Universität zu Köln

Nutsua, Marcel Elie, Fischer, Annegret, Nebel, Almut, Hofmann, Sylvia, Schreiber, Stefan, Krawczak, Michael ORCID: 0000-0003-2603-1502 and Nothnagel, Michael ORCID: 0000-0001-8305-7114 (2015). Family-Based Benchmarking of Copy Number Variation Detection Software. PLoS One, 10 (7). SAN FRANCISCO: PUBLIC LIBRARY SCIENCE. ISSN 1932-6203

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Abstract

The analysis of structural variants, in particular of copy-number variations (CNVs), has proven valuable in unraveling the genetic basis of human diseases. Hence, a large number of algorithms have been developed for the detection of CNVs in SNP array signal intensity data. Using the European and African HapMap trio data, we undertook a comparative evaluation of six commonly used CNV detection software tools, namely Affymetrix Power Tools (APT), QuantiSNP, PennCNV, GLAD, R-gada and VEGA, and assessed their level of pair-wise prediction concordance. The tool-specific CNV prediction accuracy was assessed in silico by way of intra-familial validation. Software tools differed greatly in terms of the number and length of the CNVs predicted as well as the number of markers included in a CNV. All software tools predicted substantially more deletions than duplications. Intra-familial validation revealed consistently low levels of prediction accuracy as measured by the proportion of validated CNVs (34-60%). Moreover, up to 20% of apparent family-based validations were found to be due to chance alone. Software using Hidden Markov models (HMM) showed a trend to predict fewer CNVs than segmentation-based algorithms albeit with greater validity. PennCNV yielded the highest prediction accuracy (60.9%). Finally, the pair-wise concordance of CNV prediction was found to vary widely with the software tools involved. We recommend HMM-based software, in particular PennCNV, rather than segmentation-based algorithms when validity is the primary concern of CNV detection. QuantiSNP may be used as an additional tool to detect sets of CNVs not detectable by the other tools. Our study also reemphasizes the need for laboratory-based validation, such as qPCR, of CNVs predicted in silico.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Nutsua, Marcel Elie UNSPECIFIED UNSPECIFIED UNSPECIFIED Fischer, Annegret UNSPECIFIED UNSPECIFIED UNSPECIFIED Nebel, Almut UNSPECIFIED UNSPECIFIED UNSPECIFIED Hofmann, Sylvia UNSPECIFIED UNSPECIFIED UNSPECIFIED Schreiber, Stefan UNSPECIFIED UNSPECIFIED UNSPECIFIED Krawczak, Michael UNSPECIFIED orcid.org/0000-0003-2603-1502 UNSPECIFIED Nothnagel, Michael UNSPECIFIED orcid.org/0000-0001-8305-7114 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-398704
DOI:	10.1371/journal.pone.0133465
Journal or Publication Title:	PLoS One
Volume:	10
Number:	7
Date:	2015
Publisher:	PUBLIC LIBRARY SCIENCE
Place of Publication:	SAN FRANCISCO
ISSN:	1932-6203
Language:	English
Faculty:	Faculty of Mathematics and Natural Sciences
Divisions:	Faculty of Mathematics and Natural Sciences > Department of Biology > Institute for Genetics
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language NUCLEOTIDE POLYMORPHISM ARRAYS; INTERNATIONAL HAPMAP PROJECT; GENOME-WIDE ASSOCIATION; HIDDEN-MARKOV MODEL; SNP GENOTYPING DATA; STRUCTURAL VARIATION; MISSING HERITABILITY; GENETIC-VARIATION; HAPLOTYPE MAP; SEQUENCE Multiple languages Multidisciplinary Sciences Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/39870