Nishio Motohide, Satoh Masahiro
NARO Institute of Livestock and Grassland Science, Tsukuba, Japan.
PLoS One. 2014 Jan 8;9(1):e85792. doi: 10.1371/journal.pone.0085792. eCollection 2014.
We evaluated the performance of GBLUP including dominance genetic effect (GBLUP-D) by estimating variances and predicting genetic merits in a computer simulation and 2 actual traits (T4 and T5) in pigs. In simulation data, GBLUP-D explained more than 50% of dominance genetic variance. Moreover, GBLUP-D yielded estimated total genetic effects over 1.2% more accurate than those yielded by GBLUP. In particular, when the dominance genetic variance was large, the accuracy could be substantially improved by increasing the number of markers. The dominance genetic variances in T4 and T5 accounted for 9.6% and 6.3% of the phenotypic variances, respectively. Estimates of such small dominance genetic variances contributed little to the improvement of the accuracies of estimated total genetic effects. In both simulation and pig data, there were nearly no differences in the estimates of additive genetic effects or their variance between GBLUP-D and GBLUP. Therefore, we conclude GBLUP-D is a feasible approach to improve genetic performance in crossbred populations with large dominance genetic variation and identify mating systems with good combining ability.
我们通过在计算机模拟以及猪的两个实际性状（T4和T5）中估计方差和预测遗传价值，评估了包含显性遗传效应的基因组最佳线性无偏预测（GBLUP-D）的性能。在模拟数据中，GBLUP-D解释了超过50%的显性遗传方差。此外，GBLUP-D产生的估计总遗传效应比GBLUP产生的估计总遗传效应准确超过1.2%。特别是，当显性遗传方差较大时，通过增加标记数量可大幅提高准确性。T4和T5中的显性遗传方差分别占表型方差的9.6%和6.3%。如此小的显性遗传方差估计值对提高估计总遗传效应的准确性贡献不大。在模拟数据和猪数据中，GBLUP-D和GBLUP在加性遗传效应估计值或其方差方面几乎没有差异。因此，我们得出结论，GBLUP-D是一种可行的方法，可用于提高具有大显性遗传变异的杂交群体的遗传性能，并识别具有良好配合力的交配系统。
