Efficient detection of copy-number variations using exome data: Batch- and sex-based analyses.
XHMM
copy number variation
exome sequencing
jNord
mendelian disorder
Journal
Human mutation
ISSN: 1098-1004
Titre abrégé: Hum Mutat
Pays: United States
ID NLM: 9215429
Informations de publication
Date de publication:
01 2021
01 2021
Historique:
received:
20
06
2020
revised:
29
09
2020
accepted:
15
10
2020
pubmed:
2
11
2020
medline:
1
4
2022
entrez:
1
11
2020
Statut:
ppublish
Résumé
Many algorithms to detect copy number variations (CNVs) using exome sequencing (ES) data have been reported and evaluated on their sensitivity and specificity, reproducibility, and precision. However, operational optimization of such algorithms for a better performance has not been fully addressed. ES of 1199 samples including 763 patients with different disease profiles was performed. ES data were analyzed to detect CNVs by both the eXome Hidden Markov Model (XHMM) and modified Nord's method. To efficiently detect rare CNVs, we aimed to decrease sequencing biases by analyzing, at the same time, the data of all unrelated samples sequenced in the same flow cell as a batch, and to eliminate sex effects of X-linked CNVs by analyzing female and male sequences separately. We also applied several filtering steps for more efficient CNV selection. The average number of CNVs detected in one sample was <5. This optimization together with targeted CNV analysis by Nord's method identified pathogenic/likely pathogenic CNVs in 34 patients (4.5%, 34/763). In particular, among 142 patients with epilepsy, the current protocol detected clinically relevant CNVs in 19 (13.4%) patients, whereas the previous protocol identified them in only 14 (9.9%) patients. Thus, this batch-based XHMM analysis efficiently selected rare pathogenic CNVs in genetic diseases.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
50-65Informations de copyright
© 2020 Wiley Periodicals LLC.
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