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 versus 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.

中文翻译：

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在挪威红牛的单步基因组预测中校正基群差异和未知亲本组

几项研究报告了使用单步方法进行基因组评估的偏差和膨胀。基于谱系的基础种群与基因组关系矩阵（G）之间的不相容性可能是这些偏差的原因。解释失踪父母的不当方法可能是导致有或没有基因组信息的遗传评估存在偏差的另一个原因。为了处理这些问题，我们拟合并评估了一个固定协变量 (J)，其中包含基因型动物的一个和无关的非基因型动物的零，或相关非基因型动物的基于谱系的回归系数。我们还评估了将 J 协变量与遗传组一起拟合育种值估计的偏差和稳定性的替代方法，并将其作为随机效应纳入 G 中。在整体与部分数据集比较中，对部分数据研究了四种情况：基因型缺失、表型缺失、基因型和表型缺失以及谱系缺失。与既未拟合 J 也未拟合遗传组的基本模型相比，将 J 拟合为固定或随机减少了水平偏差和膨胀，并增加了基因组预测的稳定性。在大多数模型中，基因组预测在很大程度上偏向于缺少基因型和表型信息的情景。对于组合组效应和 J 效应的模型，偏差减少了。具有这些校正组协变量的模型比最近发布的模型表现更好，其中遗传组被封装并通过 Quaas 和 Pollak 变换随机拟合。在我们的挪威红牛数据中，