Abstract
PurposeAge-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.MethodsA 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.ResultsBased 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.ConclusionThe 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.
Highlights
Age-related cataract (ARC), a leading cause of visual impairment and blindness worldwide [1], is a growing global public health problem that affects approximately 37 million people and accounts for 48% of all cases of blindness[2, 3]
classification and regression tree (CART) analyses revealed that the combination of five single nucleotide polymorphisms (SNPs) above was the best polymorphic signature for discriminating between the cases and the controls
The use of multifactorial analysis rather than a single methodology could be an improved strategy for identifying complex gene interactions
Summary
Age-related cataract (ARC), a leading cause of visual impairment and blindness worldwide [1], is a growing global public health problem that affects approximately 37 million people and accounts for 48% of all cases of blindness[2, 3]. The development of ARC can be influenced by multiple factors, ranging from degenerative processes or personal characteristics to environmental and dietary factors. The majority of studies have analyzed individual genes by directly testing the effects of one or several SNPs in a candidate gene on disease development. Because of the weak marginal effects of these disease-associated SNPs, each individual SNP has limited power to predict the risk of ARC. To evaluate whether interactions and combined effects among multiple SNPs contribute to the susceptibility to ARC, researchers have turned to multifactorial analysis. The analysis of such interactions and combined effects in case-control studies is hampered by one major concern: dimensionality
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