Bayesian genetic analysis of milk yield in Tunisian Holstein dairy cattle population


The genetic determinism of 305-d milk yield and its genetic parameters were investigated in Tunisian Holstein dairy cattle population through Bayesian segregation analyses using a Monte Carlo Markov Chains (MCMC) method. Data included 49,709 records of 305-d milk yield collected between 1996 and 2003 from 114 dairy herds. The postulated major gene was assumed to be additive biallelic locus with Mendelian transmission probabilities and priors used for variance components were uniform. Gibbs sampling was used to generate a chain of 500,000 samples, which were used to obtain posterior means of genetic parameters. Estimated marginal posterior means ± posterior standard deviations of variance components of milk yield were 402866.28 ± 23629.97, 271256.66 ± 34477.83, 68276.83 ± 233027.62 and 1098855.75 ± 10009.52 for polygenic variance (σ2u), permanent environmental variance (σ2ne), major gene variance (σ2G) and error variance (σ2e), respectively. The main finding of this paper showed the postulated major locus was not significant, since the 95% highest posterior density regions (HPDs95%) of most major gene parameters included 0, and particularly for the major gene variance. Estimated transmission probabilities for the 95% highest posterior density regions (HPDs95%) were overlapped. Genetic parameters of 305-d milk yield were very similar under both mixed inheritance and polygenic models. These results indicated that the genetic determinism of milk yield in Tunisian Holstein dairy cattle population is purely polygenic. Based on 50,000 Gibbs samples, heritability and repeatability estimates using polygenic model were h2 = 0.22 ± 0.012 and r = 0.38 ± 0.006, respectively.

