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Annual Review of Biomedical Data Science

Volume 7, 2024

Generating Clinical-Grade Gene–Disease Validity Classifications Through the ClinGen Data Platforms

Abstract

Clinical genetic laboratories must have access to clinically validated biomedical data for precision medicine. A lack of accessibility, normalized structure, and consistency in evaluation complicates interpretation of disease causality, resulting in confusion in assessing the clinical validity of genes and genetic variants for diagnosis. A key goal of the Clinical Genome Resource (ClinGen) is to fill the knowledge gap concerning the strength of evidence supporting the role of a gene in a monogenic disease, which is achieved through a process known as Gene–Disease Validity curation. Here we review the work of ClinGen in developing a curation infrastructure that supports the standardization, harmonization, and dissemination of Gene–Disease Validity data through the creation of frameworks and the utilization of common data standards. This infrastructure is based on several applications, including the ClinGen GeneTracker, Gene Curation Interface, Data Exchange, GeneGraph, and website.

Keyword(s): biocuration, clinical genetics, data harmonization, data standards, precision medicine, research informatics
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/content/journals/10.1146/annurev-biodatasci-102423-112456
2024-08-23
2024-10-06
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Literature Cited

  1. 1.
    Amberger JS, Bocchini CA, Scott AF, Hamosh A. 2019.. OMIM.org: leveraging knowledge across phenotype–gene relationships. . Nucleic Acids Res. 47::D1038–43
     [Crossref] [Google Scholar]
  2. 2.
    Povey S, Lovering R, Bruford E, Wright M, Lush M, Wain H. 2001.. The HUGO Gene Nomenclature Committee (HGNC). . Hum. Genet. 109::678–80
     [Crossref] [Google Scholar]
  3. 3.
    Claussnitzer M, Cho JH, Collins R, Cox NJ, Dermitzakis ET, et al. 2020.. A brief history of human disease genetics. . Nature 577::179–89
     [Crossref] [Google Scholar]
  4. 4.
    Crespi S. 2021.. Looking back at 20 years of human genome sequencing. . Science Podcast, Feb. 4. https://www.science.org/content/podcast/looking-back-20-years-human-genome-sequencing
    [Google Scholar]
  5. 5.
    Rehm HL, Berg JS, Brooks LD, Bustamante CD, Evans JP, et al. 2015.. ClinGen—the Clinical Genome Resource. . N. Engl. J. Med. 372::2235–42
     [Crossref] [Google Scholar]
  6. 6.
    MacArthur DG, Manolio TA, Dimmock DP, Rehm HL, Shendure J, et al. 2014.. Guidelines for investigating causality of sequence variants in human disease. . Nature 508::469–76
     [Crossref] [Google Scholar]
  7. 7.
    Strande NT, Riggs ER, Buchanan AH, Ceyhan-Birsoy O, DiStefano M, et al. 2017.. Evaluating the clinical validity of gene-disease associations: an evidence-based framework developed by the Clinical Genome Resource. . Am. J. Hum. Genet. 100::895–906
     [Crossref] [Google Scholar]
  8. 8.
    Bean LJH, Funke B, Carlston CM, Gannon JL, Kantarci S, et al. 2020.. Diagnostic gene sequencing panels: from design to report—a technical standard of the American College of Medical Genetics and Genomics (ACMG). . Genet. Med. 22::453–61
     [Crossref] [Google Scholar]
  9. 9.
    Köhler S, Gargano M, Matentzoglu N, Carmody LC, Lewis-Smith D, et al. 2021.. The Human Phenotype Ontology in 2021. . Nucleic Acids Res. 49::D1207–17
     [Crossref] [Google Scholar]
  10. 10.
    Mungall CJ, McMurry JA, Köhler S, Balhoff JP, Borromeo C, et al. 2017.. The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species. . Nucleic Acids Res. 45::D712–22
     [Crossref] [Google Scholar]
  11. 11.
