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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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.
Integrated supplementary information
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.
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.
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.
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.
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 Figures 1–5, Supplementary Note and Supplementary Tables 1,3 and 5
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.
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.
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).
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.
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.
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.
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.
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.
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).