通过整合NIR预测的表型,改进了小麦最终产品质量特征的多特征预测
Shiva Azizinia1, Daniel Mullan2, Allan Rattey2
1Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia.
Frontiers in plant science
|June 5, 2023
概括
近红外 (NIR) 谱法可以在小麦质量特征的早期选择. 这种具有成本效益的方法显著提高了基因组预测的准确性,加速了繁殖周期,以获得更好的最终产品.
科学领域:
- 农业科学 农业科学
- 植物育种 植物育种
- 基因组学就是基因组学.
背景情况:
- 在小麦育种中,传统的最终产品质量检测是昂贵的,耗时的,限制了其在早期发展阶段的使用.
- 像近红外 (NIR) 光谱这样的高通量,非破坏性方法为早期特征评估提供了潜力.
- 基因组预测模型可以整合次要特征,以提高复杂质量特征的选择效率.
研究的目的:
- 评估将NIR预测的次要特征纳入多特征基因组最佳线性无偏预测 (GBLUP) 模型对面包小麦六种最终产品质量特征的预测准确性的影响.
- 确定NIR预测的数据是否可以增强对有价值的品质特征的早期选择,从而降低育种成本并提高有效性.
主要方法:
- 利用了1,400-1,900个面包小麦品种的数据,用InfiniumTM Wheat Barley 40K BeadChip进行基因定型,并使用外体序列数据进行归算.
- 在8年的时间里 (2012-2019) 使用标准测试和NIR光谱收集了最终产品质量特征数据,生成了约27,000行的预测数据.
- 应用了GBLUP模型,将单特征分析与多特征分析进行比较,包括NIR预测的次要特征.
主要成果:
- 最终产品特征与NIR预测数据之间的遗传相关性在0.5到0.83之间 (除了0.19的面粉膨胀体积).
- 与单一特征分析相比,将NIR预测数据纳入最终产品特征的预测准确度提高了高达30%.
- 多特征预测准确度显示,最终产品和NIR数据 (0.69-0.77) 之间的遗传相关性与高相关性.
- 未来预测验证表明,随着多年数据被添加到多特征模型中,准确性逐渐增加.
结论:
- 将NIR预测的次要特征集成到GBLUP模型中,可以显著提高面包小麦关键最终产品质量特征的预测准确度.
- 这种方法可以在繁殖周期的早期有效地选择有价值的品质特征,从而带来巨大的成本和效率效益.
- 这项研究验证了NIR光谱学与基因组预测相结合的实用性,以加速以最终产品质量为重点的小麦育种计划.
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