    Thaxton C, Goldstein J, DiStefano M, Wallace K, Witmer PD, et al. 2022.. Lumping versus splitting: how to approach defining a disease to enable accurate genomic curation. . Cell Genom. 2::100131
     [Crossref] [Google Scholar]
  12. 12.
    Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, et al. 2016.. The FAIR Guiding Principles for scientific data management and stewardship. . Sci. Data 3::160018
     [Crossref] [Google Scholar]
  13. 13.
    Preston CG, Wright MW, Madhavrao R, Harrison SM, Goldstein JL, et al. 2022.. ClinGen Variant Curation Interface: a variant classification platform for the application of evidence criteria from ACMG/AMP guidelines. . Genome Med. 14::6
     [Crossref] [Google Scholar]
  14. 14.
    Zahn L. 1990.. Network Computing Architecture. Englewood Cliffs, NJ:: Prentice Hall
     [Google Scholar]
  15. 15.
    Arp R, Smith B, Spear AD. 2015.. Building Ontologies with Basic Formal Ontology. Cambridge, MA:: MIT Press
     [Google Scholar]
  16. 16.
    Visser U, Abeyruwan S, Vempati U, Smith RP, Lemmon V, Schürer SC. 2011.. BioAssay Ontology (BAO): a semantic description of bioassays and high-throughput screening results. . BMC Bioinform. 12::257
     [Crossref] [Google Scholar]
  17. 17.
    Diehl AD, Meehan TF, Bradford YM, Brush MH, Dahdul WM, et al. 2016.. The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability. . J. Biomed. Semant. 7::44
     [Crossref] [Google Scholar]
  18. 18.
    Pawliczek P, Patel RY, Ashmore LR, Jackson AR, Bizon C, et al. 2018.. ClinGen Allele Registry links information about genetic variants. . Hum. Mutat. 39::1690–701
     [Crossref] [Google Scholar]
  19. 19.
    Malone J, Holloway E, Adamusiak T, Kapushesky M, Zheng J, et al. 2010.. Modeling sample variables with an Experimental Factor Ontology. . Bioinformatics 26::1112–18
     [Crossref] [Google Scholar]
  20. 20.
    Aleksander SA, Balhoff J, Carbon S, Cherry JM, Drabkin HJ, et al. 2023.. The Gene Ontology knowledgebase in 2023. . Genetics 224::iyad031
     [Crossref] [Google Scholar]
  21. 21.
    Seal RL, Braschi B, Gray K, Jones TEM, Tweedie S, et al. 2023.. Genenames.org: the HGNC resources in 2023. . Nucleic Acids Res. 51::D1003–9
     [Crossref] [Google Scholar]
  22. 22.
    den Dunnen JT, Dalgleish R, Maglott DR, Hart RK, Greenblatt MS, et al. 2016.. HGVS recommendations for the description of sequence variants: 2016 update. . Hum. Mutat. 37::564–69
     [Crossref] [Google Scholar]
  23. 23.
    Smith CL, Eppig JT. 2009.. The mammalian phenotype ontology: enabling robust annotation and comparative analysis. . Wiley Interdiscip. Rev. Syst. Biol. Med. 1::390–99
     [Crossref] [Google Scholar]
  24. 24.
    Smith JR, Park CA, Nigam R, Laulederkind SJ, Hayman GT, et al. 2013.. The clinical measurement, measurement method and experimental condition ontologies: expansion, improvements and new applications. . J. Biomed. Semantics 4::26
     [Crossref] [Google Scholar]
  25. 25.
    Smith B, Ceusters W, Klagges B, Köhler J, Kumar A, et al. 2005.. Relations in biomedical ontologies. . Genome Biol. 6::R46
     [Crossref] [Google Scholar]
  26. 26.
    Brush MH, Shefchek K, Haendel M. 2016.. SEPIO: a semantic model for the integration and analysis of scientific evidence. . In Proceedings of the Joint International Conference on Biological Ontology and BioCreative, Corvallis, Oregon, United States, Aug. 1–4, 2016, ed. P Jaiswal, R Hoehndorf, C Arighi, A Meier . Corvallis, OR:: Oregon State Univ. https://ceur-ws.org/Vol-1747/IT605_ICBO2016.pdf
    [Google Scholar]
  27. 27.
    Eilbeck K, Lewis SE, Mungall CJ, Yandell M, Stein L, et al. 2005.. The Sequence Ontology: a tool for the unification of genome annotations. . Genome Biol. 6::R44
     [Crossref] [Google Scholar]
  28. 28.
