Elsevier

Ophthalmology

Volume 126, Issue 12, December 2019, Pages 1607-1614
Ophthalmology

Original Article
Contribution of Genome-Wide Significant Single Nucleotide Polymorphisms in Myopia Prediction: Findings from a 10-year Cohort of Chinese Twin Children

https://doi.org/10.1016/j.ophtha.2019.06.026Get rights and content
Under a Creative Commons license
open access

Purpose

To determine the added predictive ability of genome-wide significant single nucleotide polymorphisms (SNPs) in refraction prediction in children and investigate the earliest age threshold for an accurate prediction of high myopia.

Design

Prospective longitudinal study.

Participants

A total of 1063 first-born twins followed annually between 2006 and 2015 in China. The exposures were genetic factors (parental myopia, SNPs) and environmental factors (near work, outdoor activity).

Methods

Five linear mixed-effect models, consisting of different combinations of age, gender, genetic, and environmental factors, were built to predict myopia development. All predictions were performed on the basis of spherical equivalent (SE) at baseline and the measurements on the second and third visits.

Main Outcome Measures

The primary outcome measure was SE at the last visit among all subjects, and the secondary outcome measure was the presence of high myopia at the age of 18 years.

Results

Mean age of the study population was 10.5±2.2 years (range, 7–15 years) at baseline, and 48.6% were male. In linear mixed-effect models, age, age square, gender, paternal SE, maternal SE, and genetic risk scores (GRSs) showed a significant fixed effect, whereas outdoor and near-work time were not significant to SE at the last visit. Incorporating more follow-up data into the model showed better performance across all models. In the prediction of the presence of high myopia at 18 years of age, the model consisting of only age and gender showed a good performance (area under the curve [AUC] = 0.95), whereas the addition of SNPs did not enhance the model performance significantly. The AUC for predicting high myopia was >0.95 after the age of 13 years for participants with a single visit and after the age of 12 years for those with 1 more visit data.

Conclusions

A simple model incorporating age, sex, and relevant refraction data is sufficient to accurately predict high myopia; there was limited improvement in the prediction model after adding genetic information. Furthermore, this prediction on the outcome at 18 years is possible when the child is aged 12 to 13 years.

Abbreviations and Acronyms

AIC
Akaike information criterion
AUC
area under the curve
D
diopter
GRS
genetic risk score
GTES
Guangzhou Twin Eye Study
GWAS
genome-wide association study
MSE
mean squared error
SE
spherical equivalent
SNP
single nucleotide polymorphism

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See Commentary on page 1615.

Supplemental material available at www.aaojournal.org.

Financial Disclosure(s): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Supported by the Fundamental Research Funds of the State Key Laboratory in Ophthalmology, National Natural Science Foundation of China81125007, Science and Technology Planning Project of Guangdong Province, China 2013B20400003, China Postdoctoral Science Foundation (2019TQ0365).

M.H.: Support – University of Melbourne at Research Accelerator Program and the CERA Foundation. The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian State Government. The sponsor or funding organization had no role in the design or conduct of this research. The sponsor or funding organization had no role in the design or conduct of this research.

HUMAN SUBJECTS: Human subjects were included in this study. The human ethics committees at the Zhongshan University Ethical Review Board and Ethics Committee of Zhongshan Ophthalmic Center approved the study. All research adhered to the tenets of the Declaration of Helsinki. All participants provided informed consent.

No animal subjects were used in this study.

Author Contributions:

Conception and design: Chen, He

Data collection: Chen, Han, Guo, Li

Analysis and interpretation: Chen, Guo, Li, Lee

Obtained funding: He, Han, Guo

Overall responsibility: Chen, Han, Guo, Lee, He

Y.C., X.H., and X.G. contributed equally to the work.