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Utilizing random regression models for genomic prediction of a longitudinal trait derived from high‐throughput phenotyping

机译：利用随机回归模型对高通量表型衍生的纵向性状进行基因组预测

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The accessibility of high‐throughput phenotyping platforms in both the greenhouse and field, as well as the relatively low cost of unmanned aerial vehicles, has provided researchers with an effective means to characterize large populations throughout the growing season. These longitudinal phenotypes can provide important insight into plant development and responses to the environment. Despite the growing use of these new phenotyping approaches in plant breeding, the use of genomic prediction models for longitudinal phenotypes is limited in major crop species. The objective of this study was to demonstrate the utility of random regression (RR) models using Legendre polynomials for genomic prediction of shoot growth trajectories in rice ( Oryza sativa ). An estimate of shoot biomass, projected shoot area (PSA), was recorded over a period of 20?days for a panel of 357 diverse rice accessions using an image‐based greenhouse phenotyping platform. A RR that included a fixed second‐order Legendre polynomial, a random second‐order Legendre polynomial for the additive genetic effect, a first‐order Legendre polynomial for the environmental effect, and heterogeneous residual variances was used to model PSA trajectories. The utility of the RR model over a single time point (TP) approach, where PSA is fit at each time point independently, is shown through four prediction scenarios. In the first scenario, the RR and TP approaches were used to predict PSA for a set of lines lacking phenotypic data. The RR approach showed a 11.6% increase in prediction accuracy over the TP approach. Much of this improvement could be attributed to the greater additive genetic variance captured by the RR approach. The remaining scenarios focused forecasting future phenotypes using a subset of early time points for known lines with phenotypic data, as well new lines lacking phenotypic data. In all cases, PSA could be predicted with high accuracy ( r : 0.79 to 0.89 and 0.55 to 0.58 for known and unknown lines, respectively). This study provides the first application of RR models for genomic prediction of a longitudinal trait in rice and demonstrates that RR models can be effectively used to improve the accuracy of genomic prediction for complex traits compared to a TP approach.
机译：温室和田间高通量表型平台的可及性以及无人驾驶航空器的相对低廉的价格，为研究人员提供了表征整个生长季节大量人口的有效手段。这些纵向表型可以提供对植物发育和对环境的反应的重要见解。尽管在植物育种中越来越多地使用这些新的表型方法，但在主要农作物中，纵向表型的基因组预测模型的使用受到限制。这项研究的目的是证明使用勒让德多项式的随机回归（RR）模型对水稻（Oryza sativa）的芽生长轨迹进行基因组预测的效用。使用基于图像的温室表型平台，在20天的时间内记录了一组357种不同水稻品种的新芽生物量，即预计出芽面积（PSA）。 RR包括固定的二阶Legendre多项式，用于累加遗传效应的随机二阶Legendre多项式，用于环境效应的一阶Legendre多项式以及异构残差用于PSA轨迹建模。通过四个预测方案显示了在单个时间点（TP）方法上的RR模型的效用，其中在每个时间点独立地拟合PSA。在第一种情况下，使用RR和TP方法来预测一组缺乏表型数据的品系的PSA。与TP方法相比，RR方法的预测准确性提高了11.6％。这种改进的大部分归因于RR方法捕获的更大的附加遗传方差。其余方案着重于使用已知表型数据的品系以及缺乏表型数据的新品系的早期时间点子集来预测未来的表型。在所有情况下，都可以以较高的准确度预测PSA（已知线和未知线的r分别为0.79至0.89和0.55至0.58）。这项研究提供了RR模型在水稻纵向性状的基因组预测中的首次应用，并证明与TP方法相比，RR模型可以有效地用于提高复杂性状的基因组预测的准确性。

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