Dias Kaio O G, Dos Santos Jhonathan P R, Krause Matheus D, Piepho Hans-Peter, Guimarães Lauro J M, Pastina Maria M, Garcia Antonio A F
Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, SP, Brazil.
Department of General Biology, Federal University of Viçosa, Viçosa, Brazil.
Theor Appl Genet. 2022 Apr;135(4):1385-1399. doi: 10.1007/s00122-022-04041-y. Epub 2022 Feb 22.
We propose using probability concepts from Bayesian models to leverage a more informed decision-making process toward cultivar recommendation in multi-environment trials. Statistical models that capture the phenotypic plasticity of a genotype across environments are crucial in plant breeding programs to potentially identify parents, generate offspring, and obtain highly productive genotypes for target environments. In this study, our aim is to leverage concepts of Bayesian models and probability methods of stability analysis to untangle genotype-by-environment interaction (GEI). The proposed method employs the posterior distribution obtained with the No-U-Turn sampler algorithm to get Hamiltonian Monte Carlo estimates of adaptation and stability probabilities. We applied the proposed models in two empirical tropical datasets. Our findings provide a basis to enhance our ability to consider the uncertainty of cultivar recommendation for global or specific adaptation. We further demonstrate that probability methods of stability analysis in a Bayesian framework are a powerful tool for unraveling GEI given a defined intensity of selection that results in a more informed decision-making process toward cultivar recommendation in multi-environment trials.
我们建议使用贝叶斯模型中的概率概念，以便在多环境试验中利用更明智的决策过程进行品种推荐。能够捕捉基因型在不同环境下表型可塑性的统计模型，对于植物育种计划至关重要，有助于潜在地识别亲本、培育后代，并获得适合目标环境的高产基因型。在本研究中，我们的目标是利用贝叶斯模型的概念和稳定性分析的概率方法来解析基因型与环境互作（GEI）。所提出的方法采用通过无回转采样器算法获得的后验分布，以得到适应和稳定性概率的哈密顿蒙特卡罗估计值。我们将所提出的模型应用于两个热带实证数据集。我们的研究结果为增强我们考虑全球或特定适应性品种推荐不确定性的能力提供了基础。我们进一步证明，在给定选择强度的情况下，贝叶斯框架下的稳定性分析概率方法是解析GEI的有力工具，这会在多环境试验中产生更明智的品种推荐决策过程。
