The Singapore Cohort Study for Risk Factors in Myopia cohort has been previously described.3- 4 The students enrolled in the study are examined annually, and serial eye measurements are taken using standardized protocols. These include cycloplegic refraction and axial length measurement of the eyeball. To remove ethnicity as a potential source of population heterogeneity, we only included children of Chinese descent in this genotyping exercise (n = 978). Phenotypic classification of the children into those with severe myopia, those with nonsevere myopia, and nonmyopic controls was made at visit 4 when the children were aged 10 to 12 years. The SE was defined as sphere plus half-negative cylinder. High myopia was defined as an SE of −5.0 D or less; mild to moderate myopia was defined as an SE between −5.0 D and −0.5 D; and nonmyopic controls included those with an SE greater than −0.5 D. The axial length of the globe was measured by contact ultrasound A-scan biometry as previously described.3- 4 The SNP rs4803455 was analyzed in an opportunistic but hypothesis-driven manner as the data were available from an ongoing genome-wide association study using the Illumina HumanHap 550 Beadchips (Illumina, Inc, San Diego, California; http://www.illumina.com). Rigorous quality-control steps were performed, including genotype success rate, missingness, population stratification, departure from Hardy-Weinberg equilibrium in controls, monomorphism, excess heterozygosity, cryptic relatedness, and sex discrepancy. Data analysis was performed using SPSS version 17 statistical software (SPSS Inc, Chicago, Illinois). Pairwise linkage disequilibrium between markers was computed based on the squared Pearson correlation coefficient (r2) using the overall data set. We used the linkage disequilibrium information to select a set of 4000 independent autosomal markers (r2 < 0.16) with approximately equal intermarker distance (approximately 670 kilobases [kb]) across the genome. This set of markers was used to examine sample relationships with the Graphical Representation of Relationships program (Center for Statistical Genetics, University of Michigan, Ann Arbor; http://www.sph.umich.edu/csg/abecasis/GRR/) and to examine population structure with Structure software (Department of Statistics, University of Oxford, Oxford, England).5 Additional evaluative testing of population structure was performed with the Eigenstrat method6 using all markers with no Hardy-Weinberg equilibrium deviation and with greater than 1% minor allele frequency. For related samples identified by Graphical Representation of Relationships, we retained those with a higher call rate. Meta-analysis of all available data was performed using inverse-variance weighting as previously described.7