Leaving no patient behind! Expert recommendation in the use of innovative technologies for diagnosing rare diseases.

Humans Rare Diseases / diagnosis Genomics Genetic Testing / methods
Genomics IRDiRC Innovative technologies Molecular diagnostics Rare disease Rare disease diagnosis Rare disease research

Journal

Orphanet journal of rare diseases
ISSN: 1750-1172
Titre abrégé: Orphanet J Rare Dis
Pays: England
ID NLM: 101266602

Informations de publication

Date de publication:
27 Sep 2024
Historique:
received: 26 03 2024
accepted: 11 09 2024
medline: 28 9 2024
pubmed: 28 9 2024
entrez: 28 9 2024
Statut: epublish

Résumé

Genetic diagnosis plays a crucial role in rare diseases, particularly with the increasing availability of emerging and accessible treatments. The International Rare Diseases Research Consortium (IRDiRC) has set its primary goal as: "Ensuring that all patients who present with a suspected rare disease receive a diagnosis within one year if their disorder is documented in the medical literature". Despite significant advances in genomic sequencing technologies, more than half of the patients with suspected Mendelian disorders remain undiagnosed. In response, IRDiRC proposes the establishment of "a globally coordinated diagnostic and research pipeline". To help facilitate this, IRDiRC formed the Task Force on Integrating New Technologies for Rare Disease Diagnosis. This multi-stakeholder Task Force aims to provide an overview of the current state of innovative diagnostic technologies for clinicians and researchers, focusing on the patient's diagnostic journey. Herein, we provide an overview of a broad spectrum of emerging diagnostic technologies involving genomics, epigenomics and multi-omics, functional testing and model systems, data sharing, bioinformatics, and Artificial Intelligence (AI), highlighting their advantages, limitations, and the current state of clinical adaption. We provide expert recommendations outlining the stepwise application of these innovative technologies in the diagnostic pathways while considering global differences in accessibility. The importance of FAIR (Findability, Accessibility, Interoperability, and Reusability) and CARE (Collective benefit, Authority to control, Responsibility, and Ethics) data management is emphasized, along with the need for enhanced and continuing education in medical genomics. We provide a perspective on future technological developments in genome diagnostics and their integration into clinical practice. Lastly, we summarize the challenges related to genomic diversity and accessibility, highlighting the significance of innovative diagnostic technologies, global collaboration, and equitable access to diagnosis and treatment for people living with rare disease.

Identifiants

DOI: 10.1186/s13023-024-03361-0 PMID: 39334316
pubmed: 39334316
doi: 10.1186/s13023-024-03361-0
pii: 10.1186/s13023-024-03361-0
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

357

Subventions

Organisme : Horizon 2020 Framework Programme
ID : 825575
Organisme : Feilman Foundation
ID : Channel 7 Telethon Trust
Organisme : Stan Perron Charitable Foundation
ID : Channel 7 Telethon Trust
Organisme : NHGRI NIH HHS
ID : U01HG011762
Pays : United States
Organisme : NHGRI NIH HHS
ID : U01HG011755
Pays : United States
Organisme : The McCusker Charitable Foundation
ID : Channel 7 Telethon Trust

Informations de copyright

© 2024. The Author(s).

