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The predictive ability of indirect genetic models is reduced when culled animals are omitted from the data.
Genetics Selection Evolution ( IF 4.1 ) Pub Date : 2020-02-10 , DOI: 10.1186/s12711-020-0527-x
Birgitte Ask 1 , Ole F Christensen 2 , Marzieh Heidaritabar 2 , Per Madsen 2 , Hanne M Nielsen 1, 2, 2
Affiliation  

BACKGROUND Physical removal of individuals from groups causes reductions in group sizes and changes in group composition, which may affect the predictive ability of estimates of indirect genetic effects of animals on phenotypes of group mates. We hypothesized that including indirect genetic effects of culled animals and of animals without phenotypes in the analysis affects estimates of genetic parameters, improves predictive ability, and reduces bias of predicted breeding values. We tested this by applying different editing procedures, i.e. omission of individuals or groups from the data, and genetic models, i.e. a classical and an indirect genetic model (IGM) without or with weighting of indirect genetic effects based on the relative proportion of time spent in the pen or space allowance. Data consisted of average daily gain for 123,567 pigs in 11,111 groups, from which 3% of individuals in 25% of groups were prematurely removed from the group. RESULTS The estimate of total heritability was higher (0.29 to 0.34) than that of direct heritability (0.23 to 0.25) regardless of the editing procedures and IGM used. Omission of individuals or groups from the data reduced the predictive ability of estimates of indirect genetic effects by 8 to 46%, and the predictive ability of estimates of the combined direct and indirect genetic effects by up to 4%. Omission of full groups introduced bias in predicted breeding values. Weighting of indirect genetic effects reduced the predictive ability of their estimates by at least 19% and of the estimates of the combined direct and indirect genetic effects by 1%. CONCLUSIONS We identified significant indirect genetic effects for growth in pigs. Culled animals should neither be removed from the data nor accounted for by weighting their indirect genetic effects in the model based on the relative proportion of time spent in the pen or space allowance, because it will reduce predictive ability and increase bias of predicted breeding values. Information on culled animals is important for prediction of indirect genetic effects and must be accounted for in IGM analyses by including fixed regressions based on relative time spent within the pen or relative space allowance.

中文翻译:

当从数据中剔除被淘汰的动物时,间接遗传模型的预测能力会降低。

背景技术个体从群体中的物理移出导致群体规模的减小和群体组成的改变,这可能影响动物对同伴表型的间接遗传效应的估计的预测能力。我们假设在分析中包括淘汰动物和无表型动物的间接遗传效应会影响遗传参数的估计,提高预测能力,并减少预测育种值的偏差。我们通过应用不同的编辑程序(即从数据中省略个人或群体)和遗传模型(即经典或间接遗传模型(IGM))来测试了这一点,即不考虑或不考虑间接花费的时间比例来衡量间接遗传效应在笔或空间津贴。数据包括123的平均每日收益,11,111个组中的567头猪,其中25%组中的3%的个体被过早地从该组中移出。结果无论采用何种编辑程序和IGM,总遗传力的估计值均高于直接遗传力的估计值(0.23至0.25)。数据中个人或群体的遗漏将间接遗传效应估计的预测能力降低了8%至46%,将直接和间接遗传效应的组合估计的预测能力降低了4%。全组的遗漏在预测的育种值上引入了偏差。间接遗传效应的权重将其估计的预测能力降低了至少19%,将直接和间接遗传效应的组合估计的预测能力降低了1%。结论我们确定了对猪生长的显着间接遗传效应。不应将被淘汰的动物从数据中删除,也不应根据在笔或空间津贴中花费的相对时间比例在模型中加权其间接遗传效应,因为这会降低预测能力并增加预测的育种值的偏差。关于被淘汰动物的信息对于间接遗传效应的预测很重要,并且必须在IGM分析中加以考虑,包括基于笔内花费的相对时间或相对空间余量的固定回归。
更新日期:2020-04-22
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