Trisomy 12 in Epstein-Barr virus-transformed lymphoblastoid cell lines of normal individuals and patients with nonhematologic malignancies

Risin, Semyon A.; Hopwood, Vicki L.; Pathak, Sen

doi:10.1016/0165-4608(92)90010-6

Cited by 10 publications

(10 citation statements)

References 29 publications

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“…We found that these cell-batch-specific events usually occur throughout the entire chromosome or entire arm, especially on chromosomes X, 2, and 12 (Supplemental Fig. 7), which were also reported to form aneuploidy in other cell-line studies (Risin et al 1992(Risin et al , 1993Aardema et al 1997;Locke et al 2006). Thus, our analysis indicates that CNV detection algorithms based only on signal intensity can be misleading, and that genotype data on cell lines should be interpreted with caution.…”

Section: The Use Of Family Information In Cnv Characterizationmentioning

confidence: 78%

PennCNV: An integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data

Wang¹,

Li²,

Hadley³

et al. 2007

Genome Res.

1,639

1,839

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Comprehensive identification and cataloging of copy number variations (CNVs) is required to provide a complete view of human genetic variation. The resolution of CNV detection in previous experimental designs has been limited to tens or hundreds of kilobases. Here we present PennCNV, a hidden Markov model (HMM) based approach, for kilobase-resolution detection of CNVs from Illumina high-density SNP genotyping data. This algorithm incorporates multiple sources of information, including total signal intensity and allelic intensity ratio at each SNP marker, the distance between neighboring SNPs, the allele frequency of SNPs, and the pedigree information where available. We applied PennCNV to genotyping data generated for 112 HapMap individuals; on average, we detected ∼27 CNVs for each individual with a median size of ∼12 kb. Excluding common rearrangements in lymphoblastoid cell lines, the fraction of CNVs in offspring not detected in parents (CNV-NDPs) was 3.3%. Our results demonstrate the feasibility of whole-genome fine-mapping of CNVs via high-density SNP genotyping.

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Section: The Use Of Family Information In Cnv Characterizationmentioning

confidence: 78%

PennCNV: An integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data

Wang¹,

Li²,

Hadley³

et al. 2007

Genome Res.

1,639

1,839

View full text Add to dashboard Cite

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“…2). Test samples can also be from a mix of untransformed or transformed tissues, all impacting on interpretation11,46. Finally, samples used to discover structural variants from control populations may have little or no genetic (for example, parent of origin) information or phenotypic assessment protocols attached to them.…”

Section: Challenges In Characterizing Structural Variantsmentioning

confidence: 99%

Challenges and standards in integrating surveys of structural variation

et al. 2007

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There has been an explosion of data describing newly recognized structural variants in the human genome. In the flurry of reporting, there has been no standard approach to collecting the data, assessing its quality or describing identified features. This risks becoming a rampant problem, in particular with respect to surveys of copy number variation and their application to disease studies. Here, we consider the challenges in characterizing and documenting genomic structural variants. From this, we derive recommendations for standards to be adopted, with the aim of ensuring the accurate presentation of this form of genetic variation to facilitate ongoing research.Structural variation in the genome refers to cytogenetically visible and (more commonly) submicroscopic variants, including deletions, insertions, duplications and large-scale copy number variants -collectively termed copy number variations (CNVs) -as well as inversions and translocations (Box 1)1-3. Genome scanning technologies are now commonplace in many laboratories, allowing new structural variation to be recognized from general population surveys4-12 or studies of diseases13-21. In fact, the Database of Genomic Variants4,22 (see list of databases in Table 1 This first round of observations came from several studies, each using a different technology platform and data processing algorithms, with different degrees of pre-and postexperimental standardization and validation. As a result, the data vary in quality and often have both high false-positive and false-negative rates. There is the very real possibility of the entire human genome soon being presented as 'structurally variant' in one form or another, based solely on studies of nondisease samples, which would be a distortion. It will be important for all future applications of structural variation information that the scope and detail of variants in the general population be accurately cataloged. In particular, medical genetics researchinvestigating structural variation profiles in individuals or clinical cohorts -will need a reliable foundation against which to interpret possible pathogenic findings in cytogenomic ( Fig. 1), linkage and genome-wide association studies21,23-25.The field of genomic structural variation, however, is on the cusp of change. Pioneering approaches, often fragmented or fraught with technical limitations, are being supplanted by new technologies that afford much higher resolution screening of the genome at lower cost. We anticipate that, in the next year, the quantity of structural variation data will increase by orders of magnitude owing to microarray-based experiments alone, not to mention the plethora soon to flow from clone-end6,26 or whole-genome sequencing experiments27-30. Many of these studies will survey nondisease samples for structural variation discovery to create control databases. Moreover, in little more than two years from the first description of global CNV distribution4,5, the field is poised to make structural variation analyses standard in the...

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“…Finally, the number of falsepositive and false-negative CNVs in different studies may be variable, depending both on the measurement technology and on the sample quality, including the sample source. For example, many of the CNVs reported thus far have been discovered in DNA derived from cell-culture lines (4,6,8,9), which are known to be susceptible to genomic changes during propagation (19).…”

Section: Introductionmentioning

confidence: 99%

Array CGH analysis of copy number variation identifies 1284 new genes variant in healthy white males: implications for association studies of complex diseases

Smith

Tsalenko²,

Sampas³

et al. 2007

Human Molecular Genetics

203

182

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The discovery of copy number variation in healthy individuals is far from complete, and owing to the resolution of detection systems used, the majority of loci reported so far are relatively large ( approximately 65%>10 kb). Applying a two-stage high-resolution array comparative genomic hybridization approach to analyse 50 healthy Caucasian males from northern France, we discovered 2208 copy number variants (CNVs) detected by more than one consecutive probe. These clustered into 1469 CNV regions (CNVRs), of which 721 are thought to be novel. The majority of these are small (median size 4.4 kb) and most have common boundaries, with a coefficient of variation less than 0.1 for 83% of endpoints in those observed in multiple samples. Only 6% of the CNVRs analysed showed evidence of both copy number losses and gains at the same site. A further 6089 variants were detected by single probes: 48% of these were observed in more than one individual. In total, 2570 genes were seen to intersect variants: 1284 in novel loci. Genes involved in differentiation and development were significantly over-represented and approximately half of the genes identified feature in the Online Mendelian Inheritance in Man database. The biological importance of many genes affected, along with the well-conserved nature of the majority of the CNVs, suggests that they could have important implications for phenotype and, thus, be useful for association studies of complex diseases.

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Trisomy 12 in Epstein-Barr virus-transformed lymphoblastoid cell lines of normal individuals and patients with nonhematologic malignancies

Cited by 10 publications

References 29 publications

PennCNV: An integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data

PennCNV: An integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data

Challenges and standards in integrating surveys of structural variation

Array CGH analysis of copy number variation identifies 1284 new genes variant in healthy white males: implications for association studies of complex diseases

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