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Ilahi, H. , Hammouda, M. and Othmane, M. (2012) Bayesian genetic analysis of milk yield in Tunisian Holstein dairy cattle population. Open Journal of Genetics, 2, 103-108. doi: 10.4236/ojgen.2012.22015.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Degroot, B.J., Keown, J.F., Van Vleck, L.D., et al. (2002) Genetic parameters and response of linear type, yield traits, and somatic cell scores to divergent selection for predicted transmitting ability for type in Holsteins. Journal of Dairy Science, 85, 1314-1323. doi:10.3168/jds.S0022-0302(02)74227-6
[2] Hammami, H., Rekik, B., Soyeurt, H., et al. (2008) Genetic parameters for Tunisian Holsteins using a test-day random regression model. Journal of Dairy Science, 91, 2118-2126. doi:10.3168/jds.2007-0382
[3] Rekik, B., Ben Gara, A., Ben Hammouda, M., et al. (2003) Fitting lactation curves of dairy cattle in different types of herds in Tunisian. Livestock Production Science, 83, 309-315. doi:10.1016/S0301-6226(03)00028-9
[4] Janss, L.L.G., Thompson, R. and Van Arendonk, J.A.M. (1995) Application of Gibbs sampling for inference in a mixed major gene-polygenic inheritance model in animal populations. Theoretical and Applied Genetics, 91, 1137-1147. doi:10.1007/BF00223932
[5] Ilahi, H. (1999) Variabilité génétique de débit de traite chez les caprins laitiers. Thèse de doctorat. ENSA-Rennes, France.
[6] Ilahi, H., Manfredi, E., Chastin, P., et al. (2000) Genetic variability in milking speed of dairy goats. Genetical Re- search, 75, 315-319. doi:10.1017/S001667230000450X
[7] Hagger, C., Janss, L.L.G., Kadarmideen, H.N., et al. (2004) Bayesian inference on major loci in related multi- generation selection lines of laying hens. Poultry Science, 83, 1932-1939.
[8] Ilahi, H. and Kadarmideen, H.N. (2004) Bayesian segregation analysis of milk flow in Swiss dairy cattle using Gibbs sampling. Genetics Selection Evolution, 36, 563- 576. doi:10.1186/1297-9686-36-5-563
[9] Ilahi, H. and Othmane, M.H. (2011) Complex segregation analysis of total milk yield in Churra dairy ewes. Asian- Australian Journal of Animal Science, 24, 330-335.
[10] Ilahi H. and Othmane, M.H. (2011) Bayesian segregation analysis of test-day milk yield in Tunisian Sicilo-Sarde dairy sheep. Journal of Animal and Feed Science, 20, 161-170.
[11] Guo, S.W. and Thompson, E.A. (1992) Monte Carlo method for combined segregation and linkage analysis. Journal of Human Genetics, 51, 1111-1126.
[12] Janss, L.L.G., Van Arendonk, J.A.M. and Brascamp, E.W. (1997) Bayesian statistical analyses for presence of single major genes affecting meat quality traits in crossed pig population. Genetics, 145, 395-408.
[13] Sorensen, D., Anderson, S., Jensen, J., et al. (1994) Infer- ences about genetic parameters using the Gibbs sampler, Proceedings of the 5th World Congress on Genetics Applied to Livestock Production, 7-12 August 1994, Guelph, 321-328.
[14] Janss, L.L.G. (2008) iBay manual version 1.46. Janss Bioinformatics, Lieden.
[15] Box, G.E.P. and Tiao, G. (1973) Bayesian inference in statistical analysis. Reading Addison-Wesley, New York.
[16] Scott, D.W. (1992) Multivariate density estimation. Wiley and Sons, New York. doi:10.1002/9780470316849
[17] Janss, L.L.G. (1998) “MAGGIC” a package of subroutines for genetic analyses with Gibbs sampling. Proceeding of 6th World Congress on Genetics Applied to Live-stock Production, 6-11 January 1998, University of New England, Armidale, 459-460.
[18] Miyake, T., Gaillard, C., Moriya, et al. (1999) Accuracy of detection of major genes segregating in outbred population by Gibbs sampling using phenotypic values of quantitative traits. Journal of Animal Breeding and Genetics, 116, 281-288. doi:10.1046/j.1439-0388.1999.00197.x
[19] Elston, R.C. (1980) Segregation analysis. Current developments in anthropological genetics. Mielke, J.H. and Crawford, M.H., Eds., Publishing Corporation, 327-354. doi:10.1007/978-1-4613-3084-4_11
[20] Elston, R.C. and Stewart, J.M. (1971) A general model for the genetic analysis of pedigree data. Human Heredity, 21, 523-542. doi:10.1159/000152448
[21] Pan, Y., Boettcher, J. and Gibson, J. (2001) Bayesian seg- regation analyses of somatic cell scores of Ontario Hol- stein cattle. Journal of Dairy Science, 84, 2796-2802. doi:10.3168/jds.S0022-0302(01)74735-2
[22] Ben Gara, A., Rekik, B. and Bouallègue, M. (2006) Genetic parameters and evaluation of Tunisian dairy cattle population for milk yield by Bayesian and BLUP analyses. Livestock Production Science, 100, 142-149. doi:10.1016/j.livprodsci.2005.08.012
[23] Haile-Mariam, R., Bowman, J.P. and Goddar, M.E. (2003) Genetic and environmental relationship among calving interval, survival, persistency of milk yield and somatic cell count in dairy cattle. Livestock Production Science, 116, 189-200. doi:10.1016/S0301-6226(02)00188-4
[24] Jiang, X.P., Lieu, G.Q., Wang, C., et al. (2004) Milk trait heritability and correlation with heterozygosity in yak. Journal of Applied Genetics, 45, 215-224.
[25] Weigel, K.A., Rekaya, R., Zwald, N.R., et al. (2001) International genetic evaluation of dairy sires using a multiple-trait model with individual animal performance records. Journal of Dairy Science, 84, 2789-2795. doi:10.3168/jds.S0022-0302(01)74734-0
[26] Bakir, G., Kaygisiz, A. and Ulher, H. (2004) Estimates of genetic parameters of milk yield in brown Swiss and Holstein Friesian cattle. Pakistan Journal of Biological Sciences, 7, 1198-1201. doi:10.3923/pjbs.2004.1198.1201
[27] Chauhan, V.P.S. and Hayes, J.F. (1991) Genetic parameters for first lactation milk production and composition for Holsteins using multivariate restricted maximum likelihood. Journal of Dairy Science, 74, 603-615. doi:10.3168/jds.S0022-0302(91)78207-6
[28] Misztal, I., Lawlor, T.J., Short, T.H., et al. (1992) Multiple trait estimation of variance components of milk yield and type traits using an animal model. Journal of Dairy Science, 75, 544-551. doi:10.3168/jds.S0022-0302(92)77791-1

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