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热带肉牛后备牛初情期的多元基因组预测。

Multivariate genomic predictions for age at puberty in tropically adapted beef heifers.

机构信息

Department of Animal Science, Texas A&M University, College Station, TX.

Central Queensland University, School of Health, Medical and Applied Sciences, Rockhampton, QLD, Australia.

出版信息

J Anim Sci. 2019 Jan 1;97(1):90-100. doi: 10.1093/jas/sky428.

Abstract

Heifers that have an earlier age at puberty often have greater lifetime productivity. Age at puberty is moderately heritable so selection should effectively reduce the number of days to puberty, and improve heifer productivity and profitability as a result. However, recording age at puberty is intensive, requiring repeat ovarian scanning to determine age at first corpus luteum (AGECL). Genomic selection has been proposed as a strategy to select for earlier age at puberty; however, large reference populations of cows with AGECL records and genotypes would be required to generate accurate GEBV for this trait. Reproductive maturity score (RMS) is a proxy trait for age at puberty for implementation in northern Australia beef herds, where large scale recording of AGECL is not feasible. RMS assigns a score of 0 to 5 from a single ovarian scan to describe ovarian maturity at ~600 d. Here we use multivariate genomic prediction to evaluate the value of a large RMS data set to improve accuracy of GEBV for age at puberty (AGECL). There were 882 Brahman and 990 Tropical Composite heifers with AGECL phenotypes, and an independent set of 974 Brahman, 1,798 Santa Gertrudis, and 910 Droughtmaster heifers with RMS phenotypes. All animals had 728,785 real or imputed SNP genotypes. The correlation of AGECL and RMS (h2 = 0.23) was estimated as -0.83 using the genomic information. This result also demonstrates that using genomic information it is possible to estimate genetic correlations between traits collected on different animals in different herds, with minimal or unknown pedigree linkage between them. Inclusion of heifers with RMS in the multi-trait model improved the accuracy of genomic evaluations for AGECL. Accuracy of RMS GEBV generally did not improve by adding heifers with AGECL phenotypes into the reference population. These results suggest that RMS and AGECL may be used together in a multi-trait prediction model to increase the accuracy of prediction for age at puberty in tropically adapted beef cattle.

摘要

育成牛的初情期年龄较早通常具有更高的终生生产力。初情期年龄具有中度的遗传力,因此选择应该有效地减少达到初情期的天数,并因此提高育成牛的生产力和盈利能力。然而,记录初情期年龄是密集型的,需要重复卵巢扫描以确定首次黄体出现的年龄(AGECL)。基因组选择已被提议作为选择更早的初情期年龄的策略;然而,需要具有 AGECL 记录和基因型的母牛的大型参考群体来为该性状生成准确的 GEBV。生殖成熟评分(RMS)是北澳大利亚肉牛群中初情期年龄的替代性状,在那里大规模记录 AGECL 是不可行的。RMS 在单个卵巢扫描中从 0 到 5 分配一个分数,以描述约 600 天的卵巢成熟度。在这里,我们使用多元基因组预测来评估大型 RMS 数据集对提高初情期年龄(AGECL)GEBV 准确性的价值。有 882 头婆罗门牛和 990 头热带复合育成牛具有 AGECL 表型,以及一组 974 头婆罗门牛、1798 头圣格特鲁迪斯牛和 910 头抗旱牛具有 RMS 表型。所有动物都有 728785 个真实或推断的 SNP 基因型。使用基因组信息估计 AGECL 和 RMS(h2 = 0.23)的相关性为-0.83。该结果还表明,使用基因组信息,可以在不同牛群中不同动物收集的性状之间估计遗传相关性,而它们之间最小或未知的系谱联系。在多性状模型中包含具有 RMS 的育成牛可以提高 AGECL 的基因组评估准确性。通过将具有 AGECL 表型的育成牛添加到参考群体中,RMS 的 GEBV 准确性通常不会提高。这些结果表明,RMS 和 AGECL 可以一起用于多性状预测模型,以提高热带适应性肉牛初情期的预测准确性。

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