Threshold and linear models for genetic evaluation of visual scores in Hereford and Braford cattle
G. S. Campos A D , F. A. Reimann A , P. I. Schimdt A , L. L. Cardoso B , B. P. Sollero B , J. Braccini C , M. J. Yokoo B , A. A. Boligon A and F. F. Cardoso A B
+ Author Affiliations
- Author Affiliations
A Departamento de Zootecnia, Universidade Federal de Pelotas, RS, Brazil, CEP 96160-000.
B Embrapa Pecuária Sul, Bagé, RS, Brazil, CEP 96401-970.
C Departamento de Zootecnia, Universidade Federal do Rio Grande do Sul, RS, Brazil, CEP 91509-900.
D Corresponding author. Email: gabrielsoarescampos@hotmail.com
Animal Production Science 59(4) 619-627 https://doi.org/10.1071/AN17436
Submitted: 30 June 2017 Accepted: 13 February 2018 Published: 9 May 2018
Abstract
Data from 127 539 Hereford and Braford cattle were used to compare estimates of genetic parameters for navel, conformation, precocity, muscling and size visual scores at yearling, using linear and threshold animal models. In a second step, these models were cross-validated using a multinomial logistic regression in order to quantify the association between phenotype and genetic merit for each trait. For navel score, higher heritability was obtained with the threshold model (0.42 ± 0.02) in relation to the linear model (0.22 ± 0.02). However, similar heritability was estimated in both models for conformation, precocity, muscling and size, with values of 0.18 ± 0.01, 0.19 ± 0.01, 0.19 ± 0.01 and 0.26 ± 0.01, respectively, using linear model, and of 0.19 ± 0.01, 0.19 ± 0.01, 0.20 ± 0.01, and 0.29 ± 0.01, respectively, using threshold model. For navel score, Spearman correlations between sires’ breeding values predicted using linear and threshold models ranged from 0.60 (1% of the best sires are selected) to 0.96 (all sires are selected). For conformation, precocity, muscling and size scores, low changes in sires’ rank are expected using these models (Spearman correlations >0.86), regardless of the proportion of sires selected. Except for navel with the linear model, the direction of the associations between phenotype and genetic merit were in accordance with its expectation, as there were increases in the phenotype per unit of change in the breeding value. Thus, the threshold model would be recommended to perform genetic evaluation of navel score in this population. However, linear and threshold models showed similar predictive ability for conformation, precocity, muscling and size scores.
Additional keywords: animal breeding, Bayesian framework, beef cattle, categorical data.
References
Bignardi AB, Gordo DGM, Albuquerque LG, Sesanaii JC (2011) Parâmetros genéticos de escore visual do umbigo em bovinos da raça Nelore. Arquivo Brasileiro de Medicina Veterinária e Zootecnia 63, 941–947.| Parâmetros genéticos de escore visual do umbigo em bovinos da raça Nelore.Crossref | GoogleScholarGoogle Scholar |
Boligon AA, Mercadante MEZ, Albuquerque LGD (2011) Genetic associations of conformation, finishing precocity and muscling visual scores with mature weight in Nelore cattle. Livestock Science 135, 238–243.
| Genetic associations of conformation, finishing precocity and muscling visual scores with mature weight in Nelore cattle.Crossref | GoogleScholarGoogle Scholar |
Boligon AA, Ayres DR, Pereira RJ, Morotti NP, Albuquerque LG (2012) Genetic associations of visual scores with subsequent rebreeding and days to first calving in Nellore cattle. Journal of Animal Breeding and Genetics 129, 448–456.
Boligon AA, Carvalheiro R, Albuquerque LG (2013) Evaluation of mature cow weight: genetic correlations with traits used in selection indices, correlated responses, and genetic trends in Nelore cattle. Journal of Animal Science 91, 20–28.
| Evaluation of mature cow weight: genetic correlations with traits used in selection indices, correlated responses, and genetic trends in Nelore cattle.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXjsFKisb8%3D&md5=44d43f5568b5f07ea582ec9c7aaa5a2cCAS |
Boligon AA, De Vargas L, Silveira DD, Roso VM, Campos GS, Vaz RZ, Souza FRP (2016) Genetic models for breed quality and navel development scores and its associations with growth traits in beef cattle. Tropical Animal Health and Production 48, 1679–1684.
