Research article
Identification of QTLs controlling gene expression networks defined a priori
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* Corresponding author: Daniel J Kliebenstein kliebenstein@ucdavis.edu
1 University of California-Davis, Department of Plant Sciences, Mail Stop 3, One Shields Ave, Davis, CA 95616-8780, USA
2 INRA, Station de Génétique et d'Amélioration des Plantes, Centre de Versailles, 78026Versailles, France
3 Purdue University, Department of Statistics, Mathematical Sciences Building, 150 North University Street, West Lafayette, IN 47907-2067, USA
BMC Bioinformatics 2006, 7:308 doi:10.1186/1471-2105-7-308
Published: 16 June 2006Abstract
Background
Gene expression microarrays allow the quantification of transcript accumulation for many or all genes in a genome. This technology has been utilized for a range of investigations, from assessments of gene regulation in response to genetic or environmental fluctuation to global expression QTL (eQTL) analyses of natural variation. Current analysis techniques facilitate the statistical querying of individual genes to evaluate the significance of a change in response, also known as differential expression. Since genes are also known to respond as groups due to their membership in networks, effective approaches are needed to investigate transcriptome variation as related to gene network responses.
Results
We describe a statistical approach that is capable of assessing higher-order a priori defined gene network response, as measured by microarrays. This analysis detected significant network variation between two Arabidopsis thaliana accessions, Bay-0 and Shahdara. By extending this approach, we were able to identify eQTLs controlling network responses for 18 out of 20 a priori-defined gene networks in a recombinant inbred line population derived from accessions Bay-0 and Shahdara.
Conclusion
This approach has the potential to be expanded to facilitate direct tests of the relationship between phenotypic trait and transcript genetic architecture. The use of a priori definitions for network eQTL identification has enormous potential for providing direction toward future eQTL analyses.