Evaluating the Calling Performance of a Rare Disease NGS Panel for Single Nucleotide and Copy Number Variants
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SHORT COMMUNICATION
Evaluating the Calling Performance of a Rare Disease NGS Panel for Single Nucleotide and Copy Number Variants P. Cacheiro1,2 • A. Ordo´n˜ez-Ugalde1 • B. Quinta´ns1,2 • S. Pin˜eiro-Hermida1 J. Amigo2,3 • M. Garcı´a-Murias1,2 • S. I. Pascual-Pascual4 • F. Grandas5 • J. Arpa6 • A. Carracedo2,3 • M. J. Sobrido1,2
•
Ó Springer International Publishing Switzerland 2017
Abstract Introduction Variant detection protocols for clinical nextgeneration sequencing (NGS) need application-specific optimization. Our aim was to analyze the performance of single nucleotide variant (SNV) and copy number (CNV) detection programs on an NGS panel for a rare disease. Methods Thirty genes were sequenced in 83 patients with hereditary spastic paraplegia. The variant calls obtained with LifeScope, GATK UnifiedGenotyper and GATK HaplotypeCaller were compared with Sanger sequencing. The calling efficiency was evaluated for 187 (56 unique) SNVs and indels. Five multiexon deletions detected by multiple ligation probe assay were assessed from the NGS panel data with ExomeDepth, panelcn.MOPS and CNVPanelizer software. Electronic supplementary material The online version of this article (doi:10.1007/s40291-017-0268-x) contains supplementary material, which is available to authorized users.
Results There were 48/51 (94%) SNVs and 1/5 (20%) indels consistently detected by all the calling algorithms. Two SNVs were not detected by any of the callers because of a rare reference allele, and one SNV in a low coverage region was only detected by two algorithms. Regarding CNVs, ExomeDepth detected 5/5 multi-exon deletions, panelcn.MOPs 4/5 and only 3/5 deletions were accurately detected by CNVPanelizer. Conclusions The calling efficiency of NGS algorithms for SNVs is influenced by variant type and coverage. NGS protocols need to account for the presence of rare variants in the reference sequence as well as for ambiguities in indel calling. CNV detection algorithms can be used to identify large deletions from NGS panel data for diagnostic applications; however, sensitivity depends on coverage, selection of the reference set and deletion size. We recommend the incorporation of several variant callers in the NGS pipeline to maximize variant detection efficiency.
& M. J. Sobrido [email protected] 1
Neurogenetics Group, Instituto de Investigacio´n Sanitaria de Santiago (IDIS), Hospital Clı´nico de Santiago, level-2, Travesı´a da Choupana s/n, 15706 Santiago de Compostela, Spain
2
Grupo de Medicina Xeno´mica, CIBERER-U711, Santiago de Compostela, Spain
3
Fundacio´n Pu´blica Galega de Medicina Xeno´mica, Santiago de Compostela, Spain
4
Servicio de Neuropediatrı´a, Hospital Universitario La Paz, Madrid, Spain
5
Unidad de Trastornos del Movimiento, Instituto de Investigacio´n Sanitaria Gregorio Maran˜o´n, Hospital General Gregorio Maran˜o´n, Madrid, Spain
6
Servicio de Neurologı´a, Hospital Universitario La Paz, Universidad Auto´noma de Madrid, Madrid, Spain
Key Points Mutation detection sensitivity of diagnostic next generatio
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