Quantitative genomics of starvation stress resistance in Drosophila

We evaluated whole-genome transcript abundance for males and females of Ore, 2b, and four recombinant inbred lines derived from them, under control and starved conditions. There were significant differences in transcript abundance between the sexes for nearly 50% of the genome, while the transcriptional response to starvation stress involved approximately 25% of the genome. Nearly 50% of P-element insertions in 160 genes with altered transcript abundance during starvation stress had mutational effects on starvation tolerance. Approximately 5% of the genome exhibited genetic variation in transcript abundance, which was largely attributable to regulation by unlinked genes. Genes exhibiting variation in transcript abundance among lines did not cluster within starvation resistance QTLs, and none of the candidate genes affecting variation in starvation resistance between Ore and 2b exhibited significant differences in transcript abundance between lines.Expression profiling is a powerful method for identifying networks of pleiotropic genes regulating complex traits, but the relationship between variation in transcript abundance among lines used to map QTLs and genes affecting variation in quantitative traits is complicated.Quantitative traits affecting morphology, physiology, behavior, disease susceptibility and reproductive fitness are controlled by multiple interacting genes whose effects are conditional on the genetic, sexual and external environments [1]. Advances in medicine, agriculture, and an understanding of adaptive evolution depend on discovering the genes that regulate these complex traits, and determining the genetic and molecular properties of alleles at loci that cause segregating genetic variation in natural populations. Assessing subtle effects of induced mutations on quantitative trait phenotypes in model organisms is a straightforward approach to identify genes regulating complex traits [1-3]. However, the large number of potential mutations to evaluate