Genotype by environment interactions for body weight in Mediterranean buffaloes using reaction norm models

Fernando B. Rodrigues; Carlos H. M. Malhado; Paulo L. S. Carneiro; Diego P. Ambrosini; Marcos P. G. Rezende; Riccardo Bozzi; Jiuzhou Song

doi:10.17533/udea.rccp.v34n2a05

Authors

Fernando B. Rodrigues Federal Institute of Northern Minas Gerais
Carlos H. M. Malhado State University of the Southwest of Bahia
Paulo L. S. Carneiro State University of the Southwest of Bahia
Diego P. Ambrosini State University of the Southwest of Bahia
Marcos P. G. Rezende State University of the Southwest of Bahia
Riccardo Bozzi University of Florence
Jiuzhou Song University of Maryland

DOI:

https://doi.org/10.17533/udea.rccp.v34n2a05

Keywords:

bayesian inference, breeding value, buffaloes, environment, environmental gradient, genetic evaluation, genotypes, genotype by environment interaction, hierarchical reaction, rank correlations, reaction norm models

Abstract

Background: Buffalo breeding has significantly increased in Brazil over recent years. However, few genetic evaluations have been conducted. Objective: To assess Genotype x Environment Interactions in the Mediterranean Water Buffalo in Brazil, for weight at 205 days of age, using reaction norm models via random regression. Methods: Data for buffaloes born between 1990 and 2014 were collected from five farms ascribed to the Brazilian Buffaloe Improvement Program, located in the North (1), Northeast (1), South (2) and Southeast (1) regions of Brazil. The initial database consisted of 5,280 observations at 205 days of age (W205). We assessed fit using two hierarchical reaction norm models: a two-step (HRNM2s) and a one-step (HRNM1s). Model fit was estimated using the Deviance Information Criterion, Deviance Based on Bayes Factors and Deviance based on Conditional Predictive Ordinate. The environmental descriptors were created to group individuals into common production environments based on year, season, herd and sex. Results: The best fit was obtained for the hierarchical reaction norm model with one-step (HRNM1s). Direct heritability estimates for this model ranged from 0.17 to 0.67 and the maternal heritability from 0.02 to 0.11 with increasing environmental gradient. Lower correlations among the sire classifications were obtained in comparison with HRNM1s in environments with low and high management, confirming the presence of genotype x environment interactions. Conclusion: We recommend a wider application of genetic evaluation in buffalo aimed at identifying optimal genotypes within specific environments.

|Abstract

= 611 veces | PDF

= 528 veces| | HTML

= 0 veces|

Downloads

Download data is not yet available.

References

Ambrosini DP, Bracini Neto J, Martins Filho R, Malhado CHM, Afonso P, Carneiro P. Reaction norms of direct and maternal effects for weight at 205 days in Polled Nellore cattle in North-eastern Brazil. Arch Tierzucht 2014; 57:1-11. https://doi.org/10.7482/0003-9438-57-032.

Ambrosini DP, Carneiro P, Bracini Neto J, Malhado CHM, Martins Filho R, Cardoso FF. Genotype x environment interaction for yearling weight in Polled Nellore cattle in Northeast Brazil. Pesq agropec bras 2012; 47:1489-1495. https://doi.org/10.1590/S0100-204X2012001000011.

Bastianetto E. Water buffalo breed in Brazil: situation and perspective. R Bras Saud Reprod Anim 2009; 6:8-103. http://www.cbra.org.br/pages/publicacoes/rbra/download/p98-103.pdf.

Bernardes O. Buffaloes breeding in Brasil: position and economic relevancy. R Bras Saud Reprod Anim 2007; 31:293-298. https://doi.org/10.4081/ijas.2007.s2.162.

Brooks SP, Roberts GO. Convergence assessment techniques for Markov chain Montes Carlo. Stat Comp 1998; 8:319-335. https://link.springer.com/content/pdf/10.1023/A:1008820505350.pdf.

