Department of Dairy Science, University of Wisconsin, Madison 53706.
Department of Dairy Science, University of Wisconsin, Madison 53706.
J Dairy Sci. 2019 Dec;102(12):11081-11091. doi: 10.3168/jds.2019-16820. Epub 2019 Sep 20.
Genomic data are widely available in the dairy industry and provide a cost-effective means of predicting genetic merit to inform selection decisions and increase genetic gains. As more dairy farms adopt genomic selection practices, dairy producers will soon have genomic data available on all of the animals within their herds. This is a very rich, but currently underused, source of information. Herdmates provide an excellent indication of how a selection candidate's genetics will perform within a given herd, noting that herdmates often include close relatives that share a similar environment. The study objective was to evaluate the utility of incorporating herdmate data into genomic predictions in a data set composed of 3,303 Holsteins from one herd in Canada and 6 herds throughout the United States. Within-herd prediction accuracy was assessed for milk-production and feed-efficiency traits determined from genomic best linear unbiased prediction under 4 different scenarios. Scenario 1 did not include herdmates in the training population. Scenarios 2 through 4 included herdmates in the training population, and scenarios 3 and 4 also included modeling of herd-specific marker effects. Leave-one-out cross validation was used to maximize the number of herdmates in the training population in scenarios 2 through 4, while maintaining constant training population size with scenario 1. Results from the present study reveal the importance of incorporating herdmate data into genomic evaluations. Inclusion of herdmates in the training population improved mean within-herd prediction accuracy for milk-production traits (± standard error) by 0.08 ± 0.03 (milk yield), 0.07 ± 0.03 (fat percentage), and 0.05 ± 0.01 (protein percentage) and feed-efficiency traits by 0.07 ± 0.02 (milk energy), 0.03 ± 0.02 (DMI), and 0.08 ± 0.01 (metabolic body weight). Modeling herd-specific marker effects further improved mean within-herd prediction accuracy for milk yield and energy by 0.03 ± 0.01 and 0.02 ± 0.01, respectively. Herds with higher within-herd heritability and low genomic correlation with the remaining herds benefitted most from the inclusion of herdmate data.
基因组数据在乳品行业中广泛可用，提供了一种具有成本效益的预测遗传优势的方法，以告知选择决策并增加遗传增益。随着越来越多的奶牛场采用基因组选择实践，奶牛生产者很快将在其牛群中的所有动物身上获得基因组数据。这是一个非常丰富但目前未被充分利用的信息来源。同群牛是候选者在特定牛群中的遗传表现的极佳指示，注意到同群牛通常包括具有相似环境的近亲。本研究的目的是评估在由加拿大一个牛群的 3303 头荷斯坦奶牛和美国 6 个牛群的数据集中，将同群牛数据纳入基因组预测的效用。在 4 种不同的情况下，使用基于基因组最佳线性无偏预测的牛奶产量和饲料效率性状，评估了群体内预测的准确性。情景 1 未将同群牛纳入训练群体。情景 2 至 4 将同群牛纳入训练群体，情景 3 和 4 还包括对特定于群体的标记效应的建模。使用留一法交叉验证来最大化情景 2 至 4 中训练群体中的同群牛数量，同时保持情景 1 中的训练群体大小不变。本研究的结果表明，将同群牛数据纳入基因组评估的重要性。将同群牛纳入训练群体提高了牛奶产量性状的群体内预测准确性（±标准误差）0.08±0.03（产奶量）、0.07±0.03（脂肪百分比）和 0.05±0.01（蛋白质百分比）和饲料效率性状的 0.07±0.02（牛奶能量）、0.03±0.02（DMI）和 0.08±0.01（代谢体重）。对特定于群体的标记效应的建模进一步提高了牛奶产量和能量的群体内预测准确性，分别为 0.03±0.01 和 0.02±0.01。具有较高群体内遗传力和与其余牛群较低基因组相关性的牛群从纳入同群牛数据中获益最多。
