760 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  63. 63.

    Davidson, A. E. et al. RP1L1 variants are associated with a spectrum of inherited retinal diseases including retinitis pigmentosa and occult macular dystrophy. Hum. Mutat. 34, 506–514 (2013).

    PubMed  Article  CAS  Google Scholar 

  64. 64.

    Hawthorne, F. et al. Association mapping of the high-grade myopia MYP3 locus reveals novel candidates UHRF1BP1L, PTPRR, and PPFIA2. Invest. Ophthalmol. Vis. Sci. 54, 2076–2086 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  65. 65.

    Feldkaemper, M. & Schaeffel, F. An updated view on the role of dopamine in myopia. Exp. Eye Res. 114, 106–119 (2013).

    PubMed  Article  CAS  Google Scholar 

  66. 66.

    Paul, M. L., Graybiel, A. M., David, J. C. & Robertson, H. A. D1-like and D2-like dopamine receptors synergistically activate rotation and c-fos expression in the dopamine-depleted striatum in a rat model of Parkinson’s disease. J. Neurosci. 12, 3729–3742 (1992).

    PubMed  Article  CAS  Google Scholar 

  67. 67.

    Stone, R. A., Lin, T., Laties, A. M. & Iuvone, P. M. Retinal dopamine and form-deprivation myopia. Proc. Natl. Acad. Sci. USA 86, 704–706 (1989).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  68. 68.

    Gardner, M., Bertranpetit, J. & Comas, D. Worldwide genetic variation in dopamine and serotonin pathway genes: implications for association studies. Am. J. Med. Genet. B. Neuropsychiatr. Genet. 147B, 1070–1075 (2008).

    PubMed  Article  CAS  Google Scholar 

  69. 69.

    D’Souza, U. M. & Craig, I. W. Functional polymorphisms in dopamine and serotonin pathway genes. Hum. Mutat. 27, 1–13 (2006).

    PubMed  Article  CAS  Google Scholar 

  70. 70.

    Beaulieu, J. M. & Gainetdinov, R. R. The physiology, signaling, and pharmacology of dopamine receptors. Pharmacol. Rev. 63, 182–217 (2011).

    PubMed  Article  CAS  Google Scholar 

  71. 71.

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

    PubMed  Article  CAS  Google Scholar 

  72. 72.

    Holden, B. A. et al. Global prevalence of myopia and high myopia and temporal trends from 2000 through 2050. Ophthalmology 123, 1036–1042 (2016).

    PubMed  Article  Google Scholar 

  73. 73.

    Cardon, L. R. & Palmer, L. J. Population stratification and spurious allelic association. Lancet 361, 598–604 (2003).

    PubMed  Article  Google Scholar 

  74. 74.

    Chua, S. Y. et al. Age of onset of myopia predicts risk of high myopia in later childhood in myopic Singapore children. Ophthalmic Physiol. Opt. 36, 388–394 (2016).

    PubMed  Article  Google Scholar 

  75. 75.

    Williams, K. M. et al. Age of myopia onset in a British population-based twin cohort. Ophthalmic Physiol. Opt. 33, 339–345 (2013).

    PubMed  Article  Google Scholar 

  76. 76.

    Dolgin, E. The myopia boom. Nature 519, 276–278 (2015).

    PubMed  Article  CAS  Google Scholar 

  77. 77.

    Connaughton, V. Glutamate and glutamate receptors in the vertebrate retina. In: H. Kolb et al. eds. Webvision: The Organization of the Retina and Visual System (Webvision, Salt Lake City, UT, USA, 1995).

    Google Scholar 

  78. 78.

    Hung, G. K., Mahadas, K. & Mohammad, F. Eye growth and myopia development: unifying theory and Matlab model. Comput. Biol. Med. 70, 106–118 (2016).

    PubMed  Article  Google Scholar 

  79. 79.

    Norton, T. T. What do animal studies tell us about the mechanism of myopia-protection by light? Optom. Vis. Sci. 93, 1049–1051 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  80. 80.

    Weiss, S. & Schaeffel, F. Diurnal growth rhythms in the chicken eye: relation to myopia development and retinal dopamine levels. J. Comp. Physiol. A 172, 263–270 (1993).

