Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China.
Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.
Nat Commun. 2021 Sep 6;12(1):5304. doi: 10.1038/s41467-021-25086-5.
Phenotypic plasticity represents a capacity by which the organism changes its phenotypes in response to environmental stimuli. Despite its pivotal role in adaptive evolution, how phenotypic plasticity is genetically controlled remains elusive. Here, we develop a unified framework for coalescing all single nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) into a quantitative graph. This framework integrates functional genetic mapping, evolutionary game theory, and predator-prey theory to decompose the net genetic effect of each SNP into its independent and dependent components. The independent effect arises from the intrinsic capacity of a SNP, only expressed when it is in isolation, whereas the dependent effect results from the extrinsic influence of other SNPs. The dependent effect is conceptually beyond the traditional definition of epistasis by not only characterizing the strength of epistasis but also capturing the bi-causality of epistasis and the sign of the causality. We implement functional clustering and variable selection to infer multilayer, sparse, and multiplex interactome networks from any dimension of genetic data. We design and conduct two GWAS experiments using Staphylococcus aureus, aimed to test the genetic mechanisms underlying the phenotypic plasticity of this species to vancomycin exposure and Escherichia coli coexistence. We reconstruct the two most comprehensive genetic networks for abiotic and biotic phenotypic plasticity. Pathway analysis shows that SNP-SNP epistasis for phenotypic plasticity can be annotated to protein-protein interactions through coding genes. Our model can unveil the regulatory mechanisms of significant loci and excavate missing heritability from some insignificant loci. Our multilayer genetic networks provide a systems tool for dissecting environment-induced evolution.
表型可塑性是指生物体根据环境刺激改变表型的能力。尽管它在适应性进化中起着关键作用，但表型可塑性的遗传控制仍然难以捉摸。在这里，我们开发了一个统一的框架，将全基因组关联研究（GWAS）中的所有单核苷酸多态性（SNP）合并到一个定量图中。该框架整合了功能遗传作图、进化博弈论和捕食者-猎物理论，将每个 SNP 的净遗传效应分解为其独立和依赖成分。独立效应源于 SNP 的内在能力，只有在孤立状态下才会表现出来，而依赖效应则源于其他 SNP 的外在影响。依赖效应在概念上超出了传统的上位性定义，不仅描述了上位性的强度，还捕捉了上位性的双向因果关系和因果关系的符号。我们实施功能聚类和变量选择，从任何遗传数据维度推断多层、稀疏和多重互作网络。我们设计并进行了两项针对金黄色葡萄球菌的 GWAS 实验，旨在测试该物种对万古霉素暴露和大肠杆菌共存的表型可塑性的遗传机制。我们重建了两种最全面的非生物和生物表型可塑性遗传网络。通路分析表明，表型可塑性的 SNP-SNP 上位性可以通过编码基因注释到蛋白质-蛋白质相互作用。我们的模型可以揭示重要基因座的调控机制，并从一些不重要的基因座中挖掘缺失的遗传率。我们的多层遗传网络为剖析环境诱导的进化提供了系统工具。
