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Inclusion of genotype with fundus phenotype improves accuracy of predicting choroidal neovascularization and geographic atrophy
Journal article   Open access   Peer reviewed

Inclusion of genotype with fundus phenotype improves accuracy of predicting choroidal neovascularization and geographic atrophy

Lorah T Perlee, Aruna T Bansal, Karen Gehrs, Jeffrey S Heier, Karl Csaky, Rando Allikmets, Paul Oeth, Toni Paladino, Daniel H Farkas, P Lyle Rawlings, …
Ophthalmology (Rochester, Minn.), Vol.120(9), pp.1880-1892
09/2013
DOI: 10.1016/j.ophtha.2013.02.007
PMCID: PMC3695024
PMID: 23523162
url
http://doi.org/10.1016/j.ophtha.2013.02.007View
Open Access

Abstract

The accuracy of predicting conversion from early-stage age-related macular degeneration (AMD) to the advanced stages of choroidal neovascularization (CNV) or geographic atrophy (GA) was evaluated to determine whether inclusion of clinically relevant genetic markers improved accuracy beyond prediction using phenotypic risk factors alone. Cohort study. White, non-Hispanic subjects participating in the Age-Related Eye Disease Study (AREDS) sponsored by the National Eye Institute consented to provide a genetic specimen. Of 2415 DNA specimens available, 940 were from disease-free subjects and 1475 were from subjects with early or intermediate AMD. DNA specimens from study subjects were genotyped for 14 single nucleotide polymorphisms (SNPs) in genes shown previously to associate with CNV: ARMS2, CFH, C3, C2, FB, CFHR4, CFHR5, and F13B. Clinical demographics and established disease associations, including age, sex, smoking status, body mass index (BMI), AREDS treatment category, and educational level, were evaluated. Four multivariate logistic models (phenotype; genotype; phenotype + genotype; and phenotype + genotype + demographic + environmental factors) were tested using 2 end points (CNV, GA). Models were fitted using Cox proportional hazards regression to use time-to-disease onset data. Brier score (measure of accuracy) was used to identify the model with the lowest prediction error in the training set. The most accurate model was subjected to independent statistical validation, and final model performance was described using area under the receiver operator curve (AUC) or C-statistic. The CNV prediction models that combined genotype with phenotype with or without age and smoking revealed superior performance (C-statistic = 0.96) compared with the phenotype model based on the simplified severity scale and the presence of CNV in the nonstudy eye (C-statistic = 0.89; P<0.01). For GA, the model that combined genotype with phenotype demonstrated the highest performance (AUC = 0.94). Smoking status and ARMS2 genotype had less of an impact on the prediction of GA compared with CNV. Inclusion of genotype assessment improves CNV prediction beyond that achievable with phenotype alone and may improve patient management. Separate assessments should be used to predict progression to CNV and GA because genetic markers and smoking status do not equally predict both end points. Proprietary or commercial disclosure may be found after the references.
 Choroidal Neovascularization - diagnosis Geographic Atrophy - genetics Geographic Atrophy - diagnosis Humans Middle Aged Male Macular Degeneration - diagnosis Apolipoproteins - genetics Aged, 80 and over Complement System Proteins - genetics Female Eye Proteins - genetics Reproducibility of Results Risk Factors Genotype Genetic Markers Disease Progression Proteins - genetics Phenotype Macular Degeneration - genetics Aged Polymorphism, Single Nucleotide Complement Factor H - genetics Choroidal Neovascularization - genetics Fundus Oculi

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