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RESEARCH ARTICLE

Genotype by environment interaction for yearling weight in Nellore cattle applying reaction norms models

S. Ribeiro A D , J. P. Eler A , V. B. Pedrosa B , G. J. M. Rosa C , J. B. S. Ferraz A and J. C. C. Balieiro A
+ Author Affiliations
- Author Affiliations

A Grupo de Melhoramento Animal e Biotecnologia, Departamento de Ciências Básicas, Universidade de São Paulo, Av. Duque de Caxias Norte, 225, 13635-000, Pirassununga, SP, Brazil.

B LeMA – Laboratório de estudos em Melhoramento Animal, Departamento de Zootecnia, Universidade Estadual de Ponta Grossa, Av. General Carlos Cavalcanti, 4748, 84010-290, Ponta Grossa, PR, Brazil.

C Department of Dairy Sciences, University of Wisconsin, 1675 Observatory Drive, Madison, WI 53706, USA.

D Corresponding author. Email: sandra_fzea@yahoo.com.br

Animal Production Science 58(11) 1996-2002 https://doi.org/10.1071/AN17048
Submitted: 27 January 2017  Accepted: 30 May 2017   Published: 21 July 2017

Abstract

In the present study, a possible existence of genotype × environment interaction was verified for yearling weight in Nellore cattle, utilising a reaction norms model. Therefore, possible changes in the breeding value were evaluated for 46 032 animals, from three distinct herds, according to the environmental gradient variation of the different contemporary groups. Under a Bayesian approach, analyses were carried out utilising INTERGEN software resulting in solutions of contemporary groups dispersed in the environmental gradient from –90 to +100 kg. The estimates of heritability coefficients ranged from 0.19 to 0.63 through the environmental gradient and the genetic correlation between intercept and slope of the reaction norms was 0.76. The genetic correlation considering all animals of the herds in the environmental gradient ranged from 0.83 to 1.0, and the correlation between breeding values of bulls in different environments ranged from 0.79 to 1.0. The results showed no effect of genotype × environment interaction on yearling weight in the herds of this study. However, it is important to verify a possible influence of the genotype × environment in the genetic evaluation of beef cattle, as different environments might cause interference in gene expression and consequently difference in phenotypic response.

Additional keywords: Bayesian inference, beef cattle, environmental sensitivity, genetic evaluation, random regression.


References

Cardoso FF (2010) ‘Application of Bayesian inference in animal breeding using the Intergen program. Manual of Version 1.2.’ (Embrapa Pecuária Sul: Bagé, RS, Brazil)

Cardoso FF, 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.
Linear reaction norm models for genetic merit prediction of Angus cattle under genotype by environment interaction.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XhtFehsbrO&md5=50c1a8a62456083e74cc9b612b9f5717CAS |

Cardoso LL, Braccini Neto J, Cardoso FF, Cobuci JA, Biassus IDO, 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.

Carvalheiro R, Fries LA, Schenkel FS, Albuquerque LGD (2002) Effects of heterogeneity of residual variance among contemporary groups on genetic evaluation of beef cattle. Brazilian Journal of Animal Science 31, 1680–1688.

Corrêa MBB, Dionello NJL, 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, 1468–1477.

de Jong G, Bijma P (2002) Selection and phenotypic plasticity in evolutionary biology and animal breeding. Livestock Production Science 78, 195–214.
Selection and phenotypic plasticity in evolutionary biology and animal breeding.Crossref | GoogleScholarGoogle Scholar |

Falconer DS (1990) Selection in different environments: effects on environmental sensitivity (reaction norm) and on mean performance. Genetical Research 56, 57–70.
Selection in different environments: effects on environmental sensitivity (reaction norm) and on mean performance.Crossref | GoogleScholarGoogle Scholar |

Falconer DS, Mackay TFC (1996) ‘Introduction to quantitative genetics.’ 4th edn. (Longmans Green: Harlow, Essex, UK)

Guidolin DGF, Buzanskas ME, Ramos SB, Venturini GC, Lôbo RB, Paz CCP, Munari DP, Oliveira JA (2012) Genotype–environment interaction for post-weaning traits in Nellore beef cattle. Animal Production Science 52, 975–980.
Genotype–environment interaction for post-weaning traits in Nellore beef cattle.Crossref | GoogleScholarGoogle Scholar |