    Mungall CJ, Torniai C, Gkoutos GV, Lewis SE, Haendel MA. 2012.. Uberon, an integrative multi-species anatomy ontology. . Genome Biol. 13::R5
     [Crossref] [Google Scholar]
  29. 29.
    Landrum MJ, Lee JM, Riley GR, Jang W, Rubinstein WS, et al. 2014.. ClinVar: public archive of relationships among sequence variation and human phenotype. . Nucleic Acids Res. 42::D980–85
     [Crossref] [Google Scholar]
  30. 30.
    Fry NK, Marshall H, Mellins-Cohen T. 2019.. In praise of preprints. . Microb. Genom. 5::e000259
     [Google Scholar]
  31. 31.
    Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, et al. 2001.. dbSNP: the NCBI database of genetic variation. . Nucleic Acids Res. 29::308–11
     [Crossref] [Google Scholar]
  32. 32.
    Martin FJ, Amode MR, Aneja A, Austine-Orimoloye O, Azov AG, et al. 2023.. Ensembl 2023. . Nucleic Acids Res. 51::D933–41
     [Crossref] [Google Scholar]
  33. 33.
    O'Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, et al. 2016.. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. . Nucleic Acids Res. 44::D733–45
     [Crossref] [Google Scholar]
  34. 34.
    Morales J, Pujar S, Loveland JE, Astashyn A, Bennett R, et al. 2022.. A joint NCBI and EMBL-EBI transcript set for clinical genomics and research. . Nature 604::310–15
     [Crossref] [Google Scholar]
  35. 35.
    Mohan S, Mayers M, Weaver M, Baudet H, De Biase I, et al. 2023.. Evaluating the strength of evidence for genes implicated in peroxisomal disorders using the ClinGen clinical validity framework and providing updates to the peroxisomal disease nomenclature. . Mol. Genet. Metab. 139::107604
     [Crossref] [Google Scholar]
  36. 36.
    Sax MJ. 2019.. Apache Kafka. . In Encyclopedia of Big Data Technologies, ed. S Sakr, A Zomaya , pp. 58–66. Cham, Switz:.: Springer
     [Google Scholar]
  37. 37.
    James CA, Jongbloed JDH, Hershberger RE, Morales A, Judge DP, et al. 2021.. International evidence based reappraisal of genes associated with arrhythmogenic right ventricular cardiomyopathy using the Clinical Genome Resource framework. . Circ. Genom. Precis. Med. 14::e003273
     [Crossref] [Google Scholar]
  38. 38.
    Jordan E, Peterson L, Ai T, Asatryan B, Bronicki L, et al. 2021.. Evidence-based assessment of genes in dilated cardiomyopathy. . Circulation 144::7–19
     [Crossref] [Google Scholar]
  39. 39.
    Renard M, Francis C, Ghosh R, Scott AF, Witmer PD, et al. 2018.. Clinical validity of genes for heritable thoracic aortic aneurysm and dissection. . J. Am. Coll. Cardiol. 72::605–15
     [Crossref] [Google Scholar]
  40. 40.
    Ingles J, Goldstein J, Thaxton C, Caleshu C, Corty EW, et al. 2019.. Evaluating the clinical validity of hypertrophic cardiomyopathy genes. . Circ. Genom. Precis. Med. 12::e002460
     [Crossref] [Google Scholar]
  41. 41.
    Adler A, Novelli V, Amin AS, Abiusi E, Care M, et al. 2020.. An international, multicentered, evidence-based reappraisal of genes reported to cause congenital long QT syndrome. . Circulation 141::418–28
     [Crossref] [Google Scholar]
  42. 42.
    DiStefano MT, Hemphill SE, Oza AM, Siegert RK, Grant AR, et al. 2019.. ClinGen expert clinical validity curation of 164 hearing loss gene-disease pairs. . Genet. Med. 21::2239–47
     [Crossref] [Google Scholar]
  43. 43.
    Lee K, Seifert BA, Shimelis H, Ghosh R, Crowley SB, et al. 2019.. Clinical validity assessment of genes frequently tested on hereditary breast and ovarian cancer susceptibility sequencing panels. . Genet. Med. 21::1497–506
     [Crossref] [Google Scholar]
  44. 44.
    Seifert BA, McGlaughon JL, Jackson SA, Ritter DI, Roberts ME, et al. 2019.. Determining the clinical validity of hereditary colorectal cancer and polyposis susceptibility genes using the Clinical Genome Resource Clinical Validity Framework. . Genet. Med. 21::1507–16
