Department of Agronomy, Iowa State University, Ames, IA, USA.
Biostatistics Unit, University of Hohenheim, Stuttgart, Germany.
Theor Appl Genet. 2023 Nov 21;136(12):252. doi: 10.1007/s00122-023-04470-3.
Simulations demonstrated that estimates of realized genetic gain from linear mixed models using regional trials are biased to some degree. Thus, we recommend multiple selected models to obtain a range of reasonable estimates. Genetic improvements of discrete characteristics are obvious and easy to demonstrate, while quantitative traits require reliable and accurate methods to disentangle the confounding genetic and non-genetic components. Stochastic simulations of soybean [Glycine max (L.) Merr.] breeding programs were performed to evaluate linear mixed models to estimate the realized genetic gain (RGG) from annual multi-environment trials (MET). True breeding values were simulated under an infinitesimal model to represent the genetic contributions to soybean seed yield under various MET conditions. Estimators were evaluated using objective criteria of bias and linearity. Covariance modeling and direct versus indirect estimation-based models resulted in a substantial range of estimated values, all of which were biased to some degree. Although no models produced unbiased estimates, the three best-performing models resulted in an average bias of [Formula: see text] kg/ha[Formula: see text]/yr[Formula: see text] ([Formula: see text] bu/ac[Formula: see text]/yr[Formula: see text]). Rather than relying on a single model to estimate RGG, we recommend the application of several models with minimal and directional bias. Further, based on the parameters used in the simulations, we do not think it is appropriate to use any single model to compare breeding programs or quantify the efficiency of proposed new breeding strategies. Lastly, for public soybean programs breeding for maturity groups II and III in North America, the estimated RGG values ranged from 18.16 to 39.68 kg/ha[Formula: see text]/yr[Formula: see text] (0.27-0.59 bu/ac[Formula: see text]/yr[Formula: see text]) from 1989 to 2019. These results provide strong evidence that public breeders have significantly improved soybean germplasm for seed yield in the primary production areas of North America.
模拟结果表明，使用区域试验的线性混合模型估计实际遗传增益在某种程度上存在偏差。因此，我们建议采用多种选择模型来获得一系列合理的估计值。离散性状的遗传改良效果明显且易于证明，而数量性状则需要可靠且准确的方法来区分遗传和非遗传因素的混杂。利用大豆[Glycine max (L.) Merr.] 育种计划的随机模拟，评估了线性混合模型，以估计年度多环境试验（MET）中实际遗传增益（RGG）。在无穷小模型下模拟真实育种值，以代表不同 MET 条件下大豆种子产量的遗传贡献。使用偏差和线性的客观标准评估估计器。协方差建模以及直接与间接估计模型导致了估计值的广泛范围，所有这些都在某种程度上存在偏差。虽然没有模型产生无偏估计值，但表现最好的三个模型导致平均偏差为 [Formula: see text] kg/ha[Formula: see text]/yr[Formula: see text]（[Formula: see text] bu/ac[Formula: see text]/yr[Formula: see text]）。我们建议应用几个最小化和有方向偏差的模型，而不是依赖单一模型来估计 RGG。此外，基于模拟中使用的参数，我们认为使用任何单一模型来比较育种计划或量化新提出的育种策略的效率都不合适。最后，对于北美成熟组 II 和 III 的公共大豆计划，从 1989 年到 2019 年，估计的 RGG 值范围为 18.16 到 39.68 kg/ha[Formula: see text]/yr[Formula: see text]（0.27-0.59 bu/ac[Formula: see text]/yr[Formula: see text]）。这些结果有力地证明了公共育种者已经显著改善了北美主要生产地区的大豆种质产量。
