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多品种肉牛群体生产寿命的遗传评估

Genetic evaluation of productive longevity in a multibreed beef cattle population.

作者信息

Passafaro Tiago Luciano, Rubio Yeni Liliana Bernal, Vukasinovic Natascha, Gonzalez-Peña Dianelys, Gordo Daniel Gustavo Mansan, Short Thomas, Leachman Lee, Andersen Kent

机构信息

Zoetis Inc, Livestock Genetics and Precision Animal Health VMRD, Kalamazoo, MI 49007, USA.

Leachman Cattle of Colorado, Fort Collins, CO 80524, USA.

出版信息

J Anim Sci. 2024 Jan 3;102. doi: 10.1093/jas/skae363.

DOI:10.1093/jas/skae363
PMID:39626265
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11683841/
Abstract

Genetic selection for traits that have a direct impact on profitability, such as productive longevity (PL), which blends cow longevity with regular reproductive performance, is fundamental for the economic success of beef cow-calf operations. The purpose of this study was to develop a data screening strategy and a statistical model to predict genetic merit for PL in a multibreed beef cattle population. Pedigree (n = 1,352,765) and phenotype (n = 978,382) information were provided by Leachman Cattle of Colorado, and genotypes (n = 26,342) were provided by the Zoetis commercial genotyping laboratory. A repeatability model (REP) including the systematic effects of age at first calving, year-season of progeny birth, pedigree-based retained heterosis, and parity number, as well as the random effects of the additive genetic, permanent environment, contemporary group, and residual were fitted to adjust PL. In addition, a random regression model (RRM) was fitted to investigate PL considering the same effects, with the difference that random effects were regressed on parity. Estimated breeding values (EBV) were obtained by single-step GBLUP (ssGBLUP) and transformed to predict differences in the number of calves through linear regression. Predictive performance was assessed in a group of 7,268 cows born in 2010. Heritability estimates for PL were relatively low, with values of 0.109 for REP and a decreasing trend for RRM with values ranging from 0.16 to 0.04. Repeatability for PL was of moderate magnitude, with values of 0.415 for REP and from 0.29 to 0.57 for RRM. Heritability estimates suggest that most of the phenotypic variation was accounted for by environmental factors, but long-term genetic selection could still be effective. REP was more efficient than RRM, showing the lower number of iterations and time to reach convergence with comparable solutions to RRM. Validation results showed that correlations between EBV and phenotypes (observed/precorrected) increased over the years ranging from 0.04 to 0.92. Repeatability values and the validation approach suggested that using a cow's first record (second parity success or failure) is a reasonably good indicator of posterior performance for PL. Therefore, the inclusion of PL in a multibreed genetic evaluation program and incorporation into selection indexes with existing economic traits can enable more profitable selection and breeding decisions in beef cattle herds.

摘要

对那些直接影响盈利能力的性状进行基因选择至关重要,例如生产寿命(PL),它将母牛寿命与正常繁殖性能相结合,对肉牛犊牛养殖业务的经济成功起着关键作用。本研究的目的是制定一种数据筛选策略和一个统计模型,以预测多品种肉牛群体中PL的遗传价值。科罗拉多州的利奇曼牛业公司提供了系谱信息(n = 1,352,765)和表型信息(n = 978,382),硕腾商业基因分型实验室提供了基因型信息(n = 26,342)。拟合了一个重复性模型(REP),该模型包括初产年龄、后代出生年份季节、基于系谱的保留杂种优势和平胎次等系统效应,以及加性遗传、永久环境、当代组和残差等随机效应,用于调整PL。此外,拟合了一个随机回归模型(RRM),考虑相同的效应来研究PL,不同之处在于随机效应是按胎次进行回归分析的。通过单步基因组最佳线性无偏预测法(ssGBLUP)获得估计育种值(EBV),并通过线性回归将其转换以预测犊牛数量的差异。在一组2010年出生的7268头母牛中评估预测性能。PL的遗传力估计值相对较低,REP模型的值为0.109,RRM模型的值呈下降趋势,范围从0.16到0.04。PL的重复性为中等水平,REP模型的值为0.415,RRM模型的值在0.29到0.57之间。遗传力估计表明,大部分表型变异是由环境因素造成的,但长期的基因选择仍然可能有效。REP模型比RRM模型更有效,在达到与RRM模型相当的解时,显示出更少的迭代次数和时间。验证结果表明,多年来EBV与表型(观察值/校正后值)之间的相关性从0.04增加到0.92。重复性值和验证方法表明,使用母牛的首次记录(第二胎成功或失败)是PL后期性能的一个相当好的指标。因此,将PL纳入多品种遗传评估计划并与现有的经济性状一起纳入选择指数,可以在肉牛群中实现更有利可图的选择和育种决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4611/11683841/d448ae8c0789/skae363_fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4611/11683841/0e02cebb9110/skae363_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4611/11683841/ebc74cef97f4/skae363_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4611/11683841/22b1bbf4672b/skae363_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4611/11683841/8bb20f5e7143/skae363_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4611/11683841/318f7030ff3f/skae363_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4611/11683841/7ba9fd13aa13/skae363_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4611/11683841/d448ae8c0789/skae363_fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4611/11683841/0e02cebb9110/skae363_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4611/11683841/ebc74cef97f4/skae363_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4611/11683841/22b1bbf4672b/skae363_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4611/11683841/8bb20f5e7143/skae363_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4611/11683841/318f7030ff3f/skae363_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4611/11683841/7ba9fd13aa13/skae363_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4611/11683841/d448ae8c0789/skae363_fig7.jpg

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