Bayesian Inference of (Co) Variance Components and Genetic Parameters for Economic Traits in Iranian Holsteins via Gibbs Sampling

Iranian Journal of Applied Animal Science

مقاله 9، دوره 5، شماره 1، خرداد 2015، صفحه 51-60   

نوع مقاله: Research/Original Article

نویسندگان

H. Faraji-Arough^{* 1}؛ A.A. Aslaminejad¹؛ M. Tahmoorespur¹؛ M. Rokouei²؛ M.M. Shariati¹

¹Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

²Department of Animal Science, Faculty of Agriculture, University of Zabol, Zabol, Iran

چکیده

The aim of this study was using Bayesian approach via Gibbs sampling (GS) for estimating genetic parameters of production, reproduction and health traits in Iranian Holstein cows. Data consisted of 320666 first- lactation records of Holstein cows from 7696 sires and 260302 dams collected by the animal breeding center of Iran from year 1991 to 2010. (Co) variance components were estimated using a multi-trait animal model analyzed via Gibbs sampling. After convergence, the highest posterior density region of heritability for milk (MY305), fat (FY305), protein (PY305), age at first calving (AFC), calving interval (CI) and somatic cell score (SCS) were 0.255-0.275, 0.195-0.215, 0.195-0.225, 0.260-0.275, 0.065-0.080 and 0.055-0.075, respectively. Genetic correlations ranged from -0.121 (between FY305 and AFC) to 0.914 (between MY305 and PY305) and for phonotypic correlations, it was from -0.083 (between MY305 and SCS) to 0.929 (between MY305 and PY305. The result of this study showed that production traits and AFC have enough genetic variation to develop breeding programs. The estimated genetic correlations suggest that milk production traits and CI would be affected if increasing milk production is the selection goal. The high genetic correlation between CI with SCS suggests that increasing calving interval trait result in an increased SCS.

