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

Area of Science:

• Genetics
• Ophthalmology
• Bioinformatics

Background:

• Dry eye disease (DED) is a prevalent ocular surface condition with complex etiology.
• Genetic factors play a role in DED development, but comprehensive genetic risk assessment is lacking.

Purpose of the Study:

• To conduct a large-scale genome-wide association study (GWAS) for DED.
• To construct a polygenic risk score (PRS) for DED risk stratification.
• To investigate genetic-environmental interactions in DED.

Main Methods:

• Retrospective case-control study of 40,112 Han Chinese individuals from the Taiwan Biobank (TWB).
• GWAS analysis using PLINK software, followed by logistic regression.
• PRS model construction using clumping and thresholding, with five-fold cross-validation.

Main Results:

• Eleven independent risk loci for DED were identified at genome-wide significance.
• The PRS model incorporated 932 (young) and 740 (old) genetic loci.
• Integrating covariates into the PRS model improved predictive accuracy (AUROC from ~0.51-0.54 to ~0.60-0.65).

Conclusions:

• Identified novel DED-associated genes and established a PRS for improved risk prediction.
• The findings support the utility of PRS in personalized medicine for DED.
• This research provides insights into DED mechanisms and future therapeutic strategies.