    PubMed  Article  CAS  Google Scholar 

  81. 81.

    Stone, R. A., Lin, T., Iuvone, P. M. & Laties, A. M. Postnatal control of ocular growth: dopaminergic mechanisms. Ciba Found. Symp. 155, 45–62 (1990).

    PubMed  CAS  Google Scholar 

  82. 82.

    Morgan, I. G. The biological basis of myopic refractive error. Clin. Exp. Optom. 86, 276–288 (2003).

    PubMed  Article  Google Scholar 

  83. 83.

    Li, X. X., Schaeffel, F., Kohler, K. & Zrenner, E. Dose-dependent effects of 6-hydroxy dopamine on deprivation myopia, electroretinograms, and dopaminergic amacrine cells in chickens. Vis. Neurosci. 9, 483–492 (1992).

    PubMed  Article  CAS  Google Scholar 

  84. 84.

    Iuvone, P. M., Tigges, M., Stone, R. A., Lambert, S. & Laties, A. M. Effects of apomorphine, a dopamine receptor agonist, on ocular refraction and axial elongation in a primate model of myopia. Invest. Ophthalmol. Vis. Sci. 32, 1674–1677 (1991).

    PubMed  CAS  Google Scholar 

  85. 85.

    Ashby, R., McCarthy, C. S., Maleszka, R., Megaw, P. & Morgan, I. G. A muscarinic cholinergic antagonist and a dopamine agonist rapidly increase ZENK mRNA expression in the form-deprived chicken retina. Exp. Eye Res. 85, 15–22 (2007).

    PubMed  Article  CAS  Google Scholar 

  86. 86.

    Ashby, R. Animal studies and the mechanism of myopia-protection by light? Optom. Vis. Sci. 93, 1052–1054 (2016).

    PubMed  Article  Google Scholar 

  87. 87.

    Rymer, J. & Wildsoet, C. F. The role of the retinal pigment epithelium in eye growth regulation and myopia: a review. Vis. Neurosci. 22, 251–261 (2005).

    PubMed  Article  Google Scholar 

  88. 88.

    Chen, S. et al. Bright light suppresses form-deprivation myopia development with activation of dopamine D1 receptor signaling in the ON pathway in retina. Invest. Ophthalmol. Vis. Sci. 58, 2306–2316 (2017).

    PubMed  Article  CAS  Google Scholar 

  89. 89.

    Chen, P. S. et al. Effects of C825T polymorphism of the GNB3 gene on availability of dopamine transporter in healthy volunteers: a SPECT study. Neuroimage 56, 1526–1530 (2011).

    PubMed  Article  CAS  Google Scholar 

  90. 90.

    Scott, M. S. & Ono, M. From snoRNA to miRNA: dual function regulatory non-coding RNAs. Biochimie 93, 1987–1992 (2011).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  91. 91.

    McFadden, S. A. Understanding and treating myopia: what more we need to know and future research priorities. Optom. Vis. Sci. 93, 1061–1063 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  92. 92.

    Smith, E. L. III, Hung, L. F. & Arumugam, B. Visual regulation of refractive development: insights from animal studies. Eye (Lond.) 28, 180–188 (2014).

    Article  Google Scholar 

  93. 93.

    Zhang, Y. & Wildsoet, C. F. RPE and choroid mechanisms underlying ocular growth and myopia. Prog. Mol. Biol. Transl. Sci. 134, 221–240 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  94. 94.

    Harper, A. R. & Summers, J. A. The dynamic sclera: extracellular matrix remodeling in normal ocular growth and myopia development. Exp. Eye Res. 133, 100–111 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  95. 95.

    Summers, J. A. The choroid as a sclera growth regulator. Exp. Eye Res. 114, 120–127 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  96. 96.

    Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G. R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  97. 97.

    Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).

    PubMed  Article  CAS  Google Scholar 

  98. 98.

    Chen, W. M. & Abecasis, G. R. Family-based association tests for genomewide association scans. Am. J. Hum. Genet. 81, 913–926 (2007).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  99. 99.

    Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  100. 100.

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  101. 101.

    Zaykin, D. V. Optimally weighted Z-test is a powerful method for combining probabilities in meta-analysis. J. Evol. Biol. 24, 1836–1841 (2011).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  102. 102.

