Detecting genetic variation in microarray expression data

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 ∼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.[Supplemental material is available online at www.genome.org. GeSNP can be accessed at

Array-based high-throughput DNA markers for crop improvement

“…Other statistical methods that are specifically dedicated to SFP/SNP detection include 'robustified projection pursuit' (Cui et al, 2005), GeSNP (Greenhall et al, 2007) and two other methods developed by Luo et al (2007). In a recent study, three methods were compared by testing their relative efficiencies to detect SFPs associated with known barley SNPs (already mapped using same population).…”

“…Two methods recently developed by Luo et al (2007) address most of these issues and can be used for SFP analysis in seed plants. A web-based program 'GeSNP', which was initially designed to detect SNPs from microarray data, was recently tested for its suitability to predict SFPs in mice, humans and chimpanzees (Greenhall et al, 2007), but its use for plants has yet to be tried.…”

Molecular Markers for Sweet Sorghum Based on Microarray Expression Data

“…1 and Table 1). Based on existing data (Greenhall et al 2007), we adopted a t value of 7 or higher as a threshold. Chromosomes 1, 2, and 3 had the highest number of genes displaying both ELPs and SFPs, whereas chromosomes 5 and 6 had the lowest number of ELPs and SFPs, respectively (Fig.…”

“…These genes were described in our previous study to be differentially expressed between grain and sweet sorghum (Calviño et al 2008). The utility of GeSNP has been successfully tested for SFP discovery in mice, humans, and chimpanzees (Greenhall et al 2007), but there is no report on plants yet. In order to experimentally validate the SFPs identified in sorghum, we sequenced fragments from 58 genes and found SNPs in 30 of them, out of which 19 genes had a validated SFP.…”

“…We report the identification of SFPs in 58 sorghum genes by using the recently developed software GeSNP (Greenhall et al 2007). These genes were described in our previous study to be differentially expressed between grain and sweet sorghum (Calviño et al 2008).…”