Expression QTLs: applications for crop improvement

Abstract

In the past, plant breeders dealt with complex agronomic traits in crops, such as drought, pest resistance, yield, and standability, through field observations and standard breeding practices. The evolution to molecular breeding has yielded a deeper understanding of the interacting quantitative trait loci (QTLs) of the complex traits and has exposed underlying genetic variation useful in marker-assisted breeding. Current momentum in QTL analysis is toward understanding the genetic regulation of gene expression as studied by the quantification of transcript levels of genes, or expression QTL (eQTL). Large scale microarray studies have elucidated the genetic regulation of entire transcriptomes, in the process known as genetical genomics, and are beginning to build biochemical pathways of interacting genes on the basis of variations in transcript levels. In addition to understanding general patterns of gene expression, these genetical genomic studies are creating caches of information useful for a multitude of applications. As one gene regulates the level of expression of another (trans-acting eQTL), novel upstream or downstream components in gene regulation pathways can be identified. In addition to steady state analysis, the induction of stimuli such as drought can lead to a deeper understanding of gene networks that are activated under such conditions. Correlation of measured transcript levels (eQTL phenotype) with classic QTL phenotypes may suggest functional roles for the allelic variation in gene expression and serve as a predictor of downstream effects on plant development, morphology, and agronomic interest. Finally, the analysis of the activation of particular genes under steady state or external stimuli treatment provides insight into the functionality of endogenous promoters. While promoters used for transgenic expression have been thoroughly analyzed in model systems and model inbred lines, the understanding of agronomically important phenotypes may benefit from the analysis of genetic polymorphisms of trans-acting regulators affecting transgene expression, and therefore can allow for the optimization of expression both of current and future transgenic lines.