Filho Juraci Souza Sampaio, Oliveira Isadora Cristina Martins, Pastina Maria Marta, Campos Marcos de Souza, de Oliveira Eder Jorge
Federal University of Recôncavo da Bahia, Centro de Ciências Agrárias, Ambientais e Biológicas, Cruz das Almas, Bahia, Brazil.
Embrapa Milho e Sorgo, Sete Lagoas, MG, Brazil.
PLoS One. 2024 Dec 9;19(12):e0315370. doi: 10.1371/journal.pone.0315370. eCollection 2024.
The variability in genetic variance and covariance due to genotype × environment interaction (G×E) can hinder genotype selection accuracy, especially for complex traits. This study analyzed G×E interactions in cassava to identify stable, high-performing genotypes and predict agronomic performance in untested environments using factor analytic multiplicative mixed models (FAMM) within multi-environment trials (METs). We evaluated 22 cassava genotypes for fresh root yield (FRY), dry root yield (DRY), shoot yield (ShY), and dry matter content (DMC) across 55 Brazilian environments. FAMM was applied to estimate genetic values and environmental loads, revealing significant genetic variance, especially for FRY (0.16-0.92) and broad-sense heritability ([Formula: see text]) above 0.70 in advanced yield trials. In joint analyses, analytic factor FA4 explained over 88% of genetic variation for all traits despite high G×E and data imbalance. Positive genetic correlations were found between environments for ShY and DRY (0.99 and 1.0, respectively), while FRY and DMC showed negative correlations (-0.82 and -0.95). Latent regression analysis identified hybrids adaptable to a range of environments, as well as genotypes suited to specific conditions. Moderate correlations between environmental covariables (rainfall, altitude, solar radiation) and FA model loadings suggest these factors contribute to high G×E interactions, notably for FRY. The FAMM model provided a robust approach to G×E analysis in cassava, yielding practical insights for breeding programs.
由于基因型×环境互作（G×E）导致的遗传方差和协方差的变异性会阻碍基因型选择的准确性，尤其是对于复杂性状而言。本研究在多环境试验（METs）中使用因子分析乘法混合模型（FAMM）分析了木薯中的G×E互作，以鉴定稳定、高性能的基因型，并预测未测试环境中的农艺性能。我们在巴西的55个环境中评估了22个木薯基因型的鲜根产量（FRY）、干根产量（DRY）、地上部产量（ShY）和干物质含量（DMC）。应用FAMM来估计遗传值和环境负荷，结果显示存在显著的遗传方差，尤其是在高级产量试验中，FRY的遗传方差为0.16 - 0.92，广义遗传力（[公式：见原文]）高于0.70。在联合分析中，尽管存在较高的G×E和数据不平衡，但分析因子FA4解释了所有性状超过88%的遗传变异。ShY和DRY在不同环境间的遗传相关性为正（分别为0.99和1.0），而FRY和DMC呈负相关（-0.82和-0.95）。潜在回归分析确定了适应一系列环境的杂交种以及适合特定条件的基因型。环境协变量（降雨量、海拔、太阳辐射）与FA模型负荷之间的中等相关性表明这些因素导致了较高的G×E互作，尤其是对于FRY。FAMM模型为木薯的G×E分析提供了一种稳健的方法，为育种计划提供了实用的见解。
