Systems genetics analysis of body weight and energy metabolism traits in Drosophila melanogaster
1 Department of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, AL 35294-3360, USA
2 Department of Genetics, North Carolina State University, Raleigh, NC 27695, USA
3 W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, NC 27695, USA
4 Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD 21250, USA
5 Nutrition Obesity Research Center, University of Alabama at Birmingham, Birmingham, AL 35294, USA
6 Diabetes Research Training Center, University of Alabama at Birmingham, Birmingham, AL 35294, USA
7 Current Address: Department of Human Genetics, Emory University, Atlanta, GA 30322, USA
BMC Genomics 2010, 11:297 doi:10.1186/1471-2164-11-297Published: 11 May 2010
Obesity and phenotypic traits associated with this condition exhibit significant heritability in natural populations of most organisms. While a number of genes and genetic pathways have been implicated to play a role in obesity associated traits, the genetic architecture that underlies the natural variation in these traits is largely unknown. Here, we used 40 wild-derived inbred lines of Drosophila melanogaster to quantify genetic variation in body weight, the content of three major metabolites (glycogen, triacylglycerol, and glycerol) associated with obesity, and metabolic rate in young flies. We chose these lines because they were previously screened for variation in whole-genome transcript abundance and in several adult life-history traits, including longevity, resistance to starvation stress, chill-coma recovery, mating behavior, and competitive fitness. This enabled us not only to identify candidate genes and transcriptional networks that might explain variation for energy metabolism traits, but also to investigate the genetic interrelationships among energy metabolism, behavioral, and life-history traits that have evolved in natural populations.
We found significant genetically based variation in all traits. Using a genome-wide association screen for single feature polymorphisms and quantitative trait transcripts, we identified 337, 211, 237, 553, and 152 novel candidate genes associated with body weight, glycogen content, triacylglycerol storage, glycerol levels, and metabolic rate, respectively. Weighted gene co-expression analyses grouped transcripts associated with each trait in significant modules of co-expressed genes and we interpreted these modules in terms of their gene enrichment based on Gene Ontology analysis. Comparison of gene co-expression modules for traits in this study with previously determined modules for life-history traits identified significant modular pleiotropy between glycogen content, body weight, competitive fitness, and starvation resistance.
Combining a large phenotypic dataset with information on variation in genome wide transcriptional profiles has provided insight into the complex genetic architecture underlying natural variation in traits that have been associated with obesity. Our findings suggest that understanding the maintenance of genetic variation in metabolic traits in natural populations may require that we understand more fully the degree to which these traits are genetically correlated with other traits, especially those directly affecting fitness.