  Genome-wide association studies (GWAS) require accurate cohort phenotyping,
but expert labeling can be costly, time-intensive, and variable. Here we
develop a machine learning (ML) model to predict glaucomatous optic nerve head
features from color fundus photographs. We used the model to predict vertical
cup-to-disc ratio (VCDR), a diagnostic parameter and cardinal endophenotype for
glaucoma, in 65,680 Europeans in the UK Biobank (UKB). A GWAS of ML-based VCDR
identified 299 independent genome-wide significant (GWS; $P\leq5\times10^{-8}$)
hits in 156 loci. The ML-based GWAS replicated 62 of 65 GWS loci from a recent
VCDR GWAS in the UKB for which two ophthalmologists manually labeled images for
67,040 Europeans. The ML-based GWAS also identified 92 novel loci,
significantly expanding our understanding of the genetic etiologies of glaucoma
and VCDR. Pathway analyses support the biological significance of the novel
hits to VCDR, with select loci near genes involved in neuronal and synaptic
biology or known to cause severe Mendelian ophthalmic disease. Finally, the
ML-based GWAS results significantly improve polygenic prediction of VCDR and
primary open-angle glaucoma in the independent EPIC-Norfolk cohort.
