Dominant genetic effects may provide a critical contribution to the total genetic variation of quantitative and complex traits. However, investigations of genome-wide markers to study the genomic prediction (GP) and genetic mechanisms of complex traits generally ignore dominant genetic effects. The increasing availability of genomic datasets and the potential benefits of the inclusion of non-additive genetic effects in GP have recently renewed attention to incorporation of these effects in genomic prediction models. In the present study, data from 498 genotyped Alpine Merino sheep were adopted to estimate the additive and dominant genetic effects of 9 wool and blood traits via two linear models: (1) an additive effect model (MAG) and (2) a model that included both additive and dominant genetic effects (MADG). Moreover, a method of 5-fold cross validation was used to evaluate the capability of GP in the two different models. The results of variance component estimates for each trait suggested that for fleece extension rate (73%), red blood cell count (28%), and hematocrit (25%), a large component of phenotypic variation was explained by dominant genetic effects. The results of cross validation demonstrated that the MADG model, comprising additive and dominant genetic effects, did not display an apparent advantage over the MAG model that included only additive genetic effects, i.e., the model that included dominant genetic effects did not improve the capability for prediction of the genomic model. Consequently, inclusion of dominant effects in the GP model may not be beneficial for wool and blood traits in the population of Alpine Merino sheep.

显性的遗传效应可能对定量和复杂性状的总遗传变异起关键作用。但是，对全基因组标记进行研究以研究复杂性状的基因组预测（GP）和遗传机制通常忽略了显性遗传效应。基因组数据集的可用性不断提高，以及在GP中纳入非累加遗传效应的潜在好处，最近引起了人们对将这些效应纳入基因组预测模型的关注。在本研究中，采用498个基因型高山美利奴绵羊的数据，通过两个线性模型估算9种羊毛和血液性状的加性和显性遗传效应：（1）加性效应模型（MAG）和（2）包括加性和显性遗传效应（MADG）。此外，一种5倍交叉验证的方法用于评估两种不同模型中GP的能力。每个特征的方差成分估计结果表明，对于羊毛延伸率（73％），红细胞计数（28％）和血细胞比容（25％），表型变异的很大一部分是由显性遗传效应解释的。交叉验证的结果表明，包含加性和显性遗传效应的MADG模型与仅包含加性遗传效应的MAG模型相比，没有显示出明显的优势，即，包含显性遗传效应的模型并没有提高抗性的能力。基因组模型的预测。因此，在GP模型中包含显性效应可能不会有益于高山美利奴绵羊群体的羊毛和血液性状。