It has been argued that metabolic content can be used as a selection marker to accelerate crop improvement because metabolic profiles in crops are often under genetic control. Evaluating the role of genetics in metabolic variation is a long‐standing challenge. Rice, one of the world's most important staple crops, is known to be sensitive to recent increases in nighttime temperatures. Quantification of metabolic levels can help measure rice responses to high night temperature (HNT) stress. However, the extent of metabolic variation that can be explained by regression on whole‐genome molecular markers remains to be evaluated. In the current study, we generated metabolic profiles for mature grains from a subset of rice diversity panel accessions grown under optimal and HNT conditions. Metabolite accumulation was low to moderately heritable, and genomic prediction accuracies of metabolite accumulation were within the expected upper limit set by their genomic heritability estimates. Genomic heritability estimates were slightly higher in the control group than in the HNT group. Genomic correlation estimates for the same metabolite accumulation between the control and HNT conditions indicated the presence of genotype‐by‐environment interactions. Reproducing kernel Hilbert spaces regression and image‐based deep learning improved prediction accuracy, suggesting that some metabolite levels are under non‐additive genetic control. Joint analysis of multiple metabolite accumulation simultaneously was effective in improving prediction accuracy by exploiting correlations among metabolites. The current study serves as an important first step in evaluating the cumulative effect of markers in influencing metabolic variation under control and HNT conditions.

中文翻译：

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响应温暖夜间条件的稻谷代谢含量的基因组预测


有人认为，代谢含量可以用作加速作物改良的选择标志，因为作物的代谢特征通常受到遗传控制。评估遗传学在代谢变异中的作用是一项长期的挑战。水稻是世界上最重要的主食作物之一，众所周知，它对最近夜间气温的升高很敏感。代谢水平的量化有助于测量水稻对夜间高温 （HNT） 胁迫的反应。然而，可以通过全基因组分子标记物的回归来解释的代谢变异程度仍有待评估。在目前的研究中，我们从在最佳和 HNT 条件下生长的水稻多样性面板种质子集中生成了成熟谷物的代谢谱。代谢物积累具有低到中等遗传性，代谢物积累的基因组预测精度在其基因组遗传力估计值设定的预期上限内。对照组的基因组遗传力估计值略高于 HNT 组。对照和 HNT 条件之间相同代谢物积累的基因组相关性估计表明存在基因型-环境相互作用。再现核希尔伯特空间回归和基于图像的深度学习提高了预测准确性，表明一些代谢物水平处于非加性遗传控制之下。通过利用代谢物之间的相关性，同时联合分析多种代谢物积累可有效提高预测准确性。目前的研究是评估标志物在控制和 HNT 条件下影响代谢变化的累积效应的重要第一步。