Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-05T15:36:15.856Z Has data issue: false hasContentIssue false
  • English
  • Français

A study of heterogeneity of environmental variance for slaughter weight in pigs

Published online by Cambridge University Press:  01 January 2008

N. Ibáñez-Escriche ,
L. Varona ,
D. Sorensen  and
J. L. Noguera
N. Ibáñez-Escriche*
Affiliation:
Genètica i Millora Animal - IRTA, 25198 Lleida, Spain Departamento de Ciencia Animal, UPV, PO Box 22012, 46071 Valencia, Spain
L. Varona
Affiliation:
Genètica i Millora Animal - IRTA, 25198 Lleida, Spain
D. Sorensen
Affiliation:
Danish Institute of Agricultural Sciences, PB50, 8830 Tjele, Denmark
J. L. Noguera
Affiliation:
Genètica i Millora Animal - IRTA, 25198 Lleida, Spain
*
Genètica i Millora Animal - IRTA, 25198 Lleida, Spain. E-mail: noelia.ibañ[email protected]
Get access

Abstract

This work presents an analysis of heterogeneity of environmental variance for slaughter weight (175 days) in pigs. This heterogeneity is associated with systematic and additive genetic effects. The model also postulates the presence of additive genetic effects affecting the mean and environmental variance. The study reveals the presence of genetic variation at the level of the mean and the variance, but an absence of correlation, or a small negative correlation, between both types of additive genetic effects. In addition, we show that both, the additive genetic effects on the mean and those on environmental variance have an important influence upon the future economic performance of selected individuals.

Keywords

Bayesian analysisheterogeneous environmental variancepigsslaughter weight
Type
Full Paper
Information
animal , Volume 2 , Issue 1 , January 2008 , pp. 19 - 26
Copyright
Copyright © The Animal Consortium 2008

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

Damgaard, LH, Rydhmer, L, Løvendahl, P, Grandinson, K 2003. Genetic parameters for within-litter variation in piglet birth weight and change in within-litter variation during suckling. Journal of Animal Science 81, 604–610.CrossRefGoogle ScholarPubMed
Foulley, JL, Quaas, RL 1995. Heterogeneous variances in Gaussian linear mixed models. Genetic Selection Evolution 27, 211–228.CrossRefGoogle Scholar
Garrick, DJ, Van Vleck, LD 1987. Aspects of selection for performance in several environments with heterogeneous variances. Journal of Animal Science 65, 409–421.CrossRefGoogle Scholar
Garrick, DJ, Pollak, EJ, Quaas, RL 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
Gelman, A, Rubin, DB 1992. Inference from iterative simulation using multiple sequences. Statistical Science 7, 457–511.CrossRefGoogle Scholar
Gelman, A, Meng, XL, Stern, H 1996. Posterior predictive assessment of model fitness via realized discrepancies (with discussion). Statistica Sinica 6, 733–807.Google Scholar
Gelman, A, Carlin, JB, Stern, HS, Rubin, DB 2004. Bayesian data analysis, second edition. Chapman and Hall, New York.Google Scholar
Geman, S, Geman, D 1984. Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721–741.CrossRefGoogle ScholarPubMed
Gutiérrez, JP, Nieto, B, Piqueras, P, Ibáñez, N, Salgado, C 2006. Genetic parameters for canalisation analysis of litter size and litter weight traits at birth in mice. Genetic Selection Evolution 38, 445–462.CrossRefGoogle ScholarPubMed
Henderson, CR 1975. Best linear unbiased estimation and prediction under a selection model. Biometrics 31, 423–447.CrossRefGoogle Scholar
Hennessy, DA 2005. Slaughterhouse rules: animal uniformity and regulating for food safety in meat packing. American Journal of Agricultural Economics 87, 600.CrossRefGoogle Scholar
Hill, WG 1984. On selection among groups with heterogeneous variance. Animal Production 39, 473–477.Google Scholar
Huby, M, Gogué, J, Maignel, L, Bidanel, JP 2003. Corrélations génétiques entre les caractéristiques numériques et pondérales de la portée, la variabilité du poids des porcelets et leur survie entre la naissance et le sevrage. Journées Recherche Porcine 35, 293–300.Google Scholar
Jaffrezic, F, White, IMS, Thompson, R, Hill, WG 2000. A link function approach to model heterogeneity of residuals variances over time in lactation curve analyses. Journal of Dairy Science 83, 1089–1093.CrossRefGoogle ScholarPubMed
Kizilkaya, K, Tempelman, RJ 2005. A general approach to mixed effects modelling of residual variances in generalized linear mixed models. Genetic Selection Evolution 37, 31–56.CrossRefGoogle ScholarPubMed
Noguera, JL, Pomar, J 2007. e-BDporc®: système électronique espagnol d’aide à la gestion des élevages. Journées Recherche Porcine 39, 232–238.Google Scholar
Ros, M, Sorensen, D, Waagepetersen, R, Dupont-Nivet, M, San Cristobal, M, Bonnet, J-C, Mallard, J 2004. Evidence for genetic control of adult weight plasticity in the snail helix aspersa. Genetics 168, 2089–2097.CrossRefGoogle ScholarPubMed
Rowe, SJ, White, IMS, Avendano, S, Hill, WG 2006. Genetic heterogeneity of residual variance in broiler chickens. Genetic Selection Evolution 38, 617–635.CrossRefGoogle ScholarPubMed
San Cristobal-Gaudy, M, Elsen, JM, Bodin, L, Chevalet, C 1998. Prediction of response to a selection for canalisation of a continuous trait in animal breeding. Genetic Selection Evolution 30, 423–451.CrossRefGoogle Scholar
San Cristobal-Gaudy, M, Bodin, L, Elsen, JM, Chevalet, C 2001. Genetic components of litter size variability in sheep. Genetics Selection Evolution 33, 249–271.CrossRefGoogle ScholarPubMed
See, MT 1998. Heterogeneity of (co)variance among herds for backfat measures of swine. Journal of Animal Science 76, 2568–2574.CrossRefGoogle ScholarPubMed
Sorensen, D, Waagepetersen, R 2003. Normal linear models with genetically structured variance heterogeneity: a case study. Genetical Research 82, 207–222.CrossRefGoogle ScholarPubMed
Spiegelhalter, DJ, Best, NG, Carlin, BP, Van der Linde, A 2002. Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society. Series B 64, 583–639.CrossRefGoogle Scholar
Wang, CS, Rutledge, JJ, Gianola, D 1994. Marginal inference about variance components in a mixed model using Gibbs sampling. Genetic Selection Evolution 26, 91–115.CrossRefGoogle Scholar
Wellock, IJ, Emmans, GC, Kyriazakis, I 2004. Describing and predicting potential growth in the pig. Animal Science 78, 379–388.CrossRefGoogle Scholar