News Release

Genome-wide network analysis of above- and below-ground co-growth in Populus euphratica

Peer-Reviewed Publication

Nanjing Agricultural University The Academy of Science

Fig. 1


The workflow of genome-wide multilayer networks that mediate complex dynamic traits.

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Credit: Plant Phenomics

Tree growth, a complex interplay of genetics between the plant's above- and below-ground parts, lacks a comprehensive understanding of its genetic architecture. Research has increasingly focused on understanding how genes regulate growth, employing advanced methods such as transcriptome analysis and genome-wide association studies. Yet, these studies often narrow their focus to a few genes or traits, overlooking the holistic view of organ integrity and the full genetic network. Therefore, there is a need for a comprehensive gene model approach, leveraging high-throughput genotyping technologies, to fully grasp the genetic mechanisms shaping complex plant phenotypes and their development.

In January 2024, Plant Phenomics published a research article titled “Genome-Wide Network Analysis of Above- and Below-Ground Co-growth in Populus euphratica”.

In this study, researchers introduce a pioneering computational model designed to uncover the genetic underpinnings of tree growth in Populus euphratica, focusing on the developmental dynamics and interactions between above- and below-ground traits. By integrating advanced methodologies like systems mapping, functional clustering, and evolutionary game theory, the model successfully delineates the genetic contributions and network topology driving phenotypic formation. Specifically, the research explores the interactions among four critical traits—stem length, taproot length, lateral root length, and average lateral root length—using a multi-dimensional interactive model (MDIM). This approach not only fits the growth curves of these traits with high accuracy but also illuminates the complex interplay among them, revealing distinct time-varying growth characteristics and the influence of genetic interactions.

The findings indicate that the taproot exhibits superior growth compared to other traits, especially in early development stages, likely catering to the plant's need for water and nutrients. Furthermore, the analysis of significant quantitative trait loci (QTLs) identifies 54 key SNPs across the genome, contributing to understanding of the genetic regulation of these traits. Notably, the model's ability to simulate the growth and genetic effects of Populus euphratica under various conditions validated its reliability and accuracy.

In addition, the construction of a multilayer large-scale interactive network, drawing inspiration from ecosystem structures, enables a holistic view of the genome-wide genetic architecture. This network, comprising multiple modules with differing genetic effects, showcases the complexity of genetic interactions in growth traits. Key QTLs are identified and their biological functions annotated, shedding light on their roles in plant development. The network's hierarchical structure, ranging from global gene interactions to specific SNP effects, provides unprecedented insights into the genetic basis of complex traits.

Overall, this study not only advances the genetic understanding of tree growth but also sets a precedent for the analysis of complex traits across different species. The model's flexibility and scalability suggest its applicability to a wide range of biological questions, highlighting its potential to revolutionize our approach to genetic research and plant breeding.




Kaiyan  Lu1†, Huiying  Gong3†, Dengcheng  Yang3†, Meixia  Ye3, Qing  Fang4, Xiao-Yu  Zhang1*, and Rongling  Wu2,3*


1College of Science, Beijing Forestry University, Beijing 100083, P. R. China.

2Yanqi Lake Beijing Institute of  Mathematical  Sciences  and  Applications,  Beijing  101408,  China.  

3Center  for  Computational  Biology,  College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P. R. China.

4Faculty of Science, Yamagata University, Yamagata 990, Japan.

About  Rongling  Wu

He is currently a researcher at Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, and also serves as editor-in-chief, associate editor, special editor and editorial board member of several journals in the fields of genetics, bioinformatics and computational biology. His research interests include: developing interdisciplinary statistical methods to reveal the genetic control mechanisms of complex traits and human complex diseases. The proposed functional mapping method can effectively discover the genetic rules of trait development and describe the key patterns of gene effects changing over time and space. Combining functional mapping with evolutionary game theory, scale theory, and prey-predator theory, a series of computational methods have been developed to construct multi-level, multi-space, and multi-scale genotype-phenotype relationships from molecules to phenotypes The three-dimensional network provides analysis tools for systems biology, systems medicine, and systems pharmacology research.

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