Evidence map›Paper›PMID 39626265›Full record

ArticleJournal of animal science2024

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

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

Abstract read
In one paragraph

Article in Journal of animal science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Genetic Associations of Functional Longevity With Sexual Precocity and Growth Traits in Nellore Beef Cattle.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2026
    Article
  2. Genetic Evaluation for Functional Longevity in Nellore Cattle.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2026
    Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Tiago Luciano PassafaroZoetis Inc, Livestock Genetics and Precision Animal Health VMRD, Kalamazoo, MI 49007, USA.ORCID 0000-0003-4490-8611
Yeni Liliana Bernal RubioZoetis Inc, Livestock Genetics and Precision Animal Health VMRD, Kalamazoo, MI 49007, USA.
Natascha VukasinovicZoetis Inc, Livestock Genetics and Precision Animal Health VMRD, Kalamazoo, MI 49007, USA.
Dianelys Gonzalez-PeñaZoetis Inc, Livestock Genetics and Precision Animal Health VMRD, Kalamazoo, MI 49007, USA.
Daniel Gustavo Mansan GordoZoetis Inc, Livestock Genetics and Precision Animal Health VMRD, Kalamazoo, MI 49007, USA.
Thomas ShortZoetis Inc, Livestock Genetics and Precision Animal Health VMRD, Kalamazoo, MI 49007, USA.
Lee LeachmanLeachman Cattle of Colorado, Fort Collins, CO 80524, USA.
Kent AndersenZoetis Inc, Livestock Genetics and Precision Animal Health VMRD, Kalamazoo, MI 49007, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

BreedingLongevityAnimalsCattleFemaleGenotypeMaleModels, GeneticPedigreePhenotypeReproductionSelection, Geneticbeef cattlemultibreednumber of calvesproductive longevityrepeatability

Identifiers

PMID39626265
PMCPMC11683841

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.