High-resolution mapping and analysis of copy number variations in the human genome: a data resource for clinical and research applications.
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ABSTRACT:
SUBMITTER: Shaikh TH
PROVIDER: S-EPMC2752118 | biostudies-literature | 2009 Sep
REPOSITORIES: biostudies-literature
Publications
High-resolution mapping and analysis of copy number variations in the human genome: a data resource for clinical and research applications.
Shaikh Tamim H TH Gai Xiaowu X Perin Juan C JC Glessner Joseph T JT Xie Hongbo H Murphy Kevin K O'Hara Ryan R Casalunovo Tracy T Conlin Laura K LK D'Arcy Monica M Frackelton Edward C EC Geiger Elizabeth A EA Haldeman-Englert Chad C Imielinski Marcin M Kim Cecilia E CE Medne Livija L Annaiah Kiran K Bradfield Jonathan P JP Dabaghyan Elvira E Eckert Andrew A Onyiah Chioma C CC Ostapenko Svetlana S Otieno F George FG Santa Erin E Shaner Julie L JL Skraban Robert R Smith Ryan M RM Elia Josephine J Goldmuntz Elizabeth E Spinner Nancy B NB Zackai Elaine H EH Chiavacci Rosetta M RM Grundmeier Robert R Rappaport Eric F EF Grant Struan F A SF White Peter S PS Hakonarson Hakon H
Genome research 20090710 9
We present a database of copy number variations (CNVs) detected in 2026 disease-free individuals, using high-density, SNP-based oligonucleotide microarrays. This large cohort, comprised mainly of Caucasians (65.2%) and African-Americans (34.2%), was analyzed for CNVs in a single study using a uniform array platform and computational process. We have catalogued and characterized 54,462 individual CNVs, 77.8% of which were identified in multiple unrelated individuals. These nonunique CNVs mapped t ...[more]
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Project description:BackgroundBtau_4.0 and UMD3.1 are two distinct cattle reference genome assemblies. In our previous study using the low density BovineSNP50 array, we reported a copy number variation (CNV) analysis on Btau_4.0 with 521 animals of 21 cattle breeds, yielding 682 CNV regions with a total length of 139.8 megabases.ResultsIn this study using the high density BovineHD SNP array, we performed high resolution CNV analyses on both Btau_4.0 and UMD3.1 with 674 animals of 27 cattle breeds. We first compared CNV results derived from these two different SNP array platforms on Btau_4.0. With two thirds of the animals shared between studies, on Btau_4.0 we identified 3,346 candidate CNV regions representing 142.7 megabases (~4.70%) of the genome. With a similar total length but 5 times more event counts, the average CNVR length of current Btau_4.0 dataset is significantly shorter than the previous one (42.7 kb vs. 205 kb). Although subsets of these two results overlapped, 64% (91.6 megabases) of current dataset was not present in the previous study. We also performed similar analyses on UMD3.1 using these BovineHD SNP array results. Approximately 50% more and 20% longer CNVs were called on UMD3.1 as compared to those on Btau_4.0. However, a comparable result of CNVRs (3,438 regions with a total length 146.9 megabases) was obtained. We suspect that these results are due to the UMD3.1 assembly's efforts of placing unplaced contigs and removing unmerged alleles. Selected CNVs were further experimentally validated, achieving a 73% PCR validation rate, which is considerably higher than the previous validation rate. About 20-45% of CNV regions overlapped with cattle RefSeq genes and Ensembl genes. Panther and IPA analyses indicated that these genes provide a wide spectrum of biological processes involving immune system, lipid metabolism, cell, organism and system development.ConclusionWe present a comprehensive result of cattle CNVs at a higher resolution and sensitivity. We identified over 3,000 candidate CNV regions on both Btau_4.0 and UMD3.1, further compared current datasets with previous results, and examined the impacts of genome assemblies on CNV calling.
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| S-EPMC3856455 | biostudies-literature
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2014-07-24 | GSE59150 | GEO
Project description:PurposeChromosomal microarray analysis to assess copy-number variation has become a first-tier genetic diagnostic test for individuals with unexplained neurodevelopmental disorders or multiple congenital anomalies. More than 100 cytogenetic laboratories worldwide use the new ultra-high resolution Affymetrix CytoScan-HD array to genotype hundreds of thousands of samples per year. Our aim was to develop a copy-number variation resource from a new population sample that would enable more accurate interpretation of clinical genetics data on this microarray platform and others.MethodsGenotyping of 1,000 adult volunteers who are broadly representative of the Ontario population (as obtained from the Ontario Population Genomics Platform) was performed with the CytoScan-HD microarray system, which has 2.7 million probes. Four independent algorithms were applied to detect copy-number variations. Reproducibility and validation metrics were quantified using sample replicates and quantitative-polymerase chain reaction, respectively.ResultsDNA from 873 individuals passed quality control and we identified 71,178 copy-number variations (81 copy-number variations/individual); 9.8% (6,984) of these copy-number variations were previously unreported. After applying three layers of filtering criteria, from our highest confidence copy-number variation data set we obtained >95% reproducibility and >90% validation rates (73% of these copy-number variations overlapped at least one gene).ConclusionThe genotype data and annotated copy-number variations for this largely Caucasian population will represent a valuable public resource enabling clinical genetics research and diagnostics.
