Advancing AMD Genetics
A look at the evolution of this dynamic field, including emerging applications for patient care.
BY JOHANNA M. SEDDON, MD, SCM
As we demonstrated in our U.S. Twin Study of AMD in 2005, genetic factors play a substantial role in the etiology of AMD and associated macular characteristics, explaining 46% to 71% of the variation in the overall severity of the disease.1 Since that report, we and other researchers have made remarkable progress. Here I will discuss the evolution of AMD genetics, including the various biological pathways involved and innovative methods of using genotypes, phenotypes and other factors to predict risk of disease.
Early Findings
Besides the Twin Study, additional research identified a genetic effect in AMD. Among the key findings:
■ Complement factor H gene (CFH) was strongly associated with AMD2-5
■ Susceptibility genes for age-related maculopathy were identified on chromosome 10q266
■ Hypothetical LOC387715 was identified as a major susceptibility gene for AMD, independent of complement factor H7
■ Common variation in three genes, including a novel noncoding variant in CFH, was found to strongly influence risk of AMD8
■ Y402H complement factor H polymorphism was associated with exudative AMD9
■ Variation in complement 3 was associated with risk of AMD.10,11
■ A variant of the HTRA1 gene was found to increase susceptibility to AMD12
■ Variation near complement factor I was discovered to be associated with risk of advanced AMD.13
Signs of Decreased Risk
More recently, hepatic lipase (LIPC) and other genes in the high-density lipoprotein (HDL) pathway and also TIMP3 have been associated with AMD.141, 5 Adjusting for demographic and environmental variables in a study of 820 subjects (545 with advanced AMD and 275 without AMD), we found that the novel locus in the LIPC gene was associated with reduced risk of advanced AMD, independent of other genes and risk factors.
Lutein was protective and independent of the gene effects in this study. We included dietary lutein in our models because HDL is the major lipoprotein transporter of lutein and zeaxanthin in the body, and the T allele of the LIPC gene increases HDL. We also found that higher HDL reduced risk independent of LIPC.14,16-18 Therefore, both genetic susceptibility and behavioral and lifestyle factors have been found to modify the risk of developing AMD.
We also recently performed the largest meta-analysis of genome-wide association studies to date at that time for advanced AMD.19 In this study, two new common susceptibility alleles — rs1999930 on 6q21-q22.3 near FRK/COL10A1 and rs4711751 on 6p12 near VEGFA — were identified. In addition, 10 previously reported loci were confirmed with genome-wide significant signals.
Loci in the previously reported genes ABCA1 and COL8A1 were also detected, suggesting an association with advanced AMD. The novel variants identified in this 2011 study suggest that angiogenesis (VEGFA) and extracellular collagen matrix (FRK/COL10A and COL8A1) pathways contribute to the development of advanced AMD.
Statistical Predictive Models
Besides identifying genes associated with the risk of AMD, we have developed statistical models to predict which patients will likely progress from the early and intermediate stages of AMD to the advanced forms of geographic atrophy and neovascular disease. Our models include genetic factors along with demographic (age, sex, education), environmental (smoking, BMI, antioxidant use) and macular characteristics.20,21
These models of AMD progression include varying rates of progression of up to 12 years, AMD status at baseline, macular drusen size in both eyes at baseline, six genetic variants, and demographic and environmental factors. When we adjusted for environmental and genetic factors, we found that presence of drusen and increasing drusen size emerged as strong risk predictors of progression to advanced AMD. For individuals with the same baseline drusen phenotype and AMD status, we added demographic, lifestyle, and genetic factors, allowing us to differentiate those who were at low, medium, and high risk for progression. Models with varying combinations of these variables have excellent probability of predicting progression to advanced AMD.
Since 2006, we have designed several predictive models for AMD.8, 20, 22-23 The concordance statistic based on an algorithm in our early models — which combined six genetic variants and demographic and environmental variables — was 0.831 +/– 0.013, with an average follow-up time of 6.3 years. Our analyses in 2011 expanded on these models and added the presence and size of macular drusen and severity of AMD in each eye at baseline, grade of AMD at each follow-up visit, longer follow-up time, and a larger sample size. We also added genes in the HDL pathway and CFI.24 As a result, we saw the concordance statistic improve to 0.885 and 0.915 for 5-year and 10-year progressions, respectively. Results have practical applications in clinical settings and for designing clinical trials. However, it is important to note these applications are restricted to Caucasian populations, the high-risk group that has been the focus of our studies to date.
| Using Genetics to Prospectively Assess Stages of AMD |
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We can now predict onset of various stages of AMD that pharmaceutical companies may be able to use to design clinical trials for future treatments. In our recent research, we derived progression events and time to each stage of AMD from the longitudinal data of 2,560 subjects without advanced AMD. Single-nucleotide polymorphisms (SNPs) in 12 AMD risk loci were genotyped. A multistate Markov model for progression from normal to intermediate drusen, then to large drusen, and eventually to neovascular disease (NV) or geographic atrophy (GA) was applied to estimate stage-specific hazard ratios for each SNP. The effects of these genetic factors were also estimated by a multivariate multistate Markov model adjusted for baseline age, sex, smoking history, body mass index, education, antioxidant treatment, and the status of AMD in the fellow eye. When controlling for demographic and behavioral factors and other SNPs, we found that the TT genotype of rs10468017 in LIPC was associated with decreased risk of progression from large drusen to neovascular AMD. This genotype also tended to reduce the risk of progression from normal to intermediate drusen. The SNP rs1883025 (T allele) in ABCA1 was associated with decreased risk of progression from normal to intermediate drusen and from intermediate drusen to large drusen. The genes CFH, C3, CFB, and ARMS2/HTRA1 were associated with progression from intermediate drusen to large drusen and from large drusen to GA or NV. We concluded that genes in different pathways influence progression to different stages of AMD. In our most recent study, our risk prediction model was validated in an independent and external cohort. The study looked at progression from non-existent, early or intermediate stages to advanced subtypes of AMD. The findings will be useful for research clincal trials and personalized medicine. References:Yu Y, Reynolds R, Rosner B, Daly MJ, Seddon JM. Prospective assessment of genetic effects on progression to different stages of age-related macular degeneration using multistate Markov models. Invest Ophthalmol Vis Sci 2012;53(3):1548-1556. Seddon J, Reynolds R, Yu Y, Rosner B. Validation of a prediction algorithm for progression to advanced macular degeneration subtypes. JAMA Ophthalmol In press. |
Future Applications
Research on predictive models continues to increase rapidly. (See “Using Genetics to Prospectively Assess Stages of AMD.”) On an individual basis, these models could be used to generate risk scores indicating when patients might develop advanced forms of AMD and lose vision. At this point, recommending genetic testing may be best for members of high-risk families or for those with a diagnosis of early to intermediate AMD. The overall benefit of improving prediction of individual progression rates should be fully realized when genetic testing becomes more a part of routine clinical practice.
