Evidence map›Paper›PMID 41007931›Full record

ArticleAnimals : an open access journal from MDPI2025

Heterogeneity of Variances in Milk Yield in Murrah Buffaloes.

Raimundo Nonato Colares Camargo Júnior, Cláudio Vieira de Araújo, José Ribamar Felipe Marques, Marina de Nadai Bonin Gomes, Welligton Conceição da Silva, Tatiane Silva Belo, Carlos Eduardo Lima Sousa, Éder Bruno Rebelo da Silva, Larissa Coelho Marques, Mauro Marinho da Silva and 6 more

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

16 authors.

Raimundo Nonato Colares Camargo JúniorPostgraduate Program in Animal Science (PPGCAN), Institute of Veterinary Medicine, Federal University of Para (UFPA), Castanhal 68746-360, PA, Brazil.ORCID 0000-0003-2362-3625
Cláudio Vieira de AraújoDepartment of Agricultural and Environmental Sciences, Federal University of Mato Grosso (UFMT), Sinop 78550-728, MT, Brazil.ORCID 0000-0001-9378-7348
José Ribamar Felipe MarquesEmbrapa Eastern Amazon, Belém 66095-903, PA, Brazil.
Marina de Nadai Bonin GomesPostgraduate Program in Animal Science, Faculty of Veterinary Medicine and Animal Science, Federal University of Mato Grosso do Sul, Campo Grande 79074-460, MS, Brazil.
Welligton Conceição da SilvaPostgraduate Program in Animal Science (PPGCAN), Institute of Veterinary Medicine, Federal University of Para (UFPA), Castanhal 68746-360, PA, Brazil.ORCID 0000-0001-9287-0465
Tatiane Silva BeloDepartment of Veterinary Medicine, University Center of the Amazon (UNAMA), Santarém 68010-200, PA, Brazil.ORCID 0000-0002-0515-6640
Carlos Eduardo Lima SousaDepartment of Veterinary Medicine, University Center of the Amazon (UNAMA), Santarém 68010-200, PA, Brazil.ORCID 0009-0007-7940-927X
Éder Bruno Rebelo da SilvaPostgraduate Program in Animal Science (PPGCAN), Institute of Veterinary Medicine, Federal University of Para (UFPA), Castanhal 68746-360, PA, Brazil.ORCID 0000-0002-2964-8471
Larissa Coelho MarquesDepartment of Veterinary Medicine, University Center of the Amazon (UNAMA), Belém 66060-902, PA, Brazil.
Mauro Marinho da SilvaPostgraduate Program in Animal Science (PPGCAN), Institute of Veterinary Medicine, Federal University of Para (UFPA), Castanhal 68746-360, PA, Brazil.
Marcio Luiz Repolho PicançoFederal Institute of Education, Science and Technology of Pará (IFPA), Santarém 68020-820, PA, Brazil.
José de Brito Lourenço-JúniorPostgraduate Program in Animal Science (PPGCAN), Institute of Veterinary Medicine, Federal University of Para (UFPA), Castanhal 68746-360, PA, Brazil.
Alison Miranda SantosPostgraduate Program in Animal Science (PPGCAN), Institute of Veterinary Medicine, Federal University of Para (UFPA), Castanhal 68746-360, PA, Brazil.
Albiane Sousa de OliveiraPostgraduate Program in Animal Science, Faculty of Veterinary Medicine and Animal Science, Federal University of Mato Grosso do Sul, Campo Grande 79074-460, MS, Brazil.
Jaqueline Rodrigues Ferreira CaraPostgraduate Program in Animal Science, Faculty of Veterinary Medicine and Animal Science, Federal University of Mato Grosso do Sul, Campo Grande 79074-460, MS, Brazil.ORCID 0000-0002-4938-1970
André Guimaraes Maciel E SilvaPostgraduate Program in Animal Science (PPGCAN), Institute of Veterinary Medicine, Federal University of Para (UFPA), Castanhal 68746-360, PA, Brazil.ORCID 0000-0002-0020-2951

Funding

Marina de Nadai Bonim Gomes 001
6 · The paper itself

Abstract

The aim of this study was to assess the presence of heterogeneity of variance in milk yield in the first lactation of buffaloes and its subsequent influence on the genetic evaluation of Murrah breed sires. The analysis utilized a dataset comprising 2392 milk yield records of buffaloes involved in the Programa de Melhoramento de Búfalos do Brasil. The standard deviation classes were established by standardizing the averages of contemporary group levels, with positive values constituting the high standard deviation class and values equaling or less than zero comprising the low standard deviation class. The linear mixed model incorporated fixed effects of sire group, buffalo age at calving, and heterozygosity as covariates, along with additive genetic random effects. Variance components were estimated via Bayesian inference employing the Gibbs sampler to derive posterior means. The average posterior heritability obtained in analyses without considering heterogeneity of variances (i.e., the "general analysis") was 0.21, while the averages 0.19 and 0.34 were obtained for the low and high standard deviation classes, respectively. The genetic correlation between standard deviation classes was 0.61. The genetic correlation estimates between the predictions of breeding values for milk yield were more closely aligned between the predictions obtained in the general analysis with the low standard deviation class, and more discrepant between the two standard deviation classes. In the animal genetic evaluation model, when heterogeneity of variance is disregarded, the variance components are substantially weighted towards the performance of individuals in the low phenotypic variability class. By disregarding the presence and heterogeneity of variance, the breeding values of the best sires were underestimated.

Indexed as

genetic parametersgenotype-environment interactionmilk productionselection

Identifiers

PMID41007931
PMCPMC12466676

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.