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Network‐based feature selection reveals substructures of gene modules responding to salt stress in rice

机译:基于网络的特征选择揭示了水稻响应盐胁迫的基因模块的亚结构

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摘要

Rice, an important food resource, is highly sensitive to salt stress, which is directly related to food security. Although many studies have identified physiological mechanisms that confer tolerance to the osmotic effects of salinity, the link between rice genotype and salt tolerance is not very clear yet. Association of gene co‐expression network and rice phenotypic data under stress has penitential to identify stress‐responsive genes, but there is no standard method to associate stress phenotype with gene co‐expression network. A novel method for integration of gene co‐expression network and stress phenotype data was developed to conduct a system analysis to link genotype to phenotype. We applied a LASSO‐based method to the gene co‐expression network of rice with salt stress to discover key genes and their interactions for salt tolerance‐related phenotypes. Submodules in gene modules identified from the co‐expression network were selected by the LASSO regression, which establishes a linear relationship between gene expression profiles and physiological responses, that is, sodium/potassium condenses under salt stress. Genes in these submodules have functions related to ion transport, osmotic adjustment, and oxidative tolerance. We argued that these genes in submodules are biologically meaningful and useful for studies on rice salt tolerance. This method can be applied to other studies to efficiently and reliably integrate co‐expression network and phenotypic data.
机译:水稻是重要的粮食资源,对盐分胁迫高度敏感,盐分胁迫与粮食安全直接相关。尽管许多研究已经确定了赋予盐分渗透作用耐受性的生理机制,但水稻基因型与耐盐性之间的联系还不是很清楚。基因共表达网络和水稻在胁迫下的表型数据之间的关联可以识别胁迫响应基因,但是尚无标准方法将胁迫表型与基因共表达网络相关联。开发了一种新的整合基因共表达网络和应激表型数据的方法,以进行系统分析以将基因型与表型联系起来。我们将基于LASSO的方法应用于盐胁迫水稻的基因共表达网络,以发现关键基因及其与盐耐受性相关的表型之间的相互作用。通过LASSO回归选择从共表达网络中识别的基因模块中的亚模块,这在基因表达谱与生理反应之间建立了线性关系,即盐胁迫下钠/钾缩合。这些子模块中的基因具有与离子转运,渗透调节和氧化耐受性有关的功能。我们认为,这些子模块中的基因具有生物学意义,可用于水稻耐盐性研究。该方法可用于其他研究,以有效,可靠地整合共表达网络和表型数据。

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