Animal Breeding and Genomics, Wageningen University and Research, P.O. Box 338, 6700 AH, Wageningen, The Netherlands.
Cobb-Vantress Inc., Siloam Springs, AR, 72761‑1030, USA.
Genet Sel Evol. 2023 Mar 22;55(1):19. doi: 10.1186/s12711-023-00787-1.
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.
在基因组预测中，通常基于当前群体中的等位基因频率对单核苷酸多态性的基因型进行中心化，而不是基于基础群体中的等位基因频率。非基因型动物的平均育种值取决于基因型亲属的平均表现，但可以通过拟合基因型个体的平均表现作为固定回归来校正。相关的协变量向量被称为“J 因子”，如果将其拟合为固定效应，可以提高父本基因组估计育种值（GEBV）的准确性和分散偏差。迄今为止，这仅在具有单一品种的群体中进行过。在这里，我们研究了在三交杂种群体中为每个品种拟合单独的 J 因子是否有任何益处，以及使用基于系谱的预期或基于基因组的估计品种分数来定义 J 因子是否有任何益处。
对于 7 天体重，当拟合多个 J 因子时，分散偏差会减小，但只有在选择性基因型个体的比例较低时才会减小。平均而言，与没有 J 因子相比，验证记录的平均回归系数对 GEBV 的增加，以及随着多个 J 因子的增加而增加。然而，对于 35 天体重，情况并非如此。无论使用哪种 J 因子方法，GEBV 的准确性都保持不变。不同 J 因子方法之间的差异有限，因为估计协变量向量、回归 J 因子的估计系数和 GEBV 的相关性接近 1。
根据我们的结果以及在特定的分析设计中，即所有具有表型的动物都是相同类型的杂种，拟合单个 J 因子应该足以减少分散偏差。拟合多个 J 因子可能会进一步减少分散偏差，但这取决于性状和基因型率。对于分析的杂交群体，拟合多个 J 因子没有不利影响，如果这样做，使用的品种分数是基于系谱预期还是基于基因组估计并不重要。最后，当从杂交数据中估计 GEBV 时，通过包括对实际品种比例的简单回归，可以减少任何观察到的偏差。
