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

Huiying Gong, Xiao Yu Zhang, Sheng Zhu, Libo Jiang, Xuli Zhu, Qing Fang, Rongling Wu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Trait covariation during multiphasic growth 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 study, we constructed a nonlinear mixed mapping framework to explore the genetic mechanisms that regulate 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 stem wood 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 machinery 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. The results may improve our understanding of the evolution of development in changing environments.

Original languageEnglish (US)
Article number711219
JournalFrontiers in Plant Science
StatePublished - Oct 5 2021

All Science Journal Classification (ASJC) codes

  • Plant Science


Dive into the research topics of 'Genetic Architecture of Multiphasic Growth Covariation as Revealed by a Nonlinear Mixed Mapping Framework'. Together they form a unique fingerprint.

Cite this