International Undiagnosed Diseases Programs (UDPs): components and outcomes

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Abstract

Over the last 15 years, Undiagnosed Diseases Programs have emerged to address the significant number of individuals with suspected but undiagnosed rare genetic diseases, integrating research and clinical care to optimize diagnostic outcomes. This narrative review summarizes the published literature surrounding Undiagnosed Diseases Programs worldwide, including thirteen studies that evaluate outcomes and two commentary papers. Commonalities in the diagnostic and research process of Undiagnosed Diseases Programs are explored through an appraisal of available literature. This exploration allowed for an assessment of the strengths and limitations of each of the six common steps, namely enrollment, comprehensive clinical phenotyping, research diagnostics, data sharing and matchmaking, results, and follow-up. Current literature highlights the potential utility of Undiagnosed Diseases Programs in research diagnostics. Since participants have often had extensive previous genetic studies, research pipelines allow for diagnostic approaches beyond exome or whole genome sequencing, through reanalysis using research-grade bioinformatics tools and multi-omics technologies. The overall diagnostic yield is presented by study, since different selection criteria at enrollment and reporting processes make comparisons challenging and not particularly informative. Nonetheless, diagnostic yield in an undiagnosed cohort reflects the potential of an Undiagnosed Diseases Program. Further comparisons and exploration of the outcomes of Undiagnosed Diseases Programs worldwide will allow for the development and improvement of the diagnostic and research process and in turn improve the value and utility of an Undiagnosed Diseases Program.

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The FAIR Guiding Principles for scientific data management and stewardship

Mark D Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg … (2017)

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

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The 100,000 Genomes Pilot on Rare Disease Diagnosis in Healthcare − A Preliminary Report

Damian Smedley, Katherine R. Smith, Antonio Rueda Martin … (2022)

Background The UK 100,000 Genomes Project is in the process of investigating the role of genome sequencing of patients with undiagnosed rare disease following usual care, and the alignment of research with healthcare implementation in the UK’s national health service. (Other parts of this Project focus on patients with cancer and infection.) Methods We enrolled participants, collected clinical features with human phenotype ontology terms, undertook genome sequencing and applied automated variant prioritization based on virtual gene panels (PanelApp) and phenotypes (Exomiser), alongside identification of novel pathogenic variants through research analysis. We report results on a pilot study of 4660 participants from 2183 families with 161 disorders covering a broad spectrum of rare disease. Results Diagnostic yields varied by family structure and were highest in trios and larger pedigrees. Likely monogenic disorders had much higher diagnostic yields (35%) with intellectual disability, hearing and vision disorders, achieving yields between 40 and 55%. Those with more complex etiologies had an overall 25% yield. Combining research and automated approaches was critical to 14% of diagnoses in which we found etiologic non-coding, structural and mitochondrial genome variants and coding variants poorly covered by exome sequencing. Cohort-wide burden testing across 57,000 genomes enabled discovery of 3 new disease genes and 19 novel associations. Of the genetic diagnoses that we made, 24% had immediate ramifications for the clinical decision-making for the patient or their relatives. Conclusion Our pilot study of genome sequencing in a national health care system demonstrates diagnostic uplift across a range of rare diseases. (Funded by National Institute for Health Research and others)

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Meta-analysis of the diagnostic and clinical utility of genome and exome sequencing and chromosomal microarray in children with suspected genetic diseases

Michelle M Clark, Zornitza Stark, Lauge Farnaes … (2018)

Genetic diseases are leading causes of childhood mortality. Whole-genome sequencing (WGS) and whole-exome sequencing (WES) are relatively new methods for diagnosing genetic diseases, whereas chromosomal microarray (CMA) is well established. Here we compared the diagnostic utility (rate of causative, pathogenic, or likely pathogenic genotypes in known disease genes) and clinical utility (proportion in whom medical or surgical management was changed by diagnosis) of WGS, WES, and CMA in children with suspected genetic diseases by systematic review of the literature (January 2011–August 2017) and meta-analysis, following MOOSE/PRISMA guidelines. In 37 studies, comprising 20,068 children, diagnostic utility of WGS (0.41, 95% CI 0.34–0.48, I 2 = 44%) and WES (0.36, 95% CI 0.33–0.40, I 2 = 83%) were qualitatively greater than CMA (0.10, 95% CI 0.08–0.12, I 2 = 81%). Among studies published in 2017, the diagnostic utility of WGS was significantly greater than CMA (P < 0.0001, I 2 = 13% and I 2 = 40%, respectively). Among studies featuring within-cohort comparisons, the diagnostic utility of WES was significantly greater than CMA (P < 0.001, I 2 = 36%). The diagnostic utility of WGS and WES were not significantly different. In studies featuring within-cohort comparisons of WGS/WES, the likelihood of diagnosis was significantly greater for trios than singletons (odds ratio 2.04, 95% CI 1.62–2.56, I 2 = 12%; P < 0.0001). Diagnostic utility of WGS/WES with hospital-based interpretation (0.42, 95% CI 0.38–0.45, I 2 = 48%) was qualitatively higher than that of reference laboratories (0.29, 95% CI 0.27–0.31, I 2 = 49%); this difference was significant among studies published in 2017 (P < .0001, I 2 = 22% and I 2 = 26%, respectively). The clinical utility of WGS (0.27, 95% CI 0.17–0.40, I 2 = 54%) and WES (0.17, 95% CI 0.12–0.24, I 2 = 76%) were higher than CMA (0.06, 95% CI 0.05–0.07, I 2 = 42%); this difference was significant for WGS vs CMA (P < 0.0001). In conclusion, in children with suspected genetic diseases, the diagnostic and clinical utility of WGS/WES were greater than CMA. Subgroups with higher WGS/WES diagnostic utility were trios and those receiving hospital-based interpretation. WGS/WES should be considered a first-line genomic test for children with suspected genetic diseases.

