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

Abstract

Purpose: To 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.

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

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

Methods: Phenotypic 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 measures: Characteristics and quality of cluster solutions, assessed by Calinski-Harabasz scores, unexplained variance, and consistency; and genotype-phenotype associations, assessed by t test.

Results: In 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).

Conclusions: Phenotypic 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.