Dr. Seddon is a macular degeneration specialist. She is director of the Ophthalmic Epidemiology and Genetics Service at the New England Eye Center at Tufts Medical Center and a Professor of Ophthalmology at Tufts University School of Medicine, Boston.
References
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2. Klein RJ, Zeiss C, Chew EY, et al. Complement factor H polymorphism in age-related macular degeneration. Science 2005;308:385-389.
3. Edwards AO, Ritter R 3rd, Abel KJ, et al. Complement factor H polymorphism and age-related macular degeneration. Science 2005;308:421-424.
4. Haines JL, Hauser MA, Schmidt S, et al. Complement factor H variant increases the risk of age-related macular degeneration. Science 2005;308:419-421.
5. Hageman GS, Anderson DH, Johnson LV et al. A common haplotype in the complement regulatory gene factor H (HF1/CFH) predisposes individuals to age-related macular degeneration. Proc Natl Acad Sci USA 2005;102:7227-7232.
6. Jakobsdottir J, Conley Y, Weeks DE, et al. Susceptiblity genes for age-related maculopathy on chromosome 10q26. Am J Hum Genet 2005;77:389-407.
7. Rivera A, Fisher SA, Fritsche LG, et al. Hypothetical LOC387715 is a second major susceptibility gene for age-related macular degeneration, contributing independently of complement factor H to disease risk. Hum Mol Genet 2005;14:3227-3236.
8. Maller J, George S, Purcell S, et al. Common variation in three genes, including a noncoding variant in CFH, strongly influences risk of age-related macular degeneration. Nat Genet 2006;38:1055-1059.
9. Souied EH, Leveziel N, Richard F, et al. Y402H complement factor H polymorphism associated with exudative age-related macular degeneration in the French population. Mol Vis 2005;11:1135-1140.
10. Maller JB, Fagerness JA, Reynolds RC, et al. Variation in complement factor 3 is associated with risk of age-related macular degeneration. Nat Genet 2007;39:1200-1201.
11. Yates JR, Sepp T, Matharu BK, et al. Genetic Factors in AMD Study Group. Complement C3 variant and the risk of age-related macular degeneration. N Engl J Med 2007;357:553-561.
12. Yang Z, Camp NJ, Sun H, et al. A variant of the HTRA1 gene increases susceptibility to age-related macular degeneration. Science 2006;314:992-993.
13. Fagerness JA, Maller JB, Neale BM, Reynolds RC, Daly MJ, Seddon JM. Variation near complement factor I is associated with risk of advanced AMD. Eur J Hum Genet 2009;17:100-104.
14. Neale BM, Fagerness J, Reynolds R, et al. Genome-wide association study of advanced age-related macular degeneration identifies a role of the hepatic lipase gene (LIPC). Proc Natl Acad Sci USA 2010;107:7395-7400.
15. Chen W, Stambolian D, Edwards AO, et al. Genetic variants near TIMP3 and high-density lipoprotein associated loci influence susceptibility to age-related macular degeneration. Proc Natl Acad Sci USA 2010;107:7401-7406.
16. Kathiresan S, Willer CJ, Peloso GM, et al. Common variants at 30 loci con- tribute to polygenic dyslipidemia. Nat Genet 2009;41:56-65.
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19. Yu Y, Bhangale TR, Fagerness J, et al. Common variants near FRK/COL10A1 and VEGFA are associated with advanced age-related macular degeneration. Hum Mol Genet 2011 Sep 15;20(18):3699-3709.
20. Seddon J, Reynolds R, Maller J,et al. Prediction model for prevalence and incidence of advanced age-related macular degeneration based on genetic, demographic, and environmental variables. Invest Ophthalmol Vis Sci 2009;50(5):2044-2053.
21. Seddon JM, Reynolds R, Yu Y. Risk models for progression to advanced age-related macular degeneration using demographic, environmental, genetic, and ocular factors. Ophthalmology 2011;118(11):2203-2211.
22. Seddon J, George S, Rosner B, Klein M. CFH gene variant Y402H, and smoking, body mass index, environmental asso-ciations with advanced age-related macular degeneration. Hum Hered 2006;61:157-165.
23. Seddon JM, Francis PJ, George S, et al. Association of CFH Y402H and LOC387715 A69S with progression of age related macular degeneration. JAMA 2007;297:1793-1800.
24. Yu Y, Reynolds R, Rosner B, Daly MJ, Seddon JM. Prospective assessment of genetic effects on progression to different stages of age-related macular degeneration using multi-state Markov models. Invest Ophthalmol Vis Sci 2012;53(3):1548-1556.
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