Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-26T04:01:20.351Z Has data issue: false hasContentIssue false

Comparison between linear and proportional hazard models for the analysis of age at first lambing in the Ripollesa breed

Published online by Cambridge University Press:  09 November 2015

J. Casellas*
Affiliation:
Grup de Recerca en Millora Genètica Molecular Veterinària, Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
*
Get access

Abstract

Age at first lambing (AFL) plays a key role on the reproductive performance of sheep flocks, although there are no genetic selection programs accounting for this trait in the sheep industry. This could be due to the non-Gaussian distribution pattern of AFL data, which must be properly accounted for by the analytical model. In this manuscript, two different parameterizations were implemented to analyze AFL in the Ripollesa sheep breed, that is, the skew-Gaussian mixed linear model (sGML) and the piecewise Weibull proportional hazards model (PWPH). Data were available from 10 235 ewes born between 1972 and 2013 in 14 purebred Ripollesa flocks located in the north-east region of Spain. On average, ewes gave their first lambing short after their first year and a half of life (590.9 days), and within-flock averages ranged between 523.4 days and 696.6 days. Model fit was compared using the deviance information criterion (DIC; the smaller the DIC statistic, the better the model fit). Model sGML was clearly penalized (DIC=200 059), whereas model PWPH provided smaller estimates and reached the minimum DIC when one cut point was added to the initial Weibull model (DIC=132 545). The pure Weibull baseline and parameterizations with two or more cut points were discarded due to larger DIC estimates (>134 200). The only systematic effect influencing AFL was the season of birth, where summer- and fall-born ewes showed a remarkable shortening of their AFL, whereas neither birth type nor birth weight had a relevant impact on this reproductive trait. On the other hand, heritability on the original scale derived from model PWPH was high, with a model estimate place at 0.114 and its highest posterior density region ranging from 0.079 and 0.143. As conclusion, Gaussian-related mixed linear models should be avoided when analyzing AFL, whereas model PWPH must be viewed as better alternative with superior goodness of fit; moreover, the additive genetic background underlying this reproductive trait supports its inclusion into current genetic selection programs given its economic importance.

