Chafai Narjice, Badaoui Bouabid
Laboratory of Biodiversity, Ecology and Genome, Department of Biology, Faculty of Sciences, Mohammed V University in Rabat, B.P. 1014 RP, Rabat 10100, Morocco.
African Sustainable Agriculture Research Institute (ASARI), Mohammed VI Polytechnic University (UM6P), Laayoune 70000, Morocco.
Animals (Basel). 2024 Dec 15;14(24):3614. doi: 10.3390/ani14243614.
Reproductive efficiency is a key element of profitability in dairy herds. However, the genetic evaluation of fertility traits is often challenged by the presence of high censorship rates due to various reasons. An easy approach to address this challenge is to remove the censored data from the dataset. However, removing data might bias the genetic evaluation. Therefore, addressing this issue is crucial, particularly for small populations and populations with limited size. This study uses a Moroccan Holstein dataset to compare two Gaussian linear models and a threshold linear model to handle censored records of days open (DO). Data contained 8646 records of days open across the first three parities of 6337 Holstein cows. The pedigree file comprised 11,555 animals and 14.51% of the dataset was censored. The genetic parameters and breeding values of DO were computed using three different methods: a linear model where all censored records were omitted (LM), a penalty method in which a constant equal to one estrus cycle in cattle was added to the maximum value of DO in each contemporary group to impute the censored records (PLM), and a bivariate threshold model with a penalty (PTM). The heritability estimates were equal to 0.021 ± 0.01 (PLM), 0.029 ± 0.01 (LM), and 0.033 ± 0.01 (PTM). The penalty method and the threshold linear model with a penalty showed better prediction accuracy calculated using the LR method (0.21, and 0.20, respectively). PLM and PTM had a high Spearman correlation (0.99) between the estimated breeding values of the validation dataset, which explains the high percentage of common animals in the top 20% of selected animals. The lack of changes in the ranking of animals between PLM and PTM suggests that both methods can be used to address censored data in this population.
繁殖效率是奶牛群盈利能力的关键要素。然而，由于各种原因，高删失率的存在常常给繁殖性状的遗传评估带来挑战。解决这一挑战的一个简单方法是从数据集中移除删失数据。然而，移除数据可能会使遗传评估产生偏差。因此，解决这个问题至关重要，特别是对于小群体和规模有限的群体。本研究使用一个摩洛哥荷斯坦数据集，比较两个高斯线性模型和一个阈值线性模型，以处理产犊间隔（DO）的删失记录。数据包含6337头荷斯坦奶牛前三胎的8646条产犊间隔记录。系谱文件包含11555只动物，数据集的14.51%被删失。使用三种不同方法计算DO的遗传参数和育种值：一种线性模型，其中所有删失记录均被省略（LM）；一种惩罚方法，其中在每个当代组中，将等于牛一个发情周期的常数加到DO的最大值上，以估算删失记录（PLM）；以及一种带惩罚的二元阈值模型（PTM）。遗传力估计值分别为0.021±0.01（PLM）、0.029±0.01（LM）和0.033±0.01（PTM）。惩罚方法和带惩罚的阈值线性模型使用LR方法计算时显示出更好的预测准确性（分别为0.21和0.20）。PLM和PTM在验证数据集的估计育种值之间具有较高的斯皮尔曼相关性（0.99），这解释了在前20%的选定动物中常见动物的高比例。PLM和PTM之间动物排名没有变化，这表明两种方法均可用于处理该群体中的删失数据。
