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

Frontiers in Plant Science

卷:12

Libo Jiang¹  Xuli Zhu¹  Sheng Zhu²  Xiao-Yu Zhang³  Huiying Gong³  Rongling Wu⁴  Qing Fang⁵ 

[1] Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China;

[2] College of Biology and the Environment, Nanjing Forestry University, Nanjing, China;

[3] College of Science, Beijing Forestry University, Beijing, China;

[4] Departments of Public Health Sciences and Statistics, Center for Statistical Genetics, The Pennsylvania State University, Hershey, PA, United States;

[5] Faculty of Science, Yamagata University, Yamagata, Japan;

关键词: trees growth;    genetic architecture;    quantitative trait loci (QTLs);    nonlinear mixed mapping;    multiphasic growth models;   

DOI  :  10.3389/fpls.2021.711219

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown