Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, 1432 Ås, Norway.
GENO SA, Storhamargata 44, 2317 Hamar, Norway.
J Anim Sci. 2022 Sep 1;100(9). doi: 10.1093/jas/skac227.
Bias and inflation in genomic evaluation with the single-step methods have been reported in several studies. Incompatibility between the base-populations of the pedigree-based and the genomic relationship matrix (G) could be a reason for these biases. Inappropriate ways of accounting for missing parents could be another reason for biases in genetic evaluations with or without genomic information. To handle these problems, we fitted and evaluated a fixed covariate (J) that contains ones for genotyped animals and zeros for unrelated non-genotyped animals, or pedigree-based regression coefficients for related non-genotyped animals. We also evaluated alternative ways of fitting the J covariate together with genetic groups on biases and stability of breeding value estimates, and of including it into G as a random effect. In a whole vs. partial data set comparison, four scenarios were investigated for the partial data: genotypes missing, phenotypes missing, both genotypes and phenotypes missing, and pedigree missing. Fitting J either as fixed or random reduced level-bias and inflation and increased stability of genomic predictions as compared to the basic model where neither J nor genetic groups were fitted. In most models, genomic predictions were largely biased for scenarios with missing genotype and phenotype information. The biases were reduced for models which combined group and J effects. Models with these corrected group covariates performed better than the recently published model where genetic groups were encapsulated and fitted as random via the Quaas and Pollak transformation. In our Norwegian Red cattle data, a model which combined group and J regression coefficients was preferred because it showed least bias and highest stability of genomic predictions across the scenarios.
在一些研究中，已经报道了单步法基因组评估中的偏差和膨胀。基于系谱的关系矩阵（G）和基础群体之间的不兼容性可能是这些偏差的原因之一。在没有或有基因组信息的遗传评估中，不适当的处理缺失父母的方法也可能是偏差的另一个原因。为了解决这些问题，我们拟合并评估了一个固定协变量（J），它包含了已基因型动物的 1 和未相关的非基因型动物的 0，或者是相关的非基因型动物的基于系谱的回归系数。我们还评估了在偏差和育种值估计的稳定性方面，将 J 协变量与遗传群体一起拟合，以及将其作为随机效应包含在 G 中的替代方法，并在全数据集与部分数据集的比较中，对部分数据集的四个场景进行了研究：基因型缺失、表型缺失、基因型和表型都缺失，以及系谱缺失。与基本模型（未拟合 J 或遗传群体）相比，无论是固定还是随机拟合 J 都降低了水平偏差和膨胀，并提高了基因组预测的稳定性。在大多数模型中，对于缺失基因型和表型信息的场景，基因组预测存在很大偏差。对于结合组和 J 效应的模型，偏差减小。与最近发表的将遗传群体封装并通过 Quaas 和 Pollak 变换作为随机拟合的模型相比，具有这些校正组协变量的模型表现更好。在我们的挪威红牛数据中，组合组和 J 回归系数的模型是首选，因为它在所有场景中表现出最小的偏差和最高的基因组预测稳定性。
