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

Weixiong He; Urmo Võsa; Teele Palumaa; Jue-Sheng Ong; Santiago Diaz Torres; Alex W Hewitt; David A Mackey; Puya Gharahkhani; Tõnu Esko; Stuart MacGregor

doi:10.1093/hmg/ddae157

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

Hum Mol Genet. 2024 Nov 13:ddae157. doi: 10.1093/hmg/ddae157. Online ahead of print.

Authors

Weixiong He^{1

2}, Urmo Võsa^{3

4}, Teele Palumaa^{4

5

6}, Jue-Sheng Ong¹, Santiago Diaz Torres^{1

2}, Alex W Hewitt^{7

8}, David A Mackey⁹, Puya Gharahkhani^{1

2

10}, Tõnu Esko⁴, Stuart MacGregor^{1

2}

Affiliations

¹ QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, Queensland 4006, Australia.
² Faculty of Medicine, The University of Queensland, Brisbane, St Lucia, Queensland 4072, Australia.
³ Department of Genetics, University Medical Centre Groningen Medical Faculty building (building 3211) 5th floor, Antonius Deusinglaan 1 9713 AV Groningen, The Netherlands.
⁴ Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, Tartu, Estonia.
⁵ Eye Clinic, East Tallinn Central Hospital, Ravi street 18, 10138 Tallinn, Estonia.
⁶ Department of Ophthalmology, Emory University, 201 Dowman Dr NE, Atlanta, GA 30322, United States.
⁷ Menzies Institute for Medical Research, University of Tasmania, 17 Liverpool St, Hobart Tasmania 7000, Australia.
⁸ Centre for Eye Research Australia, University of Melbourne, Peter Howson Wing, Level 7, 32 Gisborne Street, Melbourne East Victoria 3002, Australia.
⁹ Centre for Ophthalmology and Visual Science, Lions Eye Institute, University of Western Australia, 2 Verdun Street, Nedlands, Western Australia 6009, Australia.
¹⁰ Faculty of Health, School of Biomedical Sciences, Queensland University of Technology, R Block, Kelvin Grove Campus Victoria Park Road, Kelvin Grove, Queensland 4059, Australia.

PMID: 39535071
DOI: 10.1093/hmg/ddae157

Abstract

Purpose: This study aimed to develop and validate a comprehensive polygenic risk score (PRS) for keratoconus, enhancing the predictive accuracy for identifying individuals at increased risk, which is crucial for preventing keratoconus-associated visual impairment such as post-Laser-assisted in situ keratomileusis (LASIK) ectasia.

Methods: We applied a multi-trait analysis approach (MTAG) to genome-wide association study data on keratoconus and quantitative keratoconus-related traits and used this to construct PRS models for keratoconus risk using several PRS methodologies. We evaluated the predictive performance of the PRSs in two biobanks: Estonian Biobank (EstBB; 375 keratoconus cases and 17 902 controls) and UK Biobank (UKB: 34 keratoconus cases and 1000 controls). Scores were compared using the area under the curve (AUC) and odds ratios (ORs) for various PRS models.

Results: The PRS models demonstrated significant predictive capabilities in EstBB, with the SBayesRC model achieving the highest OR of 2.28 per standard deviation increase in PRS, with a model containing age, sex and PRS showing good predictive accuracy (AUC = 0.72). In UKB, we found that adding the best-performing PRS to a model containing corneal measurements increased the AUC from 0.84 to 0.88 (P = 0.012 for difference), with an OR of 4.26 per standard deviation increase in the PRS. These models showed improved predictive capability compared to previous keratoconus PRS.

Conclusion: The PRS models enhanced prediction of keratoconus risk, even with corneal measurements, showing potential for clinical use to identify individuals at high risk of keratoconus, and potentially help reduce the risk of post-LASIK ectasia.

Keywords: Estonian Biobank; GWAS; PRS; The UK Biobank; keratoconus.