da Silva Éder David Borges, Xavier Alencar, Faria Marcos Ventura
Department of Agronomy, Universidade Estadual do Centro-Oeste, Guarapuava, Brazil.
Department of Biostatistics, Corteva AgriscienceTM, Johnston, IA, United States.
Front Genet. 2021 Sep 1;12:637133. doi: 10.3389/fgene.2021.637133. eCollection 2021.
Genomic-assisted breeding has become an important tool in soybean breeding. However, the impact of different genomic selection (GS) approaches on short- and long-term gains is not well understood. Such gains are conditional on the breeding design and may vary with a combination of the prediction model, family size, selection strategies, and selection intensity. To address these open questions, we evaluated various scenarios through a simulated closed soybean breeding program over 200 breeding cycles. Genomic prediction was performed using genomic best linear unbiased prediction (GBLUP), Bayesian methods, and random forest, benchmarked against selection on phenotypic values, true breeding values (TBV), and random selection. Breeding strategies included selections within family (WF), across family (AF), and within pre-selected families (WPSF), with selection intensities of 2.5, 5.0, 7.5, and 10.0%. Selections were performed at the F4 generation, where individuals were phenotyped and genotyped with a 6K single nucleotide polymorphism (SNP) array. Initial genetic parameters for the simulation were estimated from the SoyNAM population. WF selections provided the most significant long-term genetic gains. GBLUP and Bayesian methods outperformed random forest and provided most of the genetic gains within the first 100 generations, being outperformed by phenotypic selection after generation 100. All methods provided similar performances under WPSF selections. A faster decay in genetic variance was observed when individuals were selected AF and WPSF, as 80% of the genetic variance was depleted within 28-58 cycles, whereas WF selections preserved the variance up to cycle 184. Surprisingly, the selection intensity had less impact on long-term gains than did the breeding strategies. The study supports that genetic gains can be optimized in the long term with specific combinations of prediction models, family size, selection strategies, and selection intensity. A combination of strategies may be necessary for balancing the short-, medium-, and long-term genetic gains in breeding programs while preserving the genetic variance.
基因组辅助育种已成为大豆育种中的一项重要工具。然而，不同的基因组选择（GS）方法对短期和长期增益的影响尚未得到充分理解。这些增益取决于育种设计，并可能因预测模型、家系大小、选择策略和选择强度的组合而有所不同。为了解决这些悬而未决的问题，我们通过一个模拟的封闭大豆育种计划，在200个育种周期内评估了各种情况。使用基因组最佳线性无偏预测（GBLUP）、贝叶斯方法和随机森林进行基因组预测，并以基于表型值、真实育种值（TBV）的选择以及随机选择作为基准。育种策略包括家系内选择（WF）、跨家系选择（AF）和预选家系内选择（WPSF），选择强度分别为2.5%、5.0%、7.5%和10.0%。选择在F4代进行，此时个体进行表型鉴定并用6K单核苷酸多态性（SNP）阵列进行基因分型。模拟的初始遗传参数是根据大豆NAM群体估计的。WF选择提供了最显著的长期遗传增益。GBLUP和贝叶斯方法优于随机森林，并在前100代内提供了大部分遗传增益，但在100代之后被表型选择超越。在WPSF选择下，所有方法表现相似。当个体进行AF和WPSF选择时，观察到遗传方差的衰减更快，因为80%的遗传方差在28 - 58个周期内耗尽，而WF选择在第184个周期之前都能保持方差。令人惊讶的是，选择强度对长期增益的影响小于育种策略。该研究支持通过预测模型、家系大小、选择策略和选择强度的特定组合，可以长期优化遗传增益。在育种计划中，可能需要结合多种策略来平衡短期、中期和长期的遗传增益，同时保持遗传方差。
