Data from: Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: prediction accuracy implications

Hybrids are broadly used in plant breeding and accurate estimation of variance components is crucial for optimizing genetic gain. Genome-wide information may be used to explore models designed to assess the extent of additive and non-additive variance and test their prediction accuracy for the genomic selection. Ten linear mixed models, involving pedigree- and marker-based relationship matrices among parents, were developed to estimate additive (A), dominance (D) and epistatic (AA, AD and DD) effects. Five complementary models, involving the gametic phase to estimate marker-based relationships among hybrid progenies, were developed to assess the same effects. The models were compared using tree height and 3303 single-nucleotide polymorphism markers from 1130 cloned individuals obtained via controlled crosses of 13 Eucalyptus urophylla females with 9 Eucalyptus grandis males. Akaike information criterion (AIC), variance ratios, asymptotic correlation matrices of estimates, goodness-of-fit, prediction accuracy and mean square error (MSE) were used for the comparisons. The variance components and variance ratios differed according to the model. Models with a parent marker-based relationship matrix performed better than those that were pedigree-based, that is, an absence of singularities, lower AIC, higher goodness-of-fit and accuracy and smaller MSE. However, AD and DD variances were estimated with high s.es. Using the same criteria, progeny gametic phase-based models performed better in fitting the observations and predicting genetic values. However, DD variance could not be separated from the dominance variance and null estimates were obtained for AA and AD effects. This study highlighted the advantages of progeny models using genome-wide information.

杂交种（Hybrids）广泛应用于植物育种领域，而方差组分的精准估计对于优化遗传增益至关重要。全基因组信息可用于开发旨在评估加性与非加性方差占比的模型，并检验这些模型在基因组选择中的预测精度。本研究构建了10个线性混合模型，其中包含亲本间基于系谱（pedigree）和标记（marker）的亲缘关系矩阵，用于估计加性（A）、显性（D）以及上位性（AA、AD和DD）效应；另有5个互补模型，借助配子阶段（gametic phase）信息估计杂交后代间基于标记的亲缘关系，同样用于评估上述三类效应。
研究利用1130个克隆个体的树高表型数据与3303个单核苷酸多态性（single-nucleotide polymorphism, SNP）标记开展模型比较，这些克隆个体来自13尾叶桉（Eucalyptus urophylla）雌性亲本与9巨桉（Eucalyptus grandis）雄性亲本的控制性杂交后代。模型比较采用的指标包括赤池信息准则（Akaike information criterion, AIC）、方差比、估计值的渐近相关矩阵、拟合优度、预测精度以及均方误差（mean square error, MSE）。
不同模型对应的方差组分与方差比存在差异。带有亲本标记亲缘关系矩阵的模型表现优于基于系谱的模型，具体体现为无奇异情况、赤池信息准则值更低、拟合优度与预测精度更高，且均方误差更小；不过，AD与DD方差的估计标准误偏高。基于相同评价标准，借助配子阶段信息的后代模型在拟合观测数据与预测遗传值方面表现更优，但该类模型无法将DD方差与显性方差区分开，且AA和AD效应的估计值为零。本研究证实了基于全基因组信息的后代模型在植物杂交育种分析中的优势。