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Johanna M. Seddon, Rachel E. Silver, Manlik Kwong, Bernard Rosner; Risk Prediction for Progression of Macular Degeneration: 10 Common and Rare Genetic Variants, Demographic, Environmental, and Macular Covariates. Invest. Ophthalmol. Vis. Sci. 2015;56(4):2192-2202. doi: 10.1167/iovs.14-15841.
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To determine the association between genetic variants and transition to advanced age-related macular degeneration (AMD), and to develop a predictive model and online application to assist in clinical decision making.
Among 2951 subjects in the Age-Related Eye Disease Study, 834 progressed from no AMD, early AMD, or intermediate AMD to advanced disease. Survival analysis was used to assess which genetic, demographic, environmental, and macular covariates were independently associated with progression. Attributable risk, area under the curve statistics (AUCs), and reclassification odds ratios (ORs) were calculated. Split-sample validation was performed. An online risk calculator was developed and is available in the public domain at www.seddonamdriskscore.org.
Ten genetic loci were independently associated with progression, including newly identified rare variant C3 K155Q (hazard ratio: 1.7, 95% confidence interval: 1.2–2.5, P = 0.002), three variants in CFH, and six variants in ARMS2/HTRA1, CFB, C3, C2, COL8A1, and RAD51B. Attributable risk calculations revealed that 80% of incident AMD is attributable to genetic factors, adjusting for demographic covariates and baseline macular phenotypes. In a model including 10 genetic loci, age, sex, education, body mass index, smoking, and baseline AMD status, the AUC for progression to advanced AMD over 10 years was 0.911. Split-sample validation showed a similar AUC (0.907). Reclassification analyses indicated that subjects were categorized into a more accurate risk category if genetic information was included (OR 3.2, P < 0.0001).
Rare variant C3 K155Q was independently associated with AMD progression. The comprehensive model may be useful for identifying and monitoring high-risk patients, selecting appropriate therapies, and designing clinical trials.
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