Cattle international genetic evaluations allow the comparison of estimated breeding values (EBV) across different environments, i.e. countries. For international evaluations, across-country genetic correlations (rg) need to be estimated. However, lack of convergence of the estimated parameters and high standard errors of the rg are often experienced for beef cattle populations due to limited across-country genetic connections. Furthermore, using all available genetic connections to estimate rg is prohibitive due to computational constraints, thus sub-setting the data is necessary. Our objective was to investigate and compare the impact of strategies of data sub-setting on estimated across-country rg and their computational requirements. Phenotype and pedigree information for age-adjusted weaning weight was available for ten European countries and 3,128,338 Limousin beef cattle males and females. Using a Monte Carlo based expectation–maximization restricted maximum likelihood (MC EM REML) methodology, we estimated across-country rg by using a multi-trait animal model where countries are modelled as different correlated traits. Values of rg were estimated using the full data and four different sub-setting strategies that aimed at selecting the most connected herds from the largest population. Using all available data, direct and maternal rg (standard errors in parentheses) were on average equal to 0.79 (0.14) and 0.71 (0.19), respectively. Direct-maternal within-country and between-country rg were on average equal to − 0.12 (0.09) and 0.00 (0.14), respectively. Data sub-setting scenarios gave similar results: on average, estimated rg were smaller compared to using all data for direct (0.02) and maternal (0.05) genetic effects. The largest differences were obtained for the direct-maternal within-country and between-country rg, which were, on average 0.13 and 0.12 smaller compared to values obtained by using all data. Standard errors always increased when reducing the data, by 0.02 to 0.06, on average. The proposed sub-setting strategies reduced the required computing time up to 22% compared to using all data. Estimating all 120 across-country rg that are required for beef cattle international evaluations, using a multi-trait MC EM REML approach, is feasible but involves long computing time. We propose four strategies to reduce computational requirements while keeping a multi-trait estimation approach. In all scenarios with data sub-setting, the estimated rg were consistently smaller (mainly for direct-maternal rg) and had larger standard errors.

中文翻译：

---

将利木赞肉牛主要种群数据的子集化对跨国遗传相关性估计的影响。

牛国际基因评估可以比较不同环境（即国家/地区）的估计育种值（EBV）。对于国际评估，需要估算跨国遗传相关性（rg）。然而，由于有限的跨国遗传联系，肉牛种群常常缺乏估计参数的收敛性，并且rg的高标准误差也经常出现。此外，由于计算限制，使用所有可用的遗传联系来估计rg是禁止的，因此需要对数据进行子设置。我们的目的是调查和比较数据子集策略对估计的全国RG的影响及其计算要求。欧洲有10个国家和3128338头利穆赞肉牛雄性和雌性提供了年龄调整后的断奶体重的表型和谱系信息。使用基于蒙特卡洛的期望最大化最大化限制最大似然（MC EM REML）方法，我们通过使用多特征动物模型（其中的国家被建模为不同的相关特征）来估算全国范围的RG。rg的值是使用完整数据和四种不同的亚组策略估算的，这些策略旨在从最大的种群中选择联系最紧密的种群。使用所有可用数据，直接和母体的rg（括号中的标准误差）平均分别等于0.79（0.14）和0.71（0.19）。国家/地区内直接孕产妇和国家间平均Rg平均分别为− 0.12（0.09）和0.00（0.14）。数据子集情景给出了相似的结果：与使用所有直接（0.02）和母体（0.05）数据的数据相比，平均估计的rg较小。与直接使用母体的国家内部和国家之间的rg相比，差异最大，与使用所有数据获得的值相比，平均差异分别小0.13和0.12。当减少数据时，平均标准误差通常会增加0.02至0.06。与使用所有数据相比，建议的子设置策略将所需的计算时间减少了22％。使用多特征MC EM REML方法估算肉牛国际评估所需的全部120个越野车是可行的，但计算时间较长。我们提出了四种策略，以减少计算需求，同时保持多特征估计方法。