Références

Zurynski Y, et al. Australian children living with rare diseases: experiences of diagnosis and perceived consequences of diagnostic delays. Orphanet J Rare Dis. 2017;12:68.
pubmed: 28399928 pmcid: 5387276 doi: 10.1186/s13023-017-0622-4
Hoytema van Konijnenburg EMM, et al. Treatable inherited metabolic disorders causing intellectual disability: 2021 review and digital app. Orphanet J Rare Dis. 2021;16:170.
pubmed: 33845862 pmcid: 8042729 doi: 10.1186/s13023-021-01727-2
Graessner H, Zurek B, Hoischen A, Beltran S. Solving the unsolved rare diseases in Europe. Eur J Hum Genet. 2021;29:1319–20.
pubmed: 34140650 pmcid: 8440595 doi: 10.1038/s41431-021-00924-8
Lübbeke A, Carr AJ, Hoffmeyer P. Registry stakeholders. EFORT Open Rev. 2019;4:330–6.
pubmed: 31210971 pmcid: 6549107 doi: 10.1302/2058-5241.4.180077
Day S, Rennie S, Luo D, Tucker JD. Open to the public: paywalls and the public rationale for open access medical research publishing. Res Involv Engagem. 2020;6:8.
pubmed: 32161664 pmcid: 7048123 doi: 10.1186/s40900-020-0182-y
Vreman RA, et al. Assessment of significant benefit for orphan medicinal products by European regulators may support subsequent relative effectiveness assessments by health technology assessment organizations. Drug Discov Today. 2020;25:1223–31.
pubmed: 32344040 doi: 10.1016/j.drudis.2020.04.012
Thorogood A, et al. International federation of genomic medicine databases using GA4GH standards. Cell Genom. 2021;1:100032.
pubmed: 35128509 pmcid: 8813094 doi: 10.1016/j.xgen.2021.100032
Sanderson K. High-profile autism genetics project paused amid backlash. Nature. 2021;598:17–8.
pubmed: 34580484 doi: 10.1038/d41586-021-02602-7
Hastings Ward J, et al. Research participants: critical friends, agents for change. Eur J Hum Genet. 2022;30:1309–13.
pubmed: 36195706 pmcid: 9712391 doi: 10.1038/s41431-022-01199-3
Köhler S, et al. Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Res. 2019;47:D1018–27.
pubmed: 30476213 doi: 10.1093/nar/gky1105
Fang H, et al. Whole genome sequencing of one complex pedigree illustrates challenges with genomic medicine. BMC Med Genom. 2017;10:10.
doi: 10.1186/s12920-017-0246-5
Smedley D, et al. A whole-genome analysis framework for effective identification of pathogenic regulatory variants in mendelian disease. Am J Hum Genet. 2016;99:595–606.
pubmed: 27569544 pmcid: 5011059 doi: 10.1016/j.ajhg.2016.07.005
Baynam G, et al. 3-dimensional facial analysis-facing precision public health. Front Public Health. 2017;5:31.
pubmed: 28443272 pmcid: 5385440 doi: 10.3389/fpubh.2017.00031
Shefchek KA, et al. The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species. Nucleic Acids Res. 2020;48:D704–15.
pubmed: 31701156 doi: 10.1093/nar/gkz997
Vasilevsky NA, et al. Plain-language medical vocabulary for precision diagnosis. Nat Genet. 2018;50:474–6.
pubmed: 29632381 pmcid: 6258202 doi: 10.1038/s41588-018-0096-x
Bastarache L, et al. Phenotype risk scores identify patients with unrecognized Mendelian disease patterns. Science. 2018;359:1233–9.
pubmed: 29590070 pmcid: 5959723 doi: 10.1126/science.aal4043
Son JH, et al. Deep phenotyping on electronic health records facilitates genetic diagnosis by clinical exomes. Am J Hum Genet. 2018;103:58–73.
pubmed: 29961570 pmcid: 6035281 doi: 10.1016/j.ajhg.2018.05.010
Jacobsen JOB, et al. The GA4GH phenopacket schema defines a computable representation of clinical data. Nat Biotechnol. 2022;40:817–20.
pubmed: 35705716 pmcid: 9363006 doi: 10.1038/s41587-022-01357-4
Swietlik EM, et al. ‘There and back again’-forward genetics and reverse phenotyping in pulmonary arterial hypertension. Genes (Basel). 2020;11:1408.
pubmed: 33256119 doi: 10.3390/genes11121408
Landini S, et al. Reverse phenotyping after whole-exome sequencing in steroid-resistant nephrotic syndrome. Clin J Am Soc Nephrol. 2020;15:89–100.
