Selection based on scrapie genotypes could improve the genetic resistance for scrapie in sheep. However, in practice, few animals are genotyped. The objectives were to define numerical values of scrapie resistance genotypes and adjust for their non-additive genetic effect; evaluate prediction accuracy of ungenotyped animals using linear animal model; and predict and assess selection response based on estimated breeding values (EBV) of ungenotyped animals. The scrapie resistance (SR) was defined by ranking scrapie genotypes from low (0) to high (4) resistance based on genotype risk groups and was also adjusted for non-additive genetic effect of the haplotypes. Genotypes were simulated for 1,671,890 animals from pedigree. The simulated alleles were assigned to scrapie haplotypes in two scenarios of high (SR_h) and low (SR_l) resistance populations. A sample of 20,000 genotyped animals were used to predict ungenotyped using animal model. Prediction accuracies for ungenotyped animals for SR_h and SR_l were 0.60 and 0.54, and for allele content were from 0.41 to 0.71, respectively. Response to selection on SR_h and SR_l increased SR by 0.52 and 0.28, and on allele content from 0.13 to 0.50, respectively. In addition, the selected animals had large proportion of homozygous for the favorable haplotypes. Thus, pre-selection prior to genotyping could reduce genotyping costs for breeding programs. Using a linear animal model to predict SR makes better use of available information for the breeding programs.

中文翻译：

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使用线性动物模型预测未基因型绵羊瘙痒病的遗传抗性

基于搔痒病基因型的选择可以提高绵羊搔痒病的遗传抗性。然而，在实践中，很少有动物被基因分型。目标是确定搔痒病抗性基因型的数值并调整它们的非加性遗传效应；使用线性动物模型评估未基因型动物的预测准确性；并根据未基因分型动物的估计育种值 (EBV) 预测和评估选择反应。痒病抗性 (SR) 是通过根据基因型风险组将痒病基因型从低 (0) 到高 (4) 抗性排列来定义的，并且还针对单倍型的非加性遗传效应进行了调整。对来自谱系的 1,671,890 只动物的基因型进行了模拟。模拟的等位基因在两种高（SR _h) 和低 (SR _l ) 抗性种群。使用动物模型使用 20,000 只基因分型动物的样本来预测未基因分型。_{SR h}和 SR _l的未基因分型动物的预测准确度分别为 0.60 和 0.54，等位基因含量分别为 0.41 到 0.71。_{对 SR h}和 SR _l选择的反应分别使SR增加 0.52 和 0.28，等位基因含量从 0.13 增加到 0.50。此外，所选择的动物对于有利的单倍型具有很大比例的纯合子。因此，基因分型之前的预选可以降低育种计划的基因分型成本。使用线性动物模型预测 SR 可以更好地利用可用信息进行育种计划。