    Dudbridge, F. & Gusnanto, A. Estimation of significance thresholds for genomewide association scans. Genet. Epidemiol. 32, 227–234 (2008).

    PubMed  PubMed Central  Article  Google Scholar 

  103. 103.

    Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  104. 104.

    Yang, H. & Wang, K. Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR. Nat. Protoc. 10, 1556–1566 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  105. 105.

    Cooper, G. M. et al. Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 15, 901–913 (2005).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  106. 106.

    Schwarz, J. M., Rödelsperger, C., Schuelke, M. & Seelow, D. MutationTaster evaluates disease-causing potential of sequence alterations. Nat. Methods 7, 575–576 (2010).

    PubMed  Article  CAS  Google Scholar 

  107. 107.

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  108. 108.

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  109. 109.

    Consortium, G. T., GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  CAS  Google Scholar 

  110. 110.

    Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  111. 111.

    Bauer-Mehren, A., Rautschka, M., Sanz, F. & Furlong, L. I. DisGeNET: a Cytoscape plugin to visualize, integrate, search and analyze gene-disease networks. Bioinformatics 26, 2924–2926 (2010).

    PubMed  Article  CAS  Google Scholar 

  112. 112.

    Günther, S. et al. SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic Acids Res. 36, D919–D922 (2008).

    PubMed  Article  CAS  Google Scholar 

  113. 113.

    Kuhn, M. et al. STITCH 4: integration of protein-chemical interactions with user data. Nucleic Acids Res. 42, D401–D407 (2014).

    PubMed  Article  CAS  Google Scholar 

  114. 114.

    Wishart, D. S. et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 34, D668–D672 (2006).

    PubMed  Article  CAS  Google Scholar 

  115. 115.

    Whirl-Carrillo, M. et al. Pharmacogenomics knowledge for personalized medicine. Clin. Pharmacol. Ther. 92, 414–417 (2012).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

Download references

Acknowledgements

We gratefully thank all study participants, their relatives and the staff at the recruitment centers for their invaluable contributions. We thank all contributors to the CREAM Consortium, 23andMe and UKEV for their generosity in sharing data and help in the production of this publication. Funding for this particular GWAS mega-analysis was provided by the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant 648268), the Netherlands Organisation for Scientific Research (NWO, grant 91815655) and the National Eye Institute (grant R01EY020483). Funding agencies that facilitated the execution of the individual studies are acknowledged in the Supplementary Note.

The Consortium for Refractive Error and Myopia (CREAM Consortium):

