A corpus of GA4GH phenopackets: Case-level phenotyping for genomic diagnostics and discovery

Daniel Danis¹, Michael J Bamshad², Yasemin Bridges³, Andrés Caballero-Oteyza⁴, Pilar Cacheiro³, Leigh C Carmody⁵, Leonardo Chimirri⁶, Jessica X Chong⁷, Ben Coleman⁵, Raymond Dalgleish⁸, Peter J Freeman⁹, Adam S L Graefe⁶, Tudor Groza¹⁰, Peter Hansen⁶, Julius O B Jacobsen³, Adam Klocperk¹¹, Maaike Kusters¹², Markus S Ladewig¹³, Allison J Marcello¹⁴, Teresa Mattina¹⁵, Christopher J Mungall¹⁶, Monica C Munoz-Torres¹⁷, Justin T Reese¹⁶, Filip Rehburg⁶, Bárbara C S Reis¹⁸, Catharina Schuetz¹⁹, Damian Smedley³, Timmy Strauss²⁰, Jagadish Chandrabose Sundaramurthi⁵, Sylvia Thun⁶, Kyran Wissink²¹, John F Wagstaff⁸, David Zocche²², Melissa A Haendel²³, Peter N Robinson²⁴

Affiliations

¹ Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany; The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington CT 06032, USA.
² Department of Pediatrics, Division of Genetic Medicine, University of Washington, 1959 NE Pacific Street, Box 357371, Seattle, WA 98195, USA; Brotman-Baty Institute for Precision Medicine, 1959 NE Pacific Street, Box 357657, Seattle, WA 98195, USA; Department of Pediatrics, Division of Genetic Medicine, Seattle Children's Hospital, Seattle, WA 98195, USA.
³ William Harvey Research Institute, Queen Mary University of London, London, UK.
⁴ Clinic for Immunology and Rheumatology, Hanover Medical School, Hanover, Germany; RESiST-Cluster of Excellence 2155, Hanover Medical School, Hanover, Germany.
⁵ The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington CT 06032, USA.
⁶ Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
⁷ Department of Pediatrics, Division of Genetic Medicine, University of Washington, 1959 NE Pacific Street, Box 357371, Seattle, WA 98195, USA; Brotman-Baty Institute for Precision Medicine, 1959 NE Pacific Street, Box 357657, Seattle, WA 98195, USA.
⁸ Department of Genetics, Genomics and Cancer Sciences, University of Leicester, Leicester, UK.
⁹ Division of Informatics, Imaging and Data Science, The University of Manchester, Manchester, UK.
¹⁰ Rare Care Centre, Perth Children's Hospital, Nedlands, WA 6009, Australia; SingHealth Duke-NUS Institute of Precision Medicine, 5 Hospital Drive Level 9, Singapore 169609, Singapore; Telethon Kids Institute, Nedlands, WA 6009, Australia.
¹¹ Department of Immunology, 2nd Faculty of Medicine, Charles University and University Hospital in Motol, Prague, Czech Republic.
¹² Department of Paediatric Immunology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK; University College London Institute of Child Health, London, UK.
¹³ Department of Ophthalmology, University Clinic Marburg - Campus Fulda, Fulda, Germany.
¹⁴ Department of Pediatrics, Division of Genetic Medicine, University of Washington, 1959 NE Pacific Street, Box 357371, Seattle, WA 98195, USA.
¹⁵ Medica Genetics University of Catania Italy, Catania, Italy; Morgagni Foundation and Clinic, Catania, Italy.
¹⁶ Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
¹⁷ Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
¹⁸ Department of Allergy and Immunology, National Institute of Women's, Children's and Adolescents' Health Fernandes Figueira, Rio de Janeiro, Brazil; High Complexity Laboratory, National Institute of Women's, Children's and Adolescents' Health Fernandes Figueira, Rio de Janeiro, Brazil.
¹⁹ Department of Pediatrics, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; University Center for Rare Diseases, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; German Center for Child and Adolescent Health (DZKJ), partner site Leipzig/Dresden, Dresden, Germany.
²⁰ Department of Pediatrics, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; University Center for Rare Diseases, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
²¹ Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany; Utrecht University, Utrecht, the Netherlands.
²² North West Thames Regional Genetics Service, Northwick Park & St Mark's Hospitals, London, UK.
²³ University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
²⁴ Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany; The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington CT 06032, USA; ELLIS-European Laboratory for Learning and Intelligent Systems. Electronic address: peter.robinson@bih-charite.de.

