Genome-Wide Mapping of Copy Number Variation in Humans: Comparative Analysis of High Resolution Array Platforms
Accurate and efficient genome-wide detection of copy number variants (CNVs) is essential for understanding human genomic variation, genome-wide CNV association type studies, cytogenetics research and diagnostics, and independent validation of CNVs identified from sequencing based technologies. Numer...
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| Published in | PloS one Vol. 6; no. 11; p. e27859 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
30.11.2011
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0027859 |
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| Abstract | Accurate and efficient genome-wide detection of copy number variants (CNVs) is essential for understanding human genomic variation, genome-wide CNV association type studies, cytogenetics research and diagnostics, and independent validation of CNVs identified from sequencing based technologies. Numerous, array-based platforms for CNV detection exist utilizing array Comparative Genome Hybridization (aCGH), Single Nucleotide Polymorphism (SNP) genotyping or both. We have quantitatively assessed the abilities of twelve leading genome-wide CNV detection platforms to accurately detect Gold Standard sets of CNVs in the genome of HapMap CEU sample NA12878, and found significant differences in performance. The technologies analyzed were the NimbleGen 4.2 M, 2.1 M and 3×720 K Whole Genome and CNV focused arrays, the Agilent 1×1 M CGH and High Resolution and 2×400 K CNV and SNP+CGH arrays, the Illumina Human Omni1Quad array and the Affymetrix SNP 6.0 array. The Gold Standards used were a 1000 Genomes Project sequencing-based set of 3997 validated CNVs and an ultra high-resolution aCGH-based set of 756 validated CNVs. We found that sensitivity, total number, size range and breakpoint resolution of CNV calls were highest for CNV focused arrays. Our results are important for cost effective CNV detection and validation for both basic and clinical applications. |
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| AbstractList | Accurate and efficient genome-wide detection of copy number variants (CNVs) is essential for understanding human genomic variation, genome-wide CNV association type studies, cytogenetics research and diagnostics, and independent validation of CNVs identified from sequencing based technologies. Numerous, array-based platforms for CNV detection exist utilizing array Comparative Genome Hybridization (aCGH), Single Nucleotide Polymorphism (SNP) genotyping or both. We have quantitatively assessed the abilities of twelve leading genome-wide CNV detection platforms to accurately detect Gold Standard sets of CNVs in the genome of HapMap CEU sample NA12878, and found significant differences in performance. The technologies analyzed were the NimbleGen 4.2 M, 2.1 M and 3×720 K Whole Genome and CNV focused arrays, the Agilent 1×1 M CGH and High Resolution and 2×400 K CNV and SNP+CGH arrays, the Illumina Human Omni1Quad array and the Affymetrix SNP 6.0 array. The Gold Standards used were a 1000 Genomes Project sequencing-based set of 3997 validated CNVs and an ultra high-resolution aCGH-based set of 756 validated CNVs. We found that sensitivity, total number, size range and breakpoint resolution of CNV calls were highest for CNV focused arrays. Our results are important for cost effective CNV detection and validation for both basic and clinical applications. Accurate and efficient genome-wide detection of copy number variants (CNVs) is essential for understanding human genomic variation, genome-wide CNV association type studies, cytogenetics research and diagnostics, and independent validation of CNVs identified from sequencing based technologies. Numerous, array-based platforms for CNV detection exist utilizing array Comparative Genome Hybridization (aCGH), Single Nucleotide Polymorphism (SNP) genotyping or both. We have quantitatively assessed the abilities of twelve leading genome-wide CNV detection platforms to accurately detect Gold Standard sets of CNVs in the genome of HapMap CEU sample NA12878, and found significant differences in performance. The technologies analyzed were the NimbleGen 4.2 M, 2.1 M and 3x720 K Whole Genome and CNV focused arrays, the Agilent 1x1 M CGH and High Resolution and 2x400 K CNV and SNP+CGH arrays, the Illumina Human Omni1Quad array and the Affymetrix SNP 6.0 array. The Gold Standards used were a 1000 Genomes Project sequencing-based set of 3997 validated CNVs and an ultra high-resolution aCGH-based set of 756 validated CNVs. We found that sensitivity, total number, size range and breakpoint resolution of CNV calls were highest for CNV focused arrays. Our results are important for cost effective CNV detection and validation for both basic and clinical applications. Accurate and efficient genome-wide detection of copy number variants (CNVs) is essential for understanding human genomic variation, genome-wide CNV association type studies, cytogenetics research and diagnostics, and independent validation of CNVs identified from sequencing based technologies. Numerous, array-based platforms for CNV detection exist utilizing array Comparative Genome Hybridization (aCGH), Single Nucleotide Polymorphism (SNP) genotyping or both. We have quantitatively assessed the abilities