Deep-learning-derived glaucoma-related endophenotypes enable novel genome-wide genetic and functional discovery.

ABSTRACT: The genetic architecture of primary open-angle glaucoma (POAG), a leading cause of irreversible blindness, remains largely unexplained due to the reliance of previous genome-wide association studies (GWAS) on imprecise phenotypes from electronic health records. Here, we overcome this with a disease-trained, task-transfer machine learning (ML) framework that learns glaucoma-related damage patterns from a large clinical repository of 8,323 glaucoma patients. We showed that ML optical coherence tomography (OCT)-derived endophenotypes trained on 18,985 OCT scans from these patients identified novel loci associated with POAG. By applying the derived endophenotypes to 47,908 UK Biobank participants, we performed GWAS in European, African, and Asian ancestral groups followed by cross-ancestry met

SUBMITTER: Chen L 

PROVIDER: S-EPMC12637757 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature