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.

尽管长期以来，上位性（epistasis）被认为在重要通路的遗传调控中发挥关键作用，同时也为物种形成过程中的变异提供了主要来源，但在植物育种背景下，上位性对于基因组选择（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280份种质资源（accessions）的带上位性效应的遗传值预测结果。本文同时报道了专为关联分析设计的自适应混合套索回归在基因组选择场景下的拓展开发。研究结果表明，自适应混合套索回归可在同时纳入标记主效应与上位性效应的前提下，成功应用于遗传值预测。尤其值得注意的是，通过引入两位点上位性效应（在部分场景中，以交叉验证相关系数衡量时，预测精度提升超过一倍），多种性状与种植环境下均观察到预测精度得到显著提升。这表明在作物育种实践中利用非加性遗传效应开展基因组选择具有可观的应用潜力。