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通过基于等位基因的品种起源,结合纯种评估的解决方案,对杂交奶牛进行基因组预测。

Genomic predictions for crossbred dairy cows by combining solutions from purebred evaluation based on breed origin of alleles.

作者信息

Eiríksson Jón H, Byskov Kevin, Su Guosheng, Thomasen Jørn Rind, Christensen Ole F

机构信息

Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark.

SEGES Innovation P/S, 8200 Aarhus N, Denmark.

出版信息

J Dairy Sci. 2022 Jun;105(6):5178-5191. doi: 10.3168/jds.2021-21644. Epub 2022 Apr 22.

Abstract

Genomic predictions have been applied for dairy cattle for more than a decade with great success, but genomic estimated breeding values (GEBV) are not widely available for crossbred dairy cows. The large reference populations already in place for genomic evaluations of many pure breeds makes it interesting to use the accurate solutions, in particular the estimated marker effects, from these evaluations for calculation of GEBV for crossbred heifers and cows. Effects of marker alleles in crossbred animals can depend on breed origin of the alleles (BOA). Therefore, our aim was to investigate if reliable GEBV for crossbred dairy cows can be obtained by combining estimated marker effects from purebred evaluations based on BOA. We used data on 5,467 Danish crossbred dairy cows with contributions from Holstein, Jersey, and Red Dairy Cattle breeds. We assessed BOA assignment on their genotypes and found that we could assign 99.3% of the alleles to a definite breed of origin. We compared GEBV for 2 traits, protein yield and interval between first and last insemination of cows, with 2 models that both combine estimated marker effects from the genomic evaluations of the pure breeds: a breed of origin model that accounts for BOA and a breed proportion model that only accounts for genomic breed proportions in the crossbred animals. We accounted for the difference in level between the purebred evaluations by including intercepts in the models based on phenotypic averages. The predictive ability for protein yield was significantly higher from the breed of origin model, 0.45 compared with 0.43 from the breed proportion model. Furthermore, for the breed proportion model, the GEBVs had level bias, which made comparison across groups with different breed composition skewed. We therefore concluded that reliable genomic predictions for crossbred dairy cows can be obtained by combining estimated marker effects from the genomic evaluations of purebreds using a model that accounts for BOA.

摘要

基因组预测已应用于奶牛领域十多年,取得了巨大成功,但基因组估计育种值(GEBV)在杂交奶牛中尚未广泛应用。许多纯种的基因组评估已经有了大量的参考群体,利用这些评估中的精确解决方案,特别是估计的标记效应,来计算杂交小母牛和奶牛的GEBV,这很有意义。杂交动物中标记等位基因的效应可能取决于等位基因的品种来源(BOA)。因此,我们的目的是研究是否可以通过结合基于BOA的纯种评估中的估计标记效应,来获得可靠的杂交奶牛GEBV。我们使用了5467头丹麦杂交奶牛的数据,这些奶牛来自荷斯坦、泽西和红丹麦奶牛品种。我们评估了它们基因型的BOA分配情况,发现我们可以将99.3%的等位基因分配到确定的品种来源。我们比较了两个性状的GEBV,即奶牛的蛋白质产量和首次与末次授精之间的间隔,使用了两个模型,这两个模型都结合了纯种基因组评估中的估计标记效应:一个考虑BOA的品种来源模型和一个只考虑杂交动物基因组品种比例的品种比例模型。我们通过在基于表型平均值的模型中纳入截距,来考虑纯种评估之间水平的差异。品种来源模型对蛋白质产量的预测能力显著更高,为0.45,而品种比例模型为0.43。此外,对于品种比例模型,GEBV存在水平偏差,这使得不同品种组成群体之间的比较产生偏差。因此,我们得出结论,通过使用考虑BOA的模型结合纯种基因组评估中的估计标记效应,可以获得可靠的杂交奶牛基因组预测。

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