pubmed: 31831576 doi: 10.2215/CJN.06060519
Amberger JS, Bocchini CA, Scott AF, Hamosh A. OMIM.org: leveraging knowledge across phenotype-gene relationships. Nucleic Acids Res. 2019;47:D1038–43.
pubmed: 30445645 doi: 10.1093/nar/gky1151
DiStefano MT, et al. The gene curation coalition: a global effort to harmonize gene-disease evidence resources. Genet Med. 2022;24:1732–42.
pubmed: 35507016 pmcid: 7613247 doi: 10.1016/j.gim.2022.04.017
Sánchez-Luquez KY, Carpena MX, Karam SM, Tovo-Rodrigues L. The contribution of whole-exome sequencing to intellectual disability diagnosis and knowledge of underlying molecular mechanisms: a systematic review and meta-analysis. Mutat Res Rev Mutat Res. 2022;790:108428.
pubmed: 35905832 doi: 10.1016/j.mrrev.2022.108428
Similuk MN, et al. Clinical exome sequencing of 1000 families with complex immune phenotypes: toward comprehensive genomic evaluations. J Allergy Clin Immunol. 2022;150:947–54.
pubmed: 35753512 pmcid: 9547837 doi: 10.1016/j.jaci.2022.06.009
Tarailo-Graovac M, Wasserman WW, Van Karnebeek CDM. Impact of next-generation sequencing on diagnosis and management of neurometabolic disorders: current advances and future perspectives. Expert Rev Mol Diagn. 2017;17:307–9.
pubmed: 28277145 doi: 10.1080/14737159.2017.1293527
Schofield D, et al. Cost-effectiveness of massively parallel sequencing for diagnosis of paediatric muscle diseases. NPJ Genom Med. 2017;2:4.
pubmed: 29152331 pmcid: 5677979 doi: 10.1038/s41525-017-0006-7
Britten-Jones AC, et al. The diagnostic yield of next generation sequencing in inherited retinal diseases: a systematic review and meta-analysis. Am J Ophthalmol. 2023;249:57–73.
pubmed: 36592879 doi: 10.1016/j.ajo.2022.12.027
Menke J. Early detection of intestinal cancer–a task for the public health office? Offentl Gesundheitswes. 1987;49:282–4.
pubmed: 2956541
Wojcik MH, et al. Beyond the exome: what’s next in diagnostic testing for Mendelian conditions. Am J Hum Genet. 2023;110:1229–48.
pubmed: 37541186 pmcid: 10432150 doi: 10.1016/j.ajhg.2023.06.009
Wortmann SB, et al. How to proceed after ‘negative’ exome: a review on genetic diagnostics, limitations, challenges, and emerging new multiomics techniques. J Inherit Metab Dis. 2022;45:663–81.
pubmed: 35506430 pmcid: 9539960 doi: 10.1002/jimd.12507
Pavan S, et al. Clinical practice guidelines for rare diseases: the orphanet database. PLoS ONE. 2017;12:e0170365.
pubmed: 28099516 pmcid: 5242437 doi: 10.1371/journal.pone.0170365
Bodenreider O. The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 2004;32:D267-270.
pubmed: 14681409 pmcid: 308795 doi: 10.1093/nar/gkh061
Haendel M, et al. How many rare diseases are there? Nat Rev Drug Discov. 2020;19:77–8.
pubmed: 32020066 pmcid: 7771654 doi: 10.1038/d41573-019-00180-y
Ehrhart F, et al. A resource to explore the discovery of rare diseases and their causative genes. Sci Data. 2021;8:124.
pubmed: 33947870 pmcid: 8096966 doi: 10.1038/s41597-021-00905-y
Umlai U-KI, Bangarusamy DK, Estivill X, Jithesh PV. Genome sequencing data analysis for rare disease gene discovery. Brief Bioinform. 2022;23:bbab363.
pubmed: 34498682 doi: 10.1093/bib/bbab363
Macken WL, et al. Specialist multidisciplinary input maximises rare disease diagnoses from whole genome sequencing. Nat Commun. 2022;13:6324.
pubmed: 36344503 pmcid: 9640711 doi: 10.1038/s41467-022-32908-7
Logsdon GA, Vollger MR, Eichler EE. Long-read human genome sequencing and its applications. Nat Rev Genet. 2020;21:597–614.
pubmed: 32504078 pmcid: 7877196 doi: 10.1038/s41576-020-0236-x
Bruels CC, et al. Diagnostic capabilities of nanopore long-read sequencing in muscular dystrophy. Ann Clin Transl Neurol. 2022;9:1302–9.
pubmed: 35734998 pmcid: 9380148 doi: 10.1002/acn3.51612
Cohen ASA, et al. Genomic answers for children: dynamic analyses of >1000 pediatric rare disease genomes. Genet Med. 2022;24:1336–48.
pubmed: 35305867 doi: 10.1016/j.gim.2022.02.007
Pauper M, et al. Correction: long-read trio sequencing of individuals with unsolved intellectual disability. Eur J Hum Genet. 2021;29:720.
pubmed: 33772160 pmcid: 8115538 doi: 10.1038/s41431-021-00868-z
Nakamichi K, Stacey A, Mustafi D. Targeted long-read sequencing allows for rapid identification of pathogenic disease-causing variants in retinoblastoma. Ophthalmic Genet. 2022;43:762–70.
pubmed: 36325802 doi: 10.1080/13816810.2022.2141797
Glinos DA, et al. Transcriptome variation in human tissues revealed by long-read sequencing. Nature. 2022;608:353–9.
pubmed: 35922509 pmcid: 10337767 doi: 10.1038/s41586-022-05035-y
Berger SL, Kouzarides T, Shiekhattar R, Shilatifard A. An operational definition of epigenetics. Genes Dev. 2009;23:781–3.
pubmed: 19339683 pmcid: 3959995 doi: 10.1101/gad.1787609
Sadikovic B, Levy MA, Aref-Eshghi E. Functional annotation of genomic variation: DNA methylation episignatures in neurodevelopmental Mendelian disorders. Hum Mol Genet. 2020;29:R27–32.
pubmed: 32644126 doi: 10.1093/hmg/ddaa144
Sadikovic B, et al. Clinical epigenomics: genome-wide DNA methylation analysis for the diagnosis of Mendelian disorders. Genet Med. 2021;23:1065–74.
pubmed: 33547396 pmcid: 8187150 doi: 10.1038/s41436-020-01096-4
Levy MA, et al. Novel diagnostic DNA methylation episignatures expand and refine the epigenetic landscapes of Mendelian disorders. HGG Adv. 2022;3:100075.
pubmed: 35047860
Aref-Eshghi E, et al. Evaluation of DNA methylation episignatures for diagnosis and phenotype correlations in 42 Mendelian neurodevelopmental disorders. Am J Hum Genet. 2021;108:1161–3.
pubmed: 34087165 pmcid: 8206389 doi: 10.1016/j.ajhg.2021.04.022
Hildonen M, et al. DNA methylation signature classification of rare disorders using publicly available methylation data. Clin Genet. 2023;103:688–92.
pubmed: 36705342 doi: 10.1111/cge.14304
Landrum MJ, et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2018;46:D1062–7.
pubmed: 29165669 doi: 10.1093/nar/gkx1153
Bamshad MJ, Nickerson DA, Chong JX. Mendelian gene discovery: fast and furious with no end in sight. Am J Hum Genet. 2019;105:448–55.
pubmed: 31491408 pmcid: 6731362 doi: 10.1016/j.ajhg.2019.07.011
Boycott KM, Azzariti DR, Hamosh A, Rehm HL. Seven years since the launch of the matchmaker exchange: the evolution of genomic matchmaking. Hum Mutat. 2022;43:659–67.
pubmed: 35537081 pmcid: 9133175
McWalter K, Torti E, Morrow M, Juusola J, Retterer K. Discovery of over 200 new and expanded genetic conditions using GeneMatcher. Hum Mutat. 2022;43:760–4.
pubmed: 35224800 pmcid: 9306743
Laurie S, et al. The RD-connect genome-phenome analysis platform: accelerating diagnosis, research, and gene discovery for rare diseases. Hum Mutat. 2022;43:717–33.
pubmed: 35178824 pmcid: 9324157
Pais LS, et al. seqr: a web-based analysis and collaboration tool for rare disease genomics. Hum Mutat. 2022;43:698–707.
pubmed: 35266241 pmcid: 9903206
Osmond M, et al. PhenomeCentral: 7 years of rare disease matchmaking. Hum Mutat. 2022;43:674–81.
pubmed: 35165961
Rodrigues EDS, et al. Variant-level matching for diagnosis and discovery: challenges and opportunities. Hum Mutat. 2022;43:782–90.
pubmed: 35191117 pmcid: 9133151
Belien JAM, Kip AE, Swertz MA. Road to FAIR genomes: a gap analysis of NGS data generation and sharing in the Netherlands. BMJ Open Sci. 2022;6:e100268.
pubmed: 35505836 pmcid: 9014103 doi: 10.1136/bmjos-2021-100268
Schatz MC, et al. Inverting the model of genomics data sharing with the NHGRI genomic data science analysis, visualization, and informatics lab-space. Cell Genom. 2022;2:100085.
