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

• Published in: Ophthalmology (Rochester, Minn.) Vol. 126; no. 12; p. 1607
• Main Authors: Chen, Yanxian, Han, Xiaotong, Guo, Xiaobo, Li, Yonghui, Lee, Jonathan, He, Mingguang
• Format: Journal Article
• Language: English
• Published: United States 01.12.2019
• Subjects: Adolescent; Area Under Curve; Asian Continental Ancestry Group - genetics; Child; China - epidemiology; Disease Progression; Diseases in Twins - diagnosis; Diseases in Twins - genetics; Female; Follow-Up Studies; Genome-Wide Association Study; Humans; Male; Myopia, Degenerative - diagnosis; Myopia, Degenerative - genetics; Polymorphism, Single Nucleotide; Prospective Studies; Refraction, Ocular - physiology; China
• ISSN: 1549-4713; 1549-4713
• DOI: 10.1016/j.ophtha.2019.06.026

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. Prospective longitudinal study. 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). 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. 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. 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. 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.