Sergouniotis, Panagiotis I 
ORCID: 0000-0003-0986-4123, Diakite, Adam, Gaurav, Kumar, UK Biobank Eye and Vision Consortium, Birney, Ewan ORCID: 0000-0001-8314-8497 and Fitzgerald, Tomas ORCID: 0000-0002-2370-8496
(2024)
Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers.
Bioinformatics (Oxford, England), 41 (1).
btae732-.
ISSN 1367-4803, 1367-4811
Abstract
<h4>Motivation</h4>Genome-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.<h4>Results</h4>We 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.<h4>Availability and implementation</h4>Code and data links available at https://github.com/tf2/autoencoder-oct.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | UK Biobank Eye and Vision Consortium, Retina, Humans, Tomography, Optical Coherence, Phenotype, Male, Genome-Wide Association Study, Genetic Loci, Biomarkers, Deep Learning, Autoencoder |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 28 Feb 2025 14:35 |
| Last Modified: | 02 May 2025 13:40 |
| DOI: | 10.1093/bioinformatics/btae732 |
| Open Access URL: | https://doi.org/10.1093/bioinformatics/btae732 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3190581 |
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