Comparison of inference methods of genetic parameters with an application to body weight in broilers

Maniatis, G.; Demiris, N.; Kranis, A.; Banos, G.; Kominakis, A.

doi:https://doi.org/10.5194/aab-58-277-2015

Articles | Volume 58, issue 2

Article
Metrics
Related articles

Articles | Volume 58, issue 2

https://doi.org/10.5194/aab-58-277-2015

Articles | Volume 58, issue 2

Article
Metrics
Related articles

27 Jul 2015

| 27 Jul 2015

Comparison of inference methods of genetic parameters with an application to body weight in broilers

G. Maniatis, N. Demiris, A. Kranis, G. Banos, and A. Kominakis

G. Maniatis

CORRESPONDING AUTHOR

gerasiman@gmail.com

Faculty of Animal Science and Aquaculture, Agricultural University of Athens, Iera Odos 75, 118 55 Athens, Greece

N. Demiris

Department of Statistics, Athens University of Economics and Business, 76 Patission Str., 10434 Athens, Greece

A. Kranis

The Roslin Institute and Royal School of Veterinary Studies, University of Edinburgh, Midlothian, EH25 9RG, Scotland, UK

G. Banos

The Roslin Institute and Royal School of Veterinary Studies, University of Edinburgh, Midlothian, EH25 9RG, Scotland, UK

A. Kominakis

Faculty of Animal Science and Aquaculture, Agricultural University of Athens, Iera Odos 75, 118 55 Athens, Greece

Cited articles

Akaike, H.: Information theory and an extension of the maximum likelihood principle, edited by: Petrov, B. N. and Csaki, F., Proceedings of the 2nd International Symposium on Information Theory Akademiai Kiado Budapest Hungary, 267–281, 1973.

Blasco, A.: The Bayesian controversy in animal breeding, J. Anim. Sci., 79, 2023–2046, 2001.

Boulinier, T. and Staszewski, V.: Maternal transfer of antibodies: raising immuno-ecology issues, Trends Ecol. Evol., 23, 282–288, 2008.

Browne, W. J. and Draper, D.: A comparison of Bayesian and likelihood-based methods for fitting multilevel models, Bayesian Anal., 1, 473–513, 2006.

Buechler, K., Fitze, P. S., Gottstein, B., Jacot, A., and Richner, H.: Parasite-induced maternal response in a natural bird population, J. Anim. Ecol., 71, 247–252, 2002.

Crainiceanu, C. M. and Ruppert, D.: Likelihood ratio tests in linear mixed models with one variance component, J. Roy. Stat. Soc. B, 66, 165–185, 2004.

Damgaard, L. H.: Technical note: How to use Winbugs to draw inferences in animal models, J. Anim. Sci., 85, 1363–1368, 2007.

Dempster, E. R. and Lerner, I. M.: Heritability of Threshold Characters, Genetics, 35, 212–236, 1950.

Gelfand, A. E. and Smith, A. F. M.: Sampling-Based Approaches to Calculating Marginal Densities, J. Am. Stat. Assoc., 85, 398–409, 1990.

Gelman, A.: Prior distributions for variance parameters in hierarchical models(Comment on an Article by Browne and Draper), Bayesian Anal., 1, 515–533, 2006.

Geman, S. and Geman, D.: Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images, IEEE T. Pattern Anal., 6, 721–741, 1984.

Gilmour, A. R., Gogel, B. J., Cullis, B. R., and Thompson, R.: Asreml User Guide Release 3.0. VSN International Ltd, Hemel Hempstead, UK, 2009.

Gorjanc, G.: Flexible Bayesian Inference of Animal Model Parameters Using BUGS Program, Contribution for 9th WCGALP, 2010.

Groothuis, T. G. G., Eising, C. M., Dijkstra, C., and Muller, W.: Balancing between costs and benefits of maternal hormone deposition in avian eggs, Biol. Lett., 1, 78–81, 2005.

Hallander, J., Waldmann, P., Wang, C. K., and Sillanpaa, M. J.: Bayesian Inference of Genetic Parameters Based on Conditional Decompositions of Multivariate Normal Distributions, Genetics, 185, 645–654, 2010.

Henderson, C. R.: A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values, Biometrics, 32, 69–83, 1976.

Hodges, J. S. and Sargent, D. J.: Counting degrees of freedom in hierarchical and other richly-parameterised models, Biometrika, 88, 367–379, 2001.