| Genetic models for breed quality and navel development scores and its associations with growth traits in beef cattle.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC2svgsVKgsw%3D%3D&md5=76392e3c1d0da2a1ad55c57323ad461fCAS |
Cardoso FF, Cardellino RA, Campos LT (2004) Componentes de (co) variância e parâmetros genéticos de caracteres pós-desmama em bovinos da raça Angus. Revista Brasileira de Zootecnia 33, 313–319.
| Componentes de (co) variância e parâmetros genéticos de caracteres pós-desmama em bovinos da raça Angus.Crossref | GoogleScholarGoogle Scholar |
Conexão Delta G (2017) Sumário de avaliação de reprodutores – Gensys Consultores Associados S/C Ltda. Available at http://www.gensys.com.br/2017 [Verified August 2015]
Everling DM, Rorato PRN, Araújo ROD, Boligon AA, Bresolin T, Dornelles MDA, Weber T, Santana PP, Campos LT (2012) Associação genética de escores de conformação com características de crescimento em bovinos da raça Angus. Pesquisa Agropecuária Brasileira 47, 1524–1532.
| Associação genética de escores de conformação com características de crescimento em bovinos da raça Angus.Crossref | GoogleScholarGoogle Scholar |
Everling DM, Bresolin T, Rorato PRN, Araújo RO, Boligon AA, Weber T, Dornelles MA, Campos LT (2014) Finishing precocity visual score and genetic associations with growth traits in Angus beef cattle. Genetics and Molecular Research 13, 7757–7765.
| Finishing precocity visual score and genetic associations with growth traits in Angus beef cattle.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC2M7pt1emsg%3D%3D&md5=a91e768cba8d00708fe98490bef748acCAS |
Faria CUD, Magnabosco CU, Albuquerque LG, De Los Reyes A, Bezerra LAF, Lobo RB (2008) Análise genética de escores de avaliação visual de bovinos com modelos bayesianos de limiar e linear. Pesquisa Agropecuária Brasileira 43, 835–841.
| Análise genética de escores de avaliação visual de bovinos com modelos bayesianos de limiar e linear.Crossref | GoogleScholarGoogle Scholar |
Gelfand AE, Smith AFM (1990) Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 85, 398–409.
| Sampling-based approaches to calculating marginal densities.Crossref | GoogleScholarGoogle Scholar |
Geweke J (1992) Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In ‘Bayesian statistics 4’. (Eds JM Bernardo, JO Berger, AP Dawid, AFM Smith) pp. 625–631. (Oxford University Press: New York)
Gianola D (1982) Theory and analysis of threshold characteres. Journal of Animal Science 54, 1079–1096.
| Theory and analysis of threshold characteres.Crossref | GoogleScholarGoogle Scholar |
Gianola D, Foulley JL (1983) Sire evaluation for ordered categorical data with a threshold model. Genetics, Selection, Evolution. 15, 201–224.
| Sire evaluation for ordered categorical data with a threshold model.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC38fpvVagsg%3D%3D&md5=4d958042a3ad3ba2bd4991f31fcdbd22CAS |
Gianola D, Sorensen D (2002) ‘Likelihood, bayesian, and MCMC methods in quantitative genetics.’ (Statistics for Biology and Health: Springer, NY)
Gordo DGM, Baldi F, Lôbo RB, Sainz RD, Albuquerque LG (2012) Genetic association between body composition measured by ultrasound and visual scores in Brazilian Nelore cattle. Journal of Animal Science 90, 4223–4229.
| Genetic association between body composition measured by ultrasound and visual scores in Brazilian Nelore cattle.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXns1SmsQ%3D%3D&md5=1bd20e7cdc657f0369a655ab71e707aeCAS |
Gordo DGM, Espigolan R, Tonussi RL, Júnior GAF, Bresolin T, Magalhães AF, Feitosa FL, Baldi F, Carvalheiro R, Tonhati H, Oliveira HN, Chardulo LAL, Albuquerque LG (2016) Genetic parameter estimates for carcass traits and visual scores including or not genomic information. Journal of Animal Science 94, 1821–1826.
