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Article|16 Dec 2025|OPEN
An omnigenic interactome model to chart the genetic architecture of individual plants
Changjian Fa1,2 ,† , Guijia Wang3 ,† , Wenqi Pan2 ,† , Yu Wang2 , Jincan Che1,2 , Ang Dong2 , Dengcheng Yang2 and Rongling Wu4 , Shing-Tung Yau2,4,5 , Lidan Sun,3 ,
1Center for Computational Biology, School of Grassland Science, Beijing Forestry University, Beijing 100083, China
2Beijing Key Laboratory of Topological Statistics and Applications for Complex Systems, Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China
3State Key Laboratory of Efficient Production of Forest Resources, Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, National Engineering Research Center for Floriculture, School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
4Program in Applied Statistics, Shanghai Institute for Mathematics and Interdisciplinary Sciences, Shanghai 200433, China
5Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China
*Corresponding author. E-mail: sunlidan@bjfu.edu.cn
†Changjian Fa and Guijia Wang,Wenqi Pan contributed equally to the study.

Horticulture Research 13,
Article number: uhaf345 (2026)
doi: https://doi.org/10.1093/hr/uhaf345
Views: 35

Received: 03 Sep 2025
Accepted: 09 Dec 2025
Published online: 16 Dec 2025

Abstract

Complex traits are controlled by many unknown genes, making it difficult to elucidate a global picture of the genotype–phenotype map. Here, we develop a statistical mechanics model to contextualize all possible genes into informative, dynamic, omnidirectional, and personalized idopNetworks. This model, derived from the combination of functional mapping and evolutionary game theory, can visualize and trace how genes act and interact with each other to shape the genetic architecture of complex traits. The model can estimate changes in the genotypic value of one gene due to the influence of other genes, specifically on individual subjects, surpassing traditional quantitative genetic studies that can only capture the marginal effect of a gene at the population level. We reconstruct growth idopNetworks from a genome-wide mapping data in a woody plant, mei, identifying unique genetic interaction architecture that distinguishes between fast-growing trees and slow-growing trees. We perform computer simulation to validate the statistical power of the model. IdopNetworks can disentangle the genetic control mechanisms of complex traits and provide guidance on how to alter phenotypic values of specific individuals by promoting or inhibiting the expression of interactive genes.