Kolmodin R, Bijma P (2004) Response to mass selection when the genotype by environment interaction is modeled as a linear reaction norm. Genetics, Selection, Evolution. 36, 435–454.
Response to mass selection when the genotype by environment interaction is modeled as a linear reaction norm.Crossref | GoogleScholarGoogle Scholar |

Lemos MVA, Chiaia HLJ, Berton MP, Feitosa FLB, Aboujaoude C, Venturini GC, Oliveira HN, Albuquerque LG, Baldi F (2015) Reaction norms for the study of genotype-environment interaction for growth and indicator traits of sexual precocity in Nellore cattle. Genetics and Molecular Research 14, 7151–7162.
Reaction norms for the study of genotype-environment interaction for growth and indicator traits of sexual precocity in Nellore cattle.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC2MbovVaktQ%3D%3D&md5=8eb9738b5f8caf4447d2509c0630b74eCAS |

Mattar M, Silva LOC, Alencar MM, 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.
Genotype × environment interaction for long-yearling weight in Canchim cattle quantified by reaction norm analysis.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXps1yqu74%3D&md5=17a824a503d54b6b1f052c7e52b5fa01CAS |

Pedrosa VB, Groeneveld E, Eler JP, Ferraz JB (2014) Comparison of bivariate and multivariate joint analyses on the selection loss of beef cattle. Genetics and Molecular Research 13, 4036–4045.
Comparison of bivariate and multivariate joint analyses on the selection loss of beef cattle.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC2cfjsVyrug%3D%3D&md5=98f9a3a24e3bf389b2d0f4498e364135CAS |

Pégolo NT, Oliveira HN, Albuquerque LG, Bezerra LAF, Lôbo RB (2009) Genotype by environment interaction for 450-day weight of Nelore cattle analyzed by reaction norm models. Genetics and Molecular Biology 32, 281–287.

Pégolo NT, Albuquerque LG, Lôbo RB, Oliveira HN (2011) Effects of sex and age on genotype × environmental interaction for beef cattle body weight studied using reaction norm models. Journal of Animal Science 89, 3410–3425.
Effects of sex and age on genotype × environmental interaction for beef cattle body weight studied using reaction norm models.Crossref | GoogleScholarGoogle Scholar |

Pfeiffer C, Fuerst C, Schwarzenbacher H, Birgit FW (2016) Genotype by environment interaction in organic and conventional production systems and their consequences for breeding objectives in Austrian Fleckvieh cattle. Livestock Science 185, 50–55.
Genotype by environment interaction in organic and conventional production systems and their consequences for breeding objectives in Austrian Fleckvieh cattle.Crossref | GoogleScholarGoogle Scholar |

Raftery AE, Lewis SM (1992) One long run with diagnostics: implementation strategies for Markov Chain Monte Carlo. Statistical Science 7, 493–497.

Ribeiro S, Eler JP, Pedrosa VB, Rosa GJM, Ferraz JBS, Balieiro JCC (2015) Genotype x environment interaction for weaning weight in Nellore cattle using reaction norm analysis. Livestock Science 176, 40–46.
Genotype x environment interaction for weaning weight in Nellore cattle using reaction norm analysis.Crossref | GoogleScholarGoogle Scholar |

Robertson A (1959) The sampling variance of the genetic correlation coefficient. Biometrics 15, 469–485.
The sampling variance of the genetic correlation coefficient.Crossref | GoogleScholarGoogle Scholar |

Shiotsuki L, Cardoso FF, Silva JA, Albuquerque LG (2013) Comparison of a genetic group and unknown paternity models for growth traits in Nellore cattle. Journal of Animal Science 91, 5135–5143.
Comparison of a genetic group and unknown paternity models for growth traits in Nellore cattle.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhslKktrjF&md5=2130efbc986a53720435039a0d821168CAS |

Smith BJ (2000) ‘Bayesian output analysis program (BOA) version 1.0.0. User’s manual.’ (University of Iowa: Ames, IA)

Sorensen DA, Gianola D (2002) ‘Likelihood, Bayesian and MCMC methods in quantitative genetics.’ (Springer-Verlag New York, Inc.: New York)

Su G, Madsen P, Lund MS, Sorensen D, Korsgaard IR, Jensen J (2006) Bayesian analysis of the linear reaction norm model with unknown covariates. Journal of Animal Science 84, 1651–1657.
Bayesian analysis of the linear reaction norm model with unknown covariates.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28Xmt1aqsbg%3D&md5=49895959d89fa9557f2202b788fa1cf6CAS |