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Evaluation of longevity modeling censored records in Nellore

Published online by Cambridge University Press:  23 May 2017

D. A. Garcia ,
G. J. M. Rosa ,
B. D. Valente ,
R. Carvalheiro ,
G. A. Fernandes Júnior  and
L. G. Albuquerque
D. A. Garcia
Affiliation:
Department of Animal Science, UNESP, Jaboticabal, SP 14884-900, Brazil
G. J. M. Rosa
Affiliation:
Department of Animal Science, University of Wisconsin-Madison, Madison, WI 53706, USA
B. D. Valente
Affiliation:
Department of Animal Science, University of Wisconsin-Madison, Madison, WI 53706, USA
R. Carvalheiro
Affiliation:
Department of Animal Science, UNESP, Jaboticabal, SP 14884-900, Brazil CNPq Fellowship, Brasília, DF 70067-900, Brazil
G. A. Fernandes Júnior
Affiliation:
Department of Animal Science, UNESP, Jaboticabal, SP 14884-900, Brazil
L. G. Albuquerque*
Affiliation:
Department of Animal Science, UNESP, Jaboticabal, SP 14884-900, Brazil CNPq Fellowship, Brasília, DF 70067-900, Brazil
*
†Present address: Universidade Estadual Paulista ‘Júlio de Mesquita Filho’, Via de Acesso Prof. Paulo Donato Castellane s/n, Jaboticabal, São Paulo, CEP 14884-900, Brazil. E-mail: lgalb@fcav.unesp.br
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Abstract

The aim of the present study was to evaluate the prediction ability of models that cope with longevity phenotypic expression as uncensored and censored in Nellore cattle. Longevity was defined as the difference between the dates of last weaned calf and cow birth. There were information of 77 353 females, being 61 097 cows with uncensored phenotypic information and 16 256 cows with censored records. These data were analyzed considering three different models: (1) Gaussian linear model (LM), in which only uncensored records were considered; and two models that consider both uncensored and censored records: (2) Censored Gaussian linear model (CLM); and (3) Weibull frailty hazard model (WM). For the model prediction ability comparisons, the data set was randomly divided into training and validation sets, containing 80% and 20% of the records, respectively. There were considered 10 repetitions applying the following restrictions: (a) at least three animals per contemporary group in the training set; and (b) sires with more than 10 progenies with uncensored records (352 sires) should have daughters in the training and validation sets. The variance components estimated using the whole data set in each model were used as true values in the prediction of breeding values of the animals in the training set. The WM model showed the best prediction ability, providing the lowest χ2 average and the highest number of sets in which a model had the smallest value of χ2 statistics. The CLM and LM models showed prediction abilities 2.6% and 3.7% less efficient than WM, respectively. In addition, the accuracies of sire breeding values for LM and CLM were lower than those obtained for WM. The percentages of bulls in common, considering only 10% of sires with the highest breeding values, were around 75% and 54%, respectively, between LM–CLM and LM–WM models, considering all sires, and 75% between LM–CLM and LM–WM, when only sires with more than 10 progenies with uncensored records were taken into account. These results are indicative of reranking of animals in terms of genetic merit between LM, CLM and WM. The model in which censored records of longevity were excluded from the analysis showed the lowest prediction ability. The WM provides the best predictive performance, therefore this model would be recommended to perform genetic evaluation of longevity in this population.

