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（multi-environment trials, MET）中，通常通过大规模网络试验开展品种性状改良评价。然而，基因型×环境互作在多环境试验框架下筛选适应性与稳定性最优的基因型过程中发挥着关键作用。本研究针对不同残差方差结构展开测试，并同时考量适应性与稳定性，评估棉花基因型的选择增益。本试验在10个环境中培育了12个棉花基因型，并测定了纤维长度（FL）、纤维强度（FS）、马克隆值（MIC）及纤维产量（FY）四项性状。采用赤池信息准则（Akaike Information Criterion, AIC）与贝叶斯信息准则（Bayesian Information Criterion, BIC）对不同残差方差结构（同质性与异质性）的模型进行选择。通过约束最大似然法估计方差组分，并利用最佳线性无偏预测预测基因型值。采用遗传值相对性能谐波均值（harmonic mean of relative performance of genetic values, HMRPGV）实现适应性、稳定性与产量的同步选择。结果表明，基于BIC准则，异质性残差方差模型为纤维产量性状的最优拟合模型，而同质性残差方差模型则更适配纤维长度、纤维强度及马克隆值性状。本研究获得的选择准确性较高，说明预测结果具有可靠性。遗传值相对性能谐波均值方法可实现稳定性、适应性与产量的同步选择，且各性状均获得了显著的选择增益。