Holand, A. M., Steinsland, I., Martino, S., and Jensen, H.: Animal Models and Integrated Nested Laplace Approximations, G3-Genes Genom Genet, 3, 1241–1251, 2013.

Ketterson, E. D. and Nolan, V.: Adaptation, exaptation, and constraint: A hormonal perspective, Am. Nat., 154, S4–S25, 1999.

Lunn, D. J., Thomas, A., Best, N., and Spiegelhalter, D.: WinBUGS – A Bayesian modelling framework: Concepts, structure, and extensibility, Stat. Comput., 10, 325–337, 2000.

Maniatis, G., Demiris, N., Kranis, A., Banos, G., and Kominakis, A.: Genetic analysis of sexual dimorphism of body weight in broilers, J. Appl. Genet., 54, 61–70, 2013.

Mathew, B., Bauer, A. M., Koistinen, P., Reetz, T. C., Leon, J., and Sillanpaa, M. J.: Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters, Heredity, 109, 235–245, 2012.

McCullagh, P. and Nelder, J. A.: Generalized Linear Models, Chapman and Hall, London, 1994.

Muller, W., Deptuch, K., Lopez-Rull, I., and Gil, D.: Elevated yolk androgen levels benefit offspring development in a between-clutch context, Behav. Ecol., 18, 929–936, 2007.

Patterson, H. D. and Thompson, R.: Recovery of Inter-Block Information When Block Sizes Are Unequal, Biometrika, 58, 545–554, 1971.

Quaas, R. L.: Transformed Mixed Model Equations: A Recursive Algorithm to Eliminate A⁻¹, J. Dairy Sci., 72, 1937–1941, 1989.

Rue, H., Martino, S., and Chopin, N.: Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations, J. Roy. Stat. Soc. B, 71, 319–392, 2009.

Schwabl, H.: Maternal testosterone in the avian egg enhances postnatal growth, Comp. Biochem. Phys. A, 114, 271–276, 1996.

Schwarz, G.: Estimating the dimension of a model, Ann. Statist., 6, 461–464, 1978.

Sorensen, D. and Gianola D.: Likelihood, Bayesian and MCMC methods in quantitative genetics, Springer-Verlag, New York, 2002.

Sorensen, D. A., Wang, C. S., Jensen, J., and Gianola, D.: Bayesian-Analysis of Genetic Change Due to Selection Using Gibbs Sampling, Genet. Sel. Evol., 26, 333–360, 1994.

Spiegelhalter, D. J., Best, N. G., Carlin, B. R., and van der Linde, A.: Bayesian measures of model complexity and fit, J. Roy. Stat. Soc. B, 64, 583–639, 2002.

Surai, P. F. and Speake, B. K.: Distribution of carotenoids from the yolk to the tissues of the chick embryo, J. Nutr. Biochem., 9, 645–651, 1998.

Vaida, F. and Blanchard, S.: Conditional Akaike information for mixed-effects models, Biometrika, 92, 351–370, 2005.

Van Tassell, C. P. and Van Vleck, L. D.: Multiple-trait Gibbs sampler for animal models: Flexible programs for Bayesian and likelihood-based (co)variance component inference, J. Anim. Sci., 74, 2586–2597, 1996.

Van Tassell, C. P., Casella, G., and Pollak, E. J.: Effects of Selection on Estimates of Variance-Components Using Gibbs Sampling and Restricted Maximum-Likelihood, J. Dairy Sci., 78, 678–692, 1995.

von Schantz, T., Bensch, S., Grahn, M., Hasselquist, D., and Wittzell, H.: Good genes, oxidative stress and condition-dependent sexual signals, P. Roy. Soc. B-Biol. Sci., 266, 1–12, 1999.

Waagepetersen, R., Ibanez-Escriche, N., and Sorensen, D.: A comparison of strategies for Markov chain Monte Carlo computation in quantitative genetics, Genet. Sel. Evol., 40, 161–176, 2008.

Waldmann, P.: Easy and Flexible Bayesian Inference of Quantitative Genetic Parameters, Evolution, 63, 1640–1643, 2009.

Waldmann, P., Hallander, J., Hoti, F., and Sillanpaa, M. J.: Efficient Markov chain Monte Carlo implementation of Bayesian analysis of additive and dominance genetic variances in noninbred pedigrees, Genetics, 179, 1101–1112, 2008.

Wang, C. S., Rutledge, J. J., and Gianola, D.: Marginal Inferences About Variance-Components in a Mixed Linear-Model Using Gibbs Sampling, Genet. Sel. Evol., 25, 41–62, 1993.