| Genetic parameter estimates for carcass traits and visual scores including or not genomic information.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC28XhslWrtLzJ&md5=f0e46f880d56d8f9ea74616cc11b76c1CAS |
Grosso JLBM, Oliveira GA, Jr, Menezes IR, Mattos EC, Ferraz JBS, Eler JP (2014) Genetic parameters for body conformation scores and carcass traits measured by real-time ultrasound in Nellore cattle. In ‘Proceedings 10th world congress on genetics applied to livestock production’. pp. 718. (Vancouver, British Columbia, Canada)
Koury Filho W, Albuquerque LGD, Forni S, Silva JAI, Yokoo MJ, Alencar MMD (2010) Estimativas de parâmetros genéticos para os escores visuais e suas associações com peso corporal em bovinos de corte. Revista Brasileira de Zootecnia 39, 1015–1022.
| Estimativas de parâmetros genéticos para os escores visuais e suas associações com peso corporal em bovinos de corte.Crossref | GoogleScholarGoogle Scholar |
Louviere JJ, Hensher DA, Swait JD (2000) ‘Stated choice methods: analysis and applications.’ (Cambridge: Cambridge University Press, England, UK)
Marcondes CR, Paneto JCC, Silva JA, Oliveira HND, Lôbo RB (2005) Comparação entre análises para permanência no rebanho de vacas Nelore utilizando modelo linear e modelo de limiar. Arquivo Brasileiro de Medicina Veterinária e Zootecnia 57, 234–240.
| Comparação entre análises para permanência no rebanho de vacas Nelore utilizando modelo linear e modelo de limiar.Crossref | GoogleScholarGoogle Scholar |
McFadden D (1970) Conditional logit analysis of qualitative choice behavior. In ‘Frontiers in econometrics’. (Ed. P Zarenblea) pp. 105–142. (Academic Press: New York)
McHugh N, Cromie AR, Evans RD, Berry DP (2014) Validation of national genetic evaluations for maternal beef cattle traits using Irish field data. Journal of Animal Science 92, 1423–1432.
| Validation of national genetic evaluations for maternal beef cattle traits using Irish field data.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2cXms1Cjs70%3D&md5=f48cf6bb192e9aabebb851c712ffe087CAS |
Meijering A, Gianola D (1985) Linear versus nonlinear methods of sire evaluation for categorical traits: a simulation study. Genetique Selection Evolution 17, 115–132.
| Linear versus nonlinear methods of sire evaluation for categorical traits: a simulation study.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC38fpvVaqtA%3D%3D&md5=26ab54449963e0443b2f1c97617d33c1CAS |
Misztal I, Tsuruta S, Strabel T, Auvray B, Druet T, Lee DH (2002) BLUPF90 and related programs (BGF90). In ‘Proceedings of the 7th world congress on genetics applied to livestock production’. Communication number 28–07. (Montpellier, France)
Pedrosa VB, Eler JP, Ferraz JBS, Silva JAI, Ribeiro S, Silva MR, Pinto LFB (2010) Parâmetros genéticos do peso adulto e características de desenvolvimento ponderal na raça Nelore. Revista Brasileira de Saúde e Produção Animal 11, 104–113.
Silva JAV, Dias LT, Albuquerque LG (2005) Estudo genético da precocidade sexual de novilhas em um rebanho Nelore. Revista Brasileira de Zootecnia 34, 1568–1572.
| Estudo genético da precocidade sexual de novilhas em um rebanho Nelore.Crossref | GoogleScholarGoogle Scholar |
Smith BJ (1997) BOA: an R package for MCMC output convergence assessment and posterior inference. Journal of Statistical Software 21, 1–37.
R Core Team (2014) ‘R: a language and environment for statistical computing.’ R Foundation for Statistical Computing. (Vienna, Austria) Available at http://www.R-project.org/ [Verified August 2015]
Sorensen DA, Andersen S, Gianola D, Korsgaard I (1995) Bayesian inference in threshold models using Gibbs sampling. Genetics, Selection, Evolution. 27, 229–249.
Van Tassel CP, Van Vleck LD (1996) Multiple-trait Gibbs sampler for animal models; flexible programs for Bayesian and likelihood-based (co)variance components inference. Journal of Animal Science 74, 2586–2597.
Venables WN, Ripley BD (2002) ‘Modern applied statistics with S.’ 4th edn. (Springer: New York)
