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Optimizing copy number variation analysis using genome-wide short sequence oligonucleotide arrays

The detection of copy number variants (CNV) by array-based platforms provides valuable insight into understanding human diversity. However, suboptimal study design and data processing negatively affect CNV assessment. We quantitatively evaluate their impact when short-sequence oligonucleotide arrays...

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Autores principales: Oldridge, Derek A., Banerjee, Samprit, Setlur, Sunita R., Sboner, Andrea, Demichelis, Francesca
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2879534/
https://www.ncbi.nlm.nih.gov/pubmed/20156996
http://dx.doi.org/10.1093/nar/gkq073
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author Oldridge, Derek A.
Banerjee, Samprit
Setlur, Sunita R.
Sboner, Andrea
Demichelis, Francesca
author_facet Oldridge, Derek A.
Banerjee, Samprit
Setlur, Sunita R.
Sboner, Andrea
Demichelis, Francesca
author_sort Oldridge, Derek A.
collection PubMed
description The detection of copy number variants (CNV) by array-based platforms provides valuable insight into understanding human diversity. However, suboptimal study design and data processing negatively affect CNV assessment. We quantitatively evaluate their impact when short-sequence oligonucleotide arrays are applied (Affymetrix Genome-Wide Human SNP Array 6.0) by evaluating 42 HapMap samples for CNV detection. Several processing and segmentation strategies are implemented, and results are compared to CNV assessment obtained using an oligonucleotide array CGH platform designed to query CNVs at high resolution (Agilent). We quantitatively demonstrate that different reference models (e.g. single versus pooled sample reference) used to detect CNVs are a major source of inter-platform discrepancy (up to 30%) and that CNVs residing within segmental duplication regions (higher reference copy number) are significantly harder to detect (P < 0.0001). After adjusting Affymetrix data to mimic the Agilent experimental design (reference sample effect), we applied several common segmentation approaches and evaluated differential sensitivity and specificity for CNV detection, ranging 39–77% and 86–100% for non-segmental duplication regions, respectively, and 18–55% and 39–77% for segmental duplications. Our results are relevant to any array-based CNV study and provide guidelines to optimize performance based on study-specific objectives.
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spelling pubmed-28795342010-06-02 Optimizing copy number variation analysis using genome-wide short sequence oligonucleotide arrays Oldridge, Derek A. Banerjee, Samprit Setlur, Sunita R. Sboner, Andrea Demichelis, Francesca Nucleic Acids Res Genomics The detection of copy number variants (CNV) by array-based platforms provides valuable insight into understanding human diversity. However, suboptimal study design and data processing negatively affect CNV assessment. We quantitatively evaluate their impact when short-sequence oligonucleotide arrays are applied (Affymetrix Genome-Wide Human SNP Array 6.0) by evaluating 42 HapMap samples for CNV detection. Several processing and segmentation strategies are implemented, and results are compared to CNV assessment obtained using an oligonucleotide array CGH platform designed to query CNVs at high resolution (Agilent). We quantitatively demonstrate that different reference models (e.g. single versus pooled sample reference) used to detect CNVs are a major source of inter-platform discrepancy (up to 30%) and that CNVs residing within segmental duplication regions (higher reference copy number) are significantly harder to detect (P < 0.0001). After adjusting Affymetrix data to mimic the Agilent experimental design (reference sample effect), we applied several common segmentation approaches and evaluated differential sensitivity and specificity for CNV detection, ranging 39–77% and 86–100% for non-segmental duplication regions, respectively, and 18–55% and 39–77% for segmental duplications. Our results are relevant to any array-based CNV study and provide guidelines to optimize performance based on study-specific objectives. Oxford University Press 2010-06 2010-02-15 /pmc/articles/PMC2879534/ /pubmed/20156996 http://dx.doi.org/10.1093/nar/gkq073 Text en © The Author(s) 2010. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.5 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Genomics
Oldridge, Derek A.
Banerjee, Samprit
Setlur, Sunita R.
Sboner, Andrea
Demichelis, Francesca
Optimizing copy number variation analysis using genome-wide short sequence oligonucleotide arrays
title Optimizing copy number variation analysis using genome-wide short sequence oligonucleotide arrays
title_full Optimizing copy number variation analysis using genome-wide short sequence oligonucleotide arrays
title_fullStr Optimizing copy number variation analysis using genome-wide short sequence oligonucleotide arrays
title_full_unstemmed Optimizing copy number variation analysis using genome-wide short sequence oligonucleotide arrays
title_short Optimizing copy number variation analysis using genome-wide short sequence oligonucleotide arrays
title_sort optimizing copy number variation analysis using genome-wide short sequence oligonucleotide arrays
topic Genomics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2879534/
https://www.ncbi.nlm.nih.gov/pubmed/20156996
http://dx.doi.org/10.1093/nar/gkq073
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