Several studies have found that the growth rate of a pig is influenced by the genetics of the group members (indirect genetic effects). Accounting for these indirect genetic effects in a selection program may increase genetic progress for growth rate. However, indirect genetic effects are small and difficult to predict accurately. Genomic information may increase the ability to predict indirect genetic effects. Thus, the objective of this study was to test whether including indirect genetic effects in the animal model increases the predictive performance when genetic effects are predicted with genomic relationships. In total, 11,255 pigs were phenotyped for average daily gain between 30 and 94 kg, and 10,995 of these pigs were genotyped. Two relationship matrices were used: a numerator relationship matrix ( $${\mathbf{A}}$$ ) and a combined pedigree and genomic relationship matrix ( $${\mathbf{H}}$$ ); and two different animal models were used: an animal model with only direct genetic effects and an animal model with both direct and indirect genetic effects. The predictive performance of the models was defined as the Pearson correlation between corrected phenotypes and predicted genetic levels. The predicted genetic level of a pig was either its direct genetic effect or the sum of its direct genetic effect and the indirect genetic effects of its group members (total genetic effect). The highest predictive performance was achieved when total genetic effects were predicted with genomic information (21.2 vs. 14.7%). In general, the predictive performance was greater for total genetic effects than for direct genetic effects (0.1 to 0.5% greater; not statistically significant). Both types of genetic effects had greater predictive performance when they were predicted with $${\mathbf{H}}$$ rather than $${\mathbf{A}}$$ (5.9 to 6.3%). The difference between predictive performances of total genetic effects and direct genetic effects was smaller when $${\mathbf{H}}$$ was used rather than $${\mathbf{A}}$$ . This study provides evidence that: (1) corrected phenotypes are better predicted with total genetic effects than with direct genetic effects only; (2) both direct genetic effects and indirect genetic effects are better predicted with $${\mathbf{H}}$$ than $${\mathbf{A}}$$ ; (3) using $${\mathbf{H}}$$ rather than $${\mathbf{A}}$$ primarily improves the predictive performance of direct genetic effects.

中文翻译：

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使用具有间接遗传效应和基因组信息的动物模型预测猪生长速度的遗传价值

几项研究发现，猪的生长速度受群体成员的遗传学影响（间接遗传效应）。在选择程序中考虑这些间接遗传效应可能会提高遗传进展速度。但是，间接遗传效应很小，难以准确预测。基因组信息可能会增加预测间接遗传效应的能力。因此，本研究的目的是检验当通过基因组关系预测遗传效应时，在动物模型中包括间接遗传效应是否能提高预测性能。总共对11,255头猪进行了表型分析，平均日增重在30到94公斤之间，并对其中的10,995头进行了基因分型。使用了两个关系矩阵：分子关系矩阵（$$ {\ mathbf {A}} $$）和谱系和基因组关系矩阵组合（$$ {\ mathbf {H}} $$）; 使用了两种不同的动物模型：仅具有直接遗传作用的动物模型和具有直接和间接遗传作用的动物模型。模型的预测性能定义为校正表型与预测遗传水平之间的皮尔逊相关性。猪的预测遗传水平是其直接遗传效应或其直接遗传效应与其组成员的间接遗传效应的总和（总遗传效应）。当用基因组信息预测总的遗传效应时，可达到最高的预测性能（21.2对14.7％）。一般来说，总遗传效应的预测性能要比直接遗传效应的预测性能更好（提高0.1％至0.5％；无统计学意义）。当用$$ {\ mathbf {H}} $$而不是$$ {\ mathbf {A}} $$进行预测时，两种类型的遗传效应都具有更好的预测性能（5.9％至6.3％）。当使用$$ {\ mathbf {H}} $$而不是$$ {\ mathbf {A}} $$时，总遗传效应和直接遗传效应的预测性能之间的差异较小。这项研究提供了证据：（1）总遗传效应比仅直接遗传效应更好地预测了校正表型；（2）使用$$ {\ mathbf {H}} $$比使用$$ {\ mathbf {A}} $$更好地预测了直接遗传效应和间接遗传效应；