Mouse genomic representational oligonucleotide microarray analysis: Detection of copy number variations in normal and tumor specimens

B. Lakshmi, Ira M. Hall, Christopher Egan, Joan Alexander, Anthony Leotta, John Healy, Lars Zender, Mona S. Spector, Wen Xue, Scott W. Lowe, Michael Wigler, Robert Lucito

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Genomic amplifications and deletions, the consequence of somatic variation, are a hallmark of human cancer. Such variation has also been observed between "normal" individuals, as well as in individuals with congenital disorders. Thus, copy number measurement is likely to be an important tool for the analysis of genetic variation, genetic disease, and cancer. We developed representational oligonucleotide microarray analysis, a high-resolution comparative genomic hybridization methodology, with this aim in mind, and reported its use in the study of humans. Here we report the development of a representational oligonucleotide microarray analysis microarray for the genomic analysis of the mouse, an important model system for many genetic diseases and cancer. This microarray was designed based on the sequence assembly MM3, and contains ≈84,000 probes randomly distributed throughout the mouse genome. We demonstrate the use of this array to identify copy number changes in mouse cancers, as well to determine copy number variation between inbred strains of mice. Because restriction endonuclease digestion of genomic DNA is an integral component of our method, differences due to polymorphisms at the restriction enzyme cleavage sites are also observed between strains, and these can be useful to follow the inheritance of loci between crosses of different strains.

Original languageEnglish
Pages (from-to)11234-11239
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume103
Issue number30
DOIs
StatePublished - Jul 25 2006

Keywords

  • CGH
  • Mouse genome
  • Polymorphism
  • Probe selection
  • Segmentation

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