Performance of In Silico Prediction Tools for the Detection of Germline Copy Number Variations in Cancer Predisposition Genes in 4208 Female Index Patients with Familial Breast and Ovarian Cancer.
CNV
HBOC
breast/ovarian cancer susceptibility genes
multigene panel sequencing
Journal
Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829
Informations de publication
Date de publication:
01 Jan 2021
01 Jan 2021
Historique:
received:
20
11
2020
revised:
17
12
2020
accepted:
22
12
2020
entrez:
6
1
2021
pubmed:
7
1
2021
medline:
7
1
2021
Statut:
epublish
Résumé
The identification of germline copy number variants (CNVs) by targeted next-generation sequencing (NGS) frequently relies on in silico CNV prediction tools with unknown sensitivities. We investigated the performances of four in silico CNV prediction tools, including one commercial (Sophia Genetics DDM) and three non-commercial tools (ExomeDepth, GATK gCNV, panelcn.MOPS) in 17 cancer predisposition genes in 4208 female index patients with familial breast and/or ovarian cancer (BC/OC). CNV predictions were verified via multiplex ligation-dependent probe amplification. We identified 77 CNVs in 76 out of 4208 patients (1.81%); 33 CNVs were identified in genes other than
Identifiants
pubmed: 33401422
pii: cancers13010118
doi: 10.3390/cancers13010118
pmc: PMC7794674
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Koeln Fortune Program, Faculty of Medicine, University of Cologne, Germany
ID : grant number not applicable
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