Genetic and multi-omic discovery
Investigating the biology underlying imaging phenotypes and ophthalmic/systemic disease
I use genetic, metabolomic, and other omics datasets to investigate the biological processes underlying imaging-derived phenotypes and ophthalmic and systemic disease.
A central aim of this work is to move beyond statistical association: identifying potential causal relationships, clarifying disease mechanisms, and finding biological pathways that may support future therapeutic or diagnostic research.
Our genome-wide association studies have explored the genetic determinants of retinal layer thickness(Julian et al., 2025), foveal morphology(Green et al., 2025), and deep learning-derived ophthalmic imaging features(Julian et al., 2026).
I have also used causal inference methods to investigate potentially modifiable disease risk factors, including work examining the relationship between strenuous exercise and motor neuron disease(Julian et al., 2021; O’Brien et al., 2025).
References
2026
- Journal articleMulti-omic analysis of deep learning-derived phenotypes links ophthalmic imaging to cardiovascular and neurological traitsNature Cardiovascular Research, 2026
2025
- Journal articlePigmentation and retinal pigment epithelium thickness: a study of the phenotypic and genotypic relationships between ocular and extraocular pigmented tissuesPigment Cell & Melanoma Research, 2025
- PreprintGenetic insights into foveal morphology and its associations with pigmentation and age-related macular degenerationmedRxiv, 2025Preprint
- Journal articleExtreme exercise in males is linked to mTOR signalling and onset of amyotrophic lateral sclerosisBrain, 2025
2021
- Journal articlePhysical exercise is a risk factor for amyotrophic lateral sclerosis: Convergent evidence from Mendelian randomisation, transcriptomics and risk genotypesEBioMedicine, 2021