Evidence That Emmetropization Buffers Against Both Genetic and Environmental Risk Factors for Myopia

Abstract

Purpose: To test the hypothesis that emmetropization buffers against genetic and environmental risk factors for myopia by investigating whether risk factor effect sizes vary depending on children's position in the refractive error distribution.

Methods: Refractive error was assessed in participants from two birth cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC) (noncycloplegic autorefraction) and Generation R (cycloplegic autorefraction). A genetic risk score for myopia was calculated from genotypes at 146 loci. Time spent reading, time outdoors, and parental myopia were ascertained from parent-completed questionnaires. Risk factors were coded as binary variables (0 = low, 1 = high risk). Associations between refractive error and each risk factor were estimated using either ordinary least squares (OLS) regression or quantile regression.

Results: Quantile regression: effects associated with all risk factors (genetic risk, parental myopia, high time spent reading, low time outdoors) were larger for children in the extremes of the refractive error distribution than for emmetropes and low ametropes in the center of the distribution. For example, the effect associated with having a myopic parent for children in quantile 0.05 vs. 0.50 was as follows: ALSPAC: age 15, -1.19 D (95% CI -1.75 to -0.63) vs. -0.13 D (-0.19 to -0.06), P = 0.001; Generation R: age 9, -1.31 D (-1.80 to -0.82) vs. -0.19 D (-0.26 to -0.11), P < 0.001. Effect sizes for OLS regression were intermediate to those for quantiles 0.05 and 0.50.

Conclusions: Risk factors for myopia were associated with much larger effects in children in the extremes of the refractive error distribution, providing indirect evidence that emmetropization buffers against both genetic and environmental risk factors.

Figure 1.

Distribution of refractive error by quantiles. (A) refractive error at the age 15 research clinic in ALSPAC participants. (B) Refractive error at the age 9 research clinic in Generation R participants. Participants in each study sample were ranked by refractive error (most myopic to most hyperopic) and then divided into 19 equally sized bins (quantiles).

Figure 2.

Comparison of effect sizes associated with risk factor exposure estimated with OLS linear regression or with quantile regression. (A) Refractive error at the age 15 research clinic in ALSPAC participants. (B) Refractive error at the age 9 research clinic in Generation R participants. The dashed line indicates the effect size associated with exposure to the risk factor, calculated with OLS linear regression (95% confidence interval shown as gray shaded region). Filled circles correspond to the effect size associated with each exposure, calculated with quantile regression (error bars indicate 95% confidence interval). Note that effect sizes can vary across quantiles of the refractive error distribution for quantile regression.

Figure 3.

Pattern of effect sizes associated with risk factor exposure estimated with quantile regression. (A) Refractive error at the age 7 to age 15 research clinics in ALSPAC participants. (B) Refractive error at the age 9 research clinic in Generation R participants. The fitted lines indicate the effect size associated with exposure to the risk factor (shaded regions indicate 95% confidence interval of Loess fit).