Multitrait analysis of glaucoma identifies new risk loci and enables polygenic prediction of disease susceptibility and progression (original) (raw)

Abstract

laucoma refers to a group of ocular conditions united by a clinically characteristic optic neuropathy associated with, but not dependent on, elevated intraocular pressure 1. It is the leading cause of irreversible blindness worldwide and is predicted to affect 76 million by 2020 (ref. 2,3). There is no single definitive biomarker for glaucoma, and diagnosis involves assessing clinical features, with characterization of the optic nerve head carrying the strongest evidential weight. Primary open-angle glaucoma (POAG) is the most prevalent subtype of glaucoma in people of European and African ancestry 2,4. POAG is asymptomatic in the early stages; currently approximately half of all cases in the community are undiagnosed even in developed countries 5. Early detection is paramount since existing treatments cannot restore vision that has been lost, and late presentation is a major risk factor for blindness 6. Thus, better strategies to identify high-risk individuals are urgently needed 7 ; more refined approaches can capitalize on the fact that POAG is one of the most heritable of all common human diseases 8-10. The lack of a currently cost-effective screening strategy for glaucoma 7 , coupled with very high heritability, make glaucoma an ideal candidate disease for the development and application of a PRS to facilitate risk stratification. Overlap of features shared by healthy optic nerves with those in the early stages of glaucoma makes it a difficult disease to diagnose early, necessitating costly ongoing monitoring of patients for progressive optic nerve degeneration 1. Once a glaucoma diagnosis is established, rates of progression vary widely between individuals, and considerable time can elapse before surveillance techniques adequately differentiate slow from more rapidly progressing cases 1. Progressive vision loss from glaucoma can be slowed, or in some cases halted, by timely intervention to reduce intraocular pressure

Loading...

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

References (54)

