Vieira I C, Dos Santos J P R, Pires L P M, Lima B M, Gonçalves F M A, Balestre M
Departamento de Biologia, Universidade Federal de Lavras, Lavras, MG, Brasil.
Departamento de Genética, Escola Superior de Agricultura Luiz de Queiroz, , Brasil.
Genet Mol Res. 2017 May 10;16(2):gmr-16-02-gmr.16029632. doi: 10.4238/gmr16029632.
Understanding non-additive effects in the expression of quantitative traits is very important in genotype selection, especially in species where the commercial products are clones or hybrids. The use of molecular markers has allowed the study of non-additive genetic effects on a genomic level, in addition to a better understanding of its importance in quantitative traits. Thus, the purpose of this study was to evaluate the behavior of the GBLUP model in different genetic models and relationship matrices and their influence on the estimates of genetic parameters. We used real data of the circumference at breast height in Eucalyptus spp and simulated data from a population of F. Three commonly reported kinship structures in the literature were adopted. The simulation results showed that the inclusion of epistatic kinship improved prediction estimates of genomic breeding values. However, the non-additive effects were not accurately recovered. The Fisher information matrix for real dataset showed high collinearity in estimates of additive, dominant, and epistatic variance, causing no gain in the prediction of the unobserved data and convergence problems. Estimates presented differences of genetic parameters and correlations considering the different kinship structures. Our results show that the inclusion of non-additive effects can improve the predictive ability or even the prediction of additive effects. However, the high distortions observed in the variance estimates when the Hardy-Weinberg equilibrium assumption is violated due to the presence of selection or inbreeding can converge at zero gains in models that consider epistasis in genomic kinship.
在基因型选择中，理解数量性状表达中的非加性效应非常重要，特别是在商业产品为克隆体或杂交种的物种中。分子标记的使用使得在基因组水平上研究非加性遗传效应成为可能，同时也有助于更好地理解其在数量性状中的重要性。因此，本研究的目的是评估GBLUP模型在不同遗传模型和关系矩阵中的表现及其对遗传参数估计的影响。我们使用了桉属树种胸径周长的实际数据以及来自F种群的模拟数据。采用了文献中常见的三种亲缘关系结构。模拟结果表明，包含上位性亲缘关系可提高基因组育种值的预测估计。然而，非加性效应并未得到准确恢复。实际数据集的Fisher信息矩阵在加性、显性和上位性方差估计中显示出高度共线性，导致在未观测数据的预测中没有增益且出现收敛问题。考虑不同亲缘关系结构时，估计的遗传参数和相关性存在差异。我们的结果表明，包含非加性效应可以提高预测能力，甚至可以改善加性效应的预测。然而，由于选择或近亲繁殖的存在而违反哈迪-温伯格平衡假设时，在考虑基因组亲缘关系中的上位性的模型中，方差估计中观察到的高度扭曲可能导致零增益收敛。
