Abstract
The effects of genes on physiological and biochemical processes are interrelated and interdependent; it is common for genes to express pleiotropic control of complex traits. However, the study of gene expression and participating pathways in vivo at the whole-genome level is challenging. Here, we develop a coupled regulatory interaction differential equation to assess overall and independent genetic effects on trait growth. Based on evolutionary game theory and developmental modularity theory, we constructed multilayer, omnigenic networks of bidirectional, weighted, and positive or negative epistatic interactions using a forest poplar tree mapping population, which were organized into metagalactic, intergalactic, and local interstellar networks that describe layers of structure between modules, submodules, and individual single nucleotide polymorphisms, respectively. These multilayer interactomes enable the exploration of complex interactions between genes, and the analysis of not only differential expression of quantitative trait loci but also previously uncharacterized determinant SNPs, which are negatively regulated by other SNPs, based on the deconstruction of genetic effects to their component parts. Our research framework provides a tool to comprehend the pleiotropic control of complex traits and explores the inherent directional connections between genes in the structure of omnigenic networks.
Highlights
The study of gene pleiotropy has become a focus of genetic research in recent years
The trees were genotyped at single nucleotide polymorphism (SNP) sites using the Applied Biosystems QuantStudio 12K Flex Real-Time Polymerase Chain Reaction (PCR) System for genome-wide mapping. 156 362 SNPs were characterized through stringent qualitycontrol filters segregating with different patterns, of which 94 591 SNPs belong to testcross markers and 61 771 SNPs belong to intercross markers, respectively
We present a computational model integrating a novel growth equation, system mapping, functional clustering, developmental modularity theory, and evolutionary game theory
Summary
The study of gene pleiotropy has become a focus of genetic research in recent years. Pleiotropy describes the phenomenon that single genes can have multiple biological effects, so that an individual exhibits multiple traits (Solovieff et al, 2013). With the development of genome-wide association statistical models, the regulatory roles of genes and their interactive effects have received sustained attention in research on pleiotropy (Sivakumaran et al, 2011; Visscher and Yang, 2016; Watanabe et al, 2019); these existing genetic studies mainly focus on the action of identified key genes, which account for only a small amount of phenotypic. The current understanding of the networks of genes that drive the development of complex traits, and how genes throughout the genome interact remains inadequate. An “omnigenic” model was proposed to take into account the activity of genes in cells, which form a broad network in which each gene exerts an influence on the occurrence of disease or development of traits, including those without any obvious connection to traits or diseases in interconnected gene networks (Boyle et al, 2017; Wray et al, 2018; Liu et al, 2019)
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