Phenotype data for QTL mapping

Metabolic Syndrome (MetS) is a cluster of symptoms including obesity, dysregulation of blood sugars and lipids, and elevated blood pressure, which increase the risk of developing type-2 diabetes and cardiovascular disease in humans. It is a disease brought on by our modern shift in environment (including diet and activity levels) and has reached epidemic levels. An individual's risk of MetS is due to complex combinations of genetic and environmental factors including diet. Despite the apparent centrality of genotype-by-environment interactions in MetS prevalence, we have a limited understanding of the genetic architecture of these environment dependent traits and how the symptoms of MetS may be linked together by genetic mechanisms. In Drosophila melanogaster, genotype-by-diet interaction effects explain a substantial amount of the variation in MetS-like traits (weight, triglyceride storage, sugar content) in a natural population. In this study, we characterized the genetic architecture of both genotypic (main) effect and genotype-by-diet interaction (plastic) effects in MetS-like traits in the Drosophila SyntheticPopulation Resource. We tested three central hypotheses in this study: 1) Loci responsible for phenotypic variation in MetS-like traits should be shared across traits. 2) Genetic loci responsible for genetic variation in environmental plasticity and epistatic interactions for MetS-like traits should also be the loci responsible for the main effects. 3) Genes responsible for variation in MetS-like traits should share common functions if they are not the same loci. By using a round-robin crossing scheme and novel computational analyses, we were able to identify additive and dominance effect loci, as well as map epistatic loci that are diet-specific or diet-independent. The compelling conceptual finding of this study is that the main effect (diet-independent) and plastic loci are largely distinct. Further, we found that loci exhibiting epistatic effects on these traits were distinct from those with main effects. Thus, identifying the particular genes responsible for the primary genetic effects of MetS are unlikely to clarify how genetic variants interact with the environment or the genome to influence disease risk. Further, tremendous cryptic genetic variation for metabolic traits is lurking in natural populations. We have explored the function of candidate genes from our study both empirically and with bioinformatics tools. While some of the candidate genes might have been expected, most would not have been identified a priori, thus with this study we have identified many new candidate mechanisms contributing to the genetic and genotype-by-diet interaction effects on MetS variance.