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首页> 外文期刊>Plant direct. >Multivariate analysis reveals environmental and genetic determinants of element covariation in the maize grain ionome
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Multivariate analysis reveals environmental and genetic determinants of element covariation in the maize grain ionome

机译：多变量分析揭示了玉米谷物离子中元变焦的环境和遗传决定因素

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The integrated responses of biological systems to genetic and environmental variation result in substantial covariance in multiple phenotypes. The resultant pleiotropy, environmental effects, and genotype-by-environmental interactions (GxE) are foundational to our understanding of biology and genetics. Yet, the treatment of correlated characters, and the identification of the genes encoding functions that generate this covariance, has lagged. As a test case for analyzing the genetic basis underlying multiple correlated traits, we analyzed maize kernel ionomes from Intermated B73 x Mo17 (IBM) recombinant inbred populations grown in 10 environments. Plants obtain elements from the soil through genetic and biochemical pathways responsive to physiological state and environment. Most perturbations affect multiple elements which leads the ionome, the full complement of mineral nutrients in an organism, to vary as an integrated network rather than a set of distinct single elements. We compared quantitative trait loci (QTL) determining single-element variation to QTL that predict variation in principal components (PCs) of multiple-element covariance. Single-element and multivariate approaches detected partially overlapping sets of loci. QTL influencing trait covariation were detected at loci that were not found by mapping single-element traits. Moreover, this approach permitted testing environmental components of trait covariance, and identified multi-element traits that were determined by both genetic and environmental factors as well as genotype-by-environment interactions. Growth environment had a profound effect on the elemental profiles and multi-element phenotypes were significantly correlated with specific environmental variables.
机译：生物系统对遗传和环境变异的综合响应导致多种表型的大量协方差。由此产生的肺炎，环境影响和基因型 - 环境互动（GXE）是我们对生物学和遗传学的理解。然而，治疗相关性的特征，以及编码产生这种协方差的功能的基因的鉴定，已经滞后。作为分析遗传基础的遗传基础的测试案例，我们分析了在10个环境中生长的宿主B73 X Mo17（IBM）重组近亲群体的玉米核离子粒子。植物通过响应生理状态和环境的遗传和生化途径从土壤中获得元素。大多数扰动会影响导致离子组的多个元素，生物体中的矿物质营养素的完全补充，变化为集成网络而不是一组不同的单一元素。我们比较了定量特征基因座（QTL）确定单元素变化对QTL，该QTL预测多元素协方差的主组件（PC）的变化。单元素和多变量方法检测到部分重叠的基因座。在通过映射单元素特征未发现的基因座中检测到影响特质协变的QTL。此外，这种方法允许测试特性协方差的环境组分，并确定由遗传和环境因素和基因型型相互作用决定的多元素特征。生长环境对元素谱具有深远的影响，多元素表型与特定环境变量显着相关。

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