Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China.
Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China.
Plant J. 2019 Aug;99(4):796-806. doi: 10.1111/tpj.14355. Epub 2019 May 25.
Despite its critical importance to our understanding of plant growth and adaptation, the question of how environment-induced plastic response is affected genetically remains elusive. Previous studies have shown that the reaction norm of an organism across environmental index obeys the allometrical scaling law of part-whole relationships. The implementation of this phenomenon into functional mapping can characterize how quantitative trait loci (QTLs) modulate the phenotypic plasticity of complex traits to heterogeneous environments. Here, we assemble functional mapping and allometry theory through Lokta-Volterra ordinary differential equations (LVODE) into an R-based computing platform, np QTL, aimed to map and visualize phenotypic plasticity QTLs. Based on LVODE parameters, np QTL constructs a bidirectional, signed and weighted network of QTL-QTL epistasis, whose emergent properties reflect the ecological mechanisms for genotype-environment interactions over any range of environmental change. The utility of np QTL was validated by comprehending the genetic architecture of phenotypic plasticity via the reanalysis of published plant height data involving 3502 recombinant inbred lines of maize planted in multiple discrete environments. np QTL also provides a tool for constructing a predictive model of phenotypic responses in extreme environments relative to the median environment.
尽管环境诱导的可塑性反应的遗传影响问题对于我们理解植物生长和适应至关重要,但仍未得到解决。先前的研究表明,生物体在环境指数上的反应规范遵循部分-整体关系的异速缩放定律。将这一现象应用于功能映射可以描述数量性状位点(QTL)如何调节复杂性状对异质环境的表型可塑性。在这里,我们通过 Lokta-Volterra 常微分方程(LVODE)将功能映射和异速生长理论集成到一个基于 R 的计算平台 np QTL 中,旨在对表型可塑性 QTL 进行映射和可视化。基于 LVODE 参数,np QTL 构建了一个双向、有符号和加权的 QTL-QTL 上位性网络,其涌现的性质反映了基因型-环境相互作用的生态机制,适用于任何范围的环境变化。np QTL 的有效性通过重新分析涉及在多个离散环境中种植的 3502 个重组自交系的已发表的株高数据来理解表型可塑性的遗传结构得到了验证。np QTL 还提供了一种构建相对于中值环境的极端环境下表型响应预测模型的工具。