Tin Aung82,83, Amutha B. Veluchamy82,84, Kathryn P. Burdon58, Harry Campbell36, Li Jia Chen85, Peng Chen83, Wei Chen86, Emily Chew45, Margaret M. Deangelis87, Xiaohu Ding88, Angela Döring66, David M. Evans89,90, Sheng Feng91, Brian Fleck92, Rhys D. Fogarty58, Jeremy R. Fondran43, Maurizio Fossarello93, Xiaobo Guo88,94, Annet E. G. Haarman1,2, Mingguang He23,88, Laura D. Howe90,95, Sarayut Janmahasatian43, Vishal Jhanji85, Mika Kähönen96, Jaakko Kaprio20,97, John P. Kemp90, Kay-Tee Khaw11, Chiea-Chuen Khor29,83,87,98, Eva Krapohl99, Jean-François Korobelnik100,101, Kris Lee9, Shi-Ming Li22, Yi Lu56, Robert N. Luben11, Kari-Matti Mäkelä49, George McMahon90, Akira Meguro102, Evelin Mihailov18, Masahiro Miyake16, Nobuhisa Mizuki102, Margaux Morrison87, Vinay Nangia103, Konrad Oexle104, Songhomitra Panda-Jonas103, Chi Pui Pang85, Mario Pirastu105, Robert Plomin99, Taina Rantanen77, Maria Schache23, Ilkka Seppälä49, George D. Smith90, Beate St Pourcain90,106, Pancy O. Tam85, J. Willem L. Tideman1,2, Nicholas J. Timpson90, Simona Vaccargiu105, Zoran Vatavuk35, Jie Jin Wang23,24, Ningli Wang22, Nick J. Wareham107, Alan F. Wright33, Liang Xu22, Maurice K. H. Yap108, Seyhan Yazar74, Shea Ping Yip109, Nagahisa Yoshimura16, Alvin L. Young9, Jing Hua Zhao107 and Xiangtian Zhou86 UK Biobank Eye and Vision Consortium: Tariq M. Aslam110, Sarah A. Barman111, Jenny H. Barrett112, Paul N. Bishop110, Peter Blows12, Catey Bunce113, Roxana O. Carare114, Usha Chakravarthy115, Michelle Chan12, Sharon Chua12, David Crabb116, Alexander Day12, Parul Desai12, Bal Dhillon117, Andrew D. Dick118, Cathy A. Egan12, Sarah Ennis114, Marcus Fruttiger12, John Gallacher119, David F. Garway-Heath12, Jane Gibson114, Dan M. Gore12, Alison Hardcastle12, Simon P. Harding120, Ruth E. Hogg121, Pearse A. Keane12, Peng Tee Khaw12, Gerassimos Lascaratos12, Andrew Lotery122, Phil J. Luthert12, Tom J. MacGillivray123, Sarah L. Mackie124, Keith R. Martin125, Michelle McGaughey126, Bernadette McGuinness126, Gareth J. McKay126, Martin McKibbin127, Danny Mitry12, Tony Moore12, James E. Morgan26, Zaynah A. Muthy12, Eoin O’Sullivan128, Chris Owen129, Praveen J. Patel12, Euan N. Paterson126, Tunde Peto115, Axel Petzold130, Alicja R. Rudnicka129, Jay E. Self122,131, Sobha Sivaprasad12, David H. W. Steel132, Irene M. Stratton133, Nicholas Strouthidis12,Cathie L. M. Sudlow134, Caroline Thaung12, Dhanes Thomas12, Emanuele Trucco135, Adnan Tufail12, Stephen A. Vernon136, Ananth C. Viswanathan12, Jayne V. Woodside126, Max Yates137, Jennifer L. Y. Yip11 and Yalin Zheng120 23andMe Research Team: Michelle Agee7, Babak Alipanahi7, Adam Auton7, Robert K. Bell7, Katarzyna Bryc7, Sarah L. Elson7, Pierre Fontanillas7, David A. Hinds7, Jennifer C. McCreight7, Karen E. Huber7, Aaron Kleinman7, Nadia K. Litterman7, Matthew H. McIntyre7, Joanna L. Mountain7, Elizabeth S. Noblin7, Carrie A. M. Northover7, Steven J. Pitts7, J. Fah Sathirapongsasuti7, Olga V. Sazonova7, Janie F. Shelton7, Suyash Shringarpure7, Chao Tian7, Vladimir Vacic7 and Catherine H. Wilson7

