Federal University of Viçosa-Department of Plant Science, University Campus, Viçosa, Minas Gerais, Brazil.
Federal University of Viçosa-Department of General Biology, University Campus, Viçosa, Minas Gerais, Brazil.
PLoS One. 2019 Apr 18;14(4):e0215315. doi: 10.1371/journal.pone.0215315. eCollection 2019.
At present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; [Formula: see text]) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of [Formula: see text]. Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny.
目前，单性状最佳线性无偏预测（BLUP）是大豆遗传选择的标准方法。然而，当基于两个或更多遗传相关性状进行遗传选择，并且这些性状分别进行分析时，可能会出现选择偏差。在这些条件下，考虑评估性状之间的相关结构可能会为评估参数提供更准确的遗传估计，即使在环境影响下也是如此。因此，本研究旨在通过剩余最大似然（REML/BLUP）和贝叶斯方法检验多性状多环境（MTME）模型在分离大豆后代遗传选择中的效率和适用性。该研究涉及在两个环境下评估的 203 个大豆 F2:4 后代的数据，用于评估以下性状：成熟所需天数（DM）、百粒重（SW）和每个小区的平均种子产量（SY）。通过 REML/BLUP 和贝叶斯方法估计方差分量和遗传和非遗传参数。通过贝叶斯程序估计的方差分量以及通过选择预测的育种值和遗传增益与通过 REML/BLUP 获得的结果相似。频率论和贝叶斯 MTME 模型为每个小区的广义遗传力（或后代总效应的遗传力；[公式：见正文]）和后代的平均准确性提供了更高的估计值，而它们各自的单性状版本则更高。贝叶斯分析为[公式：见正文]的估计值提供了可信度区间。因此，MTME 导致了更大的选择预测增益。在此基础上，该程序可有效地应用于分离大豆后代的遗传选择。
