Selection Indices for Improvement of Body Weight and Mohair Yield under the Traditional Low-Input Production System of Local Markhoz Goat in Iran
محورهای موضوعی :
F. Hosseinzadeh Shirzeyli
1
(
Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
)
S. Joezy-Shekalgorabi
2
(
Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
)
M. Aminafschar
3
(
Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
)
M. Razmkabir
4
(
Department of Animal Science, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran
)
کلید واژه: genetic evaluation, Markhoz goat, meat, mohair selection index,
چکیده مقاله :
This research aimed to evaluate alternative selection schemes through their expected selection responses and the Bulmer parameters in the Markhoz goat breed. To select the best-ranked animals the body weights at birth, weaning, 6 months, 9 months, yearling, and mohair production records were assessed to compare the selection responses using SelAction 2.2. The average economic gain of the 3-trait indices was higher than the 2-trait indices. In this regard, the use of two selection indices of I7 (3-traits) and I4 (2-traits) pro-moted simultaneous improvement in both meat and mohair productions and can be proposed for application in this population. Artificial selection through several selection schemes has reduced population parameters in the current population of Markhoz goats. The magnitude of reduction in phenotypic variance and herita-bility was greater in traits that have been directly selected (included in index) and traits that had a higher economic coefficient in the index. Considering the present conditions, provided the optimal possible selec-tion response, genetic improvement, and economic gain to improve both mohair and meat production. Al-though, the use of these indices depends on the determination of objectives and of the measurement facility of selection criteria.
This research aimed to evaluate alternative selection schemes through their expected selection responses and the Bulmer parameters in the Markhoz goat breed. To select the best-ranked animals the body weights at birth, weaning, 6 months, 9 months, yearling, and mohair production records were assessed to compare the selection responses using SelAction 2.2. The average economic gain of the 3-trait indices was higher than the 2-trait indices. In this regard, the use of two selection indices of I7 (3-traits) and I4 (2-traits) pro-moted simultaneous improvement in both meat and mohair productions and can be proposed for application in this population. Artificial selection through several selection schemes has reduced population parameters in the current population of Markhoz goats. The magnitude of reduction in phenotypic variance and herita-bility was greater in traits that have been directly selected (included in index) and traits that had a higher economic coefficient in the index. Considering the present conditions, provided the optimal possible selec-tion response, genetic improvement, and economic gain to improve both mohair and meat production. Al-though, the use of these indices depends on the determination of objectives and of the measurement facility of selection criteria.
Abraham H., Gizaw S. and Urge M. (2018). Identification of breeding objectives for Begait goat in western Tigray, North Ethiopia. Trop. Anim. Health Prod. 50, 1887-1892.
Ahmed R.M., Osman M.A., Elsayed M. and Mansour H. (2020). Genetic improvement of some productive traits in Zaraibi goats. Arab Univ. J. Agric. Sci. 28, 207-216.
Barwick S.A., Henzell A.L., Walmsley B.J., Johnston D.J. and Banks R.G. (2018). Methods and consequences of including feed intake and efficiency in genetic selection for multiple-trait merit. J. Anim. Sci. 96, 1600-1616.
Bett R.C., Kosgey I.S., Bebe B.O. and Kahi A.K. (2007). Breeding goals for the Kenya dual purpose goat. II. Estimation of economic values for production and functional traits. Trop. Anim. Health Prod. 39, 467-475.
Bulmer M.G. (1971). The Effect of selection on genetic variability. American Natural. 105, 201-11.
Burns J.G., Eory V., Butler A., Simm G. and Wall E. (2022). Review: Preference elicitation methods for appropriate breeding objectives. Animal. 16, 100535-100545.
Chomchuen K., Tuntiyasawasdikul V., Chankitisakul V. and Boonkum W. (2022). Genetic evaluation of body weights and egg production traits using a multi-trait animal model and selection index in thai native synthetic chickens (Kaimook e-san2). Animals. 12(3), 335-345.
Conington J., Bishop S.C., Grundy B., Waterhouse A. and Simm G. (2001). Multi-trait selection indexes for sustainable UK hill sheep production. Anim. Sci. 73, 413-423.
Dekkers J.C. (2007). Prediction of response to marker-assisted and genomic selection using selection index theory. J. Anim. Breed Genet. 124, 331-341.
Dostál Z. and Pospíšil L. (2018). Conjugate gradients for symmetric positive semidefinite least-squares problems. Int. J. Comput. Math. 95, 2229-2239.
Dubeuf J.P. and Boyazoglu J. (2009). An international panorama of goat selection and breeds. Livest. Sci. 120, 225-231.
El-Hag F., Tsubo M., Rekik M., Haile A., Getachew T., Hilali M., Khatir A., Eldin I., Ali Babiker I.E., Musa A., Ahmed M.K. and Zakieldeen S. (2020). Goat breeding objectives in relation to agroecological zonation under dryland farming conditions of North Kordofan, Sudan. World J. Agric. Soil. Sci. 5, 1-7.
Getachew T., Rischkowsky B., Rekik M., Mueller J., Tessema T., Solomon D. and Haile A. (2022). Optimizing breeding structures and related management in community-based goat breeding programs in the Borana pastoral system of Ethiopia. Livest. Sci. 256, 104819-104829.