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Contributions

M.S.T., V.J.M.V., S.M., J.A.G., A.I.I., R.W., P.G.H., A.I.I. and E.M.v.L. performed the analyses. C.C.W.K., V.J.M.V., M.S.T., R.W., J.A.G. and S.M. drafted the manuscript, and C.J.H., P.G.H., A.P.K., C.M.v.D., D.S., E.M.v.L., J.E.B.-W., J.Y.T., N.A.F., Q.F., S.-M.S. and V.V. critically reviewed the manuscript. A.N., A.P.K., A.T., C.B., C. Gieger, C.L.S., C.-Y.C., G. Biino, G.C.-P., I.R., J.E.B.W., J.E.H., J. S. Ried, J.W., J.X., K.M.W., K.Y., P.M.C., S.M.H., M.S.T., N.A.F., N.E., P.C., P. Gharahkhani, P.K.J., Q.F., R. Höhn, R.L.S., R.P.I., R.W., T.H., T.-H.S.-A., T.Z., V.V., W.-Y.S., W.Z., X.L.S., Y.C.T., Y.S. and Y.Y.T. performed data analysis for the individual studies; A.D.P., A.G.U., A.T., A.W.H., B.E.K.K., C.C.W.K., C.D., C. Grazal, C.H., C.J.H., C.W., C.-Y.C., D.A.M., F.R., G. Bencic, H.M.-H., J.A.G., J.B.J., J.E.B.-W., J.E.C., J.F.W., J.H.L., J.R.V., J. S. Rahi, J. S. Ried, J.Y.T., K.Y., M.A.M.-S., N.G.M., N.P., O. Polašek, O. Pärssinen, O.T.R., P. Gupta, P.J.F., P.M., P.N.B., R.K., S.K.I., S.-M.S., T.L., T.M., W.Z., Y.C.T. and Y.X.W. contributed to data assembly. A.A.B.B., A.W., C. Grazal, D.S., K.N.W., S.W.T. and T.L.Y. performed expression experiments, and M.S.T., A.A.B.B., P.J.v.d.S. and R. Hask performed in silico pathway analyses. C.C.W.K. and C.J.H. conceived and designed the outline of the current report, and supervised conduction of experiments and analyses jointly with A.M., A.H., A.W.H., C.D., C.H., C.J.H., C.M.v.D., C.W., C.-Y.C., D.A.M., D.S., E.-S.T., F.M., G. Biino, I.R., J.A.G., J.B.J., J.E.B.-W., J.E.C., J.F.W., J.H.L., J.R.V., J.Y.T., N.A., N.A.F., N.P., O. Pärssinen, O.T.R., P.J.F., P.N.B., S.K.I., S.-M.S., T.L., T.Y.W., T.L.Y., V.V., Y.X.W. and Y.Y.T. M.P.C. analyzed the data and performed statistical analyses. The 23andMe research team, CREAM and the UK Biobank Eye and Vision Consortium contributed reagents/materials/analysis tools and performed statistical analyses.

Corresponding author

Correspondence to Caroline C. W. Klaver.

Ethics declarations

Competing interests

N.A.F., N.E., J.Y.T. and the 23andMe Research Team are current or former employees of 23andMe, Inc., and hold stock or stock options in 23andMe. J.B.J. is a patent holder with Biocompatibles UK Ltd. (Franham, Surrey, UK) (Title: Treatment of eye diseases using encapsulated cells encoding and secreting neuroprotective factor and /or anti-angiogenic factor; international patent no. 20120263794) and is included in a patent application with University of Heidelberg (Heidelberg, Germany) (Title: Agents for use in the therapeutic or prophylactic treatment of myopia or hyperopia; European patent no. 3 070 101). The other authors declare no competing financial interests.

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Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12, Supplementary Note and Supplementary Tables 1, 5–9, 12 and 17

