Freitas Moreira Fabiana, Rojas de Oliveira Hinayah, Lopez Miguel Angel, Abughali Bilal Jamal, Gomes Guilherme, Cherkauer Keith Aric, Brito Luiz Fernando, Rainey Katy Martin
Department of Agronomy, Purdue University, West Lafayette, IN, United States.
Department of Animal Sciences, Purdue University, West Lafayette, IN, United States.
Front Plant Sci. 2021 Sep 3;12:715983. doi: 10.3389/fpls.2021.715983. eCollection 2021.
Understanding temporal accumulation of soybean above-ground biomass (AGB) has the potential to contribute to yield gains and the development of stress-resilient cultivars. Our main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time and to reveal its temporal quantitative genomic properties. A subset of the SoyNAM population ( = 383) was grown in multi-environment trials and destructive AGB measurements were collected along with multispectral and RGB imaging from 27 to 83 days after planting (DAP). We used machine-learning methods for phenotypic prediction of AGB, genomic prediction of breeding values, and genome-wide association studies (GWAS) based on random regression models (RRM). RRM enable the study of changes in genetic variability over time and further allow selection of individuals when aiming to alter the general response shapes over time. AGB phenotypic predictions were high ( = 0.92-0.94). Narrow-sense heritabilities estimated over time ranged from low to moderate (from 0.02 at 44 DAP to 0.28 at 33 DAP). AGB from adjacent DAP had highest genetic correlations compared to those DAP further apart. We observed high accuracies and low biases of prediction indicating that genomic breeding values for AGB can be predicted over specific time intervals. Genomic regions associated with AGB varied with time, and no genetic markers were significant in all time points evaluated. Thus, RRM seem a powerful tool for modeling the temporal genetic architecture of soybean AGB and can provide useful information for crop improvement. This study provides a basis for future studies to combine phenotyping and genomic analyses to understand the genetic architecture of complex longitudinal traits in plants.
了解大豆地上生物量（AGB）的时间积累情况，有可能有助于提高产量，并培育出抗逆性强的品种。我们的主要目标是开发一种高通量表型分析方法，以预测大豆随时间变化的AGB，并揭示其时间定量基因组特性。在多环境试验中种植了SoyNAM群体的一个子集（n = 383），并在种植后27至83天（DAP）收集了破坏性AGB测量数据以及多光谱和RGB图像。我们使用机器学习方法对AGB进行表型预测、育种值的基因组预测以及基于随机回归模型（RRM）的全基因组关联研究（GWAS）。RRM能够研究遗传变异性随时间的变化，并且在旨在改变随时间变化的一般反应形状时，还能进一步选择个体。AGB表型预测结果较高（R² = 0.92 - 0.94）。随时间估计的狭义遗传力范围从低到中等（从44 DAP时的0.02到33 DAP时的0.28）。与间隔较远的DAP相比，相邻DAP的AGB具有最高的遗传相关性。我们观察到预测的准确性高且偏差低，这表明可以在特定时间间隔内预测AGB的基因组育种值。与AGB相关的基因组区域随时间变化，在所有评估的时间点都没有显著的遗传标记。因此，RRM似乎是一种强大的工具，可用于构建大豆AGB的时间遗传结构模型，并可为作物改良提供有用信息。本研究为未来结合表型分析和基因组分析以了解植物复杂纵向性状的遗传结构的研究提供了基础。
