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The GA4GH Phenopacket schema defines a computable representation of clinical data

Nature Biotechnology volume 40, pages 817–820 (2022)Cite this article

Subjects

The ‘PhenotypicFeature’ is the central element of the Phenopacket schema. A ‘PhenotypicFeature’ can be used to describe any phenotypic characteristic, including signs and symptoms, laboratory findings, histopathology findings, and imaging and electrophysiological results, along with modifier and qualifier concepts. Each phenotypic feature is described using an ontology term. Although the Phenopacket schema does not mandate which ontology to use, it provides recommendations, such as the Human Phenotype Ontology7 (HPO) for rare diseases and the National Cancer Institute Thesaurus (NCIT) for transmission of information about a cancer specimen (for example, pathological staging or more detailed information about histology or tumor markers)8. Within the schema, it is possible to indicate whether an abnormality was excluded during the diagnostic process (for example, whether a morphological cardiac defect was excluded by echocardiography) or to use other optional HPO terms to denote the severity, frequency (for example, number of occurrences of seizures per week), laterality (for example, unilateral) or other pattern of a phenotypic feature in the patient being described. Finally, the onset (and, if applicable, the resolution) of specific features can be indicated.

Other key elements of the schema are ‘Measurement’, which is used to capture quantitative (i.e., numerical), ordinal (for example, absent/present) or categorical measurements; ‘Biosample’, a description of biological material obtained from the individual represented in the Phenopacket and used for phenotypic, genotypic or other -omics analysis; and ‘MedicalAction’, which includes a hierarchical representation of medical actions, including medications, procedures and other actions taken for clinical management. The ‘Treatment’ element is a subelement of ‘MedicalAction’ and represents the administration of a pharmaceutical agent, broadly defined as prescription and over-the-counter medicines, vaccines and other therapeutic agents, such as monoclonal antibodies or chimeric antigen receptor (CAR)-T-cell therapy.

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Fig. 1: Phenopacket schema overview.

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Acknowledgements

The authors gratefully acknowledge insight and feedback from Marian H. Adly, Pier Luigi Buttigieg, Janine Lewis, Manuel Posada de la Paz and Maria Taboada. This work was supported by 7RM1HG010860-02 (NHGRI). Additional funding was as follows. P.N.R. was supported by NLM contract 75N97019P00280, NIH NHGRI RM1HG010860, NIH OD R24OD011883 and NIH NICHD 1R01HD103805-01. H.H. was supported by NIH OD R24OD011883. G.I.S. was supported by ELIXIR, the research infrastructure for life-science data. C.G.C. was supported by NIH NCATS U24TR002306. K.C.L. was supported by NIH OD 5UM1OD023221. M.B. was supported by the BioMedIT Network project of the Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN). A.H.W. was supported by NIH NHGRI K99HG010157 and NIH NHGRI R00HG010157. C.J.M., M.A.H., M.C.M.-T., J.A.M. and D.D. were supported by NIH NHGRI RM1HG010860 and NIH OD R24OD011883. A.M.-J. was supported by Australian Genomics. Australian Genomics is supported by the National Health and Medical Research Council (GNT1113531). D. Smedley and J.O.B.J. were supported by NIH NHGRI RM1HG010860, NIH OD R24OD011883 and NIH NICHD 1R01HD103805-01. M.D. was supported by NIH NHGRI U54HG004028, NIH NHGRI 5U01HG008473-03 and NIH NCATS OT2TR003434-01S1U54HG008033-01. G.S.B. was supported by the Roy Hill Community Foundation, Angela Wright Bennett Foundation, McCusker Charitable Foundation, Borlaug Foundation and Stan Perron Charitable Foundation. L.B. was supported by NIH NHGRI U41HG006834 (Clinical Genome Resource). M.C. was supported by EMBL-EBI Core Funds and Wellcome Trust GA4GH award number 201535/Z/16/Z. A.H. was supported by NIH NHGRI 1U41HG006627, NIH NHGRI 1U54HG006542 and NIH NHGRI 1RM1HG010860. P.N.S. was supported by The Alan Turing Institute. N.L.H. was supported by NIH NHGRI RM1HG010860, NIH OD R24OD011883 and US Department of Energy Contract DE-AC02-05CH11231. N.P. was supported by Moorfields Eye Charity. N.Q.-R. was supported by EU Horizon 2020 research and innovation programme grant agreement 825575 (EJP-RD). O.E. was supported by NIH grants UL1TR002384, R01CA194547 and P01CA214274, LLS SCOR grants 180078-01 and 7021-20 and Starr Cancer Consortium grant I11-0027. H. Lochmüller was supported by the CIHR Foundation Grant on Precision Health for Neuromuscular Diseases FDN-167281. R.T. was supported by CIHR postdoctoral fellowship award MFE-171275. L.D.S. was supported by Genome Canada and NIH NHGRI U24HG011025. S.O. was supported by AMED. D.P., L.M., A.P., S.B., M.R. and R.K. were supported by EU Horizon 2020 research and innovation programme grant agreements 779257 (Solve-RD) and 825575 (EJP-RD). R.R.F. was supported by NLM contract 75N97019P00280.

