In dairy cattle, genomic selection has been implemented successfully for purebred populations, but, to date, genomic estimated breeding values (GEBV) for crossbred cows are rarely available, although they are valuable for rotational crossbreeding schemes that are promoted as efficient strategies. An attractive approach to provide GEBV for crossbreds is to use estimated marker effects from the genetic evaluation of purebreds. The effects of each marker allele in crossbreds can depend on the breed of origin of the allele (BOA), thus applying marker effects based on BOA could result in more accurate GEBV than applying only proportional contribution of the purebreds. Application of BOA models in rotational crossbreeding requires methods for detecting BOA, but the existing methods have not been developed for rotational crossbreeding. Therefore, the aims of this study were to develop and test methods for detecting BOA in a rotational crossbreeding system, and to investigate methods for calculating GEBV for crossbred cows using estimated marker effects from purebreds. For detecting BOA in crossbred cows from rotational crossbreeding for which pedigree is recorded, we developed the AllOr method based on the comparison of haplotypes in overlapping windows. To calculate the GEBV of crossbred cows, two models were compared: a BOA model where marker effects estimated from purebreds are combined based on the detected BOA; and a breed proportion model where marker effects are combined based on estimated breed proportions. The methods were tested on simulated data that mimic the first four generations of rotational crossbreeding between Holstein, Jersey and Red Dairy Cattle. The AllOr method detected BOA correctly for 99.6% of the marker alleles across the four crossbred generations. The reliability of GEBV was higher with the BOA model than with the breed proportion model for the four generations of crossbreeding, with the largest difference observed in the first generation. In rotational crossbreeding for which pedigree is recorded, BOA can be accurately detected using the AllOr method. Combining marker effects estimated from purebreds to predict the breeding value of crossbreds based on BOA is a promising approach to provide GEBV for crossbred dairy cows.

中文翻译：

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杂交奶牛等位基因起源品种及基因组预测

在奶牛中，纯种牛群的基因组选择已成功实施，但迄今为止，杂交奶牛的基因组估计育种值 (GEBV) 很少可用，尽管它们对于作为有效策略推广的轮作杂交计划很有价值。为杂交种提供 GEBV 的一种有吸引力的方法是使用来自纯种遗传评估的估计标记效应。杂交中每个标记等位基因的影响可能取决于等位基因 (BOA) 的起源品种，因此应用基于 BOA 的标记效应可以比仅应用纯种的比例贡献产生更准确的 GEBV。BOA 模型在轮作杂交中的应用需要检测 BOA 的方法，但现有的方法尚未开发用于轮作杂交。所以，本研究的目的是开发和测试轮作杂交系统中检测 BOA 的方法，并研究使用估计的纯种牛标记效应计算杂交奶牛 GEBV 的方法。为了从记录系谱的轮作杂交中检测杂交奶牛的 BOA，我们开发了基于重叠窗口中单倍型比较的 AllOr 方法。为了计算杂交奶牛的 GEBV，比较了两个模型： BOA 模型，其中根据检测到的 BOA 组合了从纯种奶牛估计的标记效应；和品种比例模型，其中根据估计的品种比例组合标记效应。这些方法在模拟荷斯坦牛、泽西牛和红奶牛之间前四代轮作杂交的模拟数据上进行了测试。AllOr 方法正确检测了四个杂交世代中 99.6% 的标记等位基因的 BOA。BOA模型的GEBV信度在四代杂交中均高于品种比例模型，其中第一代差异最大。在记录系谱的轮作杂交中，可以使用 AllOr 方法准确检测 BOA。结合从纯种估计的标记效应来预测基于 BOA 的杂交种的育种价值是一种为杂交奶牛提供 GEBV 的有前途的方法。在第一代中观察到的最大差异。在记录系谱的轮作杂交中，可以使用 AllOr 方法准确检测 BOA。结合从纯种估计的标记效应来预测基于 BOA 的杂交种的育种价值是一种为杂交奶牛提供 GEBV 的有前途的方法。在第一代中观察到的最大差异。在记录系谱的轮作杂交中，可以使用 AllOr 方法准确检测 BOA。结合从纯种估计的标记效应来预测基于 BOA 的杂交种的育种价值是一种为杂交奶牛提供 GEBV 的有前途的方法。