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Genotype by environment interaction for tick resistance of Hereford and Braford beef cattle using reaction norm models

Overview of attention for article published in Genetics Selection Evolution, January 2016
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Title
Genotype by environment interaction for tick resistance of Hereford and Braford beef cattle using reaction norm models
Published in
Genetics Selection Evolution, January 2016
DOI 10.1186/s12711-015-0178-5
Pubmed ID
Authors

Rodrigo R. Mota, Robert J. Tempelman, Paulo S. Lopes, Ignacio Aguilar, Fabyano F. Silva, Fernando F. Cardoso

Abstract

The cattle tick is a parasite that adversely affects livestock performance in tropical areas. Although countries such as Australia and Brazil have developed genetic evaluations for tick resistance, these evaluations have not considered genotype by environment (G*E) interactions. Genetic gains could be adversely affected, since breedstock comparisons are environmentally dependent on the presence of G*E interactions, particularly if residual variability is also heterogeneous across environments. The objective of this study was to infer upon the existence of G*E interactions for tick resistance of cattle based on various models with different assumptions of genetic and residual variability. Data were collected by the Delta G Connection Improvement program and included 10,673 records of tick counts on 4363 animals. Twelve models, including three traditional animal models (AM) and nine different hierarchical Bayesian reaction norm models (HBRNM), were investigated. One-step models that jointly estimate environmental covariates and reaction norms and two-step models based on previously estimated environmental covariates were used to infer upon G*E interactions. Model choice was based on the deviance criterion information. The best-fitting model specified heterogeneous residual variances across 10 subclasses that were bounded by every decile of the contemporary group (CG) estimates of tick count effects. One-step models generally had the highest estimated genetic variances. Heritability estimates were normally higher for HBRNM than for AM. One-step models based on heterogeneous residual variances also usually led to higher heritability estimates. Estimates of repeatability varied along the environmental gradient (ranging from 0.18 to 0.45), which implies that the relative importance of additive and permanent environmental effects for tick resistance is influenced by the environment. Estimated genetic correlations decreased as the tick infestation level increased, with negative correlations between extreme environmental levels, i.e., between more favorable (low infestation) and harsh environments (high infestation). HBRNM can be used to describe the presence of G*E interactions for tick resistance in Hereford and Braford beef cattle. The preferred model for the genetic evaluation of this population for tick counts in Brazilian climates was a one-step model that considered heteroscedastic residual variance. Reaction norm models are a powerful tool to identify and quantify G*E interactions and represent a promising alternative for genetic evaluation of tick resistance, since they are expected to lead to greater selection efficiency and genetic progress.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 5%
Unknown 56 95%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 19%
Student > Ph. D. Student 9 15%
Researcher 8 14%
Student > Doctoral Student 6 10%
Professor 5 8%
Other 10 17%
Unknown 10 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 44%
Veterinary Science and Veterinary Medicine 7 12%
Biochemistry, Genetics and Molecular Biology 2 3%
Psychology 2 3%
Engineering 2 3%
Other 4 7%
Unknown 16 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 May 2017.
All research outputs
#20,655,488
of 25,371,288 outputs
Outputs from Genetics Selection Evolution
#667
of 822 outputs
Outputs of similar age
#297,105
of 402,334 outputs
Outputs of similar age from Genetics Selection Evolution
#11
of 16 outputs
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