Universidade Federal de Viçosa, Viçosa, MG, Brazil.
INCT Café/Universidade Federal de Viçosa, Viçosa, MG, Brazil.
PLoS One. 2020 Dec 23;15(12):e0244021. doi: 10.1371/journal.pone.0244021. eCollection 2020.
Random regression models (RRM) are a powerful tool to evaluate genotypic plasticity over time. However, to date, RRM remains unexplored for the analysis of repeated measures in Jatropha curcas breeding. Thus, the present work aimed to apply the random regression technique and study its possibilities for the analysis of repeated measures in Jatropha curcas breeding. To this end, the grain yield (GY) trait of 730 individuals of 73 half-sib families was evaluated over six years. Variance components were estimated by restricted maximum likelihood, genetic values were predicted by best linear unbiased prediction and RRM were fitted through Legendre polynomials. The best RRM was selected by Bayesian information criterion. According to the likelihood ratio test, there was genetic variability among the Jatropha curcas progenies; also, the plot and permanent environmental effects were statistically significant. The variance components and heritability estimates increased over time. Non-uniform trajectories were estimated for each progeny throughout the measures, and the area under the trajectories distinguished the progenies with higher performance. High accuracies were found for GY in all harvests, which indicates the high reliability of the results. Moderate to strong genetic correlation was observed across pairs of harvests. The genetic trajectories indicated the existence of genotype × measurement interaction, once the trajectories crossed, which implies a different ranking in each year. Our results suggest that RRM can be efficiently applied for genetic selection in Jatropha curcas breeding programs.
随机回归模型（RRM）是评估时间上基因型可塑性的有力工具。然而，迄今为止，RRM 在麻疯树育种的重复测量分析中仍未得到探索。因此，本研究旨在应用随机回归技术，并研究其在麻疯树育种重复测量分析中的可能性。为此，对 73 个半同胞家系的 730 个个体的籽粒产量（GY）性状进行了六年的评估。通过限制最大似然法估计方差分量，通过最佳线性无偏预测法预测遗传值，并通过勒让德多项式拟合 RRM。通过贝叶斯信息准则选择最佳 RRM。根据似然比检验，麻疯树后代存在遗传变异；此外，地块和永久环境效应具有统计学意义。方差分量和遗传力估计值随时间增加。为每个后代在整个测量过程中估计了非均匀轨迹，轨迹下的面积区分了表现更好的后代。在所有收获中，GY 的准确性都很高，这表明结果的可靠性很高。在各对收获之间观察到中等至强的遗传相关性。遗传轨迹表明存在基因型×测量相互作用，一旦轨迹交叉，就意味着每年的排名不同。我们的研究结果表明，RRM 可以有效地应用于麻疯树育种计划的遗传选择。