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Project description: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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Project description:Cancer results from somatic alterations in key genes, including point mutations, copy-number alterations and structural rearrangements. A powerful way to discover cancer-causing genes is to identify genomic regions that show recurrent copy-number alterations (gains and losses) in tumor genomes. Recent advances in sequencing technologies suggest that massively parallel sequencing may provide a feasible alternative to DNA microarrays for detecting copy-number alterations. Here we present: (i) a statistical analysis of the power to detect copy-number alterations of a given size; (ii) SegSeq, an algorithm to segment equal copy numbers from massively parallel sequence data; and (iii) analysis of experimental data from three matched pairs of tumor and normal cell lines. We show that a collection of approximately 14 million aligned sequence reads from human cell lines has comparable power to detect events as the current generation of DNA microarrays and has over twofold better precision for localizing breakpoints (typically, to within approximately 1 kilobase).
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Project description:Copy number variations (CNV) include net gains or losses of part or whole chromosomal regions. They differ from copy neutral loss of heterozygosity (cn-LOH) events which do not induce any net change in the copy number and are often associated with uniparental disomy. These phenomena have long been reported to be associated with diseases and particularly in cancer. Losses/gains of genomic regions are often correlated with lower/higher gene expression. On the other hand, loss of heterozygosity (LOH) and cn-LOH are common events in cancer and may be associated with the loss of a functional tumor suppressor gene. Therefore, identifying recurrent CNV and cn-LOH events can be important as they may highlight common biological components and give insights into the development or mechanisms of a disease. However, no currently available tools allow a comprehensive whole-genome visualization of recurrent CNVs and cn-LOH in groups of samples providing absolute quantification of the aberrations leading to the loss of potentially important information.To overcome these limitations, we developed aCNViewer (Absolute CNV Viewer), a visualization tool for absolute CNVs and cn-LOH across a group of samples. aCNViewer proposes three graphical representations: dendrograms, bi-dimensional heatmaps showing chromosomal regions sharing similar abnormality patterns, and quantitative stacked histograms facilitating the identification of recurrent absolute CNVs and cn-LOH. We illustrated aCNViewer using publically available hepatocellular carcinomas (HCCs) Affymetrix SNP Array data (Fig 1A). Regions 1q and 8q present a similar percentage of total gains but significantly different copy number gain categories (p-value of 0.0103 with a Fisher exact test), validated by another cohort of HCCs (p-value of 5.6e-7) (Fig 2B).aCNViewer is implemented in python and R and is available with a GNU GPLv3 license on GitHub https://github.com/FJD-CEPH/aCNViewer and Docker https://hub.docker.com/r/fjdceph/acnviewer/.aCNViewer@cephb.fr.
| S-EPMC5736239 | biostudies-literature
Project description:Purpose: Chromosomal microarray analysis (CMA) to assess copy number variation (CNV) content is now used as a first tier genetic diagnostic test for individuals with unexplained neurodevelopmental disorders (NDD) or multiple congenital anomalies (MCA). Over 100 cytogenetic labs worldwide are using the Affymetrix CytoScan HD 2.7M array to genotype >15,000 clinical samples per month. The aim of this study is to develop a CNV resource from a population control cohort that can be used as a community resource for interpretation of clinical and research samples. Methods: We have genotyped a large population control set (1,000 individuals from our Ontario Population Genomics Platform (OPGP)) using the Affymetrix CytoScan HD microarray comprising 2.7 million probes. Four independent algorithms were applied to detect and assess high confidence CNVs. Reproducibility and validations were quantified using sample replicates and Quantitative-PCR (QPCR), respectively. Results: DNA from 873 individuals from the OPGP cohort passed quality control and we have identified 71,178 CNVs (81 CNVs/individual) distributed across 796 different cytogenetic regions in the genome; 9.8% of the CNVs were previously unreported. After applying three layers of filtering criteria, from our high confidence CNVs dataset, we obtained a >95% reproducibility and >90% validation rate. Due to the array's high probe density within genic regions, our high confidence CNV data set show 73% of the detected CNVs overlapped at least one gene. Conclusion: The genotype data and annotated CNVs presented in this study will represent a valuable public resource enabling clinical genetics research and diagnostics. For array quality control, CEL files were processed using modules from the Affymetrix power tools and genotypes were extracted from the CHP file. Samples passing the median of the absolute pairwise differences (MAPD) < 0.20 and waviness-sd < 0.11 were retained for further analysis. After multiple checks, we excluded 52 samples that do not meet quality control (QC) cutoffs. To confirm the sample's self-reported gender, we have matched the sex chromosome information from the array and identified six samples with gender mismatch, which were excluded from the analysis. We also excluded 47 samples due to excessive CNV calls. A final set of 895 samples were used for further analysis. This number included 22 sample replicates (indicated by _1 following the Sample title), which were used to determine reproducibility of the array calls. The CNV data for this study is available from dbVar (NCBI), DGVa (EBI) accession number estd212, and DGV.
2014-07-24 | E-GEOD-59150 | biostudies-arrayexpress