首页> 美国卫生研究院文献>Journal of Experimental Botany >Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments
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Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments

机译:量化VRN1和Ppd-D1在不同环境下预测春小麦抽穗时间的影响

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摘要

Heading time is a major determinant of the adaptation of wheat to different environments, and is critical in minimizing risks of frost, heat, and drought on reproductive development. Given that major developmental genes are known in wheat, a process-based model, APSIM, was modified to incorporate gene effects into estimation of heading time, while minimizing degradation in the predictive capability of the model. Model parameters describing environment responses were replaced with functions of the number of winter and photoperiod (PPD)-sensitive alleles at the three VRN1 loci and the Ppd-D1 locus, respectively. Two years of vernalization and PPD trials of 210 lines (spring wheats) at a single location were used to estimate the effects of the VRN1 and Ppd-D1 alleles, with validation against 190 trials (~4400 observations) across the Australian wheatbelt. Compared with spring genotypes, winter genotypes for Vrn-A1 (i.e. with two winter alleles) had a delay of 76.8 degree days (°Cd) in time to heading, which was double the effect of the Vrn-B1 or Vrn-D1 winter genotypes. Of the three VRN1 loci, winter alleles at Vrn-B1 had the strongest interaction with PPD, delaying heading time by 99.0 °Cd under long days. The gene-based model had root mean square error of 3.2 and 4.3 d for calibration and validation datasets, respectively. Virtual genotypes were created to examine heading time in comparison with frost and heat events and showed that new longer-season varieties could be heading later (with potential increased yield) when sown early in season. This gene-based model allows breeders to consider how to target gene combinations to current and future production environments using parameters determined from a small set of phenotyping treatments.
机译:抽穗时间是小麦适应不同环境的主要决定因素,对于最大限度地减少繁殖发展中的霜冻,高温和干旱风险至关重要。鉴于小麦中已知主要的发育基因,因此对基于过程的模型APSIM进行了修改,以将基因效应纳入抽穗期的估计中,同时最大程度地降低模型的预测能力。描述环境响应的模型参数分别替换为三个VRN1基因座和Ppd-D1基因座处的冬季和光周期(PPD)敏感等位基因数量的函数。在单个位置上进行了210个品系(春小麦)的两年春化和PPD试验,以评估VRN1和Ppd-D1等位基因的作用,并针对澳大利亚小麦带上的190个试验(〜4400个观察值)进行了验证。与春季基因型相比,Vrn-A1的冬季基因型(即有两个冬季等位基因)的抽穗时间延迟了76.8度(°Cd),是Vrn-B1或Vrn-D1冬季基因型的两倍。 。在这三个VRN1基因座中,Vrn-B1的冬季等位基因与PPD的相互作用最强,在漫长的日子里,抽穗时间延迟了99.0°Cd。对于校准和验证数据集,基于基因的模型的均方根误差分别为3.2和4.3 d。创建了虚拟基因型以检查霜冻和高温事件的抽穗时间,并表明当季节早期播种时,新的较长季节品种可能在后期抽穗(产量可能增加)。这种基于基因的模型使育种者可以考虑如何使用从少量表型处理中确定的参数将基因组合靶向当前和未来的生产环境。

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