Combined single-step evaluation of functional longevity of dairy cows including correlated traits.

IF 3.6 1区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE Genetics Selection Evolution Pub Date : 2023-10-25 DOI:10.1186/s12711-023-00839-6
Laure-Hélène Maugan, Roberta Rostellato, Thierry Tribout, Sophie Mattalia, Vincent Ducrocq
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Abstract

Background: For years, multiple trait genetic evaluations have been used to increase the accuracy of estimated breeding values (EBV) using information from correlated traits. In France, accurate approximations of multiple trait evaluations were implemented for traits that are described by different models by combining the results of univariate best linear unbiased prediction (BLUP) evaluations. Functional longevity (FL) is the trait that has most benefited from this approach. Currently, with many single-step (SS) evaluations, only univariate FL evaluations can be run. The aim of this study was to implement a "combined" SS (CSS) evaluation that extends the "combined" BLUP evaluation to obtain more accurate genomic (G) EBV for FL when information from five correlated traits (somatic cell score, clinical mastitis, conception rate for heifers and cows, and udder depth) is added.

Results: GEBV obtained from univariate SS (USS) evaluations and from a CSS evaluation were compared. The correlations between these GEBV showed the benefits of including information from correlated traits. Indeed, a CSS evaluation run without any performances on FL showed that the indirect information from correlated traits to evaluate FL was substantial. USS and CSS evaluations that mimic SS evaluations with data available in 2016 were compared. For each evaluation separately, the GEBV were sorted and then split into 10 consecutive groups (deciles). Survival curves were calculated for each group, based on the observed productive life of these cows as known in 2021. Regardless of their genotyping status, the worst group of heifers based on their GEBV in 2016 was well identified in the CSS evaluation and they had a substantially shorter herd life, while those in the best heifer group had a longer herd life. The gaps between groups were more important for the genotyped than the ungenotyped heifers, which indicates better prediction of future survival.

Conclusions: A CSS evaluation is an efficient tool to improve FL. It allows a proper combination of information on functional traits that influence culling. In contrast, because of the strong selection intensity on young bulls for functional traits, the benefit of such a "combined" evaluation of functional traits is more modest for these males.

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奶牛功能寿命的一步综合评价,包括相关性状。
背景:多年来,多性状遗传评估一直被用来利用相关性状的信息来提高估计育种值(EBV)的准确性。在法国,通过结合单变量最佳线性无偏预测(BLUP)评估的结果,对不同模型描述的性状进行了多性状评估的精确近似。功能寿命(FL)是从这种方法中受益最多的特征。目前,对于许多单步(SS)评估,只能运行单变量FL评估。本研究的目的是实施“组合”SS(CSS)评估,当添加来自五个相关性状(体细胞评分、临床乳腺炎、小母牛和奶牛的受孕率以及乳房深度)的信息时,该评估扩展了“组合”BLUP评估,以获得更准确的FL基因组(G)EBV。结果:比较了单变量SS(USS)评估和CSS评估获得的GEBV。这些GEBV之间的相关性显示了包含相关性状信息的好处。事实上,在FL上没有任何表现的CSS评估运行表明,来自相关性状的间接信息用于评估FL是实质性的。将模拟SS评估的USS和CSS评估与2016年的可用数据进行了比较。对于每个单独的评估,GEBV被分类,然后被分成10个连续的组(十分位数)。根据2021年观察到的这些奶牛的生产寿命,计算各组的生存曲线。无论其基因分型状态如何,根据2016年GEBV,最差的一组小母牛在CSS评估中得到了很好的识别,它们的群体寿命要短得多,而最好的一组则有更长的群体寿命。组间的差距对基因型小母牛来说比未基因型小奶牛更重要,这表明对未来生存的预测更好。结论:CSS评估是改进FL的有效工具。它允许对影响筛选的功能特征的信息进行适当的组合。相比之下,由于年轻公牛对功能性状的选择强度很强,对这些雄性公牛来说,对功能性状进行这种“综合”评估的好处更为温和。
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来源期刊
Genetics Selection Evolution
Genetics Selection Evolution 生物-奶制品与动物科学
CiteScore
6.50
自引率
9.80%
发文量
74
审稿时长
1 months
期刊介绍: Genetics Selection Evolution invites basic, applied and methodological content that will aid the current understanding and the utilization of genetic variability in domestic animal species. Although the focus is on domestic animal species, research on other species is invited if it contributes to the understanding of the use of genetic variability in domestic animals. Genetics Selection Evolution publishes results from all levels of study, from the gene to the quantitative trait, from the individual to the population, the breed or the species. Contributions concerning both the biological approach, from molecular genetics to quantitative genetics, as well as the mathematical approach, from population genetics to statistics, are welcome. Specific areas of interest include but are not limited to: gene and QTL identification, mapping and characterization, analysis of new phenotypes, high-throughput SNP data analysis, functional genomics, cytogenetics, genetic diversity of populations and breeds, genetic evaluation, applied and experimental selection, genomic selection, selection efficiency, and statistical methodology for the genetic analysis of phenotypes with quantitative and mixed inheritance.
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