In genomic prediction, it is common to centre the genotypes of single nucleotide polymorphisms based on the allele frequencies in the current population, rather than those in the base generation. The mean breeding value of non-genotyped animals is conditional on the mean performance of genotyped relatives, but can be corrected by fitting the mean performance of genotyped individuals as a fixed regression. The associated covariate vector has been referred to as a ‘J-factor’, which if fitted as a fixed effect can improve the accuracy and dispersion bias of sire genomic estimated breeding values (GEBV). To date, this has only been performed on populations with a single breed. Here, we investigated whether there was any benefit in fitting a separate J-factor for each breed in a three-way crossbred population, and in using pedigree-based expected or genome-based estimated breed fractions to define the J-factors. For body weight at 7 days, dispersion bias decreased when fitting multiple J-factors, but only with a low proportion of genotyped individuals with selective genotyping. On average, the mean regression coefficients of validation records on those of GEBV increased with one J-factor compared to none, and further increased with multiple J-factors. However, for body weight at 35 days this was not observed. The accuracy of GEBV remained unchanged regardless of the J-factor method used. Differences between the J-factor methods were limited with correlations approaching 1 for the estimated covariate vector, the estimated coefficients of the regression on the J-factors, and the GEBV. Based on our results and in the particular design analysed here, i.e. all the animals with phenotype are of the same type of crossbreds, fitting a single J-factor should be sufficient, to reduce dispersion bias. Fitting multiple J-factors may reduce dispersion bias further but this depends on the trait and genotyping rate. For the crossbred population analysed, fitting multiple J-factors has no adverse consequences and if this is done, it does not matter if the breed fractions used are based on the pedigree-expectation or the genomic estimates. Finally, when GEBV are estimated from crossbred data, any observed bias can potentially be reduced by including a straightforward regression on actual breed proportions.

中文翻译：

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使用单步 SNPBLUP 进行的验证表明，评估可以继续使用基因分型个体的单一平均值，即使是多个品种

在基因组预测中，通常基于当前群体中的等位基因频率而不是碱基生成中的等位基因频率来集中单核苷酸多态性的基因型。非基因分型动物的平均育种值取决于基因分型亲属的平均表现，但可以通过将基因分型个体的平均表现拟合为固定回归来校正。相关的协变量向量被称为“J 因子”，如果作为固定效应进行拟合，可以提高父系基因组估计育种值 (GEBV) 的准确性和分散偏差。迄今为止，这仅在具有单一品种的种群中进行过。在这里，我们调查了在三向杂交种群中为每个品种安装单独的 J 因子是否有任何好处，并使用基于谱系的预期或基于基因组的估计品种分数来定义 J 因子。对于 7 天时的体重，当拟合多个 J 因子时，离散偏差会降低，但只有一小部分具有选择性基因分型的基因分型个体。平均而言，与没有相比，GEBV 验证记录的平均回归系数随着一个 J 因子的增加而增加，并随着多个 J 因子的增加而进一步增加。然而，对于 35 天时的体重，没有观察到这一点。无论使用何种 J 因子方法，GEBV 的准确性都保持不变。J 因子方法之间的差异仅限于估计协变量向量、J 因子回归的估计系数和 GEBV 的相关性接近 1。根据我们的结果和此处分析的特定设计，即 所有具有表型的动物都是同一类型的杂交种，适合单个 J 因子就足够了，以减少分散偏差。拟合多个 J 因子可能会进一步降低离散偏差，但这取决于性状和基因分型率。对于分析的杂交种群，拟合多个 J 因子没有不利后果，如果这样做，使用的品种分数是基于谱系预期还是基因组估计都无关紧要。最后，当从杂交数据估计 GEBV 时，任何观察到的偏差都可以通过对实际品种比例进行直接回归来减少。拟合多个 J 因子可能会进一步降低离散偏差，但这取决于性状和基因分型率。对于分析的杂交种群，拟合多个 J 因子没有不利后果，如果这样做，使用的品种分数是基于谱系预期还是基因组估计都无关紧要。最后，当从杂交数据估计 GEBV 时，任何观察到的偏差都可以通过对实际品种比例进行直接回归来减少。拟合多个 J 因子可能会进一步降低离散偏差，但这取决于性状和基因分型率。对于分析的杂交种群，拟合多个 J 因子没有不利后果，如果这样做，使用的品种分数是基于谱系预期还是基因组估计都无关紧要。最后，当从杂交数据估计 GEBV 时，任何观察到的偏差都可以通过对实际品种比例进行直接回归来减少。