Genetic influences on growth and body composition in mice: multilocus interactions

Abstract

Background: The genetic architecture of body weight and body composition is complex because these traits are normally influenced by multiple genes and their interactions, even after controlling for the environment. Bayesian methodology provides an efficient way of estimating these interactions.

Subjects and measurements: We used Bayesian model selection techniques to simultaneously estimate the main effects, epistasis and gene-sex interactions on age-related body weight (at 3, 6 and 10 weeks, denoted as WT3wk, WT6wk and WT10wk) and body composition (organ weights and fat-related traits) in an F(2) sample obtained from a cross between high-growth (M16i) mice and low-growth (L6) mice.

Results: We observed epistatic and main-effect quantitative trait loci (QTL) that controlled both body weight and body composition. Epistatic effects were generally more significant for WT6wk than WT10wk. Chromosomes 5 and 13 interacted strongly to control body weight at 3 weeks. A pleiotropic QTL on chromosome 2 was associated with body weight and some body composition phenotypes. Testis weight was regulated by a QTL on chromosome 13 with a significantly large main effect (2log(e)BF approximately 15).

Conclusion: By analyzing epistatic interactions, we detected QTL not found in a previous analysis of this mouse population. Hence, the detection of gene-gene interactions may provide new information about the genetic architecture of complex obesity-related traits and may lead to the detection of additional obesity genes.

Figure 1

One-dimensional profiles of Bayes factors rescaled as 2log_eBF for main (dotted black lines), epistatic effects (solid black lines) and sex-specific effects (solid grey lines). A: body weight at week 3 (WK3), B: body weight at week 6 (WK6), C: body weight at week 10 (WK10). The horizontal lines represent the significance threshold of 2log_eBF = 2.1.

Figure 2

One dimensional profiles of Bayes factors rescaled as 2log_eBF for WK3, EBW, SPL, and FAT for selected chromosomes for epistatic effects – solid, dashed, dotted and solid-dotted lines represent additive-additive, additive-dominance, dominance-additive and dominance-dominance effects, respectively.

Figure 3

One dimensional profiles of Bayes factors rescaled as 2log_eBF for WK3, EBW, SPL, and FAT (adjusted for final body weight) for selected chromosomes for epistatic effects – solid, dashed, dotted and solid-dotted lines represent additive-additive, additive-dominance, dominance-additive and dominance-dominance effects, respectively.

Figure 4

Two-dimensional profiles of Bayes factors (rescaled as 2logeBF) for WK3, SPL, and FAT for selected chromosomes. The upper diagonal shows the Bayes factor for the epistatic model, the lower diagonal shows the Bayes factor for the full model with epistasis compared with no QTL.