کلیدواژه‌ها

Bayesian inference؛ production traits؛ reproduction traits؛ somatic cell score

مراجع

Ansari-Lari M., Rezagholi M. and Reiszadeh M. (2009). Trends in calving age and calving intervals for Iranian Holstein in Fars province, Southern Iran. Trop. Anim. Health Prod. 41, 1283-1288.
Arakawa A., Iwaisaki H. and Anada K. (2009). A bayesian approach to the Japanese Black carcass genetic evaluation. South African Anim. Sci.39, 77-80.
Aspilcueta-Borquis R.R., Di Palo R., Araujo Neto F.R., Baldi F., De Camargo G.M.F., De Albuquerque L.G., Zicarelli L. and Tonhati H. (2010a). Genetic parameter estimates for buffalo milk yield, milk quality and mozzarella production and Bayesian inference analysis of their relationships. Genet. Mol. Res. 9(3), 1636-1644.
Aspilcueta Borquis R.R., Araujo Neto F.R., Baladi F., Bignardi A.B., Albuquerque L.G. and Tonhati H. (2010b). Genetic parameters for buffalo milk yield and milk quality traits using Bayesian inference. J. Dairy Sci. 93, 2195-2201.
Beaumont M.A. and Rannala B. (2004). The Bayesian revolution in genetics. Nat. Rev. Genet. 5, 251-261.
Ben Gara A., Rekik B. and Bouallegue M. (2006). Genetic parameters and evaluation of the Tunisian dairy cattle populat ion for milk yield by Bayesian and BLUP analyses. Livest. Sci. 100, 142-149.
Blasco A. (2001). The Bayesian controversy in animal breeding. J. Anim. Sci. 79, 2023-2046.
Cassandro M., Comin A., Ojala M., Dal Zotto R., De Marchi M., Gallo L., Carnier P. and Bittante G. (2008). Genetic parameters of milk coagulation properties and their relationships with milk yield and quality traits in Italian Holstein cows. J. Dairy Sci. 91, 371-376.
Ceron-Munoz M.F., Tonhati H., Costa C.N., Maldonado-Estrada J. and Rojas-Sarmiento D. (2004). Genotype x environment interaction for age at first calving in Brazilian and Colombian Holsteins. J. Dairy Sci. 87, 2455-2458.
Cienfuegos-Rivas E.G., Oltenacu P.A., Blake R.W. and CastilloJuarez H. (2006). Fertility responses of Mexican Holstein cows to US sire selection. J. Dairy Sci. 89, 2755-2760.
Dechow C.D., Rogers G.W., Cooper J.B., Phelps M.I. and Mosholder A.L. (2007). Milk, fat, protein, somatic cell score, and days open among Holstein, Brown Swiss, and their crosses. J. Dairy Sci. 90, 3542-3549.
Faraji-Arough H., Aslaminejad A.A. and Farhangfar H. (2011). Estimation of genetic parameters and trends for age at first calving and calving interval in Iranian Holstein cows. J. Res. Agric. Sci. 7(1), 79-87.
Firat M.Z., Theobald C.M. and Thompson R. (1997). Multivariate analysis of test day milk yields of British Holstein-Friesian heifers using Gibbs sampling. Acta Agric. Scandinavica. 47(4), 221-229.
Gelfand A.E. and Smith A.F.M. (1990). Sampling-based approaches to calculating marginal densities. J. Am. Statist. Assoc. 85, 398-409.
Gelman A., Carlin J.B., Stern H.S. and Rubin D.B. (2004). Bayesian data analysis. Chapman and Hall / CRC, Boca Raton, FL.
Geweke J. (1992). Bayesian Statistics Oxford University Press, New York.
Ghasemi Z. (2012). Association between somatic cell count and milk production traits in Iranian Holstein cows. MS Thesis. Ferdowsi Univ., Mashahd, Iran.
Ghavi Hossein-Zadeh N. and Ardalan M. (2011). Bayesian estimates of genetic parameters for metritis, retained placenta, milk fever and clinical mastitis in Holstein dairy cows via Gibbs sampling. Res. Vet. Sci. 90, 146-149.
Ghiasi H., Pakdel A., Nejati-Javaremi A., Mehrabani-Yeganeh H., Honarvar M., Gonzalez-Recio O., Carabano M.J. and Alenda R. (2011). Genetic variance components for female fertility in Iranian Holstein cows. Livest. Sci. 139(3), 277-280.
Haile-Mariam M., Carrick M.J. and Goddard M.E. (2008). Genotype by environment interaction for fertility, survival and milk production traits in Australian dairy cattle. J. Dairy Sci. 91, 4840-4853.
Henderson C.R. (1975). Comparison of alternative sire evaluation methods. J. Anim. Sci. 41, 760-770.
Ilahi H. and Kadarmideen H.N. (2004). Bayesian segregation analysis of milk flow in Swiss dairy cattle using Gibbs sampling. Genet. Sel. Evol. 36, 563-576.
