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Multiple-breed reaction norm animal model accounting for robustness and heteroskedastic in a Nelore–Angus crossed population

Published online by Cambridge University Press:  12 January 2016

M. M. Oliveira ,
M. L. Santana  and
F. F. Cardoso
M. M. Oliveira
Affiliation:
Universidade Federal de Pelotas (UFPEL), Pelotas, RS, Brazil
M. L. Santana
Affiliation:
Grupo de Melhoramento Animal de Mato Grosso (GMAT), Instituto de Ciências Agrárias e Tecnológicas, Universidade Federal de Mato Grosso, Campus Universitário de Rondonópolis, MT-270, Km 06, CEP 78735-901, Rondonópolis, MT, Brazil
F. F. Cardoso*
Affiliation:
Universidade Federal de Pelotas (UFPEL), Pelotas, RS, Brazil Embrapa Pecuária Sul, C. Postal 242-BR 153-Km 633, CEP 96.401-970, Bagé, RS, Brazil. Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brasília, Brazil
*
†E-mail: fernando.cardoso@embrapa.br
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Abstract

Our objective was to genetically characterize post-weaning weight gain (PWG), over a 345-day period after weaning, of Brangus-Ibagé (Nelore×Angus) cattle. Records (n=4016) were from the foundation herd of the Embrapa South Livestock Center. A Bayesian approach was used to assess genotype by environment (G×E) interaction and to identify a suitable model for the estimation of genetic parameters and use in genetic evaluation. A robust and heteroscedastic reaction norm multiple-breed animal model was proposed. The model accounted for heterogeneity of residual variance associated with effects of breed, heterozygosity, sex and contemporary group; and was robust with respect to outliers. Additive genetic effects were modeled for the intercept and slope of a reaction norm to changes in the environmental gradient. Inference was based on Monte Carlo Markov Chain of 110 000 cycles, after 10 000 cycles of burn-in. Bayesian model choice criteria indicated the proposed model was superior to simpler sub-models that did not account for G×E interaction, multiple-breed structure, robustness and heteroscedasticity. We conclude that, for the Brangus-Ibagé population, these factors should be jointly accounted for in genetic evaluation of PWG. Heritability estimates increased proportionally with improvement in the environmental conditions gradient. Therefore, an increased proportion of differences in performance among animals were explained by genetic factors rather than environmental factors as rearing conditions improved. As a consequence response to selection may be increased in favorable environments.

Keywords

beef cattlegenotype×environment interactiongenetic parametersplasticitygrowth
Type
Research Article
Information
animal , Volume 10 , Issue 7 , July 2016 , pp. 1093 - 1100
Copyright
© The Animal Consortium 2016 

