Prediction of genotype performance for untested years based on additive main effects and multiplicative interaction and linear mixed models: An illustration using dolichos bean (Lablab purpureus (L.) Sweet) multiyear data,Annals of Applied Biology

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Prediction of genotype performance for untested years based on additive main effects and multiplicative interaction and linear mixed models: An illustration using dolichos bean (Lablab purpureus (L.) Sweet) multiyear data
Annals of Applied Biology ( IF 2.2 ) Pub Date : 2021-08-24 , DOI: 10.1111/aab.12726
Vinayak Spoorthi ₁ , Sampangi Ramesh ₁ , Nagenahalli Chandrappa Sunitha ₁ , Panichayil Vijayakumar Vaijayanthi ₂

Affiliation

Carrying out multi-environment trials (MET) is a regular and mandatory procedure for identifying and recommending superior genotypes as cultivars of crops with no exception of dolichos bean. The accuracy of a crop MET can be increased using more efficient statistical tools such as Additive Main effects and Multiplicative Interaction (AMMI) and mixed linear models via best linear unbiased prediction (BLUP) procedure. AMMI is not a single model, but rather, a family of models. Considering genotypes, environments or both as random variables, three types of BLUPs, namely BLUPg, BLUPe and BLUPge, respectively are possible. Diagnosis and use of the best AMMI model family member and type of BLUP is the key to identify the best genotype(s) for use as cultivars with a hypothesis that they will perform well in farmers' fields in future years. We diagnosed the best AMMI model family member and type of BLUP based on between-year predictive accuracy using a 5-year dataset in dolichos bean. Replication-wise mean fresh pod yield of different combinations of 4-years' was used as prediction datasets to build AMMI and BLUP models. The observed mean fresh pod yield of genotypes evaluated in the year, which is not used in modelling, was used as a validation dataset. Predictive accuracy was measured as root mean squared differences between AMMI and BLUP model-predicted and observed mean fresh pod yield of genotypes. Our results showed that parsimonious AMMI1 model was far better than any type of BLUP in predicting the genotype performance for untested years.

中文翻译：

基于加性主效应和乘性交互作用和线性混合模型的未经测试年份的基因型性能预测：使用 dolichos bean (Lablab purpureus (L.) Sweet) 多年数据的说明

进行多环境试验 (MET) 是识别和推荐优良基因型作为作物品种的常规和强制性程序，长豆也不例外。可以使用更有效的统计工具（例如加性主效应和乘性相互作用 (AMMI) 以及通过最佳线性无偏预测 (BLUP) 程序的混合线性模型）来提高作物 MET 的准确性。AMMI 不是一个单一的模型，而是一个模型家族。考虑到基因型、环境或两者作为随机变量，三种类型的 BLUP 分别是可能的，即 BLUPg、BLUPe 和 BLUPge。诊断和使用最佳 AMMI 模型家族成员和 BLUP 类型是确定用作栽培品种的最佳基因型的关键，假设它们将在未来几年在农民的田地中表现良好。我们使用 dolichos bean 中的 5 年数据集，根据年际预测准确性诊断出最佳 AMMI 模型家族成员和 BLUP 类型。将 4 年不同组合的复制平均新鲜豆荚产量用作预测数据集，以构建 AMMI 和 BLUP 模型。观察到的当年评估的基因型的平均新鲜豆荚产量（未用于建模）用作验证数据集。预测准确性测量为 AMMI 和 BLUP 模型预测和观察到的基因型的平均新鲜豆荚产量之间的均方根差异。我们的结果表明，简约的 AMMI1 模型在预测未经测试的年份的基因型表现方面远优于任何类型的 BLUP。将 4 年不同组合的复制平均新鲜豆荚产量用作预测数据集，以构建 AMMI 和 BLUP 模型。观察到的当年评估的基因型的平均新鲜豆荚产量（未用于建模）用作验证数据集。预测准确性测量为 AMMI 和 BLUP 模型预测和观察到的基因型的平均新鲜豆荚产量之间的均方根差异。我们的结果表明，简约的 AMMI1 模型在预测未经测试的年份的基因型表现方面远优于任何类型的 BLUP。将 4 年不同组合的复制平均新鲜豆荚产量用作预测数据集，以构建 AMMI 和 BLUP 模型。观察到的当年评估的基因型的平均新鲜豆荚产量（未用于建模）用作验证数据集。预测准确性测量为 AMMI 和 BLUP 模型预测和观察到的基因型的平均新鲜豆荚产量之间的均方根差异。我们的结果表明，简约的 AMMI1 模型在预测未经测试的年份的基因型表现方面远优于任何类型的 BLUP。预测准确性测量为 AMMI 和 BLUP 模型预测和观察到的基因型的平均新鲜豆荚产量之间的均方根差异。我们的结果表明，简约的 AMMI1 模型在预测未经测试的年份的基因型表现方面远优于任何类型的 BLUP。预测准确性测量为 AMMI 和 BLUP 模型预测和观察到的基因型的平均新鲜豆荚产量之间的均方根差异。我们的结果表明，简约的 AMMI1 模型在预测未经测试的年份的基因型表现方面远优于任何类型的 BLUP。

更新日期：2021-08-24

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