Data from: The contribution of dominance to phenotype prediction in a pine breeding and simulated population

Pedigrees and dense marker panels have been used to predict the genetic merit of individuals in plant and animal breeding, accounting primarily for the contribution of additive effects. However, non-additive effects may also impact trait variation in many breeding systems, particularly when specific combining ability is explored. Here we used models with different priors, and including additive-only and additive plus dominance effects, to predict polygenic (height) and oligogenic (fusiform rust resistance) traits in a structured breeding population of loblolly pine (Pinus taeda L.). Models were largely similar in predictive ability, and the inclusion of dominance only improved modestly the predictions for tree height. Next, we simulated a genetically similar population to assess the ability of predicting polygenic and oligogenic traits controlled by different levels of dominance. The simulation showed an overall decrease in the accuracy of total genomic predictions as dominance increases, regardless of the method used for prediction. Thus, dominance effects may not be accounted for as effectively in prediction models, compared to traits controlled by additive alleles only. When the ratio of dominance to total phenotypic variance reached 0.2, the additive-dominance prediction models were significantly better than the additive-only models. However, in the prediction of the subsequent progeny population, this accuracy increase was only observed for the oligogenic trait.在动植物育种领域，系谱（pedigrees）与高密度标记面板（dense marker panels）已被广泛用于预测个体的遗传价值，此类方法主要考量加性效应（additive effects）的贡献。然而，诸多育种体系中的性状变异同样会受到非加性效应（non-additive effects）的影响，尤其在探究特殊配合力（specific combining ability）时更为显著。本研究采用搭载不同先验的模型，涵盖仅含加性效应以及加性与显性效应（dominance effects）两类模型，对火炬松（Pinus taeda L.）结构化育种群体中的多基因性状（株高）与寡基因性状（梭形锈病抗性，fusiform rust resistance）开展预测。各类模型的预测性能整体相近，引入显性效应仅能小幅提升株高的预测精度。随后，我们模拟了一个遗传背景相似的群体，以评估不同显性水平调控的多基因与寡基因性状的预测能力。模拟结果显示，无论采用何种预测方法，随着显性效应水平升高，全基因组预测（genomic predictions）的总体精度均呈下降趋势。由此可见，相较于仅由加性等位基因（additive alleles）调控的性状，预测模型对显性效应的考量可能不够充分。当显性效应与总表型方差（total phenotypic variance）的比值达到0.2时，加性-显性预测模型的性能显著优于仅含加性效应的模型。但在对后续子代群体进行预测时，该精度提升现象仅在寡基因性状中得以观测。