  1. Weinreb, R. N. & Khaw, P. T. Primary open-angle glaucoma. Lancet 363, 1711-1720 (2004).
  2. Tham, Y.-C. et al. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology 121, 2081-2090 (2014).
  3. Quigley, H. A. & Broman, A. T. The number of people with glaucoma worldwide in 2010 and 2020. Br. J. Ophthalmol. 90, 262-267 (2006).
  4. Chan, M. P. Y. et al. Glaucoma and intraocular pressure in EPIC-Norfolk Eye Study: cross sectional study. BMJ 358, j3889 (2017).
  5. Mitchell, P., Smith, W., Attebo, K. & Healey, P. R. Prevalence of open-angle glaucoma in Australia. The Blue Mountains Eye Study. Ophthalmology 103, 1661-1669 (1996).
  6. Fraser, S., Bunce, C. & Wormald, R. Risk factors for late presentation in chronic glaucoma. Invest. Ophthalmol. Vis. Sci. 40, 2251-2257 (1999).
  7. Burr, J. M. et al. The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation. Health Technol. Assess. 11, https://doi.org/10.3310/hta11410 (2007).
  8. Wang, K., Gaitsch, H., Poon, H., Cox, N. J. & Rzhetsky, A. Classification of common human diseases derived from shared genetic and environmental determinants. Nat. Genet. 49, 1319-1325 (2017).
  9. Sanfilippo, P. G., Hewitt, A. W., Hammond, C. J. & Mackey, D. A. The heritability of ocular traits. Surv. Ophthalmol. 55, 561-583 (2010).
  10. Choquet, H. et al. A multiethnic genome-wide association study of primary open-angle glaucoma identifies novel risk loci. Nat. Commun. 9, 2278 (2018).
  11. Leske, M. C., Heijl, A., Hyman, L., Bengtsson, B. & Komaroff, E. Factors for progression and glaucoma treatment: the Early Manifest Glaucoma Trial. Curr. Opin. Ophthalmol. 15, 102-106 (2004).
  12. Garway-Heath, D. F. et al. Latanoprost for open-angle glaucoma (UKGTS): a randomised, multicentre, placebo-controlled trial. Lancet 385, 1295-1304 (2015).
  13. Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229-237 (2018).
  14. MacGregor, S. et al. Genome-wide association study of intraocular pressure uncovers new pathways to glaucoma. Nat. Genet. 50, 1067-1071 (2018).
  15. Wu, Y., Zheng, Z., Visscher, P. M. & Yang, J. Quantifying the mapping precision of genome-wide association studies using whole-genome sequencing data. Genome Biol. 18, 86 (2017).
  16. Huang, L. et al. Genome-wide analysis identified 17 new loci influencing intra ocular pressure in Chinese population. Sci. China Life Sci. 62, 153-164 (2019).
  17. Kuchenbaecker, K. B. et al. Evaluation of polygenic risk scores for breast and ovarian cancer risk prediction in BRCA1 and BRCA2 mutation carriers. J. Natl. Cancer Inst. 109, djw302 (2017).
  18. Lecarpentier, J. et al. Prediction of breast and prostate cancer risks in male BRCA1 and BRCA2 mutation carriers using polygenic risk scores. J. Clin. Oncol. 35, 2240-2250 (2017).
  19. Hewitt, A. W., Mackey, D. A. & Craig, J. E. Myocilin allele-specific glaucoma phenotype database. Hum. Mutat. 29, 207-211 (2008).
  20. Han, X. et al. Myocilin gene Gln368Ter variant penetrance and association with glaucoma in population-based and registry-based studies. JAMA Ophthalmol. 137, 28-35 (2019).
  21. Gharahkhani, P. et al. Accurate imputation-based screening of Gln368Ter myocilin variant in primary open-angle glaucoma. Invest. Ophthalmol. Vis. Sci. 56, 5087-5093 (2015).
  22. Na, J. H. et al. Detection of glaucoma progression by assessment of segmented macular thickness data obtained using spectral domain optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 53, 3817-3826 (2012).
  23. 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).
  24. Pickrell, J. K. et al. Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 48, 709-717 (2016).
  25. Verhoeven, V. J. M. 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).
  26. Lopes, M. C. et al. Identification of a candidate gene for astigmatism. Invest. Ophthalmol. Vis. Sci. 54, 1260-1267 (2013).
  27. King, R. et al. Genomic locus modulating corneal thickness in the mouse identifies POU6F2 as a potential risk of developing glaucoma. PLoS Genet. 14, e1007145 (2018).
  28. Khawaja, A. P. et al. Genome-wide analyses identify 68 new loci associated with intraocular pressure and improve risk prediction for primary open-angle glaucoma. Nat. Genet. 50, 778-782 (2018).
  29. Gao, X. R., Huang, H., Nannini, D. R., Fan, F. & Kim, H. Genome-wide association analyses identify new loci influencing intraocular pressure. Hum. Mol. Genet. 27, 2205-2213 (2018).
  30. Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219-1224 (2018).
  31. Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 9, e1003348 (2013).
  32. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
  33. © The Author(s), under exclusive licence to Springer Nature America, Inc. 2020
  34. Centre for Vision Research, Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia. 8 Clinical Ophthalmology & Eye Health, Westmead Clinical School, University of Sydney, Sydney, New South Wales, Australia. 9 Department of Ophthalmology, King's College London, St. Thomas' Hospital, London, UK. 10 Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia. 11 Centre for Eye Research Australia, University of Melbourne, Melbourne, Victoria, Australia. 12 Department of Ophthalmology, Prince of Wales Hospital Randwick, Sydney, New South Wales, Australia. 13 South Australian Institute of Ophthalmology, Royal Adelaide Hospital, Adelaide, South Australia, Australia. 14 Biomedical Sciences Research Institute, Ulster University, Coleraine, UK. 15 Royal Victoria Hospital, Belfast Health and Social Care Trust, Belfast, UK.
  35. Auckland, Auckland, New Zealand. 18 Discipline of Ophthalmology, Faculty of Medicine and Health, University of Sydney, Sydney Eye Hospital, Sydney, New South Wales, Australia. 19 Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia. 20 Medical Research Council Human Genetics Unit, Medical Research Council Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK. 21 Department of Ophthalmology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland. 22 Department of Ophthalmology, University Medical Center Mainz, Mainz, Germany. 23 Department of Epidemiology and Medicine, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. 24 Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. 25 References 32. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203-209 (2018).
  36. Bengtsson, B. The variation and covariation of cup and disc diameters. Acta Ophthalmol. (Copenh.) 54, 804-818 (1976).
  37. Han, X. et al. Genome-wide association analysis of 95,549 individuals identifies novel loci and genes influencing optic disc morphology. Hum. Mol. Genet. https://doi.org/10.1093/hmg/ddz193 (2019).
  38. 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).
  39. Souzeau, E. et al. Australian and New Zealand Registry of Advanced Glaucoma: methodology and recruitment. Clin. Exp. Ophthalmol. 40, 569-575 (2012).
  40. Gharahkhani, P. et al. Common variants near ABCA1, AFAP1 and GMDS confer risk of primary open-angle glaucoma. Nat. Genet. 46, 1120-1125 (2014).
  41. Olsen, C. M. et al. Cohort profile: the QSkin Sun and Health Study. J. Epidemiol. 41, 929-929i (2012).
  42. Wiggs, J. L. et al. The NEIGHBOR consortium primary open-angle glaucoma genome-wide association study: rationale, study design, and clinical variables. J. Glaucoma 22, 517-525 (2013).
  43. Kwon, Y. H., Fingert, J. H., Kuehn, M. H. & Alward, W. L. M. Primary open-angle glaucoma. N. Engl. J. Med. 360, 1113-1124 (2009).
  44. Weinreb, R. N., Garway-Heath, D. F., Leung, C., Medeiros, F. A. & Liebmann, J. Diagnosis of Primary Open Angle Glaucoma: WGA consensus series-10 (Kugler Publications, 2017).
  45. Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284-290 (2015).
  46. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190-2191 (2010).
  47. 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).
  48. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369-375 (2012).
  49. 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).
  50. Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).
  51. Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576-592 (2015).
  52. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236-1241 (2015).
  53. Robin, X. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12, 77 (2011).
  54. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).