Elucidating genotype-by-environment interactions and partitioning its contribution to phenotypic variation remains a challenge for plant scientists. We propose a framework that utilizes genome-wide markers to model genotype-specific shoot growth trajectories as a function of time and soil water availability. A rice diversity panel was phenotyped daily for 21 d using an automated, high-throughput image-based, phenotyping platform that enabled estimation of daily shoot biomass and soil water content. Using these data, we modeled shoot growth as a function of time and soil water content, and were able to determine the time point where an inflection in the growth trajectory occurred. We found that larger, more vigorous plants exhibited an earlier repression in growth compared with smaller, slow-growing plants, indicating a trade-off between early vigor and tolerance to prolonged water deficits. Genomic inference for model parameters and time of inflection (TOI) identified several candidate genes. This study is the first to utilize a genome-enabled growth model to study drought responses in rice, and presents a new approach to jointly model dynamic morpho-physiological responses and environmental covariates.

中文翻译：

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利用基因组驱动的生长模型研究水稻对水缺乏的芽生长响应。

阐明基因型与环境之间的相互作用并划分其对表型变异的贡献仍然是植物科学家面临的挑战。我们提出了一个框架，该框架利用全基因组标记来建模基因型特异性芽生长轨迹，作为时间和土壤水分利用的函数。使用基于高通量图像的自动化表型分型平台，每天对水稻多样性面板进行21天的表型分析，该平台能够估算每日的芽生物量和土壤含水量。使用这些数据，我们将枝条生长建模为时间和土壤水分的函数，并能够确定生长轨迹发生拐点的时间点。我们发现，与较小的，生长缓慢的植物相比，较大，有活力的植物显示出较早的生长抑制，表明在早期活力和对长期缺水的耐受性之间进行权衡。模型参数和拐点时间（TOI）的基因组推断确定了几个候选基因。这项研究是第一个利用基因组激活的生长模型来研究水稻的干旱响应的方法，并提出了一种新的方法来共同模拟动态形态生理响应和环境协变量。