Search

Little is known about how genetic variation affects the capacity for exercise to change body composition. We examined the extent to which voluntary exercise alters body composition in several lines of selectively bred mice compared to controls. Lines studied included high runner (HR) (selected for high wheel running), M16 (selected for rapid weight gain), Institute of Cancer Research (ICR) (randomly bred as control for M16), M16i (an inbred line derived from M16), HE (selected for high percentage of body fat while holding body weight constant), LF (selected for low percentage of body fat), C57BL/6J (common inbred line), and the F1 between HR and C57BL/6J. Body weight and body fat were recorded before and after 6 days of free access to running wheels in males and females that were individually caged. Total food intake was measured during this 6-day period. All pre- and postexercise measures showed significant strain effects. While HR mice predictably exercised at higher levels, all other selection lines had decreased levels of wheel running relative to ICR. The HR × B6 F1 ran at similar levels to HR demonstrating complete dominance for voluntary exercise. Also, all strains lost body fat after exercise, but the relationships between exercise and changes in percent body were not uniform across genotypes. These results indicate that there is significant genetic variation for voluntary exercise and its effects on body composition. It is important to carefully consider genetic background and/or selection history when using mice to model effects of exercise on body composition, and perhaps, other complex traits as well.Obesity (2009) 17 7, 1402–1409. doi:10.1038/oby.2009.51 [ABSTRACT FROM AUTHOR]

A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription and phenotypic information. Here we have validated our method through the characterization of transgenic and knockout mouse models of genes predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being newly confirmed, resulted in significant changes in obesity-related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high-confidence prediction of causal genes and identification of pathways and networks involved. [ABSTRACT FROM AUTHOR]

A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription and phenotypic information. Here we have validated our method through the characterization of transgenic and knockout mouse models of genes predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being newly confirmed, resulted in significant changes in obesity-related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F(2) intercross studies allows high-confidence prediction of causal genes and identification of pathways and networks involved.

Traits related to energy balance and obesity are exceptionally complex, with varying contributions of genetic susceptibility and interacting environmental factors. The use of mouse models has been a powerful driving force in understanding the genetic architecture of polygenic traits such as obesity. However, the use of mouse models for analysis of complex traits is at an important crossroad. Genome-wide association studies in humans are now leading to direct identification of obesity genes. In this review, we focus on three areas representing the current and future roles of mouse models regarding genetics of complex obesity. First, we summarize increasingly powerful ways to harness the strength of mouse models for discovery of genes affecting polygenic obesity. Second, we examine the status of using a systems biology approach to dissect the genetic architecture of obesity. And third, we explore the effects of recent findings indicating increasing levels of complexity in the nature of variation underlying, and the heritability of, complex traits such as obesity.