Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping
Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping
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DOI：
10.1002/pld3.80
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发表时间：
2018-09-01
期刊：
影响因子：
3
通讯作者：
Morota, Gota
中科院分区：
文献类型：
--
作者：
Campbell, Malachy;Walia, Harkamal;Morota, Gota
温室和田间高通量表型平台的可及性，以及无人机的相对较低成本，为研究人员在整个生长季节描述大量种群提供了有效手段。这些纵向表型可以为植物发育和对环境的反应提供重要的见解。尽管在植物育种中越来越多地使用这些新的表型方法，但在主要作物物种中，纵向表型的基因组预测模型的使用受到限制。本研究的目的是证明随机回归（RR）模型在水稻（Oryza sativa）茎部生长轨迹基因组预测中的实用性。利用基于图像的温室表型平台，在20天内记录了357个不同水稻品种的茎生物量，即投影茎面积（PSA）。使用一个包含固定二阶Legendre多项式、用于加性遗传效应的随机二阶Legendre多项式、用于环境效应的一阶Legendre多项式和异质残差方差的RR来模拟PSA轨迹。RR模型在单时间点（TP）方法上的效用，其中PSA在每个时间点独立拟合，通过四个预测场景显示。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。RR方法的预测精度比TP方法提高了11.6%。这种改进在很大程度上可归因于RR方法捕获的更大的加性遗传变异。其余的方案侧重于预测未来的表型，使用一个子集的早期时间点的已知系的表型数据，以及新的系缺乏表型数据。在所有情况下，PSA的预测准确率都很高（已知和未知品系的r分别为0.79 ~ 0.89和0.55 ~ 0.58）。本研究首次将RR模型应用于水稻纵向性状的基因组预测，表明与TP方法相比，RR模型可以有效地提高复杂性状的基因组预测精度。
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.