Data from: Genomic prediction of pumpkin hybrid performance

Genomic prediction has become an increasingly popular tool for hybrid
performance evaluation in plant breeding, mainly because that it can
reduce cost and accelerate a breeding program. In this study, we propose a
systematic procedure to predict hybrid performance using a genomic
selection (GS) model that takes both additive and dominance marker effects
into account. We first demonstrate the advantage of the additive-dominance
effects model over the only additive effects model through a simulation
study. Based on the additive-dominance model, we predict genomic estimated
breeding values (GEBVs) for individual hybrid combinations and their
parental lines. The GEBV based specific combining ability (SCA) for each
hybrid, and general combining ability (GCAs) for its parental lines are
then derived to quantify the degree of mid-parent heterosis (MPH) or
better parent heterosis (BPH) of the hybrid. Finally, we estimate the
variance components due to additive and dominance gene action effects, and
heritability using a genomic BLUP model. These estimates are used to
justify the results of the genomic prediction study. A pumpkin data set is
given to illustrate the provided procedure. The data set consists of 320
parental lines with collected 61,179 SNP markers; 119, 120, 120 phenotypic
values of hybrids on three quantitative traits within C. maxima; and 89,
111, 90 phenotypic values of hybrids on the same three quantitative traits
within C. moshata.

基因组预测已日益成为植物育种中杂种性能评价的主流工具，其核心优势在于可降低育种成本、加快育种进程。本研究提出一套系统性分析流程，借助同时纳入加性与显性标记效应的基因组选择（GS）模型开展杂种性能预测。我们首先通过模拟研究，验证了加性-显性效应模型相较于仅含加性效应模型的优势。基于该加性-显性模型，我们对单个杂交组合及其亲本的基因组估计育种值（GEBVs）进行预测；进一步推导得到各杂交组合基于GEBVs的特殊配合力（SCA）以及亲本的一般配合力（GCAs），以此量化该杂种的中亲优势（MPH）与超亲优势（BPH）水平。最后，我们采用基因组最佳线性无偏预测（genomic BLUP）模型，估算了加性与显性基因作用效应的方差组分以及性状遗传力，上述估算结果用于验证本基因组预测研究的结论。本研究以南瓜数据集为例阐明所提流程：该数据集包含320份亲本材料，共采集得到61179个单核苷酸多态性（SNP）标记；涵盖墨西哥南瓜（C. maxima）内3个数量性状的杂种表型值分别为119、120、120个，以及笋瓜（C. moshata）内同一3个数量性状的杂种表型值分别为89、111、90个。