Models for optimizing selection based on adaptability and stability of cotton genotypes

ABSTRACT: In multi-environment trials (MET), large networks are assessed for results improvement. However, genotype by environment interaction plays an important role in the selection of the most adaptable and stable genotypes in MET framework. In this study, we tested different residual variances and measure the selection gain of cotton genotypes accounting for adaptability and stability, simultaneously. Twelve genotypes of cotton were bred in 10 environments, and fiber length (FL), fiber strength (FS), micronaire (MIC), and fiber yield (FY) were determined. Model selection for different residual variance structures (homogeneous and heterogeneous) was tested using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The variance components were estimated through restricted maximum likelihood and genotypic values were predicted through best linear unbiased prediction. The harmonic mean of relative performance of genetic values (HMRPGV) were applied for simultaneous selection for adaptability, stability, and yield. According to BIC heterogeneous residual variance was the best model fit for FY, whereas homogeneous residual variance was the best model fit for FL, FS, and MIC traits. The selective accuracy was high, indicating reliability of the prediction. The HMRPGV was capable to select for stability, adaptability and yield simultaneously, with remarkable selection gain for each trait.

摘要：在多环境试验（MET）中，大型网络被评估以提升结果。然而，基因型与环境交互作用在多环境试验框架中筛选最适应和稳定的基因型中起着至关重要的作用。在本研究中，我们测试了不同的残差方差，并测量了考虑到适应性和稳定性的棉花基因型的选择增益。在十个环境中培育了十二种棉花基因型，并测定了纤维长度（FL）、纤维强度（FS）、米诺雷值（MIC）和纤维产量（FY）。通过使用赤池信息量准则（AIC）和贝叶斯信息量准则（BIC）对不同的残差方差结构（同质和非同质）进行模型选择测试。通过限制最大似然估计来估计方差成分，并通过最佳线性无偏预测来预测基因型值。应用遗传值相对性能调和平均值（HMRPGV）以实现适应性和稳定性的同时选择，并对产量表现出显著的增益选择。根据贝叶斯信息量准则，非同质残差方差是纤维产量（FY）的最佳模型拟合，而同质残差方差是纤维长度（FL）、纤维强度（FS）和米诺雷值（MIC）性状的最佳模型拟合。选择准确性高，表明预测的可靠性。HMRPGV能够在稳定性和适应性以及产量方面同时进行选择，并对每个性状均实现了显著的增益选择。