Developing and validating a comprehensive polygenic risk score to enhance keratoconus risk prediction.

Area of Science:

• Ophthalmology
• Genetics
• Bioinformatics

Background:

• Keratoconus is a progressive eye condition leading to visual impairment.
• Identifying individuals at high risk is crucial for timely intervention and preventing complications like post-LASIK ectasia.
• Current risk prediction methods for keratoconus require enhancement.

Purpose of the Study:

• To develop and validate a comprehensive polygenic risk score (PRS) for keratoconus.
• To improve the accuracy of identifying individuals with elevated keratoconus risk.
• To aid in the prevention of keratoconus-associated visual impairment.

Main Methods:

• Utilized multi-trait analysis approach (MTAG) with genome-wide association study data for keratoconus and related traits.
• Constructed multiple PRS models using various methodologies.
• Evaluated PRS predictive performance in the Estonian Biobank (EstBB) and UK Biobank (UKB) using Area Under the Curve (AUC) and Odds Ratios (ORs).

Main Results:

• PRS models demonstrated significant predictive capabilities in EstBB (AUC=0.72) and UKB.
• The SBayesRC model achieved a high OR of 2.28 in EstBB.
• In UKB, incorporating PRS into a model with corneal measurements increased AUC from 0.84 to 0.88, with an OR of 4.26.

Conclusions:

• Developed PRS models significantly enhance keratoconus risk prediction.
• These models show potential for clinical utility in identifying at-risk individuals.
• The PRS may help mitigate the risk of post-LASIK ectasia.