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Genotype–environment interaction and sexual dimorphism in the genetic evaluation of yearling weight in Simmental cattle raised in Brazil

Published online by Cambridge University Press:  27 January 2023

G. F. Moura ,
C.D.S. Arce ,
J. C. G. Santos ,
D.J.A. Santos ,
R. R. Aspilcueta-Borquis ,
N. T. Pegolo ,
A. P. C. Gomide ,
L. F. A. Marques ,
H. N. Oliveira  and
F. R. Araujo Neto Open the ORCID record for F. R. Araujo Neto [Opens in a new window]
G. F. Moura
Affiliation:
Instituto Federal Goiano (IFGoiano), Campus Rio Verde, Rio Verde, Goiás, Brazil
C.D.S. Arce
Affiliation:
Department of Animal Science, Universidade Estadual Paulista Júlio de Mesquita (UNESP), Campus Jaboticabal, Jaboticabal, SP, Brazil
J. C. G. Santos
Affiliation:
Department of Animal Science, Universidade Estadual Paulista Júlio de Mesquita (UNESP), Campus Jaboticabal, Jaboticabal, SP, Brazil
D.J.A. Santos
Affiliation:
Department of Animal Science, Universidade Estadual Paulista Júlio de Mesquita (UNESP), Campus Jaboticabal, Jaboticabal, SP, Brazil
R. R. Aspilcueta-Borquis
Affiliation:
Universidade Federal Tecnologia do Paraná (UFTPR), Campus Dois Vizinhos, Paraná, Brazil
N. T. Pegolo
Affiliation:
Instituto Federal de São Paulo (IFSP), Campus Avaré, Avaré, SP, Brazil
A. P. C. Gomide
Affiliation:
Instituto Federal Goiano (IFGoiano), Campus Rio Verde, Rio Verde, Goiás, Brazil
L. F. A. Marques
Affiliation:
Centro de Ciências Agrárias, Universidade Federal do Espírito Santo, Alegre, Espirito Santo 29500-000, Brazil
H. N. Oliveira
Affiliation:
Department of Animal Science, Universidade Estadual Paulista Júlio de Mesquita (UNESP), Campus Jaboticabal, Jaboticabal, SP, Brazil
F. R. Araujo Neto*
Affiliation:
Instituto Federal Goiano (IFGoiano), Campus Rio Verde, Rio Verde, Goiás, Brazil
*
Author for correspondence: F. R. Araujo Neto, E-mail: francisco.neto@ifgoiano.edu.br
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Abstract

The aim of this study was to evaluate the effect of genotype–environment interaction (GEI) on the yearling weight of Simmental cattle raised in Brazil, including the sex dimorphism in reaction norm models. The environmental gradient (EG) was formed using the average weight at 365 days of the contemporary groups. Two approaches were adopted in this study to evaluate reaction norms for weight at 365 days: a single-trait model and a multitrait model in which the data for males and females were separated and considered different traits for the analysis of sexual dimorphism. The genetic parameters were estimated using the Bayesian inference and Gibbs sampling. Analysis of the trend of the heritability estimates obtained with the single-trait model along the EG revealed a value of about 0.33 (EG: −21) in the worst environments, which decreased in the intermediate environments and reached a value of 0.24 in EG: −8, with a subsequent increase of the estimates up to 0.51 in EG: +23. Using the multitrait model, similar trends were observed for the heritability estimates, which ranged from 0.25 to 0.54 for males and from 0.23 to 0.50 for females. The results show that the weight of Simmental cattle raised in the tropics is influenced by GEI and greater genetic progress could be obtained by selecting better environments. However, no significant differences in the response to most environmental changes were observed between sexes and there is only evidence of genetic heteroscedasticity in environments with lower production levels.

