Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: prediction accuracy implications
Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: prediction accuracy implications
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DOI：
10.1038/hdy.2015.78
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发表时间：
2016-02-01
期刊：
影响因子：
3.8
通讯作者：
Vigneron, Ph
中科院分区：
文献类型：
--
作者：
Bouvet, J-M;Makouanzi, G.;Vigneron, Ph
杂交种在植物育种中有着广泛的应用，其方差分量的准确估计是优化遗传增益的关键。全基因组信息可用于探索用于评估加性和非加性变异程度的模型，并测试其对基因组选择的预测准确性。建立了10个线性混合模型，包括基于家系和标记的亲本关系矩阵，用于估计加性(A)、显性(D)和上位性（AA、AD和DD）效应。五个互补模型，包括配子体阶段，以估计杂交后代之间基于标记的关系，被开发来评估相同的效果。利用13株尾叶桉雌桉与9株大桉雄桉对照杂交获得的1130个克隆个体的树高和3303个单核苷酸多态性标记对模型进行比较。采用赤池信息准则（AIC）、方差比、估计的渐近相关矩阵、拟合优度、预测精度和均方误差（MSE）进行比较。不同模型的方差成分和方差比不同。基于亲本标记的关系矩阵的模型比基于系谱的模型表现更好，即没有奇点，AIC更低，拟合优度和准确性更高，MSE更小。然而，AD和DD的方差估计具有很高的s.es。使用相同的标准，基于子代配子期的模型在拟合观察结果和预测遗传值方面表现更好。然而，DD方差不能从优势方差中分离出来，AA和AD效应的估计为零。这项研究强调了使用全基因组信息的后代模型的优势。
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.