Genomics

We are using genetic data to explore the biology of health and imaging traits

Using genetic data - we can study the biological determinants of traits, develop our understanding of how closely related disorders are on a genetic level, and even study causal pathways using methods such as “Mendelian randomisation”.

Our team have undertaken a range of studies in these areas.

Our ‘genome wide association studies’ (GWAS) have included work exploring the genetic determinants of retinal layer thicknesses(Julian et al., 2025), foveal morphology(Green et al., 2025), and deep learning derived ophthalmic imaging features(Julian et al., 2025).

We have implemented causal inference methodologies to identify important, modifiable disease risk factors - for instance, identifying that strenuous, frequent exercise is associated with increased risk of motor neuron disease in some indiviudals(“Physical Exercise Is a Risk Factor for Amyotrophic Lateral Sclerosis: Convergent Evidence from Mendelian Randomisation, Transcriptomics and Risk Genotypes,” 2021; O’Brien et al., 2025), work which had significant impact and featured in mainsteam media reporting including in BBC News and The Guardian

Our current work is focussed on genetic studies of deep learning derived phenotypes, an area we are playing a leading role, having published work on genetic determinants of phenotypes assertained using autoencoders and adversarial autoencoders(Julian et al., 2025).

References

2025

  1. Pigmentation and retinal pigment epithelium thickness: a study of the phenotypic and genotypic relationships between ocular and extraocular pigmented tissues
    Thomas H Julian, Tomas Fitzgerald, UK Biobank Eye, and 3 more authors
    Pigment Cell & Melanoma Research, 2025
  2. Genetic insights into foveal morphology and its associations with pigmentation and age-related macular degeneration
    David J Green, David Romero-Bascones, Thomas H Julian, and 8 more authors
    medRxiv, 2025
  3. Ophthalmic imaging as a measure of cardiovascular and neurological health: a multi-omic analysis of deep-learning derived phenotypes
    Thomas H Julian, Haoran Dou, Jinming Duan, and 9 more authors
    medRxiv, 2025
  4. Extreme exercise in males is linked to mTOR signalling and onset of amyotrophic lateral sclerosis
    David O’Brien, Elham Alhathli, Ceryl Harwood, and 8 more authors
    Brain, 2025

2021

  1. Physical exercise is a risk factor for amyotrophic lateral sclerosis: Convergent evidence from Mendelian randomisation, transcriptomics and risk genotypes
    EBioMedicine, 2021