of twelve leading genome-wide CNV detection platforms to accurately detect Gold Standard sets of CNVs in the genome of HapMap CEU sample NA12878, and found significant differences in performance. The technologies analyzed were the NimbleGen 4.2 M, 2.1 M and 3×720 K Whole Genome and CNV focused arrays, the Agilent 1×1 M CGH and High Resolution and 2×400 K CNV and SNP+CGH arrays, the Illumina Human Omni1Quad array and the Affymetrix SNP 6.0 array. The Gold Standards used were a 1000 Genomes Project sequencing-based set of 3997 validated CNVs and an ultra high-resolution aCGH-based set of 756 validated CNVs. We found that sensitivity, total number, size range and breakpoint resolution of CNV calls were highest for CNV focused arrays. Our results are important for cost effective CNV detection and validation for both basic and clinical applications.Accurate and efficient genome-wide detection of copy number variants (CNVs) is essential for understanding human genomic variation, genome-wide CNV association type studies, cytogenetics research and diagnostics, and independent validation of CNVs identified from sequencing based technologies. Numerous, array-based platforms for CNV detection exist utilizing array Comparative Genome Hybridization (aCGH), Single Nucleotide Polymorphism (SNP) genotyping or both. We have quantitatively assessed the abilities of twelve leading genome-wide CNV detection platforms to accurately detect Gold Standard sets of CNVs in the genome of HapMap CEU sample NA12878, and found significant differences in performance. The technologies analyzed were the NimbleGen 4.2 M, 2.1 M and 3×720 K Whole Genome and CNV focused arrays, the Agilent 1×1 M CGH and High Resolution and 2×400 K CNV and SNP+CGH arrays, the Illumina Human Omni1Quad array and the Affymetrix SNP 6.0 array. The Gold Standards used were a 1000 Genomes Project sequencing-based set of 3997 validated CNVs and an ultra high-resolution aCGH-based set of 756 validated CNVs. We found that sensitivity, total number, size range and breakpoint resolution of CNV calls were highest for CNV focused arrays. Our results are important for cost effective CNV detection and validation for both basic and clinical applications. |
| Audience | Academic |
| Author | Abyzov, Alexej Urban, Alexander E. Snyder, Michael Haraksingh, Rajini R. Gerstein, Mark |
| AuthorAffiliation | 6 Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, California, United States of America 1 Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America 4 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America 2 Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut, United States of America 3 Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America 5 Department of Computer Science, Yale University, New Haven, Connecticut, United States of America Tel Aviv University, Israel |
| AuthorAffiliation_xml | – name: 3 Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America – name: 5 Department of Computer Science, Yale University, New Haven, Connecticut, United States of America – name: 6 Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, California, United States of America – name: 4 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America – name: 1 Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America – name: Tel Aviv University, Israel – name: 2 Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut, United States of America |
| Author_xml | – sequence: 1 givenname: Rajini R. surname: Haraksingh fullname: Haraksingh, Rajini R. – sequence: 2 givenname: Alexej surname: Abyzov fullname: Abyzov, Alexej – sequence: 3 givenname: Mark surname: Gerstein fullname: Gerstein, Mark – sequence: 4 givenname: Alexander E. surname: Urban fullname: Urban, Alexander E. – sequence: 5 givenname: Michael surname: Snyder fullname: Snyder, Michael |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22140474$$D View this record in MEDLINE/PubMed |
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| Copyright | COPYRIGHT 2011 Public Library of Science 2011 Haraksingh et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Haraksingh et al. 2011 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 Conceived and designed the experiments: RRH AA AEU MS. Performed the experiments: RRH. Analyzed the data: RRH AA. Contributed reagents/materials/analysis tools: RRH AA MG AEU MS. Wrote the paper: RRH AA AEU MS. |
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| SubjectTerms | Algorithms Arrays Base Pairing - genetics Biochemistry Biology Biophysics Chromosome Mapping - methods Chromosomes, Human, Pair 1 - genetics Comparative analysis Congenital diseases Consortia Copy number Cytogenetics Deoxyribonucleic acid Developmental disabilities Diagnostic tests DNA DNA Copy Number Variations - genetics Gene mapping Gene sequencing Genes Genome, Human - genetics Genomes Genomics Genotyping High resolution Humans Hybridization Intellectual disabilities Medicine Oligonucleotide Array Sequence Analysis - methods Platforms Polymorphism Reference Standards Single nucleotide polymorphisms Single-nucleotide polymorphism Studies Therapeutic applications |
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| Title | Genome-Wide Mapping of Copy Number Variation in Humans: Comparative Analysis of High Resolution Array Platforms |
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