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.