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Author and article information

Contributors

Elizabeth Emma Palmer:

ORCID: http://orcid.org/0000-0003-1844-215X

Elizabeth.palmer@unsw.edu.au

Journal

Journal ID (nlm-ta): Orphanet J Rare Dis

Journal ID (iso-abbrev): Orphanet J Rare Dis

Title: Orphanet Journal of Rare Diseases

Publisher: BioMed Central (London )

ISSN (Electronic): 1750-1172

Publication date (Electronic): 9 November 2023

Publication date PMC-release: 9 November 2023

Publication date Collection: 2023

Volume: 18

Electronic Location Identifier: 348

Affiliations

[1 ]Discipline of Paediatrics and Child Health, Faculty of Medicine and Health, School of Clinical Medicine, University of New South Wales, ( https://ror.org/03r8z3t63) Bright Alliance Building, Level 8, Randwick, NSW Australia

[2 ]Centre for Clinical Genetics, Sydney Children’s Hospital, ( https://ror.org/02tj04e91) Randwick, NSW Australia

[3 ]Genomics and Inherited Disease Program, Garvan Institute of Medical Research, ( https://ror.org/01b3dvp57) Darlinghurst, NSW Australia

[4 ]Department of Clinical Genetics, The Children’s Hospital at Westmead, ( https://ror.org/05k0s5494) Westmead, NSW Australia

[5 ]Department of Molecular Medicine and Surgery, Center for Molecular Medicine, Karolinska Institutet, ( https://ror.org/056d84691) Stockholm, Sweden

[6 ]Department of Clinical Genetics and Genomics, Karolinska University Hospital, ( https://ror.org/00m8d6786) Stockholm, Sweden

[7 ]Division of Medical Genetics, Department of Pediatrics, University of Utah, ( https://ror.org/03r0ha626) Salt Lake City, Utah USA

[8 ]Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, ( https://ror.org/01tm6cn81) Gothenburg, Sweden

[9 ]Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, ( https://ror.org/04vgqjj36) Gothenburg, Sweden

[10 ]GRID grid.94365.3d, ISNI 0000 0001 2297 5165, Medical Genetics Branch, National Human Genome Research Institute, , National Institutes of Health, ; Bethesda, MD 20892 USA

Author information

Ela Curic http://orcid.org/0009-0007-0328-3498

Lisa Ewans http://orcid.org/0000-0001-8419-5666

Ryan Pysar http://orcid.org/0009-0001-2221-9367

Fulya Taylan http://orcid.org/0000-0002-2907-0235

Lorenzo D. Botto http://orcid.org/0000-0002-5322-7116

Ann Nordgren http://orcid.org/0000-0003-3285-4281

William Gahl http://orcid.org/0000-0002-2494-6752

Elizabeth Emma Palmer http://orcid.org/0000-0003-1844-215X

Article

Publisher ID: 2966

DOI: 10.1186/s13023-023-02966-1

PMC ID: 10633944

PubMed ID: 37946247

SO-VID: 8f65c792-6f33-48f8-b859-b6b7658ad763

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 27 April 2023

Date accepted : 30 October 2023

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ScienceOpen disciplines: Infectious disease & Microbiology

Keywords: undiagnosed diseases programs,rare diseases,genomics

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ScienceOpen disciplines: Infectious disease & Microbiology

Keywords: undiagnosed diseases programs, rare diseases, genomics

International Undiagnosed Diseases Programs (UDPs): components and outcomes

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Microbial Genomics

Most cited references 68

The FAIR Guiding Principles for scientific data management and stewardship

The 100,000 Genomes Pilot on Rare Disease Diagnosis in Healthcare − A Preliminary Report

Meta-analysis of the diagnostic and clinical utility of genome and exome sequencing and chromosomal microarray in children with suspected genetic diseases

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