Professor Damian Smedley
Professor of Computational Genomics
Centre: Clinical Pharmacology and Precision Medicine
Email: d.smedley@qmul.ac.uk
Website: https://whri-phenogenomics.github.io/index.html
Profile
Professor Smedley’s research focusses on utilising clinical and model organism phenotype data to better understand human disease.
As a principal investigator for the International Mouse Phenotyping Consortium (IMPC), his team analyses the genotype to phenotype associations emerging from this comprehensive effort to produce the first catalogue of mammalian gene function by knocking out and systematically phenotyping every protein-coding gene in the mouse. A similar approach is taken in human cellular systems as a PI in the Molecular phenotypes of null alleles in cells (MorPhic) project. Utilising phenotype comparison methods developed with his co-PIs in the Monarch Initiative, his team is able to automatically identify new animal models of known disease genes as well as suggest new candidates for diseases where the causative variants have not yet been identified in human.
This work is extended upon in the Exomiser software package, also developed in conjunction with his collaborators in the Monarch Initiative. Exomiser automates the filtering and prioritisation of coding and non-coding variants called from whole exome or genome sequencing of rare disease families using novel methodologies to prioritise the genes based on the similarity of the patient’s phenotypes to reference knowledge of genotype to phenotype associations from human disease and animal models. This software is widely used by academic researchers, diagnostic laboratories, commercial offering and in large-scale disease sequencing projects such as the US Undiagnosed Disease Network, the UK’s 100,000 Genomes Project as well as being a key component of the ISO-accredited interpretation pipeline for the NHS Genomic Medicine Service.
The team is contributing to a better understanding of the role of missense variants and post-translational modifications in rare disease as part of the MRC-funded human functional genomics initiative. Finally, as part of the Horizon Europe funded NextGen grant the team investigates federated machine learning approaches on multiomics data.