Costa-Neto Germano, Crossa Jose, Fritsche-Neto Roberto
Department of Genetics, "Luiz de Queiroz" Agriculture College, University of São Paulo (ESALQ/USP), Piracicaba, Brazil.
Institute for Genomic Diversity, Cornell University, Ithaca, NY, United States.
Front Plant Sci. 2021 Oct 7;12:717552. doi: 10.3389/fpls.2021.717552. eCollection 2021.
Quantitative genetics states that phenotypic variation is a consequence of the interaction between genetic and environmental factors. Predictive breeding is based on this statement, and because of this, ways of modeling genetic effects are still evolving. At the same time, the same refinement must be used for processing environmental information. Here, we present an "enviromic assembly approach," which includes using ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as enviromic-aided genomic prediction, E-GP). We propose that the quality of an environment is defined by the core of environmental typologies and their frequencies, which describe different zones of plant adaptation. From this, we derived markers of environmental similarity cost-effectively. Combined with the traditional additive and non-additive effects, this approach may better represent the putative phenotypic variation observed across diverse growing conditions (i.e., phenotypic plasticity). Then, we designed optimized multi-environment trials coupling genetic algorithms, enviromic assembly, and genomic kinships capable of providing realization of the genotype-environment combinations that must be phenotyped in the field. As proof of concept, we highlighted two E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity exerting optimized phenotyping efforts. Our approach was tested using two tropical maize sets, two types of enviromics assembly, six experimental network sizes, and two types of optimized training set across environments. We observed that E-GP outperforms benchmark GP in all scenarios, especially when considering smaller training sets. The representativeness of genotype-environment combinations is more critical than the size of multi-environment trials (METs). The conventional genomic best-unbiased prediction (GBLUP) is inefficient in predicting the quality of a yet-to-be-seen environment, while enviromic assembly enabled it by increasing the accuracy of yield plasticity predictions. Furthermore, we discussed theoretical backgrounds underlying how intrinsic envirotype-phenotype covariances within the phenotypic records can impact the accuracy of GP. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios.
数量遗传学表明，表型变异是遗传因素与环境因素相互作用的结果。预测性育种基于这一观点，因此，遗传效应的建模方法仍在不断发展。与此同时，处理环境信息也必须采用同样的优化方法。在此，我们提出一种“环境组学组装方法”，该方法包括利用生态生理学知识，将环境相关性转化为用于植物育种的全基因组预测（GP）（称为环境组学辅助基因组预测，E-GP）。我们认为，环境的质量由环境类型学的核心及其频率来定义，它们描述了植物适应的不同区域。据此，我们经济高效地得出了环境相似性标记。结合传统的加性和非加性效应，这种方法可能能更好地体现不同生长条件下观察到的推定表型变异（即表型可塑性）。然后，我们设计了优化的多环境试验，结合遗传算法、环境组学组装和基因组亲缘关系，能够实现必须在田间进行表型鉴定的基因型-环境组合。作为概念验证，我们重点介绍了两种E-GP应用：（1）在跨不同环境训练准确的GP模型时，解决表型信息不足的问题；（2）指导早期筛选产量可塑性，以进行优化的表型鉴定工作。我们的方法使用了两个热带玉米数据集、两种环境组学组装类型、六种实验网络规模以及两种跨环境的优化训练集进行了测试。我们观察到，在所有情况下，E-GP均优于基准GP，尤其是在考虑较小训练集时。基因型-环境组合的代表性比多环境试验（METs）的规模更为关键。传统的基因组最佳无偏预测（GBLUP）在预测未知环境的质量方面效率低下，而环境组学组装通过提高产量可塑性预测的准确性实现了这一点。此外，我们还讨论了表型记录中内在的环境类型-表型协方差如何影响GP准确性的理论背景。E-GP是一种有效的方法，可以更好地利用环境数据库，提供气候智能型解决方案，降低田间成本，并预测未来情况。
