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Katarzyna Gawlik, Hongrong Luo, Joe Lopez, Danni Lin, Cindy Wen, Henry Alexander Ferreyra, Sherman Chang, Kang Zhang; A genetic risk prediction model for age-related macular degeneration. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):2817.
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© ARVO (1962-2015); The Authors (2016-present)
Age-related macular degeneration (AMD) is characterized by complex interactions between genetic and environmental factors. With recent progress of personalized medicine, it is likely that AMD management will be influenced by assessment of genetic risk. The aim of this study is to develop a class prediction algorithm for AMD risk assessment based on individual genetic profile.
DNA samples were genotyped using the AutoGenomics INFINITI platform. AMD panel (for research use only) included ABCA1(rs1883025); APOE(rs429358, rs7412); ARMS2/HTRA1(rs10490924, EU427539, rs11200638); C3(rs2230199); CCDC109B(rs17440077); CETP(rs3764261); CFB(rs4151669, rs522162); CFH(rs1048663, rs1061170, rs10737680, rs1329428, rs2274700, rs3766405, rs412852, rs800292); CFI(rs10033900); COL8A1(rs13095226); LIPC(rs493258, rs10468017); LPL(rs12678919); TIMP3(rs9621532) and VEGFA(rs3025000, rs943080). Class prediction model building and testing were performed using TreeNet software (Salford Systems, San Diego, CA).
We genotyped the 27 SNPs from 935 AMD patients (277 dry AMD and 658 wet AMD) and 421 normal control patients. For AMD risk assessment, we tested if TreeNet can be applied to genotyping data (categorical variables). The known published odds ratios for 21 SNPs from the panel were also used as numeric variables in the model building for 369 AMD patients and 237 controls. Receiver operating characteristic curve (ROC) for both models were highly comparable (ROC=0.87 for numeric and ROC=0.91 for categorical) suggesting that TreeNet can be used for descriptive genotyping data. Next, we analyzed the performance of the prediction model for all AMD patients vs controls based on the 27 AMD panel. The ROC for the model was 0.75 with 74% sensitivity and 62% specificity. The variable ranking showed the most important SNPs associated with AMD at the top of the list including rs412852 (CFH), rs2230199 (C3) and rs10490924 (ARMS2/HTRA1) which is in accordance with previously published data.
These results suggest that the prediction algorithm can be used for the AMD risk assessment. It can also incorporate environmental factors such as age, smoking and other variables into the model building. Having a larger group of patients with well characterized AMD phenotypes, the algorithm could also be applied to predict disease progression and therapeutic response, and aid personalized care.
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