Carvalho Humberto Fanelli, Rio Simon, García-Abadillo Julian, Isidro Y Sánchez Julio
Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA)-Universidad Politécnica de Madrid (UPM)-Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223, Pozuelo de Alarcón, Madrid, Spain.
CIRAD, UMR AGAP Institut, 34398, Montpellier, France.
Plant Methods. 2024 Jun 6;20(1):85. doi: 10.1186/s13007-024-01207-1.
The selection of highly productive genotypes with stable performance across environments is a major challenge of plant breeding programs due to genotype-by-environment (GE) interactions. Over the years, different metrics have been proposed that aim at characterizing the superiority and/or stability of genotype performance across environments. However, these metrics are traditionally estimated using phenotypic values only and are not well suited to an unbalanced design in which genotypes are not observed in all environments. The objective of this research was to propose and evaluate new estimators of the following GE metrics: Ecovalence, Environmental Variance, Finlay-Wilkinson regression coefficient, and Lin-Binns superiority measure. Drawing from a multi-environment genomic prediction model, we derived the best linear unbiased prediction for each GE metric. These derivations included both a squared expectation and a variance term. To assess the effectiveness of our new estimators, we conducted simulations that varied in traits and environment parameters. In our results, new estimators consistently outperformed traditional phenotype-based estimators in terms of accuracy. By incorporating a variance term into our new estimators, in addition to the squared expectation term, we were able to improve the precision of our estimates, particularly for Ecovalence in situations where heritability was low and/or sparseness was high. All methods are implemented in a new R-package: GEmetrics. These genomic-based estimators enable estimating GE metrics in unbalanced designs and predicting GE metrics for new genotypes, which should help improve the selection efficiency of high-performance and stable genotypes across environments.
由于基因型与环境（GE）互作的存在，在不同环境中选择具有稳定高产表现的基因型是植物育种计划面临的一项重大挑战。多年来，人们提出了不同的指标，旨在表征基因型在不同环境中表现的优越性和/或稳定性。然而，这些指标传统上仅使用表型值进行估计，并不适用于不平衡设计，即并非在所有环境中都能观察到所有基因型。本研究的目的是提出并评估以下GE指标的新估计方法：协方差、环境方差、芬利 - 威尔金森回归系数和林 - 宾斯优势度量。基于多环境基因组预测模型，我们推导出了每个GE指标的最佳线性无偏预测值。这些推导包括平方期望和方差项。为了评估新估计方法的有效性，我们进行了性状和环境参数不同的模拟。在我们的结果中，新估计方法在准确性方面始终优于传统的基于表型的估计方法。通过在新估计方法中纳入方差项，除了平方期望项之外，我们能够提高估计的精度，特别是在遗传力低和/或稀疏度高的情况下对协方差的估计。所有方法都在一个新的R包GEmetrics中实现。这些基于基因组的估计方法能够在不平衡设计中估计GE指标，并预测新基因型的GE指标，这将有助于提高跨环境选择高性能和稳定基因型的效率。
