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评估奶牛的间接遗传效应

Towards assessing indirect genetic effects in dairy cattle.

作者信息

Hansson Ida, Bijma Piter, Fikse Freddy, Rönnegård Lars

机构信息

Department of Animal Biosciences, Swedish University of Agricultural Sciences, Box 7023, 750 07, Uppsala, Sweden.

Animal Breeding and Genomics, Wageningen University and Research, P.O. Box 338, 6700 AH, Wageningen, The Netherlands.

出版信息

Genet Sel Evol. 2025 Jul 21;57(1):42. doi: 10.1186/s12711-025-00988-w.

Abstract

BACKGROUND

Social interactions in a dairy herd may impact an individual's production, e.g., milk yield. These interactions can have a genetic component, so-called indirect genetic effects (IGE). IGEs contribute to heritable variation in other species, but studies on IGEs in cows are limited. Knowledge is needed on appropriate methods to monitor social interactions in cows. We evaluated with simulations whether we can estimate IGEs in cows. We used milk yield as an example trait, and we assessed how herd size, direct and indirect genetic correlations, and magnitude of IGE affected the variance component estimations and breeding value accuracies. We investigated the importance of knowing the contact intensity and direction by either including or ignoring them in the estimation model. Additionally, we investigated how random noise added to the intensities would affect the estimates and breeding values.

RESULTS

The estimated variance components were unbiased and precise for scenarios with different herd sizes of 50, 100, or 200 cows and direct and indirect genetic correlations of either - 0.6, 0, or 0.6. The IGE breeding value accuracies were 0.55-0.65 for cows when the IGE explained 30% of the phenotypic variance. When the magnitude of the IGE became smaller, the precision of the estimated variances became lower. The IGE breeding value accuracies were 0.16-0.52 for cows when the IGE explained 1.5-15% of the phenotypic variance. Using imprecise intensities or ignoring the contact direction underestimated the variance of the indirect effects, and the breeding value accuracies became lower. Ignoring the variation in intensities in the model led to unbiased variance component estimates but a larger residual variance and lower breeding value accuracies than if we used imprecise intensities.

CONCLUSIONS

We could estimate IGE in dairy cattle with high accuracy and precision in a simulated population of 10,000 phenotyped cows distributed over 50-200 herds. A smaller IGE variance led to less precise estimates and lower breeding value accuracies. Ignoring information about the intensity of contact in the model would be worse than using imprecise intensities, and using technology that also monitors the direction of contact may be beneficial to estimate variance components of IGE.

摘要

背景

奶牛群体中的社会互动可能会影响个体的生产性能,例如产奶量。这些互动可能具有遗传成分,即所谓的间接遗传效应(IGE)。IGE在其他物种中对遗传变异有贡献,但关于奶牛IGE的研究有限。需要了解监测奶牛社会互动的合适方法。我们通过模拟评估了是否能够估计奶牛的IGE。我们以产奶量为例性状,评估了群体规模、直接和间接遗传相关性以及IGE的大小如何影响方差成分估计和育种值准确性。我们通过在估计模型中纳入或忽略接触强度和方向来研究了解它们的重要性。此外,我们研究了添加到强度中的随机噪声如何影响估计值和育种值。

结果

对于奶牛数量分别为50、100或200头且直接和间接遗传相关性分别为 -0.6、0或0.6的不同群体规模的情景,估计的方差成分无偏且精确。当IGE解释30%的表型方差时,奶牛的IGE育种值准确性为0.55 - 0.65。当IGE的大小变小时,估计方差的精度降低。当IGE解释1.5 - 15%的表型方差时,奶牛的IGE育种值准确性为0.16 - 0.52。使用不精确的强度或忽略接触方向会低估间接效应的方差,并且育种值准确性会降低。在模型中忽略强度的变化会导致无偏的方差成分估计,但与使用不精确强度相比,残差方差更大且育种值准确性更低。

结论

在分布于50 - 200个牛群的10000头有表型记录的奶牛模拟群体中,我们能够高精度地估计奶牛的IGE。较小的IGE方差导致估计精度较低和育种值准确性较低。在模型中忽略接触强度信息比使用不精确强度更糟糕,并且使用能够监测接触方向的技术可能有利于估计IGE的方差成分。

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