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Authors and Affiliations

  1. William Harvey Research Institute, Queen Mary University of London, London, UK

    Julius O. B. Jacobsen & Damian Smedley

  2. Department of Molecular Life Sciences, University of Zurich, Zürich, Switzerland

    Michael Baudis

  3. Computational Oncogenomics Group, Swiss Institute of Bioinformatics, Zürich, Switzerland

    Michael Baudis

  4. Western Australian Register of Developmental Anomalies and Genetic Services of WA, King Edward Memorial Hospital, Perth, Western Australia, Australia

    Gareth S. Baynam

  5. Faculty of Health and Medical Sciences, Division of Paediatrics, University of Western Australia, Perth, Western Australia, Australia

    Gareth S. Baynam

  6. Genetic and Rare Diseases, Telethon Kids Institute, Perth, Western Australia, Australia

    Gareth S. Baynam

  7. Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland

    Jacques S. Beckmann

  8. CNAG-CRG, Centre for Genomic Regulation (CRG), Bioinformatics Unit, The Barcelona Institute of Science and Technology, Barcelona, Spain

    Sergi Beltran, Leslie Matalonga, Anastasios Papakonstantinou & Davide Piscia

  9. Universitat Pompeu Fabra (UPF), Barcelona, Spain

    Sergi Beltran

  10. Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona (UB), Barcelona, Spain

    Sergi Beltran

  11. PhenoTips, Toronto, Ontario, Canada

    Orion J. Buske

  12. Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Tiffany J. Callahan, Brian J. Laraway, Julie A. McMurry, Monica C. Munoz-Torres, Nicole A. Vasilevsky & Melissa A. Haendel

  13. Schools of Medicine, Public Health, and Nursing, Baltimore, Johns Hopkins University, Baltimore, MD, USA

    Christopher G. Chute

  14. European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Hinxton, UK

    Mélanie Courtot

  15. Genome Informatics, Ontario Institute for Cancer Research, Toronto, Ontario, Canada

    Mélanie Courtot & Lindsay D. Smith

  16. Genomic Medicine, The Jackson Laboratory, Farmington, CT, USA

    Daniel Danis, Michael A. Gargano, Jagadish Chandrabose Sundaramurthi & Peter N. Robinson

  17. Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA

    Olivier Elemento

  18. Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité–Universitätsmedizin Berlin, Berlin, Germany

    Andrea Essenwanger, Julian Sass & Sylvia Thun

  19. Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA

    Robert R. Freimuth & Aly Khalifa

  20. EMBL-EBI, Cambridge, UK

    Tudor Groza

  21. Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA

    Ada Hamosh

  22. Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Nomi L. Harris & Christopher J. Mungall

  23. Human Genetics, Leiden University Medical Center, Leiden, the Netherlands

    Rajaram Kaliyaperumal, Núria Queralt-Rosinach & Marco Roos

  24. Mouse Biology Program, University of California Davis, Davis, CA, USA

    Kevin C. Kent Lloyd

  25. Department of Surgery, University of California Davis School of Medicine, Sacramento, CA, USA

    Kevin C. Kent Lloyd

  26. University Hospital Bonn, Bonn, Germany, and Institute for Genomic Statistics and Bioinformatics, Bonn, Germany

    Peter M. Krawitz

  27. Ada Health GmbH, Berlin, Germany

    Sebastian Köhler

  28. Sensitive Data Services, CSC–IT Center for Science, Espoo, Finland

    Heikki Lehväslaiho

  29. The Australian e-Health Research Centre, CSIRO, Herston, Queensland, Australia

    Alejandro Metke-Jimenez & David P. Hansen

  30. Tohoku University, INGEM, Sendai, Japan

    Soichi Ogishima

  31. Institute of Ophthalmology, University College London, London, UK

    Nikolas Pontikos

  32. Genetics Service, Moorfields Eye Hospital, London, UK

    Nikolas Pontikos

  33. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK

    Paul N. Schofield

  34. Mammalian Genetics, The Jackson Laboratory, Bar Harbor, ME, USA

    Paul N. Schofield

  35. The Alan Turing Institute, London, UK

    Paul N. Schofield

  36. Bioinformatics and Translational Genetics, Berlin Institute of Health at Charité–Universitätsmedizin Berlin, Berlin, Germany

    Dominik Seelow & Robin Steinhaus

  37. Institute of Medical Genetics and Human Genetics, Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany

    Dominik Seelow & Robin Steinhaus

  38. Lifebit Biotech Ltd., London, UK

    Anastasios Siapos

  39. Global Alliance for Genomics and Health, N/A, Toronto, Ontario, Canada

    Lindsay D. Smith

  40. 40Medicine Department, University of Cambridge, Cambridge, UK

    Emilia M. Swietlik

  41. Respiratory Medicine Department, Addenbrooke’s Hospital, Cambridge, UK

    Emilia M. Swietlik

  42. Cambridge Centre for Lung Infection, Royal Papworth Hospital, Cambridge, UK

    Emilia M. Swietlik

  43. The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, USA

    Alex H. Wagner & Bimal P. Chaudhari

  44. Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA

    Alex H. Wagner & Bimal P. Chaudhari

  45. Departments of Medicine and Biomedical Informatics, Vanderbilt University, Nashville, TN, USA

    Jeremy L. Warner

  46. Senckenberg–Leibniz Institution for Biodiversity and Earth System Research, Data and Modelling Centre, Frankfurt/Main, Germany

    Claus Weiland

  47. Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA

    Peter N. Robinson

  48. John Wiley & Sons, Hoboken, NJ, USA

    Myles Axton

  49. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Lawrence Babb

  50. Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada

    Cornelius F. Boerkoel

  51. Khoo Teck Puat–National University Children’s Medical Institute, National University Hospital, Department of Paediatrics, Singapore, Singapore

    Hui-Lin Chin

  52. Provincial Medical Genetics, Women’s Hospital of British Columbia, Vancouver, British Columbia, Canada

    Hui-Lin Chin & Nour Gazzaz

  53. Institute of Data Science, Maastricht University, Maastricht, The Netherlands

    Michel Dumontier

  54. Department of Pediatrics, King Abdulaziz University Hospital, Jeddah, Saudi Arabia

    Nour Gazzaz

  55. Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA

    Harry Hochheiser

  56. Paediatric Dermatology, Great Ormond Street Hospital for Children, London, UK

    Veronica A. Kinsler

  57. Mosaicism and Precision Medicine Laboratory, Francis Crick Institute, London, UK

    Veronica A. Kinsler

  58. Molecular Biomedicine, Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada

    Hanns Lochmüller & Rachel Thompson

  59. Brain and Mind Research Institute, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada

    Hanns Lochmüller

  60. Neuromuscular Centre, The Ottawa Hospital, Ottawa, Ontario, Canada

    Hanns Lochmüller

  61. Precision Diagnosis & Image-Guided Therapy, Philips Research North America, Cambridge, MA, USA

    Alexander R. Mankovich

  62. ELIXIR Hub, ELIXIR, Cambridge, UK

    Gary I. Saunders

  63. Division of Evolution, Infection and Genomics, University of Manchester, Manchester, UK

    Panagiotis I. Sergouniotis

  64. Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia

    Andreas Zankl

  65. Department of Clinical Genetics, The Children’s Hospital at Westmead, Westmead, New South Wales, Australia

    Andreas Zankl

  66. Kinghorn Centre for Clinical Genomics and Bone Division, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia

    Andreas Zankl

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Consortia

The GAGH Phenopacket Modeling Consortium

  • Myles Axton
  • , Lawrence Babb
  • , Cornelius F. Boerkoel
  • , Bimal P. Chaudhari
  • , Hui-Lin Chin
  • , Michel Dumontier
  • , Nour Gazzaz
  • , David P. Hansen
  • , Harry Hochheiser
  • , Veronica A. Kinsler
  • , Hanns Lochmüller
  • , Alexander R. Mankovich
  • , Gary I. Saunders
  • , Panagiotis I. Sergouniotis
  • , Rachel Thompson
  •  & Andreas Zankl

Corresponding authors

Correspondence to Julius O. B. Jacobsen, Melissa A. Haendel or Peter N. Robinson.

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Competing interests

S.K. is an employee of Ada Health GmbH. N.P. is a director of Phenopolis Ltd. O.E. is supported by Janssen, Johnson & Johnson, Volastra Therapeutics, AstraZeneca and Eli Lilly research grants, and is a scientific advisor and equity holder in Freenome, Owkin, Volastra Therapeutics and One Three Biotech. A.R.M. is an employee of Philips Research North America. O.J.B. is an employee of PhenoTips. M.A. is an editor employed by Wiley. A.S. is an employee of Lifebit Biotech Ltd. The remaining authors declare no competing interests.

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Nature Biotechnology thanks Kai Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Jacobsen, J.O.B., Baudis, M., Baynam, G.S. et al. The GA4GH Phenopacket schema defines a computable representation of clinical data. Nat Biotechnol 40, 817–820 (2022). https://doi.org/10.1038/s41587-022-01357-4

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