Welcome

The Web-based Myopia Genetics Hub (WMGH) platform incorporates three specialized genetic databases focused on myopia: the Simple Myopia-Associated Pathogenic Variants Dataset, the Syndromic Myopia-Associated Pathogenic Variants Dataset, and the Myopia-Associated Risk Variants Dataset. Researchers can upload their existing genotype data to generate personalized assessments of myopia risk. Two formats of test-ready data templates are offered to streamline validation experiments.

How to Use

Navigate between sections with just a few clicks:

  • Simple Myopia-Associated Pathogenic Variants Dataset: Pathogenic variants linked to simple myopia ( 308 records ).
  • Syndromic Myopia-Associated Pathogenic Variants Dataset: Pathogenic variants linked to 23 myopia-related disorders ( 45,422 records ).
  • Myopia-Associated Risk Variants Dataset: Genetic risk variants associated with myopia ( 785 records ).
  • Myopia Risk Prediction: PRS-based myopia risk prediction ( processes 1,000 samples in seconds ).

Data Sources and Model Construction

Figure 1. Construction of the WMGH Platform for Myopia Genetic Analysis

This platform was constructed as a modular, reproducible resource for the collection, annotation, integration, and visualization of genetic variants associated with myopia. Figure 1 describes the detailed collection and processing workflow of the database. The platform comprises three primary variant datasets as described above, alongside a polygenic risk score (PRS) model optimized for the Han Chinese population. Each dataset is stored as a tab-delimited plain-text file (.txt) and aligned to the hg38 human reference genome assembly via liftOver. All variant identifiers (RSIDs) were cross-checked between the original sources (GWAS Catalog, ClinVar, and OMIM) and dbSNP to confirm consistency in genomic coordinates (hg38) and allele designation. All identifiers follow dbSNP conventions. Gene names are standardized to HGNC symbols. Each dataset is accompanied by machine-readable metadata including date of last update, source database version (GWAS Catalog, ClinVar, and OMIM), genome build version (GRCh38/hg38), reference URLs to original publications and database entries.

Figure 2. Construction and Application of Myopia PRS Prediction Models

The PRS model was trained on genotype data from 2,900 unrelated Han Chinese individuals provided by WeGene, including 2,308 samples in the training set and 592 samples in the independent test set. Figure 2 shows how the model is built in detail. We extracted 416 myopia-associated SNPs from GWAS summary statistics as candidate predictors. Genotypes were imputed to the hg38 build and subjected to quality control (call rate ≥ 98%, MAF ≥ 1%). The output of the myopia risk prediction model is stored in sum_final.txt, which serves as the parameter file for model inference. This file contains crucial information for each variant, including variant identifier (RSID), chromosome number (CHR), genomic position (POS), reference allele (a0), effect allele (a1), effect size/SNP weight (beta), and genotype coding (geno). When predicting personal myopia risk, the platform matches uploaded genotype data against the RSIDs in sum_final.txt. It calculates weighted PRS scores using the beta values and turns them into personal myopia risk probabilities, as illustrated in Figure 2. A probability ≥ 0.5 indicates predicted myopia, while a probability < 0.5 indicates predicted non-myopia. AUC consistency between the training and test sets demonstrates the model's generalizability and its ability to maintain predictive performance on new data.

Paper describing this work has been received in Frontiers of Computer Science (FCS) special column “ Code & Data ”.

Cited as: Yan WANG, Haoyi WENG, Minxi BI, Gang CHEN, Xinghu QIN, Likun WANG. WMGH: a comprehensive genetic platform to facilitate myopia research and risk prediction in Han Chinese individuals. Front. Comput. Sci. , 2026, DOI: 10.1007/s11704-026-60291-9

Citations

  • Li Z, Qu J, Xu X, et al. A genome-wide association study reveals association between common variants in an intergenic region of 4q25 and high-grade myopia in the Chinese Han population[J]. Human molecular genetics, 2011, 20(14): 2861-2868.
  • Guggenheim J A, Clark R, Cui J, et al. Whole exome sequence analysis in 51,624 participants identifies novel genes and variants associated with refractive error and myopia[J]. Human molecular genetics, 2022, 31(11): 1909-1919.
  • Meguro A, Yamane T, Takeuchi M, et al. Genome-wide association study in Asians identifies novel loci for high myopia and highlights a nervous system role in its pathogenesis[J]. Ophthalmology, 2020, 127(12): 1612-1624.
  • Tang S M, Li F F, Lu S Y, et al. Association of the ZC3H11B, ZFHX1B and SNTB1 genes with myopia of different severities[J]. British Journal of Ophthalmology, 2020, 104(10): 1472-1476.
  • Lanca C, Kassam I, Patasova K, et al. New polygenic risk score to predict high myopia in Singapore Chinese children[J]. Translational vision science & technology, 2021, 10(8): 26-26.
  • Jiang C, Melles R B, Yin J, et al. A multiethnic genome-wide analysis of 19,420 individuals identifies novel loci associated with axial length and shared genetic influences with refractive error and myopia[J]. Frontiers in Genetics, 2023, 14: 1113058.

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Email: qinxh@bjfu.edu.cn, wanglk@pku.edu.cn

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