In sorghum [Sorghum bicolor (L.) Moench], hybrid cultivars for the biofuel industry are desired. Along with selection based on testcross performance, evaluation of the breeding population per se is also important for the success of hybrid breeding. In addition to additive genetic effects, non-additive (i.e., dominance and epistatic) effects are expected to contribute to the performance of early generations. Unfortunately, studies on early generations in sorghum breeding programs are limited. In this study, we analyzed a breeding population for bioenergy sorghum, which was previously developed based on testcross performance, to compare genomic selection models both trained on and evaluated for the per se performance of the 3^rd generation S₀ individuals. Of over 200 ancestral inbred accessions in the base population, only 13 founders contributed to the 3^rd generation as progenitors. Compared to the founders, the performances of the population per se were improved for target traits. The total genetic variance within the S₀ generation progenies themselves for all traits was mainly additive, although non-additive variances contributed to each trait to some extent. For genomic selection, linear regression models explicitly considering all genetic components showed a higher predictive ability than other linear and non-linear models. Although the number and effect distribution of underlying loci was different among the traits, the influence of priors for marker effects was relatively small. These results indicate the importance of considering non-additive effects for dissecting the genetic architecture of early breeding generations and predicting the performance per se.

在高粱[ Sorghum bicolor（L.）Moench]中，需要用于生物燃料工业的杂交品种。除了基于testcross性能的选择之外，对繁殖种群本身的评估对于杂交育种的成功也很重要。除加性遗传效应外，非加性（即显性和上位性）效应有望促进早代的表现。不幸的是，对高粱育种计划的早期研究有限。在这项研究中，我们分析了以前基于testcross性能开发的生物能源高粱育种种群，以比较在第三代S ₀上受过训练并对其本身性能进行评估的基因组选择模型个人。在基本种群的200多个祖传近交种质中，只有13个创始人作为祖细胞贡献给第三代。与创始人相比，针对目标特征的人口本身的绩效得到了改善。S ₀内的总遗传变异所有性状的后代本身主要是累加性的，尽管非累加性状在某种程度上对每个性状有贡献。对于基因组选择，明确考虑所有遗传成分的线性回归模型显示出比其他线性和非线性模型更高的预测能力。尽管性状中潜在基因座的数量和效应分布不同，但先验对标记效应的影响相对较小。这些结果表明考虑非加性效应对解剖早期育种代的遗传结构并预测其本身性能的重要性。