pubmed: 35199087 pmcid: 8863334 doi: 10.1016/j.xgen.2021.100085
Montgomery SB, Bernstein JA, Wheeler MT. Toward transcriptomics as a primary tool for rare disease investigation. Cold Spring Harb Mol Case Stud. 2022;8:a006198.
pubmed: 35217565 pmcid: 8958920 doi: 10.1101/mcs.a006198
Cummings BB, et al. Improving genetic diagnosis in Mendelian disease with transcriptome sequencing. Sci Transl Med. 2017;9:eaal5209.
pubmed: 28424332 pmcid: 5548421 doi: 10.1126/scitranslmed.aal5209
Kremer LS, et al. Genetic diagnosis of Mendelian disorders via RNA sequencing. Nat Commun. 2017;8:15824.
pubmed: 28604674 pmcid: 5499207 doi: 10.1038/ncomms15824
Frésard L, et al. Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts. Nat Med. 2019;25:911–9.
pubmed: 31160820 pmcid: 6634302 doi: 10.1038/s41591-019-0457-8
Yépez VA, et al. Clinical implementation of RNA sequencing for Mendelian disease diagnostics. Genome Med. 2022;14:38.
pubmed: 35379322 pmcid: 8981716 doi: 10.1186/s13073-022-01019-9
Fear VS, et al. CRISPR single base editing, neuronal disease modelling and functional genomics for genetic variant analysis: pipeline validation using Kleefstra syndrome EHMT1 haploinsufficiency. Stem Cell Res Ther. 2022;13:69.
pubmed: 35139903 pmcid: 8827184 doi: 10.1186/s13287-022-02740-3
FANTOM. https://fantom.gsc.riken.jp/ .
ENCODE. https://www.encodeproject.org/ .
GTEx Portal. https://gtexportal.org/home/ .
Towards a Human Cell Atlas: Taking notes from the past. https://www.humancellatlas.org/ .
Larizza L, Finelli P. Developmental disorders with intellectual disability driven by chromatin dysregulation: clinical overlaps and molecular mechanisms. Clin Genet. 2019;95:231–40.
pubmed: 29672823 doi: 10.1111/cge.13365
Latypova X, et al. Haploinsufficiency of the Sin3/HDAC corepressor complex member SIN3B causes a syndromic intellectual disability/autism spectrum disorder. Am J Hum Genet. 2021;108:929–41.
pubmed: 33811806 pmcid: 8206166 doi: 10.1016/j.ajhg.2021.03.017
Wang J, et al. ATAC-Seq analysis reveals a widespread decrease of chromatin accessibility in age-related macular degeneration. Nat Commun. 2018;9:1364.
pubmed: 29636475 pmcid: 5893535 doi: 10.1038/s41467-018-03856-y
Corces MR, et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat Methods. 2017;14:959–62.
pubmed: 28846090 pmcid: 5623106 doi: 10.1038/nmeth.4396
Haijes HA, et al. Direct infusion based metabolomics identifies metabolic disease in patients’ dried blood spots and plasma. Metabolites. 2019;9:12.
pubmed: 30641898 pmcid: 6359237 doi: 10.3390/metabo9010012
Miller MJ, et al. Untargeted metabolomic analysis for the clinical screening of inborn errors of metabolism. J Inherit Metab Dis. 2015;38:1029–39.
pubmed: 25875217 pmcid: 4626538 doi: 10.1007/s10545-015-9843-7
Almontashiri NAM, et al. Clinical validation of targeted and untargeted metabolomics testing for genetic disorders: a 3 year comparative study. Sci Rep. 2020;10:9382.
pubmed: 32523032 pmcid: 7287104 doi: 10.1038/s41598-020-66401-2
Herzog K, et al. Plasma lipidomics as a diagnostic tool for peroxisomal disorders. J Inherit Metab Dis. 2018;41:489–98.
pubmed: 29209936 doi: 10.1007/s10545-017-0114-7
Gasperini M, Starita L, Shendure J. The power of multiplexed functional analysis of genetic variants. Nat Protoc. 2016;11:1782–7.
pubmed: 27583640 pmcid: 6690347 doi: 10.1038/nprot.2016.135
Weile J, Roth FP. Multiplexed assays of variant effects contribute to a growing genotype-phenotype atlas. Hum Genet. 2018;137:665–78.
pubmed: 30073413 pmcid: 6153521 doi: 10.1007/s00439-018-1916-x
Starita LM, et al. Variant Interpretation: functional assays to the rescue. Am J Hum Genet. 2017;101:315–25.
pubmed: 28886340 pmcid: 5590843 doi: 10.1016/j.ajhg.2017.07.014
Findlay GM, et al. Accurate classification of BRCA1 variants with saturation genome editing. Nature. 2018;562:217–22.
