ArticleBioinformatics (Oxford, England)2024
Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Genetic analysis of imaging-derived phenotypes.Nature reviews. Genetics · 2026Review
- Genome-wide association and multi-omics functional screens reveal the genetic architecture of foveal development.medRxiv : the preprint server for health sciences · 2026Article
- Multi-omic analysis of deep learning-derived phenotypes links ophthalmic imaging to cardiovascular and neurological traits.Nature cardiovascular research · 2026Article
- Replicability of unsupervised deep learning derived image phenotypes.bioRxiv : the preprint server for biology · 2026Article
- HistoGWAS: an AI-enabled framework for automated genetic analysis of tissue phenotypes in histology cohorts.Genome biology · 2026Article
- Deep-learning-derived glaucoma-related endophenotypes enable novel genome-wide genetic and functional discovery.Research square · 2026Article
- DINO-EYE: self-supervised learning for identification of different optic disc phenotypes in primary open angle glaucoma.Scientific reports · 2026Article
- REECAP: Contrastive learning of retinal aging reveals genetic loci linking morphology to eye disease.medRxiv : the preprint server for health sciences · 2025Article
- Deep-learning-derived glaucoma-related endophenotypes enable novel genome-wide genetic and functional discovery.medRxiv : the preprint server for health sciences · 2025Article
- Ophthalmic imaging as a measure of cardiovascular and neurological health: a multi-omic analysis of deep-learning derived phenotypes.medRxiv : the preprint server for health sciences · 2025Article
- Genome-Wide Insights Into the Genes and Pathways Shaping Human Foveal Development: Redefining the Genetic Landscape of Foveal Hypoplasia.Investigative ophthalmology & visual science · 2025Article
- Phenotypic screening and genetic insights for predicting major vascular-related diseases using retinal imaging.NPJ digital medicine · 2025Article
- Genetic insights into foveal morphology and its associations with pigmentation and age-related macular degeneration.medRxiv : the preprint server for health sciences · 2025Article
- Artificial Intelligence in Glaucoma: Advances in Diagnosis, Progression Forecasting, and Surgical Outcome Prediction.International journal of molecular sciences · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
motivationGenome-wide association studies (GWAS) have been remarkably successful in identifying associations between genetic variants and imaging-derived phenotypes. To date, the main focus of these analyses has been on established, clinically-used imaging features. We sought to investigate if deep learning approaches can detect more nuanced patterns of image variability.
resultsWe used an autoencoder to represent retinal optical coherence tomography (OCT) images from 31 135 UK Biobank participants. For each subject, we obtained a 64-dimensional vector representing features of retinal structure. GWAS of these autoencoder-derived imaging parameters identified 118 statistically significant loci; 41 of these associations were also significant in a replication study. These loci encompassed variants previously linked with retinal thickness measurements, ophthalmic disorders, and/or neurodegenerative conditions. Notably, the generated retinal phenotypes were found to contribute to predictive models for glaucoma and cardiovascular disorders. Overall, we demonstrate that self-supervised phenotyping of OCT images enhances the discoverability of genetic factors influencing retinal morphology and provides epidemiologically informative biomarkers. AVAILABILITY AND IMPLEMENTATION: Code and data links available at https://github.com/tf2/autoencoder-oct.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.