Detecting cryptic clinically relevant structural variation in exome-sequencing data increases diagnostic yield for developmental disorders.
bioinformatics
developmental disorders
diagnostics
insertions/deletions
structural variation
Journal
American journal of human genetics
ISSN: 1537-6605
Titre abrégé: Am J Hum Genet
Pays: United States
ID NLM: 0370475
Informations de publication
Date de publication:
04 11 2021
04 11 2021
Historique:
received:
08
10
2020
accepted:
15
09
2021
pubmed:
10
10
2021
medline:
23
11
2021
entrez:
9
10
2021
Statut:
ppublish
Résumé
Structural variation (SV) describes a broad class of genetic variation greater than 50 bp in size. SVs can cause a wide range of genetic diseases and are prevalent in rare developmental disorders (DDs). Individuals presenting with DDs are often referred for diagnostic testing with chromosomal microarrays (CMAs) to identify large copy-number variants (CNVs) and/or with single-gene, gene-panel, or exome sequencing (ES) to identify single-nucleotide variants, small insertions/deletions, and CNVs. However, individuals with pathogenic SVs undetectable by conventional analysis often remain undiagnosed. Consequently, we have developed the tool InDelible, which interrogates short-read sequencing data for split-read clusters characteristic of SV breakpoints. We applied InDelible to 13,438 probands with severe DDs recruited as part of the Deciphering Developmental Disorders (DDD) study and discovered 63 rare, damaging variants in genes previously associated with DDs missed by standard SNV, indel, or CNV discovery approaches. Clinical review of these 63 variants determined that about half (30/63) were plausibly pathogenic. InDelible was particularly effective at ascertaining variants between 21 and 500 bp in size and increased the total number of potentially pathogenic variants identified by DDD in this size range by 42.9%. Of particular interest were seven confirmed de novo variants in MECP2, which represent 35.0% of all de novo protein-truncating variants in MECP2 among DDD study participants. InDelible provides a framework for the discovery of pathogenic SVs that are most likely missed by standard analytical workflows and has the potential to improve the diagnostic yield of ES across a broad range of genetic diseases.
Identifiants
pubmed: 34626536
pii: S0002-9297(21)00346-3
doi: 10.1016/j.ajhg.2021.09.010
pmc: PMC8595893
pii:
doi:
Substances chimiques
MECP2 protein, human
0
Methyl-CpG-Binding Protein 2
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2186-2194Subventions
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Department of Health
Pays : United Kingdom
Organisme : Wellcome Trust
ID : WT098051
Pays : United Kingdom
Informations de copyright
Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of interests M.E.H. is a founder of, consultant to, director of, and holds shares in Congenica Ltd and is a consultant to the AZ Centre for Genomics Research. H.V.F. is a Section Editor for genetics for UpToDate. All other authors declare no conflict of interest.
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