Data from: Prediction of genetic values of quantitative traits with epistatic effects in plant breeding populations

Though epistasis has long been postulated to play a critical role in genetic regulation of important pathways as well as provide a major source of variation in the process of speciation, the importance of epistasis for genomic selection in the context of plant breeding is still being debated. In this paper, we report the results on the prediction of genetic values with epistatic effects for 280 accessions in the Nebraska Wheat Breeding Program using adaptive mixed LASSO. The development of adaptive mixed LASSO, originally designed for association mapping, for the context of genomic selection is reported. The results show that adaptive mixed LASSO can be successfully applied to the prediction of genetic values while incorporating both marker main effects and epistatic effects. Especially, the prediction accuracy is substantially improved by the inclusion of two-locus epistatic effects (more than one fold in some cases as measured by cross validation correlation coefficient), which is observed for multiple traits and planting locations. This points to significant potential in using non-additive genetic effects for genomic selection in crop breeding practices.

尽管上位性（epistasis）长期以来被假定在重要通路的遗传调控以及物种形成过程的主要变异来源中发挥关键作用，但在植物育种背景下，上位性对基因组选择（genomic selection）的重要性仍存在争议。本文报道了利用自适应混合套索回归（adaptive mixed LASSO），对内布拉斯加小麦育种项目中的280份种质材料开展带上位性效应的遗传值预测的研究结果。本文同时报道了为适配基因组选择场景而开发的自适应混合套索回归方法——该方法最初是为关联作图（association mapping）设计的。结果表明，在同时纳入标记主效应（marker main effects）与上位性效应的情况下，自适应混合套索回归可成功应用于遗传值预测。尤为关键的是，引入二座位上位性效应（two-locus epistatic effects）可大幅提升预测精度：在部分案例中，以交叉验证相关系数（cross validation correlation coefficient）衡量时，预测精度提升幅度可达一倍以上；这一现象在多个性状与种植环境中均有观测到。这指出了在作物育种实践中利用非加性遗传效应（non-additive genetic effects）开展基因组选择具备显著的应用潜力。