Refractive errors, in particular myopia, are a leading cause of morbidity and disability worldwide. Genetic investigation can improve understanding of the molecular mechanisms that underlie abnormal eye development and impaired vision. We conducted a meta-analysis of genome-wide association studies (GWAS) that involved 542,934 European participants and identified 336 novel genetic loci associated with refractive error. Collectively, all associated genetic variants explain 18.4% of heritability and improve the accuracy of myopia prediction (area under the curve (AUC) = 0.75). Our results suggest that refractive error is genetically heterogeneous, driven by genes that participate in the development of every anatomical component of the eye. In addition, our analyses suggest that genetic factors controlling circadian rhythm and pigmentation are also involved in the development of myopia and refractive error. These results may enable the prediction of refractive error and the development of personalized myopia prevention strategies in the future.
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Summary statistics from the cohorts that participated in the meta-analysis can be downloaded from ftp://twinr-ftp.kcl.ac.uk/Refractive_Error_MetaAnalysis_2020 and public repositories such as the GWAS Catalog (https://www.ebi.ac.uk/gwas/downloads/summary-statistics). These freely downloadable summary statistics are calculated using all cohorts described in this manuscript, except for the 23andMe participants. This is due to a non-negotiable clause in the 23andMe data transfer agreement, intended to protect the privacy of the 23andMe research participants.
To fully recreate our meta-analytic results, all bona fide researchers can obtain the 23andMe summary statistics by emailing 23andMe (email@example.com) and subsequently meta-analyzing them along with the freely accessible summary statistics for all the other cohorts.
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P.T.K. and P.J.F oversaw the UK Biobank eye data acquisition with support from the National Institute for Health Research (NIHR), Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology. The UK Biobank Eye and Vision Consortium was supported by grants from UK NIHR (BRC3_026), Moorfields Eye Charity (ST 15 11 E), Fight for Sight (1507/1508), The Macular Society, The International Glaucoma Association (IGA, Ashford UK) and Alcon Research Institute. V.V. is supported by a core UK Medical Research Council (MRC) grant MC_UU_00007/10. 23andMe thanks research participants and employees of 23andMe for making this work possible (a list of contributing staff is available in the Supplementary Note). Genotyping of the GERA cohort was funded by the US National Institute on Aging, the National Institute of Mental Health and the National Institute of Health Common Fund (RC2 AG036607); data analyses were funded by the National Eye Institute (NEI R01 EY027004, E.J.) and the National Institute of Diabetes and Digestive and Kidney Diseases (R01 DK116738, E.J.). The CREAM GWAS meta-analysis was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant 648268 to C.C.W.K), the Netherlands Organisation for Scientific Research (NWO, 91815655 to C.C.W.K) and the National Eye Institute (R01EY020483). V.J.M.V. acknowledges funding from the Netherlands Organisation for Scientific Research (NWO, grant 91617076). S.M. acknowledges support from the National Health and Medical Research Council (NHMRC) of Australia (grants 1150144, 1116360, 1154543, 1121979). EPIC-Norfolk infrastructure and core functions are supported by the MRC (G1000143) and Cancer Research UK (C864/A14136). Genotyping was funded by the MRC (MC_PC_13048). A.K.P. is supported by a Moorfields Eye Charity grant. P.J.F. received support from the Richard Desmond Charitable Trust, the National Institute for Health Research to Moorfields Eye Hospital and the Biomedical Research Centre for Ophthalmology. RW and PGH were supported by the National Eye Institute of the National Institutes of Health under award number R21EY029309. M.J.S. is a recipient of a Fight for Sight PhD studentship. K.P. is a recipient of a Fight for Sight PhD studentship. P.G.H. is the recipient of a FfS ECI fellowship. P.G.H. and C.J.H. acknowledge the TFC Frost Charitable Trust Support for the KCL Department of Ophthalmology. Statistical analyses were run in King’s College London on the Rosalind HPC LINUX Clusters and cloud servers. The UK Biobank data were accessed as part of the UK Biobank projects 669 and 17615. J.S.R. is supported in part by the NIHR Biomedical Research Centres at Moorfields Eye Hospital and the UCL Institute of Ophthalmology, and at the UCL Institute of Child Health and Great Ormond Street Hospital, and is an NIHR Senior Investigator. P.M.C. was funded by the Ulverscroft Foundation. O.A.M is supported by Wellcome Trust grant 206619_Z_17_Z and the NIHR Biomedical Research Centre at Moorfields Eye Hospital and the UCL Institute of Ophthalmology.
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a) UK Biobank (including the 102,117 subjects with direct refraction measurement and the imputed 108,956 likely myopes to 70,941 likely non-myopes, for a total of 179,897 subjects), b) Genetic Epidemiology Research on Adult Health and Aging (GERA, N = 34,998), c) 23andMe (106,086 cases and 85,757 controls, or 191,843 subjects in total).
Effect sizes are from two analyses, discovery (UK Biobank analysis on spherical equivalent + GERA, spherical equivalent + 23andMe, self-reported myopia cases and controls + UK Biobank inferred myopia cases and controls, for a total of N = 508,855 subjects) and the replication from the non-British CREAM Consortium participants (N = 34,079), used as replication. The z-scores for the discovery are on the y-axis and those from the CREAM cohort in the x-axis.