Cardoso FF. Application of Bayesian inference in animal breeding using the Intergen Program Manual of version 1. 2. Bagé-RS: Embrapa Pecuária Sul. 2010.

Cardoso FF, Tempelman RJ. Linear reaction norm models for genetic merit prediction of angus cattle under genotype by environment interaction. J Anim Sci 2012; 90:2130-2141. https://doi.org/10.2527/jas.2011-4333.

Cardoso LL, Barccini Neto J, Cardoso FF, Cobuci JA, Biassus IO, Barcellos JOJ. Hierarchical bayesian models for genotype x environment estimates in post-weaning gain of Hereford bovine via reaction norms. R Bras Zootec 2011; 40:294-300. https://doi.org/10.1590/S1516-35982011000200009.

Corrêa MBB, Dionello NJL, Cardoso FF. Genotype by environment interaction characterization and model comparison for post weaning gain adjustment of Devon cattle via reaction norms. R Bras Zootec 2009; 38:1468-1477. https://doi.org/10.1590/S1516-35982009000800010.

De Jong G. Phenotypic plasticity as a product of selection in a variable environment. Amer Nat 1995; 145:493-512. https://www.jstor.org/stable/2462965.

De Jong G. Bijma P. Selection and phenotypic plasticity in evolutionary biology and animal breeding. Livest Prod Sci 2002; 78:195-214. https://doi.org/10.1016/S0301-6226(02)00096-9.

Fikse WF, Rekaya R, Weigel KA. Assessment of environmental descriptors for studying genotype by environment interaction. Livest Prod Sci 2003; 82:223-231. https://doi.org/10.1016/S0301-6226(03)00009-5.

Garcia HA, Ramirez OJ, Rodrigues CMF, Sanchez RG, Bethencourt AM, Perez GDM, Minervino AHH, Rodrigues AC, Teixeira MMG. Trypanosoma vivax in water buffalo of the Venezuelan Llanos: An unusual outbreak of wasting disease in an endemic area of typically asymptomatic infections. Vet Parasitol 2016; 230:49-55. https://doi.org/10.1016/j.vetpar.2016.10.013.

Gelfand AE. Model determination using sampling-based methods. In: Markov Chain Monte Carlo in practice. (Eds: WR Gilks, S Richardson, DJ Spiegelhalter) pp. 145-161. (London: Champman and Hall). 1996.

Geweke J. Evaluating the accuracy of sampling-basead approaches to the calculation of posterior moments. In: Bayesian statisti. (Eds: JM Bernardo, JO Berger, AP Dawid, AFM Smit) pp. 1-21. (New York: Oxford University). 1992.

Heidelberger P, Welch P. Simulation run length control in the presence of an initial transient. Operat Res 1983; 31:1109-1144. https://www.jstor.org/stable/170841.

Knap PW, Su G. Genotype by environment interaction for litter size in pigs as quantified by reaction norms analysis. Anim 2008; 2:1742-1747. https://doi.org/10.1017/S1751731108003145.

Kolmodin R, Strandberg E, Madsen P, Jensen J, Jorjani H. Genotype by environment interaction in Nordic dairy cattle studied using reaction norms. Acta Agric Scand A Anim Sci 2002; 52:11-24. https://doi.org/10.1080/09064700252806380.

Malhado CHM, Rezende MPG, Malhado ACM, Azevedo DMMR, Souza JC, Carneiro PLS. Comparison of Nonlinear Models to Describe the Growth Curves of Jaffarabaddi, Mediterranean and Murrah buffaloes. J Agr Sci Tech 2017; 19:1485-1494. https://pdfs.semanticscholar.org/aa77/12ea8e271a886b8b1520030d06651e76241e.pdf?_ga=2.59968621.1129624986.1595351376-1607104821.1592931039.