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References

Arthur, PF, Hearnshaw, H and Stephenson, PD 1999. Direct and maternal additive and heterosis effects from crossing Bos indicus and Bos taurus cattle: cow and calf performance in two environments. Livestock Production Science 57, 231–241.CrossRefGoogle Scholar
Calus, MP, Groen, AF and de Jong, G 2002. Genotype×environment interaction for protein yield in Dutch dairy cattle as quantified by different models. Journal of Dairy Science 85, 3115–3123.CrossRefGoogle Scholar
Cantet, RJC and Fernando, RL 1995. Prediction of breeding values with additive animal-models for crosses from 2 populations. Genetics Selection Evolution 27, 323–334.CrossRefGoogle Scholar
Cardoso, FF 2010. Application of bayesian inference in animal breeding using the Intergen program: Manual of version 1.2. In Documents 112, Embrapa Southern Region Animal Husbandry, Bagé, RS, Brazil.Google Scholar
Cardoso, FF, Rosa, GJM and Tempelman, RJ 2005. Multiple-breed genetic inference using heavy-tailed structural models for heterogeneous residual variances. Journal of Animal Science 83, 1766–1779.CrossRefGoogle ScholarPubMed
Cardoso, FF and Tempelman, RJ 2004. Hierarchical Bayes multiple-breed inference with an application to genetic evaluation of a Nelore-Hereford population. Journal of Animal Science 82, 1589–1601.CrossRefGoogle ScholarPubMed
Cardoso, FF and Tempelman, RJ 2012. Linear reaction norm models for genetic merit prediction of Angus cattle under genotype by environment interaction. Journal of Animal Science 90, 2130–2141.CrossRefGoogle ScholarPubMed
Cardoso, LL, Braccini Neto, J, Cardoso, FF, Cobuci, JA, IdO, Biassus and Barcellos, JOJ 2011. Hierarchical Bayesian models for genotype × environment estimates in post-weaning gain of Hereford bovine via reaction norms. Brazilian Journal of Animal Science 40, 294–300.Google Scholar
Carvalheiro, R, Pimentel, ECG, Cardoso, V, Queiroz, SA and Fries, LA 2006. Genetic effects on preweaning weight gain of Nelore-Hereford calves according to different models and estimation methods. Journal of Animal Science 84, 2925–2933.CrossRefGoogle ScholarPubMed
Corrêa, MBB, Dionello, NJL and Cardoso, FF 2009. Genotype by environment interaction characterization and model comparison for post weaning gain adjustment of Devon cattle via reaction norms. Brazilian Journal of Animal Science 38, 1460–1467.Google Scholar
Costa, CN, Blake, RW, Pollak, EJ, Oltenacu, PA, Quaas, RL and Searle, SR 2000. Genetic analysis of Holstein cattle populations in Brazil and the United States. Journal of Dairy Science 83, 2963–2974.CrossRefGoogle ScholarPubMed
de Mattos, D, Bertrand, JK and Misztal, I 2000. Investigation of genotype×environment interactions for weaning weight for Herefords in three countries. Journal of Animal Science 78, 2121–2126.CrossRefGoogle Scholar
Falconer, DS and Mackay, TFC 1996. Introduction to quantitative genetics. Longman Group Ltd, Harlow, England.Google Scholar
Ferreira, VCP, Penna, VM, Bergmann, JAG and Torres, RA 2001. Genotype environmental interaction in some growth traits of beef cattle in Brazil. Arquivo Brasileiro De Medicina Veterinaria e Zootecnia 53, 385–392.CrossRefGoogle Scholar
Garrick, DJ, Pollak, EJ, Quaas, RL and Vanvleck, LD 1989. Variance heterogeneity in direct and maternal weight traits by sex and percent purebred for Simmental-sired calves. Journal of Animal Science 67, 2515–2528.CrossRefGoogle ScholarPubMed
Gelfand, AE 1996. Model determination using sampling-based methods. In Markov Chain Monte Carlo in practice (ed. WR Gilks, S Richardson and DJ Spiegelhalter), pp. 145–161. Champman & Hall, London.Google Scholar
Kinghorn, B 1980. The expression of recombination loss in quantitative traits. Zeitschrift Fur Tierzuchtung Und Zuchtungsbiologie-Journal of Animal Breeding and Genetics 97, 138–143.Google Scholar
Kirkpatrick, M, Lofsvold, D and Bulmer, M 1990. Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124, 979–993.CrossRefGoogle ScholarPubMed
Kizilkaya, K and Tempelman, RJ 2005. A general approach to mixed effects modeling of residual variances in generalized linear mixed models. Genetics Selection Evolution 37, 31–56.CrossRefGoogle ScholarPubMed
Kolmodin, R, Strandberg, E, Madsen, P, Jensen, J and Jorjani, H 2002. Genotype by environment interaction in Nordic dairy cattle studied using reaction norms. Acta Agriculturae Scandinavica Section a-Animal Science 52, 11–24.Google Scholar