Type
Research Article
Copyright
© The Animal Consortium 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Caraviello, DZ, Weigel, KA and Gianola, D 2004. Comparison between a Weibull proportional hazards model and a linear model for predicting the genetic merit of US Jersy sires for daughter longevity. Journal of Dairy Science 87, 14691476.CrossRefGoogle Scholar
Casellas, J 2007. Bayesian inference in a piecewise Weibull proportional hazards model with unknown change points. Journal of Animal Breeding and Genetics 124, 176184.CrossRefGoogle Scholar
Casellas, J and Bach, R 2012. Comparison of linear, skewed-linear, and proportional hazard models for the analysis of lambing interval in Ripollesa ewes. Journal Animal Science 90, 17881797.CrossRefGoogle ScholarPubMed
Casellas, J, Fina, M, Bach, R and Piedrafita, J 2014. Null genetic defferentiation among flocks contributing to the Ripollesa sheep herdbook. Proceedings of the World Congress on Genetics applied to Livestock Production, Vancouver, Canada.Google Scholar
Casellas, J, Varona, L, Ibáñez-Escriche, N, Quintanilla, R and Noguera, JL 2008. Skew distribution of founder-specific inbreeding depression effects on the longevity of Landrace sows. Genetics Research 90, 499508.CrossRefGoogle ScholarPubMed
Cox, DR 1972. Regression models and life tables (with discussion). Journal of the Royal Statistical Society B 34, 187220.Google Scholar
Damgaard, LH and Korsgaard, IR 2006. A bivariate quantitative genetic model for a linear Gaussian trait and a survival trait. Genetics, Selection, Evolution 38, 4564.Google Scholar
de la Fuente, LF, Gabiña, D, Carolino, N and Ugarte, E 2006. The Awassi and Assaf breeds in Spain and Portugal. 57th Annual Meeting of the European Federation of Animal Science, Antalya, Turkey.Google Scholar
Ducrocq, W, Quaas, RL, Pollak, EJ and Casella, G 1988. Length of productive life of dairy cows. 1. Justification of a Weibull model. Journal of Dairy Science 71, 30613070.CrossRefGoogle Scholar
El-Saied, UM, de la Fuente, LF, Carriedo, JA and San Primitivo, F 2005. Genetic and phenotypic parameter estimates of total and partial lifetime traits for dairy ewes. Journal of Dairy Science 88, 32653272.CrossRefGoogle ScholarPubMed
Esquivelzeta, C, Fina, M, Bach, R, Madruga, C, Caja, G, Casellas, J, Piedrafita, J 2011. Morphological analysis and subpopulation characterization of Ripollesa sheep breed. Animal Genetic Resources 49, 9--17.CrossRefGoogle Scholar
Fogarty, NM 1995. Genetic parameters for live weight, fat and muscle measurements, wool production and reproduction in sheep: A review. Animal Breeding Abstracts 63, 101143.Google Scholar
Gabiña, D 1989. Improvement of the reproductive performance of Rasa Aragonesa flocks in frequent lambing systems. I. Effects of management system, age of ewe and season. Livestestock Production Science 22, 6985.Google Scholar
Gallivan, C, Kemp, RA, Berger, YM and Young, LD 1993. Comparison of Finnish Landrace and Romanov as prolific breeds in a terminal-sire crossbreeding system. Journal of Animal Science 71, 29102918.Google Scholar
Garcia, DA, Carvalheiro, R, Rosa, GJM, Valente, BD and Albuquerque, LG 2014. Analysis of age at first calving dealing with censored data. In Proceedings, of the 10th World Congress of Genetics Applied to Livestock Production, Vancouver, Canada.Google Scholar
Gelfand, A and Smith, AFM 1990. Sampling based approaches to calculating marginal densities. Journal of the American Statistical Association 85, 398409.Google Scholar
Gianola, D and Fernando, RL 1986. Bayesian methods in animal breeding theory. Journal of Animal Science 63, 217244.CrossRefGoogle Scholar
Guo, S, Gianola, D, Rekaya, R, Short, T 2001. Bayesian analysis of lifetime performance and prolificacy in Landrace sows using a linear mixed model with censoring. Livestock Production Science 72, 243252.CrossRefGoogle Scholar
Hernandez, F, Elvira, L, Gonzalez-Martin, JV, Gonzalez-Bulnes, A and Astiz, S 2011. Influence of the age at first lambing on reproductive and productive performance of Lacaune dairy sheep under an intensive management system. Journal of Dairy Research 78, 160167.CrossRefGoogle Scholar
Kaplan, EL and Meier, P 1958. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53, 457481.CrossRefGoogle Scholar
Korsgaard, IR, Andersen, AH and Jensen, J 2002. Prediction error variance and expected response to selection, when selection is based on the best predictor for Gaussian and threshold characters, traits following a Poisson mixed model and survival traits. Genetics, Selection, Evolution 34, 307333.Google Scholar
Lôbo, AM, Lôbo, RN, Paiva, SR, de Oliveira, SM and Falcó, O 2009. Genetic parameters for growth, reproductive and maternal traits in a multibreed meat sheep population. Genetics and Molecular Biology 32, 761770.CrossRefGoogle Scholar
MacNeil, MD and Vukasinovic, N 2011. A prototype national cattle evaluation for sustained reproductive success in Hereford cattle. Journal of Animal Science 89, 17121718.CrossRefGoogle ScholarPubMed
Metropolis, N, Rosenbluth, AW, Rosenbluth, MN, Teller, AH and Teller, E 1953. Equations of state calculations by fast computing machines. Journal of Chemical Physics 21, 10871092.Google Scholar
Nguyen, HT, Rogers, GS and Walker, EA 1984. Estimation in change-point hazard rate models. Biometrika 71, 299304.Google Scholar
Noura, AA and Read, KLQ 1990. Proportional hazards change-point models in survival analysis. Applied Statistics 39, 241253.Google Scholar
Osuhor, CU, Osinowo, OA, Nwagu, BI, Dennar, FO and Abdullahi-Adee, A 1997. Factors affecting age at first lambing in Yankasa ewes. Animal Reproduction Science 47, 205209.CrossRefGoogle ScholarPubMed
Raftery, AE and Lewis, SM 1992. How many iterations in the Gibbs sampler?. In Bayesian Statistics IV. (ed. Bernardo JM, Berger JO, Dawid AP and Smith AFM), pp. 763773. Oxford University Press, Oxford, UK.CrossRefGoogle Scholar
Sahu, SK, Dey, DK and Branco, MD 2003. A new class of multivariate skew distributions with applications to Bayesian regression models. Canadian Journal of Statistics 31, 129150.CrossRefGoogle Scholar
Schneider, Mdel P, Strandberg, E, Ducrocq, V and Roth, A 2005. Survival analysis applied to genetic evaluation for fertility in dairy cattle. Journal of Dairy Science 88, 22532259.CrossRefGoogle ScholarPubMed
Schneider, Mdel P, Strandberg, E, Ducrocq, V and Roth, A 2006. Genetic evaluation of the interval from first to last insemination with survival analysis and linear models. Journal of Dairy Science 89, 49034906.CrossRefGoogle ScholarPubMed
Spiegelhalter, DJ, Best, NG, Carlin, BP and van der Linder, A 2002. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society B 64, 583639.CrossRefGoogle Scholar
Tarrés, J, Casellas, J and Piedrafita, J 2005. Genetic and environmental factors influencing mortality up to weaning of Bruna dels Pirineus beef calves in mountain areas. A survival analysis. Journal of Animal Science 83, 543551.Google Scholar
Varona, L, Ibáñez-Escriche, N, Quintanilla, R, Noguera, JL and Casellas, J 2008. Bayesian inference of quantitative traits using skewed distributions. Genetics Research 90, 179190.CrossRefGoogle Scholar
Westell, RA, Quaas, RL and Van Vleck, LD 1988. Genetic groups in an animal model. Journal of Dairy Science 71, 13101318.CrossRefGoogle Scholar
Wright, S 1922. Coefficient of inbreeding and relationship. The American Naturalist 56, 330338.CrossRefGoogle 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, 15631577.Google Scholar