Association analysis of genetic and environmental risk factors in the cuticular drusen subtype of age-related macular degeneration

PURPOSE: To assess the association of gender, cigarette smoking, body-mass index, and nine genetic risk variants with cuticular drusen (CD), a well recognized subtype of age-related macular degeneration (AMD). METHODS: A total of 757 patients with AMD, including 217 patients with CD, and 553 control individuals were interviewed with a questionnaire and underwent an ophthalmic examination. Venous blood samples were obtained for genomic DNA extraction, and genotyping was performed of single nucleotide polymorphisms previously associated with AMD. Odds ratios were calculated for patients with CD, using unaffected control individuals as a reference. Furthermore, odds ratios in patients with CD were compared to those in patients with “non-CD” AMD. RESULTS: The CD subtype of AMD was significantly associated with current smoking as well as variants in the complement factor H (CFH), age-related maculopathy susceptibility 2 (ARMS2), complement factor B/complement component 2 (CFB/C2), complement component 3 (C3), and apolipoprotein E (APOE) genes. In patients with CD, the association with the CFH Y402H risk allele was significantly higher (p=0.022), whereas the association with current smoking was significantly lower (p<0.001) than in the heterogeneous group of patients with “non-CD” AMD. CONCLUSIONS: The AMD subtype of CD was associated with previously identified genetic AMD risk factors. However, the association with the CFH Y402H risk allele appeared to be stronger, whereas the association with smoking was less pronounced when compared to AMD as a whole. This study suggests a more important role for genetic factors than environmental factors in the development of this well defined subtype of AMD. These findings stress the importance of detailed phenotyping in AMD to identify homogeneous AMD subtypes, which may be associated with different risk factors and disease mechanisms. Such studies will improve the accuracy of predictive models and the effectiveness of preventive and therapeutic options in AMD.