Systems genomics of metabolic phenotypes in wild-type Drosophila melanogaster

Laura K. Reed, Kevin Lee, Zhi Zhang, Lubna Rashid, Amy Poe, Benjamin Hsieh, Nigel Deighton, Norm Glassbrook, Rolf Bodmer, Greg Gibson

Research output: Contribution to journalArticlepeer-review

55 Scopus citations


Systems biology is an approach to dissection of complex traits that explicitly recognizes the impact of genetic, physiological, and environmental interactions in the generation of phenotypic variation. We describe comprehensive transcriptional and metabolic profiling in Drosophila melanogaster across four diets, finding little overlap in modular architecture. Genotype and genotype-by-diet interactions are a major component of transcriptional variation (24 and 5.3% of the total variation, respectively) while there were no main effects of diet (<1%). Genotype was also a major contributor to metabolomic variation (16%), but in contrast to the transcriptome, diet had a large effect (9%) and the interaction effect was minor (2%) for the metabolome. Yet specific principal components of these molecular phenotypes measured in larvae are strongly correlated with particular metabolic syndrome-like phenotypes such as pupal weight, larval sugar content and triglyceride content, development time, and cardiac arrhythmia in adults. The second principal component of the metabolomic profile is especially informative across these traits with glycine identified as a key loading variable. To further relate this physiological variability to genotypic polymorphism, we performed evolve-and-resequence experiments, finding rapid and replicated changes in gene frequency across hundreds of loci that are specific to each diet. Adaptation to diet is thus highly polygenic. However, loci differentially transcribed across diet or previously identified by RNAi knockdown or expression QTL analysis were not the loci responding to dietary selection. Therefore, loci that respond to the selective pressures of diet cannot be readily predicted a priori from functional analyses.

Original languageEnglish (US)
Pages (from-to)781-783
Number of pages3
Issue number2
StatePublished - Jun 2014

All Science Journal Classification (ASJC) codes

  • Genetics


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