Reporting Summary

Supplementary Table 2

Stage 1 - 3 Meta-Analyses and Conditional Analysis

Supplementary Table 3

Stage 3 Index Variants of FE and RE analyses

Supplementary Table 4

HWE p-value per cohort TopSNPs Stage 3

Supplementary Table 10

DEPICT analysis - gene-set enrichment

Supplementary Table 11

Ranking of associated genes according to biological plausibility

Supplementary Table 13

Genes with Human Ocular Phenotypes

Supplementary Table 14

Genes with Mice Ocular Phenotypes

Supplementary Table 15

Expression of Candidate Genes in Ocular Tissues

Supplementary Table 16

Genetic variants harboring known drug targets

Supplementary Data 1

Locus Zoom Plots

Supplementary Data 2

Forest Plots Stage 3 and Conditional Loci

Supplementary Data 3

Summary Statistics Stage 3 Meta-analysis

Appendices

The CREAM Consortium

Tin Aung82,83, Amutha B. Veluchamy82, 84, Kathryn P. Burdon58, Harry Campbell36, Li Jia Chen85, Peng Chen83, Wei Chen86, Emily Chew45, Margaret M. Deangelis87, Xiaohu Ding88, Angela Döring66, David M. Evans89, 90, Sheng Feng91, Brian Fleck92, Rhys D. Fogarty58, Jeremy R. Fondran43, Maurizio Fossarello93, Xiaobo Guo88, 94, Annet E. G. Haarman1, 2, Mingguang He23, 88, Laura D. Howe90, 95, Sarayut Janmahasatian43, Vishal Jhanji85, Mika Kähönen96, Jaakko Kaprio20, 97, John P. Kemp90, Kay-Tee Khaw11, Chiea-Chuen Khor29, 83, 87, 98, Eva Krapohl99, Jean-François Korobelnik100, 101, Kris Lee9, Shi-Ming Li22, Yi Lu56, Robert N. Luben11, Kari-Matti Mäkelä49, George McMahon90, Akira Meguro102, Evelin Mihailov18, Masahiro Miyake16, Nobuhisa Mizuki102, Margaux Morrison87, Vinay Nangia103, Konrad Oexle104, Songhomitra Panda-Jonas103, Chi Pui Pang85, Mario Pirastu105, Robert Plomin99, Taina Rantanen77, Maria Schache23, Ilkka Seppälä49, George D. Smith90, Beate St Pourcain90, 106, Pancy O. Tam85, J. Willem L. Tideman1, 2, Nicholas J. Timpson90, Simona Vaccargiu105, Zoran Vatavuk35, Jie Jin Wang23, 24, Ningli Wang22, Nick J. Wareham107, Alan F. Wright33, Liang Xu22, Maurice K. H. Yap108, Seyhan Yazar74, Shea Ping Yip109, Nagahisa Yoshimura16, Alvin L. Young9, Jing Hua Zhao107 and Xiangtian Zhou86

23andMe Research Team

Michelle Agee7, Babak Alipanahi7, Adam Auton7, Robert K. Bell7, Katarzyna Bryc7, Sarah L. Elson7, Pierre Fontanillas7, David A. Hinds7, Jennifer C. McCreight7, Karen E. Huber7, Aaron Kleinman7, Nadia K. Litterman7, Matthew H. McIntyre7, Joanna L. Mountain7, Elizabeth S. Noblin7, Carrie A. M. Northover7, Steven J. Pitts7, J. Fah Sathirapongsasuti7, Olga V. Sazonova7, Janie F. Shelton7, Suyash Shringarpure7, Chao Tian7, Vladimir Vacic7 and Catherine H. Wilson7

UK Biobank Eye and Vision Consortium

Tariq M. Aslam110, Sarah A. Barman111, Jenny H. Barrett112, Paul N. Bishop110, Peter Blows12, Catey Bunce113, Roxana O. Carare114, Usha Chakravarthy115, Michelle Chan12, Sharon Chua12, David Crabb116, Alexander Day12, Parul Desai12, Bal Dhillon117, Andrew D. Dick118, Cathy A. Egan12, Sarah Ennis114, Marcus Fruttiger12, John Gallacher119, David F. Garway-Heath12, Jane Gibson114, Dan M. Gore12, Alison Hardcastle12, Simon P. Harding120, Ruth E. Hogg121, Pearse A. Keane12, Peng Tee Khaw12, Gerassimos Lascaratos12, Andrew Lotery122, Phil J. Luthert12, Tom J. MacGillivray123, Sarah L. Mackie124, Keith R. Martin125, Michelle McGaughey126, Bernadette McGuinness126, Gareth J. McKay126, Martin McKibbin127, Danny Mitry12, Tony Moore12, James E. Morgan26, Zaynah A. Muthy12, Eoin O’Sullivan128, Chris Owen129, Praveen J. Patel12, Euan N. Paterson126, Tunde Peto115, Axel Petzold130, Alicja R. Rudnicka129, Jay E. Self122,131, Sobha Sivaprasad12, David H. W. Steel132, Irene M. Stratton133, Nicholas Strouthidis12, Cathie L. M. Sudlow134, Caroline Thaung12, Dhanes Thomas12, Emanuele Trucco135, Adnan Tufail12, Stephen A. Vernon136, Ananth C. Viswanathan12, Jayne V. Woodside126, Max Yates137, Jennifer L. Y. Yip11 and Yalin Zheng120

82Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore. 83Department of Ophthalmology, National University HealthSystems, National University of Singapore, Singapore, Singapore. 84Duke-NUS Medical School, Singapore, Singapore, Singapore. 85Department ofOphthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong Eye Hospital, Kowloon, Hong Kong. 86School of Ophthalmologyand Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China. 87Department of Ophthalmology and Visual Sciences, John Moran EyeCenter, University of Utah, Salt Lake City, UT, USA. 88State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University,Guangzhou, China. 89Translational Research Institute, University of Queensland Diamantina Institute, Brisbane, Queensland, Australia. 90MRC IntegrativeEpidemiology Unit, University of Bristol, Bristol, UK. 91Department of Pediatric Ophthalmology, Duke Eye Center For Human Genetics, Durham, NC, USA. 92Princess Alexandra Eye Pavilion, Edinburgh, UK. 93University Hospital ‘San Giovanni di Dio’, Cagliari, Italy. 94Department of Statistical Science, Schoolof Mathematics, Sun Yat-Sen University, Guangzhou, China. 95School of Social and Community Medicine, University of Bristol, Bristol, UK. 96Departmentof Clinical Physiology, Tampere University Hospital and School of Medicine, University of Tampere, Tampere, Finland. 97Institute for Molecular MedicineFinland FIMM, HiLIFE Unit, University of Helsinki, Helsinki, Finland. 98Division of Human Genetics, Genome Institute of Singapore, Singapore, Singapore. 99MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK. 100Université de Bordeaux, Bordeaux, France. 101Institut National de la Santé Et de la Recherche Médicale (INSERM), Institut de Santé Publiqued’Épidémiologie et de Développement (ISPED), Centre INSERM U897–Epidemiologie-Biostatistique, Bordeaux, France. 102Department of Ophthalmology, Yokohama City University School of Medicine, Yokohama, Japan. 103Suraj Eye Institute, Nagpur, Maharashtra, India. 104Institute of Neurogenomics, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany. 105Institute of Genetic and Biomedic Research, National Research Council, Cagliari, Italy. 106Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands. 107MRC Epidemiology Unit, Instituteof Metabolic Sciences, University of Cambridge, Cambridge, UK. 108Centre for Myopia Research, School of Optometry, Hong Kong Polytechnic University, Hong Kong, Hong Kong. 109Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong, Hong Kong. 110ManchesterRoyal Eye Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK. 111School of ComputerScience and Mathematics, Kingston University, Surrey, UK. 112Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, Universityof Leeds, Leeds, UK. 113Primary Care & Public Health Sciences, King’s College London, London, UK. 114Faculty of Medicine University of Southampton, Southampton General Hospital, Southampton, UK. 115School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast, NorthernIreland, UK. 116Optometry and Visual Science, School of Health Science, City, University of London, London, UK. 117Division of Health Sciences & Centrefor Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK. 118School of Clinical Sciences, Faculty of Medicine and Dentistry, University of Bristol,Bristol, UK. 119Department of Psychiatry, Oxford University, Warneford Hospital, Oxford, UK. 120Department of Eye and Vision Science, University ofLiverpool, Liverpool, UK. 121Centre for Experimental Medicine, Queen’s University Belfast, Belfast, Northern Ireland, UK. 122Department of Ophthalmology, University of Southampton NHS Foundation Trust, Southampton, UK. 123Edinburgh Imaging, University of Edinburgh, Edinburgh, UK. 124Leeds Instituteof Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK. 125Department of Ophthalmology, Cambridge University Hospitals NHSFoundation Trust, Cambridge, UK. 126Centre for Public Health, Queen’s University Belfast, Belfast, Northern Ireland, UK. 127Department of Ophthalmology,Leeds Teaching Hospitals NHS Trust, Leeds, UK. 128Department of Ophthalmology, King’s College Hospital NHS Foundation Trust, London, UK. 129StGeorge’s, University of London, London, UK. 130UCL Institute of Neurology, London, UK. 131Clinical and Experimental Sciences, Faculty of Medicine,University of Southampton, Southampton, UK. 132Institute of Genetic Medicine, Newcastle University, Newcastle Upon Tyne, UK. 133Gloucestershire RetinalResearch Group, Gloucestershire Hospitals NHS Foundation Trust, Cheltenham General Hospital, Cheltenham, UK. 134Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK. 135School of Science and Engineering, University of Dundee, Dundee, UK. 136Nottingham University Hospitals NHSTrust, Nottingham, UK. 137Norwich Medical School, University of East Anglia, Norwich, Norfolk, UK.

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Tedja, M.S., Wojciechowski, R., Hysi, P.G. et al. Genome-wide association meta-analysis highlights light-induced signaling as a driver for refractive error. Nat Genet 50, 834–848 (2018). https://doi.org/10.1038/s41588-018-0127-7

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