Lu Kaiyan, Wang Xueshun, Gong Huiying, Yang Dengcheng, Ye Meixia, Fang Qing, Zhang Xiao-Yu, Wu Rongling
College of Science, Beijing Forestry University, Beijing, China.
Department of Artificial Intelligence and Data Science, Guangzhou Xinhua University, Guangzhou, China.
Front Plant Sci. 2023 Apr 5;14:1149879. doi: 10.3389/fpls.2023.1149879. eCollection 2023.
The cooperative strategy of phenotypic traits during the growth of plants reflects how plants allocate photosynthesis products, which is the most favorable decision for them to optimize growth, survival, and reproduction response to changing environment. Up to now, we still know little about why plants make such decision from the perspective of biological genetic mechanisms.
In this study, we construct an analytical mapping framework to explore the genetic mechanism regulating the interaction of two complex traits. The framework describes the dynamic growth of two traits and their interaction as Differential Interaction Regulatory Equations (DIRE), then DIRE is embedded into QTL mapping model to identify the key quantitative trait loci (QTLs) that regulate this interaction and clarify the genetic effect, genetic contribution and genetic network structure of these key QTLs. Computer simulation experiment proves the reliability and practicability of our framework.
In order to verify that our framework is universal and flexible, we applied it to two sets of data from , namely, aboveground stem length - underground taproot length, underground root number - underground root length, which represent relationships of phenotypic traits in two spatial dimensions of plant architecture. The analytical result shows that our model is well applicable to datasets of two dimensions.
Our model helps to better illustrate the cooperation-competition patterns between phenotypic traits, and understand the decisions that plants make in a specific environment that are most conducive to their growth from the genetic perspective.
植物生长过程中表型性状的协同策略反映了植物如何分配光合作用产物，这是它们在不断变化的环境中优化生长、生存和繁殖反应的最有利决策。到目前为止，从生物遗传机制的角度来看，我们对植物为何做出这样的决策仍然知之甚少。
在本研究中，我们构建了一个分析映射框架来探索调控两个复杂性状相互作用的遗传机制。该框架将两个性状的动态生长及其相互作用描述为差异相互作用调控方程（DIRE），然后将DIRE嵌入到QTL映射模型中，以识别调控这种相互作用的关键数量性状位点（QTL），并阐明这些关键QTL的遗传效应、遗传贡献和遗传网络结构。计算机模拟实验证明了我们框架的可靠性和实用性。
为了验证我们的框架具有通用性和灵活性，我们将其应用于来自[具体来源未给出]的两组数据，即地上茎长 - 地下主根长、地下根数 - 地下根长，它们代表了植物结构两个空间维度上的表型性状关系。分析结果表明，我们的模型很好地适用于二维数据集。
我们的模型有助于更好地阐明表型性状之间的合作 - 竞争模式，并从遗传角度理解植物在特定环境中做出的最有利于其生长的决策。
