To test the hypothesis that refractive errors such as myopia and hyperopia cause an increased risk of age-related macular degeneration (AMD) and to quantify the degree of risk.

Two-sample Mendelian randomization analysis of data from a genome-wide association study.

As instrumental variables for refractive error, 126 genome-wide significant genetic variants identified by the Consortium for Refractive Error and Myopia and 23andMe Inc. were chosen. The association with refractive error for the 126 variants was obtained from a published study for a sample of 95,505 European ancestry participants from UK Biobank. Association with AMD for the 126 genetic variants was determined from a genome-wide association study (GWAS) published by the International Age-related Macular Degeneration Genomics consortium of 33,526 (16,144 cases and 17,832 controls) European ancestry participants.

Two-sample Mendelian randomization (MR) analysis was used to assess the causal role of refractive error on AMD risk, using the 126 genetic variants associated with refractive error as instrumental variables, under the assumption that the relationship between refractive error and AMD risk is linear. Main outcome measurement: the risk AMD was caused by a 1-diopter (D) change in refractive error.

MR analysis suggested that refractive error had very limited influence on the risk of AMD. Specifically, 1 D more hyperopic refractive error was associated with an odds ratio (OR) of 1.080 (95% confidence interval [CI], 1.021-1.142; P = 0.007) increased risk of AMD. MR-Egger, MR pleiotropy residual sum and outlier, weighted median, and Phenoscanner-based sensitivity analyses detected minimal evidence to suggest that this result was biased by horizontal pleiotropy.

Under the assumption of a linear relationship between refractive error and the risk of AMD, myopia and hyperopia only minimally influence the causal risk for AMD. Thus, inconsistently reported strong associations between refractive error and AMD are likely to be the result of noncausal factors such as stochastic variation, confounding, or selection bias.