Keywords

Animal breedingbeef cattleplasticityreaction norm
Type
Animal Research Paper
Information
The Journal of Agricultural Science , Volume 161 , Issue 1 , January 2023 , pp. 148 - 156
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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References

Araujo Neto, FR, Lobo, RB, Mota, MDS and Oliveira, HN (2011) Genetic parameter estimates and response to selection for weight and testicular traits in Nelore cattle. Genetics and Molecular Research 10, 3127–3140.CrossRefGoogle ScholarPubMed
Araujo Neto, FR, Pegolo, NT, Aspilcueta-Borquis, RR, Pessoa, MC, Bonifácio, A, Lobo, RB and Oliveira, HN (2018) Study of the effect of genotype–environment interaction on age at first calving and production traits in Nellore cattle using multi-trait reaction norms and Bayesian inference. Animal Science Journal 89, 939–945.CrossRefGoogle ScholarPubMed
Araujo Neto, FR, Santos, JCG, Arce, CDS, Aspilcueta Borquis, RR, Santos, DJA, Guimarães, KC, Nascimento, AV, Oliveira, HN and Tonhati, H (2022) Genomic study of the resilience of buffalo cows to a negative energy balance. Journal of Applied Genetics 1, 14.Google Scholar
Barr, DJ, Levy, R, Scheepers, C and Tily, HJ (2013) Random effects structure for confirmatory hypothesis testing: keep it maximal. Journal of Memory and Language 68, 255–278.CrossRefGoogle ScholarPubMed
Bates, D, Kliegl, R, Vasishth, S and Baayeb, RH (2015) Parsimonious mixed models. Available at https://arXiv:1506.04967 (stat.ME) (Accessed 13 March 2021).Google Scholar
Brauner, N and Shacham, M (1998) Identifying and removing sources of imprecision in polynomial regression. Mathematics and Computers in Simulation 48, 75–91.CrossRefGoogle Scholar
Braz, CU, Rowan, TN, Schnabel, RD and Decker, JE (2021) Genome-wide association analyses identify genotype-by-environment interactions of growth traits in Simmental cattle. Scientific Reports 11, 13335.CrossRefGoogle ScholarPubMed
Budescu, DV (1980) A note on polynomial regression. Multivariate Behavioral Research 15, 497–506.CrossRefGoogle ScholarPubMed
Caetano, GC, Silva, FF, Calderano, A, Silva, LP, Ribeiro, JC, Oliveira, LT and Mota, RR (2017) Genotype and protein level interaction in growth traits of meat-type quail through reaction norm models. Journal of Animal and Feed Sciences 26, 333–338.CrossRefGoogle Scholar
Carvalheiro, R, Costilla, R, Neves, HHR, Albuquerque, LG, Moore, S and Hayes, BJ (2019) Unraveling genetic sensitivity of beef cattle to environmental variation under tropical conditions. Genetics Selection Evolution 51, 29.CrossRefGoogle ScholarPubMed
Cesarani, A, Garcia, A, Hidalgo, J, Degano, L, Vicario, D, Macciotta, NPP and Lourenco, D (2021) Genomic information allows for more accurate breeding values for milkability in dual-purpose Italian Simmental cattle. Journal of Dairy Science 104, 5719–5727.CrossRefGoogle ScholarPubMed
Chapuis, H, Tixier-Boichard, M, Delabrosse, Y and Ducrocq, V (1996) Multivariate restricted maximum likelihood estimation of genetic parameters for production traits in three selected Turkey strains. Genetics Selection Evolution 28, 197–215.CrossRefGoogle Scholar
Chiaia, HLJ, Lemos, MVA, Venturini, GC, Aboujaoude, C, Berton, MP, Feitosa, FB, Carvalheiro, R, Albuquerque, LG, Oliveira, HN and Baldi, F (2015) Genotype × environment interaction for age at first calving, scrotal circumference, and yearling weight in Nellore cattle using reaction norms in multitrait random regression models. Journal of Animal Science 93, 1503–1510.CrossRefGoogle ScholarPubMed
Corrêa, MBB, Dionello, NJL and Cardoso, FF (2009) Caracterização da interação genótipo-ambiente e comparação entre modelos para ajuste do ganho pós-desmama de bovinos Devon via normas de reação. Revista Brasileira de Zootecnia 38, 1468–1477.Google Scholar