Extended Data Fig. 3 Distribution of the base-pair length (red) of the 449 regions associated in the meta-analysis of all available cohorts (from Supplementary Table 3), alongside the distribution of number of SNPs (blue) for each region.
Numbers in each of the axes in the figure are differentially colored to match the density curve they correspond to: red for the length of the region and blue for the number of SNPs.
Extended Data Fig. 4 Expression of genes located in the associated loci (from Supplementary Table 3) along the x-axis, across several human body tissues (y-axis).
The colors represent the centile ranking of the expression level of the gene in the tissue of interest. The hotter colors represent higher ranking of the gene expression and the colder colors low expression. Both genes and tissues are clustered in accordance with their pattern similarity. The symbol of all the genes could not be visualized and therefore are removed for the sake of clarity. Eye tissues, whether fetal or adult, appear to have similar patterns of gene expressions (clustered together at the bottom of the figure). Genes that are highly expressed in eye tissues fall in three clusters, shown with a black box. These clusters are displayed in more detail in Figure 4A, B and C.
Extended Data Fig. 5 Genes from the regions associated with RE (from Supplementary Table 3) that are particularly expressed in eye tissues, compared to non-ocular tissues.
These clusters are those highlighted in Supplementary Figure 3, but for the sake of clarity they are shown in transposed orientation compared to the previous figure (here genes in the y-axis and eye tissues in the x-axis), but same color codes as before. The dendrograms represent the degree of similarity observed for both tissues and gene expressions. The clusters are given in the order in which they were clustered together, from left to right: a) genes that are expressed more in other ocular tissues (fetal and adult) but much less in the adult retina. b) genes that are highly expressed in the retina and other ocular tissues, and c) genes that are expressed in the retina, but less in the other ocular tissues tested.
Extended Data Fig. 6 Results of the LD score regression analysis applied to specifically expressed genes on multiple tissue for the meta-analysis results.
Each point represents one tissue or cell line (along the x-axis) and the log10 value of the p-value for the enrichment of the meta-analysis results among genes expressed in these tissues. There were 205 tests carried out, one in each tissue and cell line, therefore only tissues with a correlation p-value< 0.00025 (Log_P > 3.6 in this figure), would have been significant after multiple testing. This condition was not fulfilled for any of the available tissues.
Extended Data Fig. 7 Mendelian randomization results on causality of IOP over refractive error. Single points in the graph represent coordinates determined by the effect of each specific SNP over IOP (x-axis, mmHg) and spherical equivalent (y-axis, Diopter units).
A total of 73 SNPs associated with IOP, but not directly associated with refractive error (that is p > 0.05) were selected as instruments. Values of associations with IOP were obtained from a meta-analysis of 139,555 European participants (Reference 50 in the manuscript) and the refractive error associations from 102,117 UK Biobank subjects. The lines represent the regression lines from each model, as specified in the figure legend. In some cases, these lines may not visible because they overlap (please refer to the values underneath the figure).
Extended Data Fig. 8 Venn’s Diagram of the number of SNPs considered in each of the stages of this study.
The different circles represent various stages, inclusion in the meta-analysis (blue), identification of significant loci (green), conditional analysis results identifying independent effects (red) and the total number of SNPs available for inclusion in prediction and heritability estimation in the independent (that is not part of the original meta-analysis) EPIC-Norfolk cohort (orange).
Extended Data Fig. 9 Prediction for the total number of SNPs and phenotypic variance explained as a function of GWAS sample size in future studies, based on the distribution of effects observed in the current meta-analysis.
The plot lines show the predicted relationship between the number of loci associated with refractive error (left vertical axis, blue line) and the variance they help explain (red line, right vertical axis), as a function of the sample size (x-axis) used in future GWAS or meta-analyses. These projections are consistent with the observed results, where an effective sample of 379,227 identified 904 independent signals after a conditional analysis, explaining 12–16% of refractive error variability.
Extended Data Fig. 10 The distribution of various natural selection test scores for SNPs associated with refractive error.
The values on the x-axis represent the ranking in terms of natural selection observed and the y-axis the density of that rank. The different tests shown are iHS, XP-EHH (CEU vs YRI), XP-EHH average score, XP-EHH maximum score and Tajima scores (black, green, red, blue and yellow respectively).
Supplementary Tables 1–26
Comparison of effect sizes illustrating the consistent effects observed in three of the major components of the meta-analysis.
LocusZoom Plots of association signals originating from autosomal regions and overlapping with genes indicated in the main manuscript. When available (not all SNPs are present and have linkage disequilibrium data available in the LocusZoom server files), the index SNP is highlighted, and the different colors denote LD with adjacent SNPs in that region. Colder colors usually denote independence (low LD). Occasionally, within the plot window, there will be different SNPs with stronger association compared to the index SNP, but their effects are sufficiently independent. The SNPs are annotated to the nearest protein coding gene, within a 250 kb interval.
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Hysi, P.G., Choquet, H., Khawaja, A.P. et al. Meta-analysis of 542,934 subjects of European ancestry identifies new genes and mechanisms predisposing to refractive error and myopia. Nat Genet 52, 401–407 (2020). https://doi.org/10.1038/s41588-020-0599-0
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