Accurate distinction of pathogenic from benign CNVs in mental retardation.

Copy number variants (CNVs) have recently been recognized as a common form of genomic variation in humans. Hundreds of CNVs can be detected in any individual genome using genomic microarrays or whole genome sequencing technology, but their phenotypic consequences are still poorly understood. Rare CN...

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Autores principales:	Jayne Y Hehir-Kwa, Nienke Wieskamp, Caleb Webber, Rolph Pfundt, Han G Brunner, Christian Gilissen, Bert B A de Vries, Chris P Ponting, Joris A Veltman
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/b1e8eb218cdc4f14be97291e55b4e2e5
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spelling	oai:doaj.org-article:b1e8eb218cdc4f14be97291e55b4e2e52021-11-25T05:42:32ZAccurate distinction of pathogenic from benign CNVs in mental retardation.1553-734X1553-735810.1371/journal.pcbi.1000752https://doaj.org/article/b1e8eb218cdc4f14be97291e55b4e2e52010-04-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20421931/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Copy number variants (CNVs) have recently been recognized as a common form of genomic variation in humans. Hundreds of CNVs can be detected in any individual genome using genomic microarrays or whole genome sequencing technology, but their phenotypic consequences are still poorly understood. Rare CNVs have been reported as a frequent cause of neurological disorders such as mental retardation (MR), schizophrenia and autism, prompting widespread implementation of CNV screening in diagnostics. In previous studies we have shown that, in contrast to benign CNVs, MR-associated CNVs are significantly enriched in genes whose mouse orthologues, when disrupted, result in a nervous system phenotype. In this study we developed and validated a novel computational method for differentiating between benign and MR-associated CNVs using structural and functional genomic features to annotate each CNV. In total 13 genomic features were included in the final version of a Naïve Bayesian Tree classifier, with LINE density and mouse knock-out phenotypes contributing most to the classifier's accuracy. After demonstrating that our method (called GECCO) perfectly classifies CNVs causing known MR-associated syndromes, we show that it achieves high accuracy (94%) and negative predictive value (99%) on a blinded test set of more than 1,200 CNVs from a large cohort of individuals with MR. These results indicate that this classification method will be of value for objectively prioritizing CNVs in clinical research and diagnostics.Jayne Y Hehir-KwaNienke WieskampCaleb WebberRolph PfundtHan G BrunnerChristian GilissenBert B A de VriesChris P PontingJoris A VeltmanPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 6, Iss 4, p e1000752 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Jayne Y Hehir-Kwa Nienke Wieskamp Caleb Webber Rolph Pfundt Han G Brunner Christian Gilissen Bert B A de Vries Chris P Ponting Joris A Veltman Accurate distinction of pathogenic from benign CNVs in mental retardation.
description	Copy number variants (CNVs) have recently been recognized as a common form of genomic variation in humans. Hundreds of CNVs can be detected in any individual genome using genomic microarrays or whole genome sequencing technology, but their phenotypic consequences are still poorly understood. Rare CNVs have been reported as a frequent cause of neurological disorders such as mental retardation (MR), schizophrenia and autism, prompting widespread implementation of CNV screening in diagnostics. In previous studies we have shown that, in contrast to benign CNVs, MR-associated CNVs are significantly enriched in genes whose mouse orthologues, when disrupted, result in a nervous system phenotype. In this study we developed and validated a novel computational method for differentiating between benign and MR-associated CNVs using structural and functional genomic features to annotate each CNV. In total 13 genomic features were included in the final version of a Naïve Bayesian Tree classifier, with LINE density and mouse knock-out phenotypes contributing most to the classifier's accuracy. After demonstrating that our method (called GECCO) perfectly classifies CNVs causing known MR-associated syndromes, we show that it achieves high accuracy (94%) and negative predictive value (99%) on a blinded test set of more than 1,200 CNVs from a large cohort of individuals with MR. These results indicate that this classification method will be of value for objectively prioritizing CNVs in clinical research and diagnostics.
format	article
author	Jayne Y Hehir-Kwa Nienke Wieskamp Caleb Webber Rolph Pfundt Han G Brunner Christian Gilissen Bert B A de Vries Chris P Ponting Joris A Veltman
author_facet	Jayne Y Hehir-Kwa Nienke Wieskamp Caleb Webber Rolph Pfundt Han G Brunner Christian Gilissen Bert B A de Vries Chris P Ponting Joris A Veltman
author_sort	Jayne Y Hehir-Kwa
title	Accurate distinction of pathogenic from benign CNVs in mental retardation.
title_short	Accurate distinction of pathogenic from benign CNVs in mental retardation.
title_full	Accurate distinction of pathogenic from benign CNVs in mental retardation.
title_fullStr	Accurate distinction of pathogenic from benign CNVs in mental retardation.
title_full_unstemmed	Accurate distinction of pathogenic from benign CNVs in mental retardation.
title_sort	accurate distinction of pathogenic from benign cnvs in mental retardation.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/b1e8eb218cdc4f14be97291e55b4e2e5
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Accurate distinction of pathogenic from benign CNVs in mental retardation.

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