Diaz, IDPS, Araujo Neto, FR, Marques, LFA and Oliveira, HN (2011) Interação genótipo × ambiente e características pré-desmama em animais da raça Simental em duas estações de nascimento. Pesquisa Agropecuária Brasileira 46, 323–330.CrossRefGoogle Scholar
Freitas, GR, Hurtado-Lugo, NA, Santos, DJA, Aspilcueta-Borquis, RR, Pegolo, NT, Tonhati, H and Araujo Neto, FR (2019) Genotype–environment interaction for age at first calving in buffaloes, using the reaction norm model. Reproduction in Domestic Animals 54, 727–732.CrossRefGoogle ScholarPubMed
Garrick, DJ, Pollak, EJ, Quaas, RL and Van Vleck, 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
Ghafouri-Kesbi, F, Mianji, GR, Pirsaraei, ZA, Hafezian, SH, Baneh, H and Soleimani, B (2014) A genetic study on sexual dimorphism of bodyweight in sheep. Animal Production Science 55, 101–106.CrossRefGoogle Scholar
Hammond, J (1947) Animal breeding in relation to nutrition and environmental conditions. Biological Reviews 22, 195–213.CrossRefGoogle ScholarPubMed
Hawkins, DM (2004) The problem of overfitting. Journal of Chemical Information and Computer Sciences 44, 1–12.Google ScholarPubMed
Iung, LHS, Neves, HHR, Mulder, HA and Carvalheiro, R (2017) Genetic control of residual variance of yearling weight in Nellore beef cattle. Journal of Animal Science 95, 1425–1433.CrossRefGoogle ScholarPubMed
Knap, PW and Su, G (2008) Genotype by environment interaction for litter size in pigs as quantified by reaction norms analysis. Animal: An International Journal of Animal Bioscience 2, 1742–1747.Google ScholarPubMed
Lever, J, Krzywinski, M and Altman, N (2016) Model selection and overfitting. Nature Methods 13, 703–704.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 ScholarPubMed
Matuschek, H, Kliegl, R, Vasishth, S, Baayen, H and Bates, D (2017) Balancing type I error and power in linear mixed models. Journal of Memory and Language 94, 305–315.CrossRefGoogle Scholar
Mignon-Grasteau, S, Beaumont, C, Poivey, J and Rochambeau, H (1998) Estimation of the genetic parameters of sexual dimorphism of body weight in label chickens and Muscovy ducks. Genetics Selection Evolution 30, 481–491.Google Scholar
Misztal, I, Tsuruta, S, Strabel, T, Auvray, B, Druet, T and Lee, DH (2002) BLUPF90 and related programs (BGF90). 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France. Available at http://nce.ads.uga.edu/wiki/lib/exe/fetch.php?media=28-07.pdf/ (Accessed 10 September 2021).Google Scholar
Näsholm, A (2004) Influence of sex on genetic expressions and variance of 4-month weight of Swedish lambs. Livestock Production Science 86, 137–142.CrossRefGoogle Scholar
Oikawa, T, Hammond, K and Tier, B (1999) Heterogeneity of variance by sex in postweaning gain of Angus calves under different environment levels. Asian-Australasian Journal of Animal Sciences 12, 846–849.CrossRefGoogle Scholar
Oliveira, DP, Lourenço, DA, Tsuruta, S, Misztal, I, Santos, DJA, Araujo Neto, FR, Aspilcueta-Borquis, RR, Baldi, F, Carvalheiro, R, Camargo, GMF, Albuquerque, LG and Tonhati, H (2018) Reaction norm for yearling weight in beef cattle using single-step genomic evaluation. Journal of Animal Science 96, 27–34.CrossRefGoogle ScholarPubMed
Pegolo, NT, Oliveira, HN, Albuquerque, LG, Bezerra, LAF and 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.CrossRefGoogle ScholarPubMed
Pegolo, NT, Albuquerque, LG, Lôbo, RB and Oliveira, HN (2011) Effects of sex and age on genotype × environment interaction for beef cattle body weight studied using reaction norm models. Journal of Animal Science 89, 3410–3425.CrossRefGoogle ScholarPubMed
Pereira, RN, Serodio, RL, Ventrua, HT, Araujo Neto, FR and Pegolo, NT (2018) Clusters de robustez como critério de seleção no melhoramento genético para mitigação de impactos das mudanças climáticas. Brazilian Journal of Biosystems Engineering 12, 152–163.CrossRefGoogle Scholar
Plummer, M, Best, N, Cowles, K and Vines, K (2006) CODA: Convergence Diagnosis and Output Analysis for MCMC. R News. Available at https://cran.r-project.org/doc/Rnews/Rnews_2006-1.pdf#page=7/ (Accessed 10 September 2021).Google Scholar
Raidan, FSS, Porto-Neto, L and Reverter, A (2019) Across-sex genomic-assisted genetic correlations for sex-influenced traits in Brahman cattle. Genetics Selection Evolution 51, 41.CrossRefGoogle ScholarPubMed
R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at https://www.R-project.org/.Google Scholar
Ribeiro, S, Eler, JP, Pedrosa, VB, Rosa, GJM, Ferraz, JBS and Balieiro, JCC (2015) Genotype × environment interaction for weaning weight in Nellore cattle using reaction norm analysis. Livestock Science 176, 40–46.CrossRefGoogle Scholar
Ritchie, HD (1995) The optimum cow: what criteria must she meet? In: Beef Improvement Federation Annual Conference, Sheridan: BIF. Available at http://www.beefcowefficiency.com/pdf/optcow.pdf/ (Accessed 20 August 2021).Google Scholar
Rodrigues, FB, Carneiro, P, Ramos, AA, Ambrosini, DP and Malhado, CHM (2015) Genotype × environment interaction for weight at 365 days in Mediterranean buffaloes. Pesquisa Agropecuária Brasileira 50, 615–621.CrossRefGoogle Scholar
Rosa, AN, Lôbo, RB, Oliveira, HN and Borjas, AR (2000) Variabilidade genética do peso adulto de matrizes em um rebanho Nelore do Estado de São Paulo. Revista Brasileira de Zootecnia 29, 1706–1711.CrossRefGoogle Scholar
Santana, ML Jr, Bignardi, AB, Eler, JP, Cardoso, FF and Ferraz, JBS (2013) Genotype by environment interaction and model comparison for growth traits of Santa Ines sheep. Journal of Animal Breeding and Genetics 130, 394–403.CrossRefGoogle ScholarPubMed
Santana, ML Jr, Eler, JP, Bignardi, AB, Menéndez-Buxadera, A, Cardoso, FF and Ferraz, JBS (2015) Multi-trait linear reaction norm model to describe the pattern of phenotypic expression of some economic traits in beef cattle across a range of environments. Journal of Applied Genetics 56, 219–229.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
Souza, LA, Malhado, CHM, Neto, JB, Martins Filho, R and Carneiro, P (2016) Genotype–environment interactions on the weight of Tabapuã cattle in the northeast of Brazil. Revista Caatinga 29, 206–215.CrossRefGoogle Scholar
Van Der Heide, EMM, Lourenço, DAL, Chen, CY, Herring, WO, Sapp, RL, Moser, DW, Tsuruta, S, Masuda, Y, Ducro, BJ and Misztal, I (2016) Sexual dimorphism in livestock species selected for economically important traits. Journal of Animal Science 94, 3684–3692.Google ScholarPubMed
Van Vleck, LD and Cundiff, LV (1998) Sex effects on breed of sire differences for birth, weaning and yearling weights. Journal of Animal Science 76, 1528–1534.CrossRefGoogle ScholarPubMed
Veloso, RC, Abreu, LRA, Mota, LFM, Castro, MR, Silva, MA, Pires, AV, Lima, HJD'A and Boari, CA (2015) Modelos de norma de reação para estudo das características de qualidade da carne de codornas de corte em função das razões (metionina + cistina): lisina da dieta. Arquivo Brasileiro de Medicina Veterinária e Zootecnia 67, 1438–1448.CrossRefGoogle Scholar
Wang, X, Miao, J, Chang, T, Xia, J, An, B, Li, Y, Lingyang, X, Lupei, Z, Xue, G, Junya, L and Huijiang, G (2019) Evaluation of GBLUP, BayesB and elastic net for genomic prediction in Chinese Simmental beef cattle. PLoS ONE 14, e0210442.CrossRefGoogle ScholarPubMed
Wittenburg, D, Teuscher, F and Reinsch, N (2011) Statistical tools to detect genetic variation for a se dimorphism in piglet birth weight. Journal of Animal Science 89, 622–629.CrossRefGoogle Scholar