Rodrigo R Mota1,2, Robert J Tempelman2, Paulo S Lopes1, Ignacio Aguilar3, Fabyano F Silva1, Fernando F Cardoso4. 1. Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil. 2. Department of Animal Science, Michigan State University, East Lansing, USA. 3. Instituto Nacional de Investigación Agropecuaria-INIA Las Brujas-Canelones, Rincón del Colorado, Canelones, Uruguay. 4. Embrapa South Livestock, Bage, Rio Grande do Sul, Brazil and Department of Animal Science, Universidade Federal de Pelotas, Pelotas, Rio Grande do Sul, Brazil. fernando.cardoso@embrapa.br.
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.
BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.
Authors: F F Cardoso; C C G Gomes; B P Sollero; M M Oliveira; V M Roso; M L Piccoli; R H Higa; M J Yokoo; A R Caetano; I Aguilar Journal: J Anim Sci Date: 2015-06 Impact factor: 3.159
Authors: D R Ayres; R J Pereira; A A Boligon; F F Silva; F S Schenkel; V M Roso; L G Albuquerque Journal: J Anim Breed Genet Date: 2013-04-10 Impact factor: 2.380
Authors: Lucas L Verardo; Marcos S Lopes; Pramod Mathur; Ole Madsen; Fabyano F Silva; Martien A M Groenen; Egbert F Knol; Paulo S Lopes; Simone E F Guimarães Journal: Mamm Genome Date: 2017-06-02 Impact factor: 2.957
Authors: Ivan Carvalho Filho; Delvan A Silva; Caio S Teixeira; Thales L Silva; Lucio F M Mota; Lucia G Albuquerque; Roberto Carvalheiro Journal: Animals (Basel) Date: 2022-09-29 Impact factor: 3.231