Hazel L.N., Dickerson G.E. and Freeman A.E. (1994). The selection index—then, now, and for the future1. J. Dairy Sci. 77, 3236-3251.
Hill W.G., Goddard M.E. and Visscher P.M. (2008). Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet. 4, e1000008.
Jahufer M.Z.Z. and Casler M.D. (2015). Application of the smith-hazel selection index for improving biomass yield and quality of switchgrass. Crop Sci. 55, 1212-1222.
Kargar Borzi N., Ayatollahi Mehrgardi A. and Abassi M.A. (2017). Breeding objectives and desired-gain selection index for rayeni cashmere goat in pasture system. Iranian J. Appl. Anim. Sci. 7, 631-636.
Kheirabadi K. and Rashidi A. (2016). Genetic description of growth traits in Markhoz goat using random regression models. Small Rumin. Res. 144, 305-312.
Kheirabadi K. and Rashidi A. (2019). Modelling and genetic evaluation of Markhoz goat growth curve parameters. Small Rumin. Res. 170, 43-50.
Madsen P., Milkevych V., Gao H., Christensen O.F. and Jensen J. (2006). DMU - A Package for Analyzing Multivariate Mixed Models in Quantitative Genetics and Genomics. Pp. 525-540 in Proc. World Congr. Genet. Appl. Livest. Prod., Electronic Poster Session - Methods and Tools - Software.
Mueller J.P., Ansari-Renani H.R., Seyed Momen S.M., Ehsani M., Alipour O. and Rischkowsky B. (2015). Implementation of a cashmere goat breeding program amongst nomads in Southern Iran. Small Rumin. Res. 129, 69-76.
Nazari-Ghadikolaei A., Mehrabani-Yeganeh H., Miarei-Aashtiani S.R., Staiger E.A., Rashidi A. and Huson H.J. (2018). Genome-wide association studies identify candidate genes for coat color and mohair traits in the Iranian Markhoz goat. Front. Genet. 9, 105-115.
Pook T., Schlather M. and Simianer H. (2020). MoBPS - modular breeding program simulator. G3 (Bethesda). 10, 1915-1923.
Rashidi A., Bishop S.C. and Matika O. (2011). Genetic parameter estimates for pre-weaning performance and reproduction traits in Markhoz goats. Small Rumin. Res. 100, 100-106.
Razmkabir M. and Mahmoudi P. (2019). Monitoring genetic diversity and population structure of Markhoz goat by pedigree analysis. Anim. Prod. Res. 7, 13-22.
Rezende F.M., Rodriguez E., Leal-Gutiérrez J.D., Elzo M.A., Johnson D.D., Carr C. and Mateescu R.G. (2021). Genomic approaches reveal pleiotropic effects in crossbred beef cattle. Front. Genet. 12, 23-29.
Rutten M.J.M., Bijma P., Woolliams J.A. and van Arendonk J.A.M. (2002). SelAction: Software to predict selection response and rate of inbreeding in livestock breeding programs. J. Hered. 93, 456-464.
Sargolzaei M., Iwaisaki H. and Colleau J.J. (2006). CFC: A tool for monitoring genetic diversity. Pp. 85-95 in Proc. 8th World Congr. Genet. Appl. Livest. Prod., Belo Horizonte, Brazil.
Scholtens M., Lopez-Villalobos N., Lehnert K., Snell R., Garrick D. and Blair H.T. (2020). Advantage of including genomic information to predict breeding values for lactation yields of milk, fat, and protein or somatic cell score in a New Zealand dairy goat herd. Animals. 11, 23-32.
Şenyüz H.H. (2021). Fertility, live weight, survival rate, greasy fleece weight, and quality traits of angora goats in Turkey. Small Rumin. Res. 197, 1-11.
Shokrollahi B. and Baneh H. (2012). (Co)variance components and genetic parameters for growth traits in Arabi sheep using different animal models. Genet. Mol. Res. 11, 305-314.
Simões M.R.S., Leal J.J.B., Minho A.P., Gomes C.C., MacNeil M.D., Costa R.F., Junqueira V.S., Schmidt P.I., Cardoso F.F., Boligon A.A. and Yokoo M.J. (2020). Breeding objectives of Brangus cattle in Brazil. J. Anim. Breed. Genet. 137, 177-188.
Snyman M.A. (2012). Genetic analysis of body weight in South African Angora kids and young goats. South African J. Anim. Sci. 42, 146-155.
Snyman M.A. (2020). Genetic analysis of reproduction, body weight and mohair production in South African Angora goats. Small Rumin. Res. 192, 106183-106194.
Sölkner J., Grausgruber H., Okeyo A.M., Ruckenbauer P. and Wurzinger M. (2008). Breeding objectives and the relative importance of traits in plant and animal breeding: A comparative review. Euphytica. 161, 273-282.
Theodoridis A., Ragkos A., Rose G., Roustemis D. and Arsenos G. (2018). Defining the breeding goal for a sheep breed including production and functional traits using market data. Animal. 12, 