pubmed: 30209399 pmcid: 6181777 doi: 10.1038/s41586-018-0461-z
Scott A, et al. Saturation-scale functional evidence supports clinical variant interpretation in Lynch syndrome. Genome Biol. 2022;23:266.
pubmed: 36550560 pmcid: 9773515 doi: 10.1186/s13059-022-02839-z
Ng C-A, et al. A massively parallel assay accurately discriminates between functionally normal and abnormal variants in a hotspot domain of KCNH2. Am J Hum Genet. 2022;109:1208–16.
pubmed: 35688148 pmcid: 9300756 doi: 10.1016/j.ajhg.2022.05.003
Erwood S, et al. Saturation variant interpretation using CRISPR prime editing. Nat Biotechnol. 2022;40:885–95.
pubmed: 35190686 doi: 10.1038/s41587-021-01201-1
Tewhey R, et al. Direct identification of hundreds of expression-modulating variants using a multiplexed reporter assay. Cell. 2016;165:1519–29.
pubmed: 27259153 pmcid: 4957403 doi: 10.1016/j.cell.2016.04.027
Griesemer D, et al. Genome-wide functional screen of 3’UTR variants uncovers causal variants for human disease and evolution. Cell. 2021;184:5247-5260.e19.
pubmed: 34534445 pmcid: 8487971 doi: 10.1016/j.cell.2021.08.025
Rosenberg AB, Patwardhan RP, Shendure J, Seelig G. Learning the sequence determinants of alternative splicing from millions of random sequences. Cell. 2015;163:698–711.
pubmed: 26496609 doi: 10.1016/j.cell.2015.09.054
Luppino F, Adzhubei IA, Cassa CA, Toth-Petroczy A. DeMAG predicts the effects of variants in clinically actionable genes by integrating structural and evolutionary epistatic features. Nat Commun. 2023;14:2230.
pubmed: 37076482 pmcid: 10115847 doi: 10.1038/s41467-023-37661-z
Livesey BJ, Marsh JA. Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations. Mol Syst Biol. 2020;16:e9380.
pubmed: 32627955 pmcid: 7336272 doi: 10.15252/msb.20199380
Fowler DM, et al. An atlas of variant effects to understand the genome at nucleotide resolution. Genome Biol. 2023;24:147.
pubmed: 37394429 pmcid: 10316620 doi: 10.1186/s13059-023-02986-x
Vadgama N, et al. De novo and inherited variants in coding and regulatory regions in genetic cardiomyopathies. Hum Genom. 2022;16:55.
doi: 10.1186/s40246-022-00420-0
Xu K, et al. Integrative analyses of scRNA-seq and scATAC-seq reveal CXCL14 as a key regulator of lymph node metastasis in breast cancer. Hum Mol Genet. 2021;30:370–80.
pubmed: 33564857 doi: 10.1093/hmg/ddab042
Graham E, et al. Integration of genomics and metabolomics for prioritization of rare disease variants: a 2018 literature review. J Inherit Metab Dis. 2018;41:435–45.
pubmed: 29721916 pmcid: 5959954 doi: 10.1007/s10545-018-0139-6
Kerkhofs MHPM, et al. Cross-omics: integrating genomics with metabolomics in clinical diagnostics. Metabolites. 2020;10:206.
pubmed: 32443577 pmcid: 7281020 doi: 10.3390/metabo10050206
Hmeljak J, Justice MJ. From gene to treatment: supporting rare disease translational research through model systems. Dis Model Mech. 2019;12:dmm039271.
pubmed: 30819728 pmcid: 6398488 doi: 10.1242/dmm.039271
Birling M-C, et al. A resource of targeted mutant mouse lines for 5,061 genes. Nat Genet. 2021;53:416–9.
pubmed: 33833456 pmcid: 8397259 doi: 10.1038/s41588-021-00825-y
Montoro R, Heine VM, Kemp S, Engelen M. Evolution of adrenoleukodystrophy model systems. J Inherit Metab Dis. 2021;44:544–53.
pubmed: 33373044 pmcid: 8248356 doi: 10.1002/jimd.12357
Segers K, et al. Rapid prenatal diagnosis of fetal Zellweger syndrome by biochemical tests, complementation studies, and molecular analyses. Prenat Diagn. 2013;33:201–3.
pubmed: 23299724 doi: 10.1002/pd.4038
Hopkins CE, Brock T, Caulfield TR, Bainbridge M. Phenotypic screening models for rapid diagnosis of genetic variants and discovery of personalized therapeutics. Mol Aspects Med. 2023;91:101153.
pubmed: 36411139 doi: 10.1016/j.mam.2022.101153
Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380:1347–58.
pubmed: 30943338 doi: 10.1056/NEJMra1814259
Cliniface. Cliniface. https://cliniface.org .
Jamuar S, et al. 3D facial analysis for rare disease diagnosis and treatment monitoring: proof-of-concept plan for hereditary angioedema. PLOS Digit Health. 2023;2:e0000090.
pubmed: 36947507 pmcid: 10032512 doi: 10.1371/journal.pdig.0000090
Robinson PN, et al. The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am J Hum Genet. 2008;83:610–5.
pubmed: 18950739 pmcid: 2668030 doi: 10.1016/j.ajhg.2008.09.017
Köhler S, et al. Clinical diagnostics in human genetics with semantic similarity searches in ontologies. Am J Hum Genet. 2009;85:457–64.
pubmed: 19800049 pmcid: 2756558 doi: 10.1016/j.ajhg.2009.09.003
100,000 Genomes Project Pilot Investigators et al. 100,000 genomes pilot on rare-disease diagnosis in health care-preliminary report. N Engl J Med 385, 1868–1880 (2021).
Wojcik MH, et al. Unique capabilities of genome sequencing for rare disease diagnosis. BMJ Yale. 2023. https://doi.org/10.1101/2023.08.08.23293829v1 .
doi: 10.1101/2023.08.08.23293829v1
van Eeghen AM, et al. Personalized medicine for rare neurogenetic disorders: can we make it happen? Cold Spring Harb Mol Case Stud. 2022;8:a006200.
pubmed: 35332073 pmcid: 8958924
Kotulska K, et al. Prevention of epilepsy in infants with tuberous sclerosis complex in the EPISTOP trial. Ann Neurol. 2021;89:304–14.
pubmed: 33180985 doi: 10.1002/ana.25956
Tarailo-Graovac M, et al. Exome sequencing and the management of neurometabolic disorders. N Engl J Med. 2016;374:2246–55.
pubmed: 27276562 pmcid: 4983272 doi: 10.1056/NEJMoa1515792
Tumiene B, Graessner H. Rare disease care pathways in the EU: from odysseys and labyrinths towards highways. J Community Genet. 2021;12:231–9.
pubmed: 33738760 pmcid: 8141079 doi: 10.1007/s12687-021-00520-9
Bachoud-Lévi A-C, et al. International guidelines for the treatment of huntington’s disease. Front Neurol. 2019;10:710.
pubmed: 31333565 pmcid: 6618900 doi: 10.3389/fneur.2019.00710
de Silva RN, et al. Diagnosis and management of progressive ataxia in adults. Pract Neurol. 2019;19:196–207.
pubmed: 31048364 pmcid: 6585307 doi: 10.1136/practneurol-2018-002096
Rotter T, et al. The effects of clinical pathways on professional practice, patient outcomes, length of stay, and hospital costs: cochrane systematic review and meta-analysis. Eval Health Prof. 2012;35:3–27.
pubmed: 21613244 doi: 10.1177/0163278711407313
Adachi T, et al. Enhancing equitable access to rare disease diagnosis and treatment around the world: a review of evidence, policies, and challenges. Int J Environ Res Public Health. 2023;20:4732.
pubmed: 36981643 pmcid: 10049067 doi: 10.3390/ijerph20064732
Global Indigenous Data Alliance. Global Indigenous Data Alliance. https://www.gida-global.org (2023).
D’Angelo CS, et al. Barriers and considerations for diagnosing rare diseases in indigenous populations. Front Pediatr. 2020;8:579924.
pubmed: 33381478 pmcid: 7767925 doi: 10.3389/fped.2020.579924
Bonne G. The treatabolome, an emerging concept. J Neuromuscul Dis. 2021;8:337–9.
pubmed: 33935101 pmcid: 8203244 doi: 10.3233/JND-219003
Incerti D, et al. Cost-effectiveness of genome sequencing for diagnosing patients with undiagnosed rare genetic diseases. Genet Med. 2022;24:109–18.
pubmed: 34906478 doi: 10.1016/j.gim.2021.08.015
Soilly AL, et al. Cost of exome analysis in patients with intellectual disability: a micro-costing study in a French setting. BMC Health Serv Res. 2023;23:386.
pubmed: 37085862 pmcid: 10120135 doi: 10.1186/s12913-023-09373-z
Yeung A, et al. A cost-effectiveness analysis of genomic sequencing in a prospective versus historical cohort of complex pediatric patients. Genet Med. 2020;22:1986–93.
pubmed: 32773771 doi: 10.1038/s41436-020-0929-8