Lange, K and Sinsheimer, JS 1993. Normal/independent distributions and their applications in robust regression. Journal of the American Statistical Association 2, 175–198.Google Scholar
Lee, DH and Bertrand, JK 2002. Investigation of genotype×country interactions for growth traits in beef cattle. Journal of Animal Science 80, 330–337.CrossRefGoogle ScholarPubMed
Lo, LL, Fernando, RL and Grossman, M 1993. Covariance between relatives in multibreed populations: additive model. Theoretical and Applied Genetics 87, 423–430.CrossRefGoogle ScholarPubMed
Long, CR 1980. Crossbreeding for beef-production – experimental results. Journal of Animal Science 51, 1197–1223.CrossRefGoogle Scholar
Mattar, M, Silva, LOC, Alencar, MM and Cardoso, FF 2011. Genotype×environment interaction for long-yearling weight in Canchim cattle quantified by reaction norm analysis. Journal of Animal Science 89, 2349–2355.CrossRefGoogle Scholar
Oliveira, MM, Cardoso, FF and Osório, JCS 2010. Variance components and genetic parameters in a Nelore-Angus multibreed population under Bayesian Approach. Brazilian Journal of Animal Science 39, 2426–2433.Google Scholar
Oliveira, MM, Cardoso, FF and Osório, JCS 2011. Robust and heteroskedastic inference in multibreed variance components, genetic parameters and breeding values. Brazilian Journal of Animal Science 40, 772–780.Google Scholar
Oliveira, NM, Salomoni, E, Leal, JJB, Moraes, CF and Del Duca, LOA 1998. Genetic and environmental effects on growth of 3/4 Nelore×5/8 Aberdeen Angus beef cattle derived from different crossbreeding schemes. Archivos Latinoamericanos de Produccion Animal 6, 173–188.Google Scholar
Paschal, JC, Sanders, JO, Kerr, JL, Lunt, DK and Herring, AD 1995. Postweaning and feedlot growth and carcass characteristics of Angus-sired, Gray-Brahman-sired, Gir-sired, Indu-Brazil-sired, Nellore-sired, and Red-Brahman-sired F1 calves. Journal of Animal Science 73, 373–380.CrossRefGoogle Scholar
Robertson, A. 1959. The sampling variance of the genetic correlation coefficient. Biometrics 15, 469–485.CrossRefGoogle Scholar
Rodriguez-Almeida, FA, Vanvleck, LD, Cundiff, LV and Kachman, SD 1995. Heterogeneity of variance by sire breed, sex, and dam breed in 200-day and 365-day weights of beef-cattle from a top cross experiment. Journal of Animal Science 73, 2579–2588.CrossRefGoogle Scholar
Roso, VM and Schenkel, FS 2006. AMC – A computer program to assess the degree of connectedness among contemporary groups. In 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, MG, Brasil, pp. communication no 27–26.Google Scholar
Santana, ML, Eler, JP, Cardoso, FF, Albuquerque, LG and Ferraz, JBS 2013. Phenotypic plasticity of composite beef cattle performance using reaction norms model with unknown covariate. Animal 7, 202–210.CrossRefGoogle ScholarPubMed
Shariati, MM, Su, G, Madsen, P and Sorensen, D 2007. Analysis of milk production traits in early lactation using a reaction norm model with unknown covariates. Journal of Dairy Science 90, 5759–5766.CrossRefGoogle ScholarPubMed
Sorensen, DA and Gianola, D 2002. Likelihood, Bayesian, and MCMC methods in quantitative genetics. Springer-Verlag, New York.CrossRefGoogle Scholar
Spiegelhalter, DJ, Best, NG, Carlin, BP and van derLinde, A 2002. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society Series B-Statistical Methodology 64, 583–616.CrossRefGoogle Scholar
Strandberg, E, Brotherstone, S, Wall, E and Coffey, MP 2009. Genotype by environment interaction for first-lactation female fertility traits in UK dairy cattle. Journal Dairy Science 92, 3437–3446.CrossRefGoogle ScholarPubMed
Stranden, I and Gianola, D 1998. Attenuating effects of preferential treatment with Student-t mixed linear models: a simulation study. Genetics Selection Evolution 30, 565–583.CrossRefGoogle Scholar
Su, G, Madsen, P, Lund, MS, Sorensen, D, Korsgaard, IR and Jensen, J 2006. Bayesian analysis of the linear reaction norm model with unknown covariates. Journal of Animal Science 84, 1651–1657.CrossRefGoogle ScholarPubMed
Toral, FLB, Silva, LOCd, Martins, EN, Gondo, A and Simonelli, SM 2004. Genotype×environment interaction in growth traits of Nellore cattle of Mato Grosso do Sul. Brazilian Journal of Animal Science 33, 1445–1455.Google Scholar
Williams, JL, Łukaszewicz, M, Bertrand, JK and Misztal, I 2012. Genotype by region and season interactions on weaning weight in United States Angus cattle. Journal of Animal Science 90, 3368–3374.CrossRefGoogle ScholarPubMed