Malhado CHM, Ramos AA, Carneiro PLS, Azevedo DMMR, Martins Filho R, Souza JC. Improvement and population structure of Mediterranean water buffaloes raised in Brazil. Pesq agropec bras 2008; 43:215-220. https://doi.org/10.1590/S0100-204X2008000200009.

Malhado CHM, Ramos AA, Carneiro PLS, Souza JC, Piccinin A. Genetic and phenotypic parameters for milk production of Murrah buffaloes. R Bras Zootec 2007; 36:376-379. https://doi.org/10.1590/S1516-35982007000200014.

Mattar M, Silva LOC, Alencar MM, Cardoso FF. Genotype x environment interaction for long-yearling weight in Canchim cattle quantified by reaction norm analysis. J Anim Scienc 2011; 89:2349-2355. https://doi.org/10.2527/jas.2010-3770.

Oroian T, Orain R, Pascalãu S, Oroian E, Dronca D. Aspects of the Genotype-Environment Interaction at the Japanese Quail (Coturnix-Coturnix Japonica). Anim Scienc Biotechnol 2010; 43:195-198. http://spasb.ro/index.php/spasb/article/view/756/713.

R Development Core Team. R: A language and environment for estatistical computing. Viena 2008, Áustria: R foundation for statistical computing: [http://www.R-project.org].

Raftery AE, Lewis SM. One long run with diagnostics: implementation strategies for markov chain Monte Carlo. Stat Scienc 1993; 7:493-497. https://projecteuclid.org/download/pdf_1/euclid.ss/1177011143.

Rezende MPG, Ferraz PC, Carneiro PLS, Malhado CHM. Phenotypic diversity in buffalo cows the Jafarabadi, Murrah and Mediterranean breeds. Pesq agropec bras 2017; 52:663-669. https://doi.org/10.1590/s0100-204x2017000800012

Rezende MPG, Malhado CHM., Biffani S, Carneiro PLS, Carrilo JÁ, Bozzi R. Genotype-environment interaction for age at first calving in Limousine and Charolais cattle raised in Italy, employing reaction norm model. Livest Sci 2020; 232;103912. https://doi.org/10.1016/j.livsci.2019.103912

Roso VM, Schenkel FS. AMC: a computer program to assess the degree of connectedness among contemporary groups. In: World Congress on Genetics Applied to Livestock Production. 2006. pp. 26-27. (Belo Horizonte). https://www.researchgate.net/publication/285687222_AMC_-_A_computer_program_to_assess_the_degree_of_connectedness_among_contemporary_groups.

SAS Institute Inc. SAS/STAT. SAS Institute Inc., 2019.

Sesana RC, Baldi F, Borquis RRA, Bignard AB, Hurtado-Lugo NA, El Faro L, Albuquerque LG, Tonhati H. Estimates of genetic parameters for total milk yield over multiple ages in Brazilian Murrah buffaloes using different models. Genet Mol Res 2014; 13:2784-2795. DOI http://dx.doi.org/10.4238/2014.April.14.7.

Smith BJ. Bayesian output analysis program (BOA) version 1.1.7.2 user’s manual. Iowa: University Of Iowa. 2007.

Spiegelhalter DJ, Best NG, Carlin BP, Van der Linde A. Bayesian measures of model complexity and fit (with discussion). J R Stat Soc B 2002; 64:583-639. https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/1467-9868.00353.

Streit M, Reinhardt F, Thaller G, Bennewitz J. Reaction norms and genotype-by-environment interaction in the German Holstein dairy cattle. J Anim Breed Genet 2012; 129:380-389. https://doi.org/10.1111/j.1439-0388.2012.00999.x.

Su G, Madsen P, Lund MS, Sorensen D, Korsgaard IR, Jensen J. Bayesian analysis of the linear reaction norm model with unknown covariates. J Anim Scienc 2006; 84:1651-1657. https://doi.org/10.2527/jas.2005-517.