Identification of discrete chromosomal deletion by binary recursive partitioning of microarray differential expression data.

TitleIdentification of discrete chromosomal deletion by binary recursive partitioning of microarray differential expression data.
Publication TypeJournal Article
Year of Publication2005
AuthorsZhou X, Cole SW, Rao NP, Cheng Z, Li Y, McBride J, Wong DTW
JournalJ Med Genet
Volume42
Issue5
Pagination416-9
Date Published2005 May
ISSN1468-6244
KeywordsCell Line, Chromosome Deletion, Chromosome Mapping, Cytogenetic Analysis, Gene Expression Profiling, Karyotyping, Oligonucleotide Array Sequence Analysis, RNA, Messenger
Abstract

DNA copy number abnormalities (CNA) are characteristic of tumours, and are also found in association with congenital anomalies and mental retardation. The ultimate impact of copy number abnormalities is manifested by the altered expression of the encoded genes. We previously developed a statistical method for the detection of simple chromosomal amplification using microarray expression data. In this study, we significantly advanced those analytical techniques to allow detection of localised chromosomal deletions based on differential gene expression data. Using three cell lines with known chromosomal deletions as model system, mRNA expression in those cells was compared with that observed in diploid cell lines of matched tissue origin. Results show that genes from deleted chromosomal regions are substantially over-represented (p<0.000001 by chi2) among genes identified as underexpressed in deletion cell lines relative to normal matching cells. Using a likelihood based statistical model, we were able to identify the breakpoint of the chromosomal deletion and match with the karyotype data in each cell line. In one such cell line, our analyses refined a previously identified 10p chromosomal deletion region. The deletion region was mapped to between 10p14 and 10p12, which was further confirmed by subtelomeric fluorescence in situ hybridisation. These data show that microarray differential expression data can be used to detect and map the boundaries of submicroscopic chromosomal deletions.

DOI10.1136/jmg.2004.025353
Alternate JournalJ. Med. Genet.
PubMed ID15863671
PubMed Central IDPMC1736049
Grant ListK22 DE014847-01 / DE / NIDCR NIH HHS / United States
R01 AI52737 / AI / NIAID NIH HHS / United States
R01 DE015970-01 / DE / NIDCR NIH HHS / United States
R21 AI49135 / AI / NIAID NIH HHS / United States
R21 CA97771 / CA / NCI NIH HHS / United States
T32 DE07296-07 / DE / NIDCR NIH HHS / United States