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Clinical analysis of germline copy number variation in DMD using a non-conjugate hierarchical Bayesian model

BACKGROUND: Detection of copy number variants (CNVs) is an important aspect of clinical testing for several disorders, including Duchenne muscular dystrophy, and is often performed using multiplex ligation-dependent probe amplification (MLPA). However, since many genetic carrier screens depend inste...

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Autores principales: Kozareva, Velina, Stroff, Clayton, Silver, Maxwell, Freidin, Jonathan F., Delaney, Nigel F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6195989/
https://www.ncbi.nlm.nih.gov/pubmed/30342520
http://dx.doi.org/10.1186/s12920-018-0404-4
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author Kozareva, Velina
Stroff, Clayton
Silver, Maxwell
Freidin, Jonathan F.
Delaney, Nigel F.
author_facet Kozareva, Velina
Stroff, Clayton
Silver, Maxwell
Freidin, Jonathan F.
Delaney, Nigel F.
author_sort Kozareva, Velina
collection PubMed
description BACKGROUND: Detection of copy number variants (CNVs) is an important aspect of clinical testing for several disorders, including Duchenne muscular dystrophy, and is often performed using multiplex ligation-dependent probe amplification (MLPA). However, since many genetic carrier screens depend instead on next-generation sequencing (NGS) for wider discovery of small variants, they often do not include CNV analysis. Moreover, most computational techniques developed to detect CNVs from exome sequencing data are not suitable for carrier screening, as they require matched normals, very large cohorts, or extensive gene panels. METHODS: We present a computational software package, geneCNV (http://github.com/vkozareva/geneCNV), which can identify exon-level CNVs using exome sequencing data from only a few genes. The tool relies on a hierarchical parametric model trained on a small cohort of reference samples. RESULTS: Using geneCNV, we accurately inferred heterozygous CNVs in the DMD gene across a cohort of 15 test subjects. These results were validated against MLPA, the current standard for clinical CNV analysis in DMD. We also benchmarked the tool’s performance against other computational techniques and found comparable or improved CNV detection in DMD using data from panels ranging from 4,000 genes to as few as 8 genes. CONCLUSIONS: geneCNV allows for the creation of cost-effective screening panels by allowing NGS sequencing approaches to generate results equivalent to bespoke genotyping assays like MLPA. By using a parametric model to detect CNVs, it also fulfills regulatory requirements to define a reference range for a genetic test. It is freely available and can be incorporated into any Illumina sequencing pipeline to create clinical assays for detection of exon duplications and deletions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12920-018-0404-4) contains supplementary material, which is available to authorized users.
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spelling pubmed-61959892018-10-30 Clinical analysis of germline copy number variation in DMD using a non-conjugate hierarchical Bayesian model Kozareva, Velina Stroff, Clayton Silver, Maxwell Freidin, Jonathan F. Delaney, Nigel F. BMC Med Genomics Technical Advance BACKGROUND: Detection of copy number variants (CNVs) is an important aspect of clinical testing for several disorders, including Duchenne muscular dystrophy, and is often performed using multiplex ligation-dependent probe amplification (MLPA). However, since many genetic carrier screens depend instead on next-generation sequencing (NGS) for wider discovery of small variants, they often do not include CNV analysis. Moreover, most computational techniques developed to detect CNVs from exome sequencing data are not suitable for carrier screening, as they require matched normals, very large cohorts, or extensive gene panels. METHODS: We present a computational software package, geneCNV (http://github.com/vkozareva/geneCNV), which can identify exon-level CNVs using exome sequencing data from only a few genes. The tool relies on a hierarchical parametric model trained on a small cohort of reference samples. RESULTS: Using geneCNV, we accurately inferred heterozygous CNVs in the DMD gene across a cohort of 15 test subjects. These results were validated against MLPA, the current standard for clinical CNV analysis in DMD. We also benchmarked the tool’s performance against other computational techniques and found comparable or improved CNV detection in DMD using data from panels ranging from 4,000 genes to as few as 8 genes. CONCLUSIONS: geneCNV allows for the creation of cost-effective screening panels by allowing NGS sequencing approaches to generate results equivalent to bespoke genotyping assays like MLPA. By using a parametric model to detect CNVs, it also fulfills regulatory requirements to define a reference range for a genetic test. It is freely available and can be incorporated into any Illumina sequencing pipeline to create clinical assays for detection of exon duplications and deletions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12920-018-0404-4) contains supplementary material, which is available to authorized users. BioMed Central 2018-10-20 /pmc/articles/PMC6195989/ /pubmed/30342520 http://dx.doi.org/10.1186/s12920-018-0404-4 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Technical Advance
Kozareva, Velina
Stroff, Clayton
Silver, Maxwell
Freidin, Jonathan F.
Delaney, Nigel F.
Clinical analysis of germline copy number variation in DMD using a non-conjugate hierarchical Bayesian model
title Clinical analysis of germline copy number variation in DMD using a non-conjugate hierarchical Bayesian model
title_full Clinical analysis of germline copy number variation in DMD using a non-conjugate hierarchical Bayesian model
title_fullStr Clinical analysis of germline copy number variation in DMD using a non-conjugate hierarchical Bayesian model
title_full_unstemmed Clinical analysis of germline copy number variation in DMD using a non-conjugate hierarchical Bayesian model
title_short Clinical analysis of germline copy number variation in DMD using a non-conjugate hierarchical Bayesian model
title_sort clinical analysis of germline copy number variation in dmd using a non-conjugate hierarchical bayesian model
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6195989/
https://www.ncbi.nlm.nih.gov/pubmed/30342520
http://dx.doi.org/10.1186/s12920-018-0404-4
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