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基因组预测为在面包小麦 (Triticum aestivum L) 中识别多种耐基因型铺平了道路

Vikas Kumaran¹, Anjan Kumar Pradhan², Neeraj Budhlakoti³

¹Indian Agricultural Research Institute, CROP IMPROVEMENT, SENIOR SCIENTIST, IARI REGIONAL STATION, WELLINGTON, WELLINGTON, TAMILNADU, India, 643231; vikaswtn21@gmail.com.

|August 25, 2025

概括

基因组预测模型有效预测小麦的抗性,贝叶斯B在幼苗阶段表现出色. 使用GWAS进行的优化标记选择显著提高了耐性预测的准确性.

科学领域:

植物育种
遗传学
农业科学

背景情况:

基因组预测对于加速作物改进至关重要.
精确的繁殖值对于在小麦中选择优秀的个体至关重要.

研究的目的:

评估五种GP模型来预测小麦幼苗和成年植物对三种主要生疾病的抵抗力.
研究标记密度和GWAS指导的选择对预测准确性的影响.

主要方法:

对GBLUP,RKHS,BayesA,BayesB和BayesC模型进行比较分析.
用于预测对叶子/棕色生 (LR),茎/黑色生 (SR) 和黄色/条纹生 (YR) 的耐药性.
用于指导GP的标志物选择的全基因组关联研究 (GWAS).

主要成果:

贝叶斯B模型在幼苗阶段证明了LR和SR的高精度;所有模型都在YR中扎.
对于成年植物的耐药性 (APR),GBLUP和BayesB的表现与LR相似.
通过GWAS选择的中间SNP密度 (100-500标记) 与完整标记集相比显著提高了预测准确性.

结论:

根据生类型和生长阶段,GP模型的有效性各不相同.
用GWAS指导的标记选择对于提高小麦耐性预测的GP准确性至关重要.
确定了两种具有广泛抗的小麦品种 (CRP-165/42和HGP1-435),用于未来的育种工作.

关键词:

造成的因素种植类型疾病管理田间作物菌类相关领域收益损失和经济影响谷物和谷物种类/耐药性

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