PMID: 39394689
PMCID: PMC11564936
DOI: 10.1016/j.xhgg.2024.100371

A corpus of GA4GH phenopackets: Case-level phenotyping for genomic diagnostics and discovery

Daniel Danis et al. HGG Adv. 2025.

. 2025 Jan 9;6(1):100371.

doi: 10.1016/j.xhgg.2024.100371. Epub 2024 Oct 10.

Authors

Affiliations

¹ Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany; The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington CT 06032, USA.
² Department of Pediatrics, Division of Genetic Medicine, University of Washington, 1959 NE Pacific Street, Box 357371, Seattle, WA 98195, USA; Brotman-Baty Institute for Precision Medicine, 1959 NE Pacific Street, Box 357657, Seattle, WA 98195, USA; Department of Pediatrics, Division of Genetic Medicine, Seattle Children's Hospital, Seattle, WA 98195, USA.
³ William Harvey Research Institute, Queen Mary University of London, London, UK.
⁴ Clinic for Immunology and Rheumatology, Hanover Medical School, Hanover, Germany; RESiST-Cluster of Excellence 2155, Hanover Medical School, Hanover, Germany.
⁵ The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington CT 06032, USA.
⁶ Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
⁷ Department of Pediatrics, Division of Genetic Medicine, University of Washington, 1959 NE Pacific Street, Box 357371, Seattle, WA 98195, USA; Brotman-Baty Institute for Precision Medicine, 1959 NE Pacific Street, Box 357657, Seattle, WA 98195, USA.
⁸ Department of Genetics, Genomics and Cancer Sciences, University of Leicester, Leicester, UK.
⁹ Division of Informatics, Imaging and Data Science, The University of Manchester, Manchester, UK.
¹⁰ Rare Care Centre, Perth Children's Hospital, Nedlands, WA 6009, Australia; SingHealth Duke-NUS Institute of Precision Medicine, 5 Hospital Drive Level 9, Singapore 169609, Singapore; Telethon Kids Institute, Nedlands, WA 6009, Australia.
¹¹ Department of Immunology, 2nd Faculty of Medicine, Charles University and University Hospital in Motol, Prague, Czech Republic.
¹² Department of Paediatric Immunology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK; University College London Institute of Child Health, London, UK.
¹³ Department of Ophthalmology, University Clinic Marburg - Campus Fulda, Fulda, Germany.
¹⁴ Department of Pediatrics, Division of Genetic Medicine, University of Washington, 1959 NE Pacific Street, Box 357371, Seattle, WA 98195, USA.
¹⁵ Medica Genetics University of Catania Italy, Catania, Italy; Morgagni Foundation and Clinic, Catania, Italy.
¹⁶ Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
¹⁷ Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
¹⁸ Department of Allergy and Immunology, National Institute of Women's, Children's and Adolescents' Health Fernandes Figueira, Rio de Janeiro, Brazil; High Complexity Laboratory, National Institute of Women's, Children's and Adolescents' Health Fernandes Figueira, Rio de Janeiro, Brazil.
¹⁹ Department of Pediatrics, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; University Center for Rare Diseases, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; German Center for Child and Adolescent Health (DZKJ), partner site Leipzig/Dresden, Dresden, Germany.
²⁰ Department of Pediatrics, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; University Center for Rare Diseases, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
²¹ Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany; Utrecht University, Utrecht, the Netherlands.
²² North West Thames Regional Genetics Service, Northwick Park & St Mark's Hospitals, London, UK.
²³ University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
²⁴ Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany; The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington CT 06032, USA; ELLIS-European Laboratory for Learning and Intelligent Systems. Electronic address: peter.robinson@bih-charite.de.