Kadarmideen H.N., Thompson R., Coffey M.P. and Kossaibati M.A. (2003). Genetic parameters and evaluations from single- and multiple-trait analysis of dairy cow fertility and milk prod-uction. Livest. Prod. Sci. 81, 183-195.
Lidauer M.H., Msdsen P., Matilainen K., Mantysaari E.A., Stranden I., Thompson R., Poso J., Pedersen J., Nielsen U.S., Eriksson J.A., Johansson K. and Aamand G.P. (2009). Estimation of variance components for Nordic red cattle test-day model: Bayesian Gibbs sampler vs. Monte Carlo EM REML. Interbull Bulletin. 40, 37-41.
Madsen P. (2008). Strategy for estimation of variance components for the joint Nordic yield evaluation. Interbull Bulletin. 38, 36-39.
Madsen P. and Jensen J. (2008). A user’s guide to DMU. A package for analysing multivariate mixed models. Version 6, release 4.5. http://dmu.agrsci.dk.  
Montaldo H.H., Castillo-Juarez H., Valencia-Posadas M., Cienfuegos-Rivas E.G. and Ruiz-Lopez F.J. (2010). Genetic and environmental parameters for milk production, udder health, and fertility traits in Mexican Holstein cows. J. Dairy Sci. 93, 2168-2175.
Nafez N., Zerehdaran S., Hassani S. and Samiei R. (2012). Genetic evaluation of productive and reproductive traits of Holstein dairy cows in the North of Iran. Iranian J. Anim. Sci. Res. 4(1), 69-77.
Odegard J., Gunnar K. and Berg H. (2003). Variance components and genetic trend for somatic cell count in Norwegian cattle. Livest. Prod. Sci. 79, 135-144.
Ojango J.M.K. and Pollott G.E. (2001). Genetics of milk yield and fertility traits in Holstein-Friesian cattle on large-scale Kenyan farms. J. Anim. Sci. 79, 1742-1750.
Ovaskainen O., Cano J.M. and Merila J. (2008). A Bayesian framework for comparative quantitative genetics.Proc. Royal Soc. 275, 669-678.
Pantelic V., Sretenovic L., Ostojic-Andric D., Trivunovic S., Petrovic M.M., Aleksic S. and Ruzic-Muslic D. (2011). Heritability and genetic correlation of production and reproduction traits of Simmental cows. African J. Biotechnol. 10(36), 7117-7121.
Paula M.C.D., Martins E.N., Silva L.O.C.D., Oliveira C.A.L.D., Valotto A.A. and Gasparino E. (2008). Estimates of genetic parameters for yield and composition of milk of Holstein cows in Paraná State. Rev. Brasileira Zootec. 37(5), 824-828.
Penasa M., Cecchinato A., Battagin M., De Marchi M., Pretto D. and Cassandro M. (2010). Bayesian inference of genetic parameters for test-day milk yield, milk quality traits and somatic cell score in Burlina cows. J. Appl. Genet. 51(4), 489-495.
Ruiz-Sánchez R., Blake R.W., Castro Gámez H.M.A., Sánchez F., Montaldo H.H. and Castillo-Juárez H. (2007). Short communication: changes in the association between milk yield and age at first calving in Holstein cows with herd environment level for milk yield. J. Dairy Sci. 90, 4830-4834.
Schukken Y.H., Leslie K.E., Weersink A.J. and Martin S.W. (1992). Ontario bulk milk somatic cell count program. II. Population dynamics of bulk milk somatic cell counts. J. Dairy Sci. 75, 3359-3366.
Shaw R.G. (1987). Maximum-likelihood approaches applied to quantitative genetics of natural populations. Evolution. 41, 812-826.
Smith B.J. (2007). Boa: an R package for MCMC output convergence assessment and posterior inference. J. Stat. Soft. 21(11), 1-37.
Toghiani S. (2012). Genetic relationships between production traits and reproductive performance in Holstein dairy cows. Arch. Tierz. 2, 458-468.
Van Tassell C.P. and Van Vleck L.D. (1996). Multiple-trait gibbs sampler for animal models: flexible programs for bayesian and likelihood-based covariance component inference. J. Anim. Sci. 74, 2586-2597.
VanTassell C.P., Casella G. and Pollak E.J. (1995). Effects of selection on estimates of variance components using Gibbs sampling and restricted maximum likelihood. J. Dairy Sci. 78, 678-692.
Van Raden P.M., Sanders A.H., Tooker M.E., Miller R.H., Norman H.D., Kuhn M.T. and Wiggans G.R. (2004). Development of a national genetic evaluation for cow fertility. J. Dairy Sci. 87, 2285-2292.
Veerkamp R.F., Koenen E.P.C. and De Jong G. (2001). Genetic correlations among body condition score, yield and fertility in first-parity cows estimated by random regression models. J. Dairy Sci. 84, 2327-2335.