Recommendations for next generation sequencing data reanalysis of unsolved cases with suspected Mendelian disorders: A systematic review and meta-analysis.

Artificial Intelligence High-Throughput Nucleotide Sequencing Humans Exome Sequencing / methods
Clinical genomics Data reanalysis Mendelian disorders Next generation sequencing Precision medicine

Journal

Genetics in medicine : official journal of the American College of Medical Genetics
ISSN: 1530-0366
Titre abrégé: Genet Med
Pays: United States
ID NLM: 9815831

Informations de publication

Date de publication:
08 2022
Historique:
received: 19 01 2022
revised: 18 04 2022
accepted: 18 04 2022
pubmed: 14 5 2022
medline: 11 8 2022
entrez: 13 5 2022
Statut: ppublish

Résumé

The study aimed to determine the diagnostic yield, optimal timing, and methodology of next generation sequencing data reanalysis in suspected Mendelian disorders. We conducted a systematic review and meta-analysis of studies that conducted data reanalysis in patients with suspected Mendelian disorders. Random effects model was used to pool the estimated outcome with subgroup analysis stratified by timing, sequencing methodology, sample size, segregation, use of research validation, and artificial intelligence (AI) variant curation tools. A search of PubMed, Embase, Scopus, and Web of Science between 2007 and 2021 yielded 9327 articles, of which 29 were selected. Significant heterogeneity was noted between studies. Reanalysis had an overall diagnostic yield of 0.10 (95% CI = 0.06-0.13). Literature updates accounted for most new diagnoses. Diagnostic yield was higher after 24 months, although this was not statistically significant. Increased diagnoses were obtained with research validation and data sharing. AI-based tools did not adversely affect reanalysis diagnostic rate. Next generation sequencing data reanalysis can improve diagnostic yield. Owing to the heterogeneity of the studies, the optimal time to reanalysis and the impact of AI-based tools could not be determined with confidence. We propose standardized guidelines for future studies to reduce heterogeneity and improve the quality of the conclusions.

Identifiants

DOI: 10.1016/j.gim.2022.04.021 PMID: 35550369
pubmed: 35550369
pii: S1098-3600(22)00750-X
doi: 10.1016/j.gim.2022.04.021
pii:
doi:

Types de publication

Journal Article Meta-Analysis Review Systematic Review Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1618-1629

Informations de copyright

Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Conflict of Interest The authors declare no conflicts of interests.

Auteurs

Pei Dai (P)

Precision Immunology Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia; St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia; Clinical Immunogenomics Research Consortium Australasia (CIRCA), Garvan Institute of Medical Research, Sydney, New South Wales, Australia.

Andrew Honda (A)

The Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.

Lisa Ewans (L)

Department of Clinical Genetics, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia.

Julie McGaughran (J)

Genetic Health Queensland, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia.

Leslie Burnett (L)

Precision Immunology Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia; St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia; Clinical Immunogenomics Research Consortium Australasia (CIRCA), Garvan Institute of Medical Research, Sydney, New South Wales, Australia; The Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia; Genetic Medicine Program, Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.

Matthew Law (M)

Biostatistics and Databases Program, The Kirby Institute, UNSW Sydney, Sydney, New South Wales, Australia.

Tri Giang Phan (TG)

Precision Immunology Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia; St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia; Clinical Immunogenomics Research Consortium Australasia (CIRCA), Garvan Institute of Medical Research, Sydney, New South Wales, Australia. Electronic address: t.phan@garvan.org.au.

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Classifications MeSH

questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Recommendations for next generation sequencing...
questionsmedicales.fr › Techniques d'investigation › Techniques génétiques › Analyse de séquence › Séquençage nucléotidique à haut débit › Recommendations for next generation sequencing...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Recommendations for next generation sequencing...
questionsmedicales.fr › Techniques d'investigation › Techniques génétiques › Analyse de séquence › Analyse de séquence d'ADN › Séquençage du génome entier › Séquençage de l'exome › Recommendations for next generation sequencing...