Wageningen University & Research Animal Breeding and Genomics, PO Box 338, 6700 AH, Wageningen, the Netherlands.
Wageningen University & Research Animal Breeding and Genomics, PO Box 338, 6700 AH, Wageningen, the Netherlands.
J Dairy Sci. 2020 Jan;103(1):556-571. doi: 10.3168/jds.2019-16626. Epub 2019 Nov 6.
Advances in technology and improved data collection have increased the availability of genomic estimated breeding values (gEBV) and phenotypic information on dairy farms. This information could be used for the prediction of complex traits such as survival, which can in turn be used in replacement heifer management. In this study, we investigated which gEBV and phenotypic variables are of use in the prediction of survival. Survival was defined as survival to second lactation, plus 2 wk, a binary trait. A data set was obtained of 6,847 heifers that were all genotyped at birth. Each heifer had 50 gEBV and up to 62 phenotypic variables that became gradually available over time. Stepwise variable selection on 70% of the data was used to create multiple regression models to predict survival with data available at 5 decision moments: distinct points in the life of a heifer at which new phenotypic information becomes available. The remaining 30% of the data were kept apart to investigate predictive performance of the models on independent data. A combination of gEBV and phenotypic variables always resulted in the model with the highest Akaike information criterion value. The gEBV selected were longevity, feet and leg score, exterior score, udder score, and udder health score. Phenotypic variables on fertility, age at first calving, and milk quantity were important once available. It was impossible to predict individual survival accurately, but the mean predicted probability of survival of the surviving heifers was always higher than the mean predicted probability of the nonsurviving group (difference ranged from 0.014 to 0.028). The model obtained 2.0 to 3.0% more surviving heifers when the highest scoring 50% of heifers were selected compared with randomly selected heifers. Combining phenotypic information and gEBV always resulted in the highest scoring models for the prediction of survival, and especially improved early predictive performance. By selecting the heifers with the highest predicted probability of survival, increased survival could be realized at the population level in practice.
技术进步和数据收集的改善增加了基因组估计育种值（gEBV）和奶牛场表型信息的可用性。这些信息可用于预测复杂性状，如存活，进而可用于后备牛管理。在这项研究中，我们研究了哪些 gEBV 和表型变量可用于预测存活。存活定义为第二次泌乳加 2 周的存活，是一个二元性状。获得了一组 6847 头出生时全部进行基因分型的小母牛的数据。每头小母牛有 50 个 gEBV 和多达 62 个表型变量，这些变量随着时间的推移逐渐可用。在 70%的数据上进行逐步变量选择，以创建多个回归模型，使用 5 个决策时刻的数据预测存活：小母牛生命中的不同点，新的表型信息可用。其余 30%的数据保留下来，以调查模型在独立数据上的预测性能。gEBV 和表型变量的组合总是导致具有最高 Akaike 信息准则值的模型。选择的 gEBV 是长寿、蹄腿评分、外貌评分、乳房评分和乳房健康评分。在可用时，与繁殖力、首次配种年龄和产奶量相关的表型变量很重要。不可能准确预测个体存活，但存活小母牛的平均预测存活概率总是高于非存活小母牛的平均预测存活概率（差异范围为 0.014 至 0.028）。与随机选择的小母牛相比，选择评分最高的 50%的小母牛时，获得的模型可多存活 2.0%至 3.0%的小母牛。结合表型信息和 gEBV 总是会为预测存活生成评分最高的模型，特别是提高了早期预测性能。通过选择预测存活概率最高的小母牛，可以在实践中提高群体水平的存活。
