Genetic Architecture of Multiphasic Growth Covariation as Revealed by a Nonlinear Mixed Mapping Framework

Trait covariation during multiphasic growth change is of crucial significance to optimal survival and reproduction during the entire life cycle. However, current analyses are mainly focused on the study of individual traits, but exploring how genes determine trait interdependence spanning multiphasic growth processes remains challenging. In this paper, we constructed a nonlinear mixed mapping framework to explore the genetic mechanisms that govern multiphasic growth changes between two complex traits and used this framework to study stem diameter and stem height in forest trees. The multiphasic nonlinear mixed mapping framework was implemented in system mapping, by which several key quantitative trait loci were found to interpret the process and pattern of stemwood growth by regulating the ecological interactions of stem apical and lateral growth. We quantified the timing and pattern of the vegetative phase transition between independently regulated, temporally coordinated processes. Furthermore, we visualized the genetic machineries of significant loci, including genetic effects, genetic contribution analysis and the regulatory relationship between these markers in the network structure. We validated the utility of the new mapping framework experimentally via computer simulations. These results may improve our understanding of the evolution of development in changing environments.