Evidence map›Paper›PMID 31221081›Full record

ArticleGenetics, selection, evolution : GSE2019

Unraveling genetic sensitivity of beef cattle to environmental variation under tropical conditions.

Roberto Carvalheiro, Roy Costilla, Haroldo H R Neves, Lucia G Albuquerque, Stephen Moore, Ben J Hayes

Abstract read
In one paragraph

Article in Genetics, selection, evolution : GSE, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed.

  1. Article
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  5. Beyond black and white: dissecting the genetic basis of skin depigmentation in Nellore cattle.Mammalian genome : official journal of the International Mammalian Genome Society · 2025
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  10. Analysis of Runs of Homozygosity in Aberdeen Angus Cattle.Animals : an open access journal from MDPI · 2024
    Article
  11. Article
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  15. Partitioning the forms of genotype-by-environment interaction in the reaction norm analysis of stability.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023
    Article
  16. Article
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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

6 authors.

Roberto CarvalheiroSchool of Agricultural and Veterinarian Sciences, Sao Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil. roberto.carvalheiro@unesp.br.ORCID http://orcid.org/0000-0002-4506-0555
Roy CostillaInstitute for Molecular Bioscience (IMB), University of Queensland, St. Lucia, QLD, 4072, Australia.
Haroldo H R NevesGenSys Associated Consultants, Porto Alegre, RS, 90680-000, Brazil.
Lucia G AlbuquerqueSchool of Agricultural and Veterinarian Sciences, Sao Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil.
Stephen MooreQueensland Alliance for Agriculture and Food Innovation (QAAFI), Centre for Animal Science, University of Queensland, St. Lucia, QLD, 4072, Australia.
Ben J HayesQueensland Alliance for Agriculture and Food Innovation (QAAFI), Centre for Animal Science, University of Queensland, St. Lucia, QLD, 4072, Australia.

Funding

Fundação de Amparo à Pesquisa do Estado de São Paulo 17/10630-2Fundação de Amparo à Pesquisa do Estado de São Paulo 17/18415-3
6 · The paper itself

Abstract

backgroundSelection of cattle that are less sensitive to environmental variation in unfavorable environments and more adapted to harsh conditions is of primary importance for tropical beef cattle production systems. Understanding the genetic background of sensitivity to environmental variation is necessary for developing strategies and tools to increase efficiency and sustainability of beef production. We evaluated the degree of sensitivity of beef cattle performance to environmental variation, at the animal and molecular marker levels (412 K single nucleotide polymorphisms), by fitting and comparing the results of different reaction norm models (RNM), using a comprehensive dataset of Nellore cattle raised under diverse environmental conditions.

resultsHeteroscedastic RNM (with different residual variances for environmental level) provided better fit than homoscedastic RNM. In addition, spline and quadratic RNM outperformed linear RNM, which suggests the existence of a nonlinear genetic component affecting the performance of Nellore cattle. This nonlinearity indicates that within-animal sensitivity depends on the environmental gradient (EG) level and that animals may present different patterns of sensitivity according to the range of environmental variations. The spline RNM showed that sensitivity to environmental variation from harsh to average EG is lowly correlated with sensitivity from average to good EG, at both the animal and molecular marker levels. Although the genomic regions that affect sensitivity in harsher environments were not the same as those associated with less challenging environments, the candidate genes within those regions participate in common biological processes such as those related to inflammatory and immune response. Some plausible candidate genes were identified.

conclusionsSensitivity of tropical beef cattle to environmental variation is not continuous along the environmental gradient, which implies that animals that are less sensitive to harsher conditions are not necessarily less responsive to variations in better environmental conditions, and vice versa. The same pattern was observed at the molecular marker level, i.e. genomic regions and, consequently, candidate genes associated with sensitivity to harsh conditions were not the same as those associated with sensitivity to less challenging conditions.

Indexed as

Gene-Environment InteractionAnimalsCattleFemaleGenome-Wide Association StudyMalePolymorphism, Single NucleotideTropical ClimateWeight Gain

Identifiers

PMID31221081
PMCPMC6585094

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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.