     [Crossref] [Google Scholar]
  45. 45.
    McGlaughon JL, Pasquali M, Wallace K, Ross J, Senol-Cosar O, et al. 2019.. Assessing the strength of evidence for genes implicated in fatty acid oxidation disorders using the ClinGen clinical validity framework. . Mol. Genet. Metab. 128::122–28
     [Crossref] [Google Scholar]
  46. 46.
    McCormick EM, Keller K, Taylor JP, Coffey AJ, Shen L, et al. 2023.. Expert panel curation of 113 primary mitochondrial disease genes for the Leigh syndrome spectrum. . Ann. Neurol. 94::696–712
     [Crossref] [Google Scholar]
  47. 47.
    Helbig I, Riggs ER, Barry CA, Klein KM, Dyment D, et al. 2018.. The ClinGen Epilepsy Gene Curation Expert Panel—bridging the divide between clinical domain knowledge and formal gene curation criteria. . Hum. Mutat. 39::1476–84
     [Crossref] [Google Scholar]
  48. 48.
    Riggs ER, Bingaman TI, Barry CA, Behlmann A, Bluske K, et al. 2022.. Clinical validity assessment of genes frequently tested on intellectual disability/autism sequencing panels. . Genet. Med. 24::1899–908
     [Crossref] [Google Scholar]
  49. 49.
    Dilliott AA, Al Nasser A, Elnagheeb M, Fifita J, Henden L, et al. 2023.. Clinical testing panels for ALS: global distribution, consistency, and challenges. . Amyotroph. Lateral Scler. Frontotemporal Degener. 24::420–35
     [Crossref] [Google Scholar]
  50. 50.
    Grant AR, Cushman BJ, Cave H, Dillon MW, Gelb BD, et al. 2018.. Assessing the gene-disease association of 19 genes with the RASopathies using the ClinGen gene curation framework. . Hum. Mutat. 39::1485–93
     [Crossref] [Google Scholar]
  51. 51.
    DiStefano MT, Goehringer S, Babb L, Alkuraya FS, Amberger J, et al. 2022.. The Gene Curation Coalition: a global effort to harmonize gene-disease evidence resources. . Genet. Med. 24::1732–42
     [Crossref] [Google Scholar]
  52. 52.
    Navarro Gonzalez J, Zweig AS, Speir ML, Schmelter D, Rosenbloom KR, et al. 2021.. The UCSC Genome Browser database: 2021 update. . Nucleic Acids Res. 49::D1046–57
     [Crossref] [Google Scholar]
  53. 53.
    Rangwala SH, Kuznetsov A, Ananiev V, Asztalos A, Borodin E, et al. 2021.. Accessing NCBI data using the NCBI Sequence Viewer and Genome Data Viewer (GDV). . Genome Res. 31::159–69
     [Crossref] [Google Scholar]
  54. 54.
    Firth HV, Richards SM, Bevan AP, Clayton S, Corpas M, et al. 2009.. DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources. . Am. J. Hum. Genet. 84::524–33
     [Crossref] [Google Scholar]
  55. 55.
    Rehm HL, Page AJH, Smith L, Adams JB, Alterovitz G, et al. 2021.. GA4GH: international policies and standards for data sharing across genomic research and healthcare. . Cell Genom. 1::100029
     [Crossref] [Google Scholar]
  56. 56.
    Dolman L, Page A, Babb L, Freimuth RR, Arachchi H, et al. 2018.. ClinGen advancing genomic data-sharing standards as a GA4GH driver project. . Hum. Mutat. 39::1686–89
     [Crossref] [Google Scholar]
  57. 57.
    Baxter SM, Posey JE, Lake NJ, Sobreira N, Chong JX, et al. 2022.. Centers for Mendelian Genomics: a decade of facilitating gene discovery. . Genet. Med. 24::784–97
     [Crossref] [Google Scholar]
/content/journals/10.1146/annurev-biodatasci-102423-112456
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Generating Clinical-Grade Gene–Disease Validity Classifications Through the ClinGen Data Platforms
Annual Review of Biomedical Data Science 7, 31 (2024); https://doi.org/10.1146/annurev-biodatasci-102423-112456
/content/journals/10.1146/annurev-biodatasci-102423-112456
/content/journals/10.1146/annurev-biodatasci-102423-112456
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  • Article Type: Review Article

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