PMID: 39394689
PMCID: PMC11564936
DOI: 10.1016/j.xhgg.2024.100371

Abstract

The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments. There has been a great need for a collection of phenopackets to test software pipelines and algorithms. Here, we present Phenopacket Store. Phenopacket Store v.0.1.19 includes 6,668 phenopackets representing 475 Mendelian and chromosomal diseases associated with 423 genes and 3,834 unique pathogenic alleles curated from 959 different publications. This represents the first large-scale collection of case-level, standardized phenotypic information derived from case reports in the literature with detailed descriptions of the clinical data and will be useful for many purposes, including the development and testing of software for prioritizing genes and diseases in diagnostic genomics, machine learning analysis of clinical phenotype data, patient stratification, and genotype-phenotype correlations. This corpus also provides best-practice examples for curating literature-derived data using the GA4GH Phenopacket Schema.

Keywords: global alliance for genomics and health; human phenotype ontology; phenopacket schema.

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Conflict of interest statement

Declaration of interests M.A.H. is a founder of Alamya Health. M.J.B. and J.X.C. are the Editor-in-Chief and Deputy Editor of HGG Advances, respectively, and were recused from the editorial handling of this manuscript.

Figures

**Figure 1**
Phenopacket Store summary characteristics (A) A histogram with distribution of ages of last examination. (B) The histogram of age of last examination partitioned by sex. (C) Distribution of HPO term counts per phenopacket. The boxplots show the counts of the HPO terms present in the phenopacket, the terms that were specifically excluded, and the total HPO count (present + excluded). The horizontal line of each box indicates the median term count, box borders indicate positions of the 1st and 3rd quartiles, the whiskers indicate 1.5 times the interquartile range, and the circles represent the term counts beyond the interquartile range. (D) The number of diseases for which the indicated number of phenopackets is available.

**Figure 2**
Schematic visualization of a phenopacket In this simplified representation, the major elements of the Phenopacket Schema used for the phenopackets in this collection are shown. The subject of the phenopacket is represented using the individual element, which allows the (anonymous) identifier, age at last examination, and sex to be specified. Each subject can have an arbitrary number of phenotypic features, which comprise an HPO term and, optionally, information about the age of onset of the feature. The subject can have an arbitrary number of diseases, but for the phenopackets contained in this collection, each subject has one disease. The subject can have an arbitrary number of interpretations, which must refer to a disease in the disease list. In this example, a pathogenic variant in the *FBN1* gene is interpreted to be causal for Marfan syndrome. Note that the Phenopacket Schema can additionally represent treatments, numerical measurements, and other clinical data. For a more detailed illustration, see the original publication.

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Update of

A corpus of GA4GH Phenopackets: case-level phenotyping for genomic diagnostics and discovery.
Danis D, Bamshad MJ, Bridges Y, Cacheiro P, Carmody LC, Chong JX, Coleman B, Dalgleish R, Freeman PJ, Graefe ASL, Groza T, Jacobsen JOB, Klocperk A, Kusters M, Ladewig MS, Marcello AJ, Mattina T, Mungall CJ, Munoz-Torres MC, Reese JT, Rehburg F, Reis BCS, Schuetz C, Smedley D, Strauss T, Sundaramurthi JC, Thun S, Wissink K, Wagstaff JF, Zocche D, Haendel MA, Robinson PN. Danis D, et al. medRxiv [Preprint]. 2024 May 29:2024.05.29.24308104. doi: 10.1101/2024.05.29.24308104. medRxiv. 2024. Update in: HGG Adv. 2025 Jan 09;6(1):100371. doi: 10.1016/j.xhgg.2024.100371. PMID: 38854034 Free PMC article. Updated. Preprint.

References

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A corpus of GA4GH phenopackets: Case-level phenotyping for genomic diagnostics and discovery

Affiliations

A corpus of GA4GH phenopackets: Case-level phenotyping for genomic diagnostics and discovery

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Update of

References

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources