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AMD Genetics: Methods and Analyses for Association, Progression, and Prediction

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Age-related Macular Degeneration

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1256))

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Abstract

Age-related macular degeneration (AMD) is a multifactorial neurodegenerative disease, which is a leading cause of vision loss among the elderly in the developed countries. As one of the most successful examples of genome-wide association study (GWAS), a large number of genetic studies have been conducted to explore the genetic basis for AMD and its progression, of which over 30 loci were identified and confirmed. In this chapter, we review the recent development and findings of GWAS for AMD risk and progression. Then, we present emerging methods and models for predicting AMD development or its progression using large-scale genetic data. Finally, we discuss a set of novel statistical and analytical methods that were recently developed to tackle the challenges such as analyzing bilateral correlated eye-level outcomes that are subject to censoring with high-dimensional genetic data. Future directions for analytical studies of AMD genetics are also proposed.

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References

  1. Age-Related Eye Disease Study Research Group (1999) The Age-Related Eye Disease Study (AREDS): design implications. AREDS report no. 1. Control Clin Trials 20:573–600

    Article  Google Scholar 

  2. Jager RD, Mieler WF, Miller JW (2008) Age-related macular degeneration. N Engl J Med 358:2606–2617

    Article  CAS  PubMed  Google Scholar 

  3. Swaroop A, Chew EY, Rickman CB, Abecasis GR (2009) Unraveling a multifactorial late-onset disease: from genetic susceptibility to disease mechanisms for age-related macular degeneration. Annu Rev Genomics Hum Genet 10:19–43

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Seddon JM, Ajani UA, Mitchell BD (1997) Familial aggregation of age-related maculopathy. Am J Ophthalmol 123:199–206

    Article  CAS  PubMed  Google Scholar 

  5. Seddon JM, Cote J, Page WF, Aggen SH, Neale MC (2005) The US twin study of age-related macular degeneration: relative roles of genetic and environmental influences. Arch Ophthalmol 123:321–327

    Article  PubMed  Google Scholar 

  6. Fisher SA et al (2005) Meta-analysis of genome scans of age-related macular degeneration. Hum Mol Genet 14:2257–2264

    Article  CAS  PubMed  Google Scholar 

  7. Klein RJ et al (2005) Complement factor H polymorphism in age-related macular degeneration. Science 308:385–389

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Gold B et al (2006) Variation in factor B (BF) and complement component 2 (C2) genes is associated with age-related macular degeneration. Nat Genet 38:458–462

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Fagerness JA et al (2009) Variation near complement factor I is associated with risk of advanced AMD. Eur J Hum Genet 17:100–104

    Article  CAS  PubMed  Google Scholar 

  10. Yates JR et al (2007) Complement C3 variant and the risk of age-related macular degeneration. N Engl J Med 357:553–561

    Article  CAS  PubMed  Google Scholar 

  11. Yang Z et al (2006) A variant of the HTRA1 gene increases susceptibility to age-related macular degeneration. Science 314:992–993

    Article  CAS  PubMed  Google Scholar 

  12. Jakobsdottir J et al (2005) Susceptibility genes for age-related maculopathy on chromosome 10q26. Am J Hum Genet 77:389–407

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Fritsche LG et al (2013) Seven new loci associated with age-related macular degeneration. Nat Genet 45:433–439

    Article  CAS  PubMed  Google Scholar 

  14. Yu Y et al (2011) Common variants near FRK/COL10A1 and VEGFA are associated with advanced age-related macular degeneration. Hum Mol Genet 20:3699–3709

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  16. Zhan X et al (2013) Identification of a rare coding variant in complement 3 associated with age-related macular degeneration. Nat Genet 45:1375–1379

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kondo N, Bessho H, Honda S, Negi A (2011) Complement factor H Y402H variant and risk of age-related macular degeneration in Asians: a systematic review and meta-analysis. Ophthalmology 118:339–344

    Article  PubMed  Google Scholar 

  18. Restrepo NA et al (2014) Genetic determinants of age-related macular degeneration in diverse populations from the PAGE study. Invest Ophthalmol Vis Sci 55:6839–6850

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Spencer KL, Glenn K, Brown-Gentry K, Haines JL, Crawford DC (2012) Population differences in genetic risk for age-related macular degeneration and implications for genetic testing. Arch Ophthalmol 130:116–117

    Article  PubMed  PubMed Central  Google Scholar 

  20. DeAngelis MM et al (2017) Genetics of age-related macular degeneration (AMD). Hum Mol Genet 26:R246

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. AREDS2 Research Group et al (2012) The Age-Related Eye Disease Study 2 (AREDS2): study design and baseline characteristics (AREDS2 report number 1). Ophthalmology 119:2282–2289

    Article  Google Scholar 

  22. Age-Related Eye Disease Study 2 Research Group (2013) Lutein + zeaxanthin and omega-3 fatty acids for age-related macular degeneration: the Age-Related Eye Disease Study 2 (AREDS2) randomized clinical trial. JAMA 309:2005–2015

    Article  CAS  Google Scholar 

  23. Seddon JM et al (2007) Association of CFH Y402H and LOC387715 A69S with progression of age-related macular degeneration. JAMA 297:1793–1800

    Article  CAS  PubMed  Google Scholar 

  24. Seddon JM, Reynolds R, Yu Y, Rosner B (2014) Three new genetic loci (R1210C in CFH, variants in COL8A1 and RAD51B) are independently related to progression to advanced macular degeneration. PLoS One 9:e87047

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Perlee LT et al (2013) Inclusion of genotype with fundus phenotype improves accuracy of predicting choroidal neovascularization and geographic atrophy. Ophthalmology 120:1880–1892

    Article  PubMed  Google Scholar 

  26. Farwick A, Wellmann J, Stoll M, Pauleikhoff D, Hense HW (2010) Susceptibility genes and progression in age-related maculopathy: a study of single eyes. Invest Ophthalmol Vis Sci 51:731–736

    Article  PubMed  Google Scholar 

  27. Yu Y, Reynolds R, Rosner B, Daly MJ, Seddon JM (2012) Prospective assessment of genetic effects on progression to different stages of age-related macular degeneration using multistate Markov models. Invest Ophthalmol Vis Sci 53:1548–1556

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Gangnon RE et al (2014) Misclassification can explain most apparent regression of age-related macular degeneration: results from multistate models with misclassification. Invest Ophthalmol Vis Sci 55:1780–1786

    Article  PubMed  PubMed Central  Google Scholar 

  29. Sardell RJ et al (2016) Progression rate from intermediate to advanced age-related macular degeneration is correlated with the number of risk alleles at the CFH locus. Invest Ophthalmol Vis Sci 57:6107–6115

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ding Y et al (2017) Bivariate analysis of age-related macular degeneration progression using genetic risk scores. Genetics 206:119–133

    Article  PubMed  PubMed Central  Google Scholar 

  31. Klein R, Klein BE, Myers CE (2011) Risk assessment models for late age-related macular degeneration. Arch Ophthalmol 129:1605–1606

    Article  PubMed  Google Scholar 

  32. Yan Q et al (2018) Genome-wide analysis of disease progression in age-related macular degeneration. Hum Mol Genet 27:929–940

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Sun T, Liu Y, Cook RJ, Chen W, Ding Y (2018) Copula-based score test for bivariate time-to-event data, with application to a genetic study of AMD progression. Lifetime Data Anal

    Google Scholar 

  34. Sun T, Ding Y (2019) Copula-based semiparametric regression method for bivariate data under general interval censoring. Biostatistics

    Google Scholar 

  35. Seddon JM et al (2009) Prediction model for prevalence and incidence of advanced age-related macular degeneration based on genetic, demographic, and environmental variables. Invest Ophthalmol Vis Sci 50:2044–2053

    Article  PubMed  Google Scholar 

  36. Seddon JM, Reynolds R, Yu Y, Daly MJ, Rosner B (2011) Risk models for progression to advanced age-related macular degeneration using demographic, environmental, genetic, and ocular factors. Ophthalmology 118:2203–2211

    Article  PubMed  Google Scholar 

  37. Seddon JM (2013) Genetic and environmental underpinnings to age-related ocular diseases. Invest Ophthalmol Vis Sci 54:ORSF28–ORSF30

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Abramoff MD et al (2016) Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 57:5200–5206

    Article  PubMed  Google Scholar 

  39. Gulshan V et al (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316:2402–2410

    Article  PubMed  Google Scholar 

  40. Kermany DS et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172:1122–1131.e9

    Article  CAS  PubMed  Google Scholar 

  41. Devalla SK et al (2018) A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head. Invest Ophthalmol Vis Sci 59:63–74

    Article  PubMed  Google Scholar 

  42. Grassmann F et al (2018) A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology 125:1410–1420

    Article  PubMed  Google Scholar 

  43. Burlina PM et al (2017) Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol 135:1170–1176

    Article  PubMed  PubMed Central  Google Scholar 

  44. Poplin R et al (2018) Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng 2:158–164

    Article  PubMed  Google Scholar 

  45. Peng Y et al (2019) DeepSeeNet: a deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs. Ophthalmology 126:565–575

    Article  PubMed  Google Scholar 

  46. Yan Q et al (2020) Deep-learning-based prediction of late age-related macular degeneration progression. Nat Mach Intell 2:141–150

    Article  PubMed  PubMed Central  Google Scholar 

  47. Restrepo NA et al (2015) Mitochondrial variation and the risk of age-related macular degeneration across diverse populations. Pac Symp Biocomput 2015:243–254

    Google Scholar 

  48. Riazi-Esfahani M, Kuppermann BD, Kenney MC (2017) The role of mitochondria in AMD: current knowledge and future applications. J Ophthalmic Vis Res 12:424–428

    Article  PubMed  PubMed Central  Google Scholar 

  49. Udar N et al (2009) Mitochondrial DNA haplogroups associated with age-related macular degeneration. Invest Ophthalmol Vis Sci 50:2966–2974

    Article  PubMed  Google Scholar 

  50. Askou AL, Alsing S, Holmgaard A, Bek T, Corydon TJ (2018) Dissecting microRNA dysregulation in age-related macular degeneration: new targets for eye gene therapy. Acta Ophthalmol 96:9–23

    Article  PubMed  Google Scholar 

  51. Elshelmani H, Rani S (2017) Exosomal MicroRNA discovery in age-related macular degeneration. Methods Mol Biol 1509:93–113

    Article  CAS  PubMed  Google Scholar 

  52. Desmettre TJ (2018) Epigenetics in age-related macular degeneration (AMD). J Fr Ophtalmol 41:e407–e415

    Article  CAS  PubMed  Google Scholar 

  53. Gemenetzi M, Lotery AJ (2014) The role of epigenetics in age-related macular degeneration. Eye (Lond) 28:1407–1417

    Article  CAS  Google Scholar 

  54. Liu MM, Chan CC, Tuo J (2012) Genetic mechanisms and age-related macular degeneration: common variants, rare variants, copy number variations, epigenetics, and mitochondrial genetics. Hum Genomics 6:13

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Ratnapriya R et al (2019) Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration. Nat Genet

    Google Scholar 

  56. Wei Y, Liu Y, Sun T, Chen W, Ding Y (2019) Gene-based association analysis for bivariate time-to-event data through functional regression with copula models. Biometrics. https://doi.org/10.1111/biom.13165

  57. Russakoff DB, Lamin A, Oakley JD, Dubis AM, Sivaprasad S (2019) Deep learning for prediction of AMD progression: a pilot study. Invest Ophthalmol Vis Sci 60:712–722

    Article  PubMed  Google Scholar 

  58. Wei Y, Hsu JC, Chen W, Chew EY,Ding Y (2020) A simultaneous inference procedure to identify subgroups from RCTs with survival outcomes: application to analysis of AMD progression studies. ar**v preprint ar**v:2003.10528

    Google Scholar 

  59. Ding Y, Kong S, Kang S, Chen W (2018) A semiparametric imputation approach for regression with censored covariate with application to an AMD progression study. Stat Med 37:3293–3308

    Article  PubMed  Google Scholar 

  60. Handa JT et al (2019) A systems biology approach towards understanding and treating non-neovascular age-related macular degeneration. Nat Commun 10:3347

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Peng Y et al (2018) DeepSeeNet: a deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs. Ophthalmology. https://doi.org/10.1016/j.ophtha.2018.11.015

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Author information

Authors and Affiliations

  1. Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, USA

    Qi Yan

  2. Division of Pulmonary Medicine, Department of Pediatrics, UPMC Children’s Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA

    Wei Chen

  3. Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA

    Ying Ding, Daniel E. Weeks & Wei Chen

  4. Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA

    Daniel E. Weeks & Wei Chen

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  1. Qi Yan
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  2. Ying Ding
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  3. Daniel E. Weeks
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  4. Wei Chen
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Corresponding author

Correspondence to Wei Chen .

Editor information

Editors and Affiliations

  1. Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD, USA

    Emily Y. Chew

  2. Neurobiology-Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD, USA

    Anand Swaroop

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Yan, Q., Ding, Y., Weeks, D.E., Chen, W. (2021). AMD Genetics: Methods and Analyses for Association, Progression, and Prediction. In: Chew, E.Y., Swaroop, A. (eds) Age-related Macular Degeneration. Advances in Experimental Medicine and Biology, vol 1256. Springer, Cham. https://doi.org/10.1007/978-3-030-66014-7_7

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