Keywords

beef cattlemodel comparisonsurvival analysisWeibull model
Type
Research Article
Information
animal , Volume 11 , Issue 12 , December 2017 , pp. 2113 - 2119
Copyright
© The Animal Consortium 2017 

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References

Caetano, SL 2011. Estudo da idade da vaca ao último parto para avaliar longevidade em rebanhos da raça Nelore por análise de sobrevivência. Tese, (Genética e Melhoramento Animal), Universidade Estadual Paulista Júlio de Mesquita Filho, Jaboticabal, Brazil, p. 107.Google Scholar
Cardoso, FF, Rosa, GJM, Tempelman, RJ and Torres Junior, RAA 2009. Modelos hierárquicos bayesianos para estimação robusta e análise de dados censurados em melhoramento animal. Revista Brasileira de Zootecnia 38, 72–80.Google Scholar
Caraviello, DZ, Weigel, KA and Gianola, D 2004. Prediction of longevity breeding values for US Holstein sires using survival analysis methodology. Journal of Dairy Science 87, 3518–3525.Google Scholar
Donoghue, KA, Rekaya, R and Bertrand, JK 2004a. Comparison methods for handling censored records in beef fertility data: simulation study. Journal of Animal Science 82, 51–356.Google ScholarPubMed
Donoghue, KA, Rekaya, R and Bertrand, JK 2004b. Comparison methods for handling censored records in beef fertility data: field study. Journal of Animal Science 82, 357–361.Google Scholar
Ducrocq, V 2005. An improved model for the French genetic evaluation of dairy bulls on length of productive life of their daughters. Animal Science 80, 249–256.CrossRefGoogle Scholar
Ducrocq, V, Boichard, D, Barbat, A and Larroque, H 2001. Implementation of an approximate multitrait BLUP evaluation to combine production traits and functional traits into a total merit index. 52th Annual Meeting of the European Association for Animal Production, 26–29 August 2001, Budapest, Hungary, Paper G1.4.Google Scholar
Ducrocq, V and Casella, G 1996. A Bayesian analysis of mixed survival models. Genetic Selection Evolution 28, 505–529.CrossRefGoogle Scholar
Ducrocq, V, Sölkner, J and Meszaros, G 2010. Survival Kit v6 – a software package for survival analysis. In Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, 1–6 August 2010, Leipzig, Germany, pp.1–4, Communication 0232.Google Scholar
Ducrocq, V, Quaas, RL, Pollak, EJ and Casella, G 1988. Length of productive life of dairy cows. 2. Variance component estimation and sire evaluation. Journal of Dairy Science 71, 3071–3079.Google Scholar
Forabosco, F, Bozzi, R, Filippini, F, Boettcher, P, Van Arendonk, JAM and Bijma, P 2006. Linear model vs. survival analysis for genetic evaluation of sires for longevity in Chianina beef cattle. Livestock Science 101, 191–198.CrossRefGoogle Scholar
Garcia, DA, Rosa, GJM, Valente, BD, Carvalheiro, R and Albuquerque, LG 2016. Comparison of models for the genetic evaluation of reproductive traits with censored data in Nellore cattle. Journal of Animal Science 94, 2297–2306.CrossRefGoogle ScholarPubMed
Gelman, A and Rubin, DB 1992. Inference from iterative simulation using multiple sequences. Statistical Science 7, 457–511.Google Scholar
González-Recio, O, Chang, YM, Gianola, D and Weigel, KA 2006. Comparison of models using different censoreing scenarios for days open in Spanish Holstein cows. British Society of Animal Science 82, 223–239.Google Scholar
Guo, SF, Gianola, D and Short, RRT 2001. Bayesian analysis of lifetime performance and prolificacy in Landrace sows using a linear mixed model with censoring. Livestock Production Science 72, 243–252.CrossRefGoogle Scholar
Heldelberger, P and Welch, P 1983. Simulation run length control in the presence of an initial transient. Operations Research 31, 1109–1144.CrossRefGoogle Scholar
Hou, Y, Madsen, P, Labouriau, R, Zhang, Y, Lund, MS and Su, G 2009. Genetic analysis of days from calving to first insemination and days open in Danish Holstein using different models and censoring scenarios. Journal of Dairy Science 92, 1229–1239.Google Scholar
