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豆类对目标外的迪坎巴耐受性的遗传结构

Caio Canella Vieira¹, Jing Zhou², Diego Jarquin³

¹Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States.

Frontiers in plant science

|October 25, 2023

概括

研究人员确定了与大豆中迪坎巴耐受性相关的基因组区域. 这一发现有助于培育抗迪坎巴漂移的作物,最大限度地减少对非迪坎巴耐受大豆品种的损害.

科学领域:

植物遗传学植物遗传学
农业科学农业科学
分子生物学分子生物学

背景情况:

迪坎巴耐受性 (DT) 豆类的采用导致了美国广泛的非目标迪坎巴损伤.
大豆对迪坎巴的敏感性因遗传背景而异,影响症状强度和产量损失.

研究的目的:

为了确定大豆对目标外的迪坎巴反应的新型标记特征关联.
扩展已知的基因组区域,与大豆中的迪坎巴耐受性相关.

主要方法:

在三年内在九个环境中对551个非DT大豆育种线进行了表型化,以获得非目标的迪坎巴反应.
使用Illumina Infinium BARCSoySNP6K BeadChip进行基因型化线路.
采用随机森林 (RF) 和支持矢量机 (SVM) 模型,并逐步选择SNP以确定与耐受性相关的基因组区域.

主要成果:

射频和SVM模型实现了高分类准确度 (0.76和0.79) 的迪坎巴反应.
鉴定了6号,8号,9号,10号和19号染色体上的8个基因组区域,与非目标迪坎巴耐受性相关.
对于高分类准确度来说,SNP对6号染色体的重大影响是足够的.
参与除草剂排毒 (第二阶段和第三阶段) 的同局部候选基因,具有重要的标记物.

结论:

基因组预测模型有效地识别了大豆基因型,对目标外的Dicamba有增强的耐受性.
了解迪坎巴耐受性的遗传结构对于开发耐药大豆品种至关重要.
这项研究为旨在减轻迪坎巴诱导的作物损伤的育种计划提供了宝贵的见解.

关键词:

无生物压力是无生物压力.迪坎巴迪坎巴 (Dicamba) 是一个全基因组关联研究研究.机器学习是机器学习.植物育种植物育种豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆

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