Cupping of the optic nerve head, a highly heritable trait, is a hallmark of glaucomatous optic neuropathy. Two key parameters are vertical cup-to-disc ratio (VCDR) and vertical disc diameter (VDD). However, manual assessment often suffers from poor accuracy and is time intensive. Here, we show convolutional neural network models can accurately estimate VCDR and VDD for 282,100 images from both UK Biobank and an independent study (Canadian Longitudinal Study on Aging), enabling cross-ancestry epidemiological studies and new genetic discovery for these optic nerve head parameters. Using the AI approach, we perform a systematic comparison of the distribution of VCDR and VDD and compare these with intraocular pressure and glaucoma diagnoses across various genetically determined ancestries, which provides an explanation for the high rates of normal tension glaucoma in East Asia. We then used the large number of AI gradings to conduct a more powerful genome-wide association study (GWAS) of optic nerve head parameters. Using the AI-based gradings increased estimates of heritability by ∼50% for VCDR and VDD. Our GWAS identified more than 200 loci associated with both VCDR and VDD (double the number of loci from previous studies) and uncovered dozens of biological pathways; many of the loci we discovered also confer risk for glaucoma.

Item Type:	Refereed Article
Keywords:	artificial intelligence, image, optic nerve head, glaucoma, GWAS, UK Biobank, CLSA
Research Division:	Biomedical and Clinical Sciences
Research Group:	Ophthalmology and optometry
Research Field:	Ophthalmology
Objective Division:	Health
Objective Group:	Clinical health
Objective Field:	Diagnosis of human diseases and conditions
UTAS Author:	Hewitt, AW (Professor Alex Hewitt)
ID Code:	152367
Year Published:	2021
Web of Science® Times Cited:	9
Deposited By:	Menzies Institute for Medical Research
Deposited On:	2022-08-18
Last Modified:	2022-09-08
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