Efficient detection of copy-number variations using exome data: Batch- and sex-based analyses.
Hum Mutat
; 42(1): 50-65, 2021 01.
Article
in En
| MEDLINE
| ID: mdl-33131168
ABSTRACT
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
DNA Copy Number Variations
/
Exome
Type of study:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Limits:
Female
/
Humans
/
Male
Language:
En
Journal:
Hum Mutat
Journal subject:
GENETICA MEDICA
Year:
2021
Document type:
Article
Affiliation country: