Cluster Analysis and Genotype-Phenotype Assessment of Geographic Atrophy in Age-Related Macular Degeneration: Age-Related Eye Disease Study 2 Report 25.

purposeTo explore whether phenotypes in geographic atrophy (GA) secondary to age-related macular degeneration can be separated into 2 or more partially distinct subtypes and if these have different genetic associations. This is important because distinct GA subtypes associated with different genetic factors might require customized therapeutic approaches.

designCluster analysis of participants within a controlled clinical trial, followed by assessment of phenotype-genotype associations.

participantsAge-Related Eye Disease Study 2 participants with incident GA during study follow-up: 598 eyes of 598 participants.

methodsPhenotypic features from reading center grading of fundus photographs were subjected to cluster analysis, by k-means and hierarchical methods, in cross-sectional analyses (using 15 phenotypic features) and longitudinal analyses (using 14 phenotypic features). The identified clusters were compared by 4 pathway-based genetic risk scores (complement, extracellular matrix, lipid, and ARMS2). The analyses were repeated in reverse (clustering by genotype and comparison by phenotype).

main outcome measuresCharacteristics and quality of cluster solutions, assessed by Calinski-Harabasz scores, unexplained variance, and consistency; and genotype-phenotype associations, assessed by t test.

resultsIn cross-sectional phenotypic analyses, k-means identified 2 clusters (labeled A and B), whereas hierarchical clustering identified 4 clusters (C-F); cluster membership differed principally by GA configuration but in few other ways. In longitudinal phenotypic analyses, k-means identified 2 clusters (G and H) that differed principally by smoking status but in few other ways. These 3 sets of cluster divisions were not similar to each other (r ≤ 0.20). Despite adequate power, pairwise cluster comparison by the 4 genetic risk scores demonstrated no significant differences (P > 0.05 for all). In clustering by genotype, k-means identified 2 clusters (I and J). These differed principally at ARMS2, but no significant genotype-phenotype associations were observed (P > 0.05 for all).

conclusionsPhenotypic clustering resulted in GA subtypes defined principally by GA configuration in cross-sectional analyses, but these were not replicated in longitudinal analyses. These negative findings, together with the absence of significant phenotype-genotype associations, indicate that GA phenotypes may vary continuously across a spectrum, rather than consisting of distinct subtypes that arise from separate genetic causes.