Combining classical trait and microarray data to dissect transcriptional regulation: a case study.

The selective transcriptional profiling approach involves selecting an optimal subset of individuals to microarray from a larger set of individuals for which relatively inexpensive quantitative trait and molecular marker data are available. The goal of the selection and subsequent analyses is to identify genes whose expression is associated with a quantitative trait or quantitative trait locus (QTL). In this paper, we applied the selective transcriptional profiling approach to data sets concerning flowering time and gene transcription levels of Arabidopsis recombinant inbred lines. Our results confirm that the selective transcriptional profiling approach can achieve much greater power for uncovering associations than standard approaches that ignore information from classical traits. In addition, we show that selective transcriptional profiling can achieve power similar to standard approaches at a fraction of the cost and effort. We also identified three groups of genes which show distinctive patterns with regard to gene expression levels, QTL genotype, and a classical trait. This study represents the first application of selective transcriptional profiling to real data and serves as a template for dissecting gene regulation networks related to a classical trait using the selective transcriptional profiling approach.