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Professor Damian Smedley

Damian

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.

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.

Key Publications

Full list of publications

  • Cacheiro P, Lawson S, Van den Veyver IB, Marengo G, Zocche D, Murray SA, Duyzend M, Robinson PN, Smedley D. Lethal phenotypes in Mendelian disorders. Genet Med. 2024
  • Cacheiro P, Pava D, Parkinson H, VanZanten M, Wilson R, Gunes O, The International Mouse Phenotyping Consortium, Smedley D. Computational identification of disease models through cross-species phenotype comparison. Dis Model Mech. 2024 Jun 1;17(6):dmm050604.
  • Duyzend MH, Cacheiro P, Jacobsen JOB, Giordano J, Brand H, Wapner RJ, Talkowski ME, Robinson PN, Smedley D. Improving prenatal diagnosis through standards and aggregation. Prenat Diagn. 2024 Apr;44(4):454-464.
  • Cacheiro P, Westerberg CH, Mager J, Dickinson ME, Nutter LMJ, Muñoz-Fuentes V, Hsu CW, Van den Veyver IB, Flenniken AM, McKerlie C, Murray SA, Teboul L, Heaney JD, Lloyd KCK, Lanoue L, Braun RE, White JK, Creighton AK, Laurin V, Guo R, Qu D, Wells S, Cleak J, Bunton-Stasyshyn R, Stewart M, Harrisson J, Mason J, Haseli Mashhadi H, Parkinson H, Mallon AM; International Mouse Phenotyping Consortium; Genomics England Research Consortium; Smedley D. Mendelian gene identification through mouse embryo viability screening. Genome Med. 2022 Oct 13;14(1):119.
  • Jacobsen JOB, Kelly C, Cipriani V, …  Smedley D. Phenotype-driven approaches to enhance variant prioritization and diagnosis of rare disease. Human Mutation. 2022 Aug;43(8):1071-1081.
  • Smedley D, Smith KR, Martin A et al. 100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care - Preliminary Report. New England Journal of Medicine 2021 Nov 11;385(20):1868-1880.
  • Robinson PN, Ravanmehr V, Jacobsen JOB, …, Smedley D. Interpretable Clinical Genomics with a Likelihood Ratio Paradigm. American Journal of Human Genetics. 2020 Sep 3;107(3):403-417.
  • Cacheiro P, Muñoz-Fuentes V, Murray SA …, Smedley D. Human and mouse essentiality screens as a resource for disease gene discovery. Nature Communications. 2020 11(1):655.
  • Cipriani V, Pontikos N, Arno G …, Smedley D An improved phenotype-driven tool for rare Mendelian variant prioritization: benchmarking Exomiser on real patient whole-exome data. 2020 Genes 11(4):460.
  • Konopka T, Smedley D. Incremental data integration for tracking genotype-disease associations. PLoS Computational Biology 2020;16(1).
  • Cacheiro P, Haendel MA, Smedley D; International Mouse Phenotyping Consortium and the Monarch Initiative. New models for human disease from the International Mouse Phenotyping Consortium. Mammalian Genome. 2019 Jun;30(5-6):143-150.
  • Meehan TF, Conte N, West DB, Jacobsen JO … Smedley D. Disease model discovery from 3,328 gene knockouts by The International Mouse Phenotyping Consortium. Nature Genetics 2017 Aug;49(8):1231-1238.
  • Smedley D, Schubach M, Jacobsen JOB et al. A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease. American Journal of Human Genetics 2016 Sep 1;99(3):595-606.
  • Smedley D, Jacobsen JO, Jäger M, Köhler S, Holtgrewe M, Schubach M, Siragusa E, Zemojtel T, Buske OJ, Washington NL, Bone WP, Haendel MA, Robinson PN. Next-generation diagnostics and disease-gene discovery with the Exomiser. Nature Protocols. 2015 Dec;10(12):2004-15.
  • Bone WP, Washington NL, Buske OJ, …, Smedley D. Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency. Genetics in Medicine 2016 Jun;18(6):608-17.
  • Smedley D, Robinson PN. Phenotype-driven strategies for exome prioritization of human Mendelian disease genes. Genome Medicine 2015 Jul 30;7(1):81.
  • Smedley D, Haider S, Durinck S et al. The BioMart community portal: an innovative alternative to large, centralized data repositories. Nucleic Acids Research 2015 Jul 1;43(W1): W589-98.
  • Smedley D, Köhler S, Czeschik JC et al. Walking the interactome for candidate prioritization in exome sequencing studies of Mendelian diseases. Bioinformatics. 2014 Nov 15;30(22):3215-22. 
  • Robinson PN, Köhler S, Oellrich A … Smedley D. Improved exome prioritization of disease genes through cross-species phenotype comparison. Genome Research. 2014 Feb;24(2):340-8.

Sponsors

Collaborators

Internal

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):

Disclosures

No disclosures

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