Professor Damian Smedley

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.

Research

Group members

Julius Jacobsen, Pilar Cacheiro, Valentina Cipriani, Letizia Vestito, Carlo Kroll, Yasemin Bridges, Gabriel Marengo, Diego Pava, Marta Delfino, Krishna Amin, Emma Magavern.

Summary 

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. 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.

Publications

• Vestito L, Cipriani V, Smedley D (2025). 3 Computational genomics and bioinformatics. nameOfConference

  
  

  DOI: 10.1016/b978-0-323-91799-5.00001-2

  
  

  QMRO: qmroHref
• Vestito L, Jacobsen JOB, Walker S et al. (publicationYear). Efficient reinterpretation of rare disease cases using Exomiser. nameOfConference

  
  

  DOI: 10.1038/s41525-024-00456-2

  
  

  QMRO: qmroHref
• Danis D, Bamshad MJ, Bridges Y et al. (2025). A corpus of GA4GH phenopackets: Case-level phenotyping for genomic diagnostics and discovery. nameOfConference

  
  

  DOI: 10.1016/j.xhgg.2024.100371

  
  

  QMRO: qmroHref
• Beckwith MA, Danis D, Bridges Y et al. (2025). Leveraging clinical intuition to improve accuracy of phenotype-driven prioritization. nameOfConference

  
  

  DOI: 10.1016/j.gim.2024.101292

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/103080
• Elrick H, Peterson KA, Willis BJ et al. (publicationYear). Impact of essential genes on the success of genome editing experiments generating 3313 new genetically engineered mouse lines. nameOfConference

  
  

  DOI: 10.1038/s41598-024-72418-8

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/100639
• Cacheiro P, Pava D, Parkinson H et al. (2024). Computational identification of disease models through cross-species phenotype comparison. nameOfConference

  
  

  DOI: 10.1242/dmm.050604

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/98117
• Benkirane M, Bonhomme M, Morsy H et al. (2024). De novo and inherited monoallelic variants in TUBA4A cause ataxia and spasticity. nameOfConference

  
  

  DOI: 10.1093/brain/awae193

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/100459
• Bridges Y, de Souza V, Cortes KG et al. (publicationYear). Towards a standard benchmark for variant and gene prioritisation algorithms: PhEval - Phenotypic inference Evaluation framework. nameOfConference

  
  

  DOI: 10.1101/2024.06.13.598672

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/100781
• Danis D, Bamshad MJ, Bridges Y et al. (publicationYear). A corpus of GA4GH Phenopackets: case-level phenotyping for genomic diagnostics and discovery. nameOfConference

  
  

  DOI: 10.1101/2024.05.29.24308104

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/98321
• Stenton SL, O’Leary MC, Lemire G et al. (publicationYear). Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project. nameOfConference

  
  

  DOI: 10.1186/s40246-024-00604-w

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/100453

Collaborators

Internal

• Prof Sir Mark Caulfield (WHRI)
• Dr Michael Barnes (WHRI)

External

• Prof Peter Robinson (Berlin Institute of Health, Germany)
• Dr. Chris Mungall (Lawrence Berkeley National Laboratory, USA)
• Prof. Melissa Haendel (University of Colorado, USA)
• Dr. Helen Parkinson (EBI, UK)
• Dr. Matthew Child (Imperial College, UK)

News

100,000 Genomes Project paper publication press (Nov 2021):

• Whole genome sequencing could save NHS millions of pounds study suggests (Guardian)
• Hundreds of patients in gene study given rare disease diagnosis (BBC)
• Scientists use genomic sequencing to pinpoint cause of rare diseases (Financial Times)

Disclosures

No disclosures