Diseases linked to Metabolic Syndrome (MetS) such at type-2 diabetes and cardiovascular disease are rapidly increasing due to the influences of a modern Westernized-life style, but the genetic, environmental, and physiological mechanisms linking the symptoms of Metabolic-syndrome remain to be elucidated. Large scale studies to systematically assess how genotype interacts with the environment to cause complex disease are very difficult in humans, but such studies are relatively tractable in genetic models systems such as Drosophila melanogaster. We have shown previously that that there is a very substantial contribution of genotype-by-environment interactions to the phenotypic variation observed for MetS-like symptoms in a naturally genetically variable population of D. melanogaster. We have also been able to demonstrate clear correlations between metabolomic and gene expression profiles and these symptoms as they vary across diet. Finally, we have shown that genetic variance in some of these traits increase with a perturbing high fat diet, indicating the exposure of cryptic genetic variation for these symptoms could contribute to increases in disease. In this study we will build off the community resources for complex genetic trait analysis of the Macdonald-Long synthetic recombinant inbred line (RIL) population and the Drosophila Genomic Reference Panel (DGRP) to map the genetic basis of genotype-by-diet interactions. First, using the 1700 Macdonald-Long Advanced Intercross synthetic RILs, we will map the genetic basis of MetS-like symptoms and the regions controlling genotype-by- environment interactions contributing to these symptoms to within 1 cM of the causal locus when the flies are raised on a normal verses high fat diet. We should be able to estimate both the effect size and population frequency of causative alleles. Second, based of the phenotypes measured in the F1 RIL population, 200 lines demonstrating the largest genotype-by-diet interaction effects will be selected for metabolomic and expression profiling. Metabolomic profiling will identify several hundred primary metabolite and whole genome expression profiles will be generated by microarray analysis. We will characterize the metabolomic and expression module structure that drives the genotype-by-environment interactions and link those pathways back to specific genetic variants. Finally, we will attempt to replicate the findings from the synthetic RIL population through association mapping in the natural variants represented in the 192 lines of the DGRP. The ultimate goal of this work is to identify genomic regions, metabolic pathways, physiological mechanisms, and dietary influences likely to be of importance to Metabolic Syndrome in humans.