Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia.
Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia.
J Exp Bot. 2023 Mar 13;74(5):1389-1402. doi: 10.1093/jxb/erac393.
Crop growth models (CGM) can predict the performance of a cultivar in untested environments by sampling genotype-specific parameters. As they cannot predict the performance of new cultivars, it has been proposed to integrate CGMs with whole genome prediction (WGP) to combine the benefits of both models. Here, we used a CGM-WGP model to predict the performance of new wheat (Triticum aestivum) genotypes. The CGM was designed to predict phenology, nitrogen, and biomass traits. The CGM-WGP model simulated more heritable GSPs compared with the CGM and gave smaller errors for the observed phenotypes. The WGP model performed better when predicting yield, grain number, and grain protein content, but showed comparable performance to the CGM-WGP model for heading and physiological maturity dates. However, the CGM-WGP model was able to predict unobserved traits (for which there were no phenotypic records in the reference population). The CGM-WGP model also showed superior performance when predicting unrelated individuals that clustered separately from the reference population. Our results demonstrate new advantages for CGM-WGP modelling and suggest future efforts should focus on calibrating CGM-WGP models using high-throughput phenotypic measures that are cheaper and less laborious to collect.
作物生长模型（CGM）可以通过采样基因型特异性参数来预测未测试环境中品种的表现。由于它们不能预测新品种的表现，因此有人提议将 CGM 与全基因组预测（WGP）相结合，以结合两种模型的优势。在这里，我们使用 CGM-WGP 模型来预测新小麦（Triticum aestivum）基因型的表现。CGM 旨在预测物候期、氮和生物量性状。与 CGM 相比，CGM-WGP 模型模拟了更多具有遗传力的 GSPs，并减少了对观测表型的误差。WGP 模型在预测产量、穗数和籽粒蛋白质含量方面表现更好，但在预测抽穗期和生理成熟日期方面与 CGM-WGP 模型表现相当。然而，CGM-WGP 模型能够预测未观测到的性状（参考群体中没有表型记录）。当预测与参考群体分离的无关联个体时，CGM-WGP 模型也表现出优越的性能。我们的结果表明 CGM-WGP 建模具有新的优势，并建议未来的努力应集中在使用更便宜、更省力的高通量表型测量来校准 CGM-WGP 模型。
