Genetic Properties of Some Economic Traits in Isfahan Native Fowl Using Bayesian and REML Methods

Document Type: Original Paper


1 Department of Animal Genetic and Breeding, Faculty of Animal Science, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

2 Department of Animal Science, College of Agriculture, Tabriz University, Tabriz, Iran.

3 Department of Animal Science, College of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.


The objective of the present study was to estimate heritability values for some performance and egg quality traits of native fowl in Isfahan breeding center using REML and Bayesian approaches. The records were about 51521 and 975 for performance and egg quality traits, respectively. At the first step, variance components were estimated for body weight at hatch (BW0), body weight at 8 weeks of age (BW8), weight at sexual maturity (WSM), egg yolk weight (YW), egg Haugh unit and eggshell thickness, via REML approach using ASREML software. At the second step, the same traits were analyzed via Bayesian approach using Gibbs3f90 software. In both approaches six different animal models were applied and the best model was determined using likelihood ratio test (LRT) and deviance information criterion (DIC) for REML and Bayesian approaches, respectively. Heritability estimates for BW0, WSM and ST were the same in both approaches. For BW0, LRT and DIC indexes confirmed that the model consisting maternal genetic, permanent environmental and direct genetic effects was significantly better than other models. For WSM, a model consisting of maternal permanent environmental effect in addition to direct genetic effect was the best. For shell thickness, the basic model consisting direct genetic effect was the best. The results for BW8, YW and Haugh unit, were different between the two approaches. The reason behind this tiny differences was that the convergence could not be achieved for some models in REML approach and thus for these traits the Bayesian approach estimated the variance components more accurately. The results indicated that ignoring maternal effects, overestimates the direct genetic variance and heritability for most of the traits. Also, the Bayesian-based software could take more variance components into account.


Browne WJ & Draper D. 2006. A comparison of Bayesian and likelihood-based methods for fitting multilevel models. Bayesian Analysis, 1: 473-514. [Link]

Dabson AJ. 1991. An Introduction to Generalized Linear Models. Chapman and Hall, London, UK. pp:74.

Dana N, Vander Waaij EH & Van Arendonk JAM. 2011. Genetic and phenotypic parameters estimates for body weights and egg production in Horro chicken of Ethiopia. Tropical Animal Health and Production, 43: 21-28. [Link]

Duangjinda M, Misztal I, Bertrand JK & Tsuruta S. 2001. The empirical bias of estimates by restricted maximum likelihood, Bayesian method, and R under selection for additive, maternal, and dominance models. Journal of Animal Science, 79: 2991-2996. [Link]

Elston RC & Stewart J. 1971. A general model for the genetic analysis of pedigree data. Human Heredity, 21: 523-542. [Link]

Gelfand AE & Smith AFM. 1990. Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85: 398-409. [Link]

Ghorbani SH, Kamali MA, Abbasi MA & Ghafouri-Kesbi F. 2012. Estimation of maternal effects on some economic traits of North Iranian native fowls using different models. Journal of Agricultural Science and Technology, 14: 95-101. [Link]

Gianola D & Fernando RL. 1986. Bayesian methods in animal breeding theory. Journal of Animal Science, 63: 217-244. [Link]

Gilmour AR, Cullis BR, Welham SJ & Thompson R. 2000. ASREML. NSW Agriculture, Orange, Australia.

Grosso JLBM, Balieiro JCC, Eler JP, Ferraz JBS, Mattos EC & Michelan Filho T. 2010. Comparison of different models to estimate genetic parameters for carcass traits in a commercial broiler line. Genetics and Molecular Research, 9: 908-918. [Link]

Hartmann C, Johansson K, Strandberg E & Rydhmer, L. 2003. Genetic correlations between the maternal genetic effect on chick weight and the direct genetic effects on egg composition traits in White Leghorn line. Poultry Science, 82: 1–8. [Link]

Le Bihan-Duval E, Mignon-Grasteau S, Millet N & Beaumont C. 1998. Genetic analysis of a selection experiment on increased body weight and breast muscle weight as well as on limited abdominal fat weight. British Poultry Science, 39: 346-353. [Link]

Le Roy P, Elsen JM & Knott S. 1989. Comparison of four statistical methods for detection of a major gene in a progeny test design. Genetics Selection Evolution, 21: 341-357. [Link]

Meyer K. 1997. Estimates of genetic parameters for weaning weight of beef cattle accounting for direct-maternal environmental covariances. Livestock Production Science, 52: 187- 199. [Link]

Misztal I. 1999. GIBBS3F90 Manual. [Link]

Muir WM, Wong GK, Zhang Y, Wang J, Groenen MA, Crooijmans RP, Megens HJ, Zhang H, Okimoto R, Vereijken A, Jungerius A, Albers GA, Lawley CT, Delany ME, Maceachern S & Cheng HH. 2008. Genome-wide assessment of worldwide chicken SNP genetic diversity indicates significant absence of rare alleles in commercial breeds. Proceedings of National Academy of Sciences of the USA, 105: 17312-17317. [Link]

Narinç D, Karaman E, Firat MZ & Aksoy T. 2011. Estimation of multiple-trait genetic parameters and BLUP using different estimation methods for some egg traits in Japanese quails. Kafkas Üniversitesi Veteriner Fakültesi Dergisi, 17: 117-123. [Link]

Rosa GJM, Padovani CR & Gianola D. 2003. Robust linear mixed models with normal/independent distributions and Bayesian MCMC implementation. Biometrical Journal, 45: 573-590. [Link]

SAS Institute. 2001. SAS /STAT user’s Guide: statistics. Release 8.2. SAS Institute Inc., Cary, NC. [Link]

Sanchez MP, Bidanel JP, Zhang S, Naveau J, Burlot T & Le Roy P. 2003. Likelihood and Bayesian analyses reveal major genes affecting body composition, carcass, meat quality and the number of false teats in Chinese European pig line. Genetics Selection Evolution, 35: 385-402. [Link]

Schenkel FS, Schaeffer LR & Boettcher PJ. 2002. Comparison between estimation of breeding values and fixed effects using Bayesian and empirical BLUP estimation under selection on parents and missing pedigree information. Genetics Selection Evolution, 34: 41-59. [Link]

Sorenson DA, Wang CS, Jensen J, Gianola D. 1994. Bayesian analysis of genetic change due to selection using Gibbs sampling. Genetics Selection Evolution, 26: 333-360. [Link]

Unver Y, Akbas Y, Firat MZ & Oguz I. 2002. Estimation of heritability for egg production in laying hens using MIVQE, ML, REML and Gibbs sampling methods. 7th world congress on applied genetics to livestock production, PP: 19-23. [Link]

Wang CS, Rutledge JJ & Gianola D. 1993. Marginal inferences about variance components in a mixed linear model using Gibbs sampling. Genetics Selection Evolution, 25: 41-62. [Link]

Wolc A, White IMS, Avendano S & Hill WG. 2009. Genetic variability in residual variation of body weight and conformation scores in broiler chickens. Poultry Science, 88: 1156-1161. [Link]

Yousefizonuz A, Alijani S, Rafat SA, Abbasi MA & Daghigh Kia H. 2013. Estimation of maternal effects on the north-iranian native chicken traits using bayesian and REML methods. Slovak Journal of Animal Science, 46: 52-60. [Link]