Abstract:
:The use of high-density oligonucleotide arrays to measure the expression levels of thousands of genes in parallel has become commonplace. To take further advantage of the growing body of data, we developed a method, termed "GeSNP," to mine the detailed hybridization patterns in oligonucleotide array expression data for evidence of genetic variation. To demonstrate the performance of the algorithm, the hybridization patterns in data obtained previously from SAMP8/Ta, SAMP10/Ta, and SAMR1/Ta inbred mice and from humans and chimpanzees were analyzed. Genes with consistent strain-specific and species-specific hybridization pattern differences were identified, and approximately 90% of the candidate genes were independently confirmed to harbor sequence differences. Importantly, the quality of gene expression data was also improved by masking the probes of regions with putative sequence differences between species and strains. To illustrate the application to human disease groups, data from an inflammatory bowel disease study were analyzed. GeSNP identified sequence differences in candidate genes previously discovered in independent association and linkage studies and uncovered many promising new candidates. This approach enables the opportunistic extraction of genetic variation information from new or pre-existing gene expression data obtained with high-density oligonucleotide arrays.
journal_name
Genome Resjournal_title
Genome researchauthors
Greenhall JA,Zapala MA,Cáceres M,Libiger O,Barlow C,Schork NJ,Lockhart DJdoi
10.1101/gr.6307307subject
Has Abstractpub_date
2007-08-01 00:00:00pages
1228-35issue
8eissn
1088-9051issn
1549-5469pii
gr.6307307journal_volume
17pub_type
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