Quantification of variation in expression networks.
Gene expression microarrays allow rapid and easy quantification of transcript accumulation for almost transcripts present in a genome. This technology has been utilized for diverse investigations from studying gene regulation in response to genetic or environmental fluctuation to global expression QTL (eQTL) analyses of natural variation. Typical analysis techniques focus on responses of individual genes in isolation of other genes. However, emerging evidence indicates that genes are organized into regulons, i.e., they respond as groups due to individual transcription factors binding multiple promoters, creating what is commonly called a network. We have developed a set of statistical approaches that allow researchers to test specific network hypothesis using a priori-defined gene networks. When applied to Arabidopsis thaliana this approach has been able to identify natural genetic variation that controls networks. In this chapter we describe approaches to develop and test specific network hypothesis utilizing natural genetic variation. This approach can be expanded to facilitate direct tests of the relationship between phenotypic trait and transcript genetic architecture. Finally, the use of a priori network definitions can be applied to any microarray experiment to directly conduct hypothesis testing at a genomics level.
Kliebenstein
Daniel J
DJ
Department of Plant Sciences, University of California, Davis, CA, USA.
D017360:Arabidopsis / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods*; D018506:Gene Expression Regulation, Plant; D053263:Gene Regulatory Networks / Q000235:genetics*; D017343:Genes, Plant / Q000235:genetics*; D005828:Genetics, Population / Q000379:methods; D001467:Hordeum / Q000235:genetics / Q000378:metabolism; D053858:Metabolic Networks and Pathways / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D040641:Quantitative Trait Loci / Q000235:genetics