Multitrait analysis of glaucoma identifies new risk loci and enables polygenic prediction of disease susceptibility and progression

Abstract

Glaucoma, a disease characterized by progressive optic nerve degeneration, can be prevented through timely diagnosis and treatment. We characterize optic nerve photographs of 67,040 UK Biobank participants and use a multitrait genetic model to identify risk loci for glaucoma. A glaucoma polygenic risk score (PRS) enables effective risk stratification in unselected glaucoma cases and modifies penetrance of the MYOC variant encoding p.Gln368Ter, the most common glaucoma-associated myocilin variant. In the unselected glaucoma population, individuals in the top PRS decile reach an absolute risk for glaucoma 10 years earlier than the bottom decile and are at 15-fold increased risk of developing advanced glaucoma (top 10% versus remaining 90%, odds ratio = 4.20). The PRS predicts glaucoma progression in prospectively monitored, early manifest glaucoma cases (P = 0.004) and surgical intervention in advanced disease (P = 3.6 × 106). This glaucoma PRS will facilitate the development of a personalized approach for earlier treatment of high-risk individuals, with less intensive monitoring and treatment being possible for lower-risk groups.

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Fig. 1: Manhattan plot displaying glaucoma-specific P values from the MTAG analysis.
Fig. 2: Comparison of the effect sizes (log ORs) for 114 genome-wide significant independent SNPs identified from the glaucoma multiple trait analysis of GWAS in the UKB versus those in independent glaucoma cohorts (meta-analysis of ANZRAG and NEIGHBORHOOD).
Fig. 3: Multitrait analysis of GWAS PRS prediction.
Fig. 4: Clinical implications of the glaucoma PRS.

Data availability

The UKB data are available through the UK Biobank Access Management System https://www.ukbiobank.ac.uk/. The GWAS summary statistics from the glaucoma MTAG analysis is available for research use at https://xikunhan.github.io/site/publication/. We will return the derived data fields following UKB policy; in due course, they will be available through the UK Biobank Access Management System.

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Acknowledgements

This work was conducted using the UK Biobank Resource (application no. 25331) and publicly available data from the IGGC. The 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. The eye and vision dataset has been developed with additional funding from the National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital and the UCL Institute of Ophthalmology, Fight for Sight charity, Moorfields Eye Charity, Macular Society, International Glaucoma Association and Alcon Research Institute. This work was also supported by grants from the National Health and Medical Research Council (NHMRC) of Australia (nos. 1107098, 1116360, 1116495, 1023911, 1150144, 1147571), the Ophthalmic Research Institute of Australia, the BrightFocus Foundation, the UK and Eire Glaucoma Society and charitable funds from the Royal Liverpool University Hospital. S.M., J.E.C., K.P.B., D.A.M. and A.W.H. are supported by NHMRC Fellowships (APP1154543, APP1154824, APP1059954, APP1154513, APP1103329). S.M. was supported by an Australian Research Council Future Fellowship (FT130101902). L.R.P. is supported by National Institutes of Health grant no. R01 EY015473. X.H. is supported by the University of Queensland Research Training Scholarship and Queensland Institute of Medical Research Berghofer PhD Top Up Scholarship. We thank D. Whiteman, R. Neale and C. Olson for providing access to the QSkin samples for use as controls as part of NHMRC grant no. 1063061. We thank S. Wood, J. Pearson and S. Gordon from the Queensland Institute of Medical Research Berghofer Research Institute for their support. The NEIGHBORHOOD consortium is supported by National Institutes of Health grant nos. P30 EY014104, R01 EY015473 and R01 EY022305.

Author information

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

Correspondence to Xikun Han.

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Competing interests

D.A.M. is consultant/advisor to Allergan, Inc. J.E.C., A.W.H. and S.M. are listed as coinventors on a patent application for the use of genetic risk scores to determine risk and guide treatment.

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Extended data

Extended Data Fig. 1 Study design.

We applied the multi-trait analysis of GWAS (MTAG) algorithm to datasets of European descent (unless otherwise specified). a, We applied MTAG to four datasets (glaucoma case-control GWAS from the UKBB; GWAS meta-analysis of intraocular pressure (IOP) from the International Glaucoma Genetics Consortium (IGGC) and the UKBB; Vertical cup-disc ratio (VCDR) GWAS data that was either adjusted for vertical disc diameter (VDD) in the UKBB dataset; or not adjusted for VDD in the IGGC). Novel variants identified through this analysis were then confirmed in two independent data sets: an Australasian cohort of advanced glaucoma (ANZRAG) and a consortium of cohorts from the United States (NEIGHBORHOOD). The clinical significance of the PRS derived from the MTAG analysis was validated in independent samples: first, in advanced glaucoma cases (ANZRAG and samples from Southampton/Liverpool in the UK), and second, in a prospectively monitored clinical cohort with early manifest glaucoma (PROGRESSA). b, Prediction in BMES, where we removed the IGGC VCDR and IGGC IOP GWAS from the training datasets, given that they contain BMES data. c, Prediction in the UKBB glaucoma and ICD-10 POAG cases. Here we removed all glaucoma cases and 3,000 controls with IOP/VCDR measurements as well as their relatives from UKBB VCDR/IOP GWAS. We also evaluated the performance of PRS in non-European ancestry (192 cases and 6,841 controls of South Asian ancestry in UKBB). d, Cumulative risk of glaucoma in UKBB. For the analysis of MYOC p.Gln368Ter carriers (n = 965; cases = 72; controls = 893), participants were stratified into tertiles of PRS. We also examined cumulative risk of glaucoma in the general population (that is in MYOC p.Gln368Ter non-carriers, n = 381,196; cases = 7,381; controls = 373,815) stratifying by deciles of the PRS. The discovery and testing datasets were designed to derive the PRS with no sample overlap (Supplementary Note).

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Supplementary Note, Figs. 1–13 and Tables 1–13

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Craig, J.E., Han, X., Qassim, A. et al. Multitrait analysis of glaucoma identifies new risk loci and enables polygenic prediction of disease susceptibility and progression. Nat Genet 52, 160–166 (2020). https://doi.org/10.1038/s41588-019-0556-y

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