Our research focuses on how the genetic landscape of an individual shapes health outcomes, phenotypes, and endotypes. Common diseases such as depression or diabetes arise from many variants with modest effect sizes acting together in the same individual. The challenge is to identify and prioritise these variants to reveal how they combine to influence disease risk. Rare Mendelian disorders provide complementary insight, as highly penetrant mutations highlight the impact of disrupting key pathways. These analyses must also account for genetic ancestry, reflecting evolutionary pressures that shape variant distributions. Comorbidities are of particular interest, as they often reveal shared or convergent pathways that determine disease aetiology.
We bring extensive expertise in phenotype analysis, cross-species gene discovery, and disease module identification. Our group developed PhenomeNET, a cross-species phenotypic similarity network created within the Virtual Physiological Human (VPH) initiative. This resource identified HIP1 as a novel candidate for Bassoe Syndrome via similarity to mouse models. Building on this foundation, we will develop computational models to interrogate shared disease mechanisms across the genome. Loci will be filtered using allele frequencies correlated with relative risk, and machine learning will integrate genomic and clinical data. These analyses will exploit prior knowledge in gene interaction networks and biomedical ontologies, represented as knowledge graphs, combined with maximum-likelihood inference. We are committed to translational research, as such this must also drive biomarker discovery and identification of therapeutic interventions.