Project description:We present a novel method of using commercial oligonucleotide expression microarrays for aCGH, enabling DNA copy number measurements and expression profiles to be combined using the same platform. This method yields aCGH data from genomic DNA without complexity reduction at a median resolution of approximately 17,500 base pairs. Due to the well-defined nature of oligonucleotide probes, DNA amplification and deletion can be defined at the level of individual genes and can easily be combined with gene expression data. Keywords: genomic DNA, CGH, Copy Number Variation
Project description:We have conducted a genome-wide analysis of spontaneous copy number variation (CNV) in the laboratory mouse. We used high resolution microarrays to identify 38 CNVs between 14 colonies of the C57BL/6 strain spanning ~967 generations of inbreeding, and examined these loci in 12 additional strains. It is clear from our results that many CNVs arise through a highly non-random process: 18 of 38 were the product of recurrent mutation, and rates of change vary roughly four orders of magnitude across different loci. These recurrent CNVs are distributed throughout the genome, affect 43 genes, and fluctuate in copy number over mere hundreds of generations, observations that raise questions about their contribution to natural variation. Keywords: Representational oligonucleotide microarray analysis, comparative genomic hybridization, DNA copy number variation, structural variation, inbred mice, spontaneous mutation rate
Project description:Gene amplifications and deletions frequently contribute to tumorigenesis. Characterization of these DNA copy-number changes is important for both the basic understanding of cancer and its diagnosis. Comparative genomic hybridization (CGH) was developed to survey DNA copy-number variations across a whole genome. With CGH, differentially labelled test and reference genomic DNAs are co-hybridized to normal metaphase chromosomes, and fluorescence ratios along the length of chromosomes provide a cytogenetic representation of DNA copy-number variation. CGH, however, has a limited ( approximately 20 Mb) mapping resolution, and higher-resolution techniques, such as fluorescence in situ hybridization (FISH), are prohibitively labour-intensive on a genomic scale. Array-based CGH, in which fluorescence ratios at arrayed DNA elements provide a locus-by-locus measure of DNA copy-number variation, represents another means of achieving increased mapping resolution. Published array CGH methods have relied on large genomic clone (for example BAC) array targets and have covered only a small fraction of the human genome. cDNAs representing over 30,000 radiation-hybrid (RH)-mapped human genes provide an alternative and readily available genomic resource for mapping DNA copy-number changes. Although cDNA microarrays have been used extensively to characterize variation in human gene expression, human genomic DNA is a far more complex mixture than the mRNA representation of human cells. Therefore, analysis of DNA copy-number variation using cDNA microarrays would require a sensitivity of detection an order of magnitude greater than has been routinely reported. We describe here a cDNA microarray-based CGH method, and its application to DNA copy-number variation analysis in breast cancer cell lines and tumours. This study is described more fully in Pollack JR et al.(1999) Nat Genet 23:41-6 Keywords: other
Project description:Gene amplifications and deletions frequently contribute to tumorigenesis. Characterization of these DNA copy-number changes is important for both the basic understanding of cancer and its diagnosis. Comparative genomic hybridization (CGH) was developed to survey DNA copy-number variations across a whole genome. With CGH, differentially labelled test and reference genomic DNAs are co-hybridized to normal metaphase chromosomes, and fluorescence ratios along the length of chromosomes provide a cytogenetic representation of DNA copy-number variation. CGH, however, has a limited ( approximately 20 Mb) mapping resolution, and higher-resolution techniques, such as fluorescence in situ hybridization (FISH), are prohibitively labour-intensive on a genomic scale. Array-based CGH, in which fluorescence ratios at arrayed DNA elements provide a locus-by-locus measure of DNA copy-number variation, represents another means of achieving increased mapping resolution. Published array CGH methods have relied on large genomic clone (for example BAC) array targets and have covered only a small fraction of the human genome. cDNAs representing over 30,000 radiation-hybrid (RH)-mapped human genes provide an alternative and readily available genomic resource for mapping DNA copy-number changes. Although cDNA microarrays have been used extensively to characterize variation in human gene expression, human genomic DNA is a far more complex mixture than the mRNA representation of human cells. Therefore, analysis of DNA copy-number variation using cDNA microarrays would require a sensitivity of detection an order of magnitude greater than has been routinely reported. We describe here a cDNA microarray-based CGH method, and its application to DNA copy-number variation analysis in breast cancer cell lines and tumours. This study is described more fully in Pollack JR et al.(1999) Nat Genet 23:41-6
Project description:<p>We present a database of copy number variations (CNVs) detected in 2,026 disease-free individuals, using high-density, SNP-based
oligonucleotide microarrays. This large cohort analyzed for CNVs in a single study using a uniform array platform and
computational tools, comprises mainly of Caucasians (65.2%) and African-Americans (34.2%), We have catalogued and
characterized 54,462 individual CNVs, 77.8% of which were identified in multiple unrelated individuals. These non-unique CNVs
mapped to 3,272 distinct regions of genomic variation spanning 5.9% of the genome; 51.5% of these were previously unreported,
and >85% are rare. Our annotation and analysis confirmed and extended previously reported correlations between CNVs and several
genomic features such as repetitive DNA elements, segmental duplications and genes. We demonstrate the utility of this data set
in distinguishing CNVs with pathologic significance from normal variants. Together, this analysis and annotation provides a
useful resource to assist with the assessment of CNVs in the contexts of human variation, disease susceptibility, and clinical
molecular diagnostics. The CNV resource is available at: <a href="http://cnv.chop.edu" target="_blank">http://cnv.chop.edu</a>.
Reprinted from Shaikh T., et al., High-Resolution Mapping and Analysis of Copy Number Variations in the Human Genome: A Data
Resource for Clinical and Research Applications Genome Research. 2009, with permission from Genome Research.</p>
<p>CHOP CNVs from 2,026 disease-free individuals are available through dbVar at
<a href="http://www.ncbi.nlm.nih.gov/dbvar/studies/nstd21/">http://www.ncbi.nlm.nih.gov/dbvar/studies/nstd21</a>.</p>
Project description:We present a novel method of using commercial oligonucleotide expression microarrays for aCGH, enabling DNA copy number measurements and expression profiles to be combined using the same platform. This method yields aCGH data from genomic DNA without complexity reduction at a median resolution of approximately 17,500 base pairs. Due to the well-defined nature of oligonucleotide probes, DNA amplification and deletion can be defined at the level of individual genes and can easily be combined with gene expression data. Experiment Overall Design: Genomic DNA isolated from peripheral blood samples and from Neuroblastoma cell lines was fragmented by DNAseI and biotin labeled by terminal transferase. Labeled DNAs were hybridized to U133plus2.0 GeneChips (Affymetrix). Hybridization and other conditions were slightly modified from those suggested for 10K Mapping Arrays (Affymetrix) and washing conditions were carried out as suggested for 100K Mapping Arrays. Probe set signals were either generated using the RMA algorithm or using the in-house developed WPP algorithm. Copy-number variation between Neuroblastoma and normal DNA was calculated.