Allelic interaction effects of DNA damage and repair genes on the predisposition to age-related cataract

Abstract

Purpose: Age-related cataract (ARC) is a leading cause of visual impairment and blindness worldwide. DNA damage and malfunction of DNA repair are believed to contribute to the pathogenesis of ARC. Aside from increasing age, the risk factors for ARC appear to be rather complex, and one or more gene variations could play critical roles in the diverse processes of ARC progression. This study aimed to investigate the combined effects of different genetic variants on ARC risk.

Methods: A cohort of 789 ARC patients and 531 normal controls from the Jiangsu Eye Study was included in this study. Genotyping of 18 single-nucleotide polymorphisms (SNPs) in 4 DNA damage/repair genes was performed using TaqMan SNP assays. SNP-SNP interactions were analyzed via multifactor dimensionality reduction (MDR), classification and regression tree (CART) and genetic risk score (GRS) analyses.

Results: Based on single-locus analyses of the 18 SNPs examined, WRN-rs11574311 (T>C) was associated with ARC risk. However, in MDR, the gene-gene interaction among the five SNPs (WRN-rs4733220 (G>A), WRN-rs1801195 (T>G), OGG1-rs2072668 (G>C) and OGG1-rs2304277 (A>G)) on ARC risk was significant (OR = 5.03, 95% CI: 3.54~7.13). CART analyses also revealed that the combination of five SNPs above was the best polymorphic signature for discriminating between the cases and the controls. The overall odds ratio for CART ranged from 4.56 to 7.90 showing an incremental risk for ARC. This result indicated that these critical SNPs participate in complex interactions. The GRS results showed an increased risk for ARC among individuals with the SNPs in this polymorphic signature.

Conclusion: The use of multifactorial analysis (or an integrated approach) rather than a single methodology could be an improved strategy for identifying complex gene interactions. The multifactorial approach used in this study has the potential to identify complex biological relationships among ARC-related genes and processes. This approach will lead to the discovery of novel biological information, ultimately improving ARC risk management.