Auteurs

Clara D M van Karnebeek (CDM)

Departments of Pediatrics and Human Genetics, Emma Center for Personalized Medicine, Amsterdam Gastro-Enterology Endocrinology Metabolism, Amsterdam University Medical Centers, Amsterdam, The Netherlands. c.d.vankarnebeek@amsterdamumc.nl.
ORCID: 0000-0002-2648-8337

Anne O'Donnell-Luria (A)

Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, USA.
Division of Genetics and Genomics, Boston Children's Hospital, Boston, USA.

Gareth Baynam (G)

Aix Marseille Univ, INSERM, Marseille Medical Genetics, MMG, Marseille, France.

Anaïs Baudot (A)

Aix Marseille Univ, INSERM, Marseille Medical Genetics, MMG, Marseille, France.

Tudor Groza (T)

Rare Care Centre, Perth Children's Hospital and Western Australian Register of Developmental Anomalies, King Edward Memorial Hospital, Perth, Australia.
European Molecular Biology Laboratory (EMBL-EBI), European Bioinformatics Institute, Hinxton, UK.

Judith J M Jans (JJM)

Department of Genetics, Section Metabolic Diagnostics, University Medical Center Utrecht, Utrecht, The Netherlands.

Timo Lassmann (T)

Telethon Kids Institute, Nedlands, Australia.

Mary Catherine V Letinturier (MCV)

IRDiRC Scientific Secretariat, French National Institute of Health and Medical Research (INSERM), Paris, France.

Stephen B Montgomery (SB)

Stanford University School of Medicine, Stanford, USA.

Peter N Robinson (PN)

The Jackson Laboratory, Farmington, CT, USA.

Stefaan Sansen (S)

Sanofi, Diegem, Belgium.

Ruty Mehrian-Shai (R)

Pediatric Brain Cancer Molecular Lab, Sheba Medical Center, Ramat Gan, Israel.

Charles Steward (C)

Genomics England, London, UK.

Kenjiro Kosaki (K)

Keio University, Minato, Japan.

Patricia Durao (P)

The Cure and Action for Tay-Sachs (CATS) Foundation, Altringham, UK.

Bekim Sadikovic (B)

Verspeeten Clinical Genome Centre, London Health Sciences, London, Canada.
Department of Pathology and Laboratory Medicine, Western University, London, Canada.

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