Predicting Risks of Dry Eye Disease Development Using a Genome-Wide Polygenic Risk Score Model.

PURPOSE: The purpose of this study was to conduct a large-scale genome-wide association study (GWAS) and construct a polygenic risk score (PRS) for risk stratification in patients with dry eye disease (DED) using the Taiwan Biobank (TWB) databases.

METHODS

This retrospective case-control study involved 40,112 subjects of Han Chinese ancestry, sourced from the publicly available TWB. Cases were patients with DED (n = 14,185), and controls were individuals without DED (n = 25,927). The patients with DED were further divided into 8072 young (<60 years old) and 6113 old participants (≥60 years old). Using PLINK (version 1.9) software, quality control was carried out, followed by logistic regression analysis with adjustments for sex, age, body mass index, depression, and manic episodes as covariates. We also built PRS prediction models using the standard clumping and thresholding method and evaluated their performance (area under the curve [AUC]) through five-fold cross-validation.

RESULTS

Eleven independent risk loci were identified for these patients with DED at the genome-wide significance levels, including DNAJB6, MAML3, LINC02267, DCHS1, SIRPB3P, HULC, MUC16, GAS2L3, and ZFPM2. Among these, MUC16 encodes mucin family protein. The PRS model incorporated 932 and 740 genetic loci for young and old populations, respectively. A higher PRS score indicated a greater DED risk, with the top 5% of PRS individuals having a 10-fold higher risk. After integrating these covariates into the PRS model, the area under the receiver operating curve (AUROC) increased from 0.509 and 0.537 to 0.600 and 0.648 for young and old populations, respectively, demonstrating the genetic-environmental interaction.

CONCLUSIONS

Our study prompts potential candidates for the mechanism of DED and paves the way for more personalized medication in the future.

TRANSLATIONAL RELEVANCE: Our study identified genes related to DED and constructed a PRS model to improve DED prediction.