Korsgaard, IR, Andersen, AH and Jensen, J 2002. Prediction error variance and expected response to selection, when selection is based on the best linear predictor – for Gaussian and threshold characters, traits following a Poisson mixed model and survival traits. Genetics Selection Evolution 34, 307–333.CrossRefGoogle ScholarPubMed
Lubbers, R, Brotherstone, S, Ducrocq, VP and Visscher, PM 2000. A comparison of a linear and proportional hazards approach to analyse discrete longevity data in dairy cows. Animal Science 70, 197–206.CrossRefGoogle Scholar
Maia, PM, Madsen, P and Labouriau, R 2013. Multivariate survival models for genetic analysis of longevity traits. Journal of Applied Statistics 41, 1286–1306.CrossRefGoogle Scholar
Mercadante, MEZ, Lôbo, RB and Oliveira, HN 2000. Estimativas de (co)variância entre características de reprodução e crescimento em fêmeas de um rebanho Nelore. Revista Brasileira de Zootecnia 29, 997–1004.CrossRefGoogle Scholar
Misztal, I, Tsuruta, S, Strabel, T, Auvray, B, Druet, T and Lee, DH 2002. BLUPF90 and related programs (BGF90). In Proceeding of 7th World Congress on Genetics Applied to Livestock Production, 19–23 August, Montpellier, France, Communication No 28-07.Google Scholar
Neves, HHR, Carvalheiro, R and Queiroz, SA 2012. Genetic parameters for an alternative criterion to improve productive longevity of Nellore cows. Journal of Animal Science 90, 4209–4216.CrossRefGoogle ScholarPubMed
Pereira, E, Oliveira, HN, Eler, JP, Silva, JAIV and Van Melis, MH 2007. Comparison among three approaches for evaluation of sexual precocity in Nellore cattle. Animal 1, 411–418.Google Scholar
R Core Team 2013. R: A language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria. . http://www.R-project.org Google Scholar
Raguž, N, Jovanovac, S, Mészáros, G and Sölkner, J 2014. Linear vs. piecewise Weibull model for genetic evaluation of sires for longevity in Simmental cattle. Mljekarstvo 64, 141–149.Google Scholar
Silva, JAIV, Eler, JP, Ferraz, JBS, Golden, BL and Oliveira, HN 2003. Short communication: Heritability estimate for stayability in Nelore cows. Livestock Production Science 79, 97–101.Google Scholar
Silva, JAIV, Formigoni, IB, Eler, JP and Ferraz, JBS 2006. Genetic relationship among stayability, scrotal circumference and post-weaning weight in Nelore cattle. Livestock Production Science 99, 51–59.Google Scholar
Sorensen, DA, Gianola, D and Korsgaard, IR 1998. Bayesian mixed-effects model analysis of a censored normal distribution with animal breeding applications. Acta Agriculturae Scandinavica 48, 222–229.Google Scholar
Tanner, MA and Wong, WH 1987. The calculation of posterior distribution by data augmentation. Journal of the American Statistical Association 81, 82–86.Google Scholar
Van Arendonk, JAM 1986. Economic importance and possibilities for improvement of dairy cow herd life. In Proceeding of World Congress on Genetics Applied to Livestock Production, 16-22 July 1986, Lincoln, United States. pp. 95–100.Google Scholar
Van Melis, MH, Eler, JP, Rosa, GJM, Ferraz, JBS, Figueiredo, LGG, Matos, EC and Oliveira, HN 2010a. Additive genetic relationship between scrotal circumference, heifer pregnancy, and stayability in Nellore cattle. Journal of Animal Science 88, 3809–3813.Google Scholar
Van Melis, MH, Oliveira, HN, Eler, JP, Ferraz, JBS, Casellas, J and Varona, L 2010b. Additive genetic relationship of longevity with fertility and production traits in Nellore cattle based on bivariate models. Genetics and Molecular Research 9, 176–187.Google Scholar
Yazdi, MH, Visscher, PM, Ducrocq, V and Thompson, R 2002. Heritability, reliability of genetic evaluations and response to selection in proportional hazard models. Journal of Dairy Science 85, 1563–1577.Google Scholar