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首页> 外文期刊>Journal of Experimental Botany >Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model
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Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model

机译：使用基于QTL的生态生理模型参数预测面包小麦（Triticum aestivum L.）的抽穗期

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摘要

Prediction of wheat phenology facilitates the selection of cultivars with specific adaptations to a particular environment. However, while QTL analysis for heading date can identify major genes controlling phenology, the results are limited to the environments and genotypes tested. Moreover, while ecophysiological models allow accurate predictions in new environments, they may require substantial phenotypic data to parameterize each genotype. Also, the model parameters are rarely related to all underlying genes, and all the possible allelic combinations that could be obtained by breeding cannot be tested with models. In this study, a QTL-based model is proposed to predict heading date in bread wheat (Triticum aestivum L.). Two parameters of an ecophysiological model (V-sat and P-base, representing genotype vernalization requirements and photoperiod sensitivity, respectively) were optimized for 210 genotypes grown in 10 contrasting location x sowing date combinations. Multiple linear regression models predicting V-sat and P-base with 11 and 12 associated genetic markers accounted for 71 and 68% of the variance of these parameters, respectively. QTL-based V-sat and P-base estimates were able to predict heading date of an independent validation data set (88 genotypes in six location x sowing date combinations) with a root mean square error of prediction of 5 to 8.6 days, explaining 48 to 63% of the variation for heading date. The QTL-based model proposed in this study may be used for agronomic purposes and to assist breeders in suggesting locally adapted ideotypes for wheat phenology.
机译：对小麦物候的预测有助于选择对特定环境具有特定适应性的品种。但是，尽管对抽穗期进行QTL分析可以确定控制物候的主要基因，但结果仅限于所测试的环境和基因型。此外，虽然生态生理模型可以在新环境中进行准确的预测，但它们可能需要大量的表型数据来参数化每个基因型。同样，模型参数很少与所有基础基因相关，并且不能通过模型​​测试通过育种可获得的所有可能的等位基因组合。在这项研究中，提出了基于QTL的模型来预测面包小麦（Triticum aestivum L.）的抽穗期。优化了生态生理模型的两个参数（V-sat和P-base，分别代表基因型春化要求和光周期敏感性），以适应在10个对比位置x播种日期组合中生长的210个基因型。预测具有11和12个相关遗传标记的V-sat和P-base的多个线性回归模型分别占这些参数方差的71％和68％。基于QTL的V-sat和P-base估计值能够预测独立验证数据集（六个位置x播种日期组合的88个基因型）的前进日期，预测的均方根误差为5到8.6天，解释了48到标题日期变化的63％。本研究中提出的基于QTL的模型可用于农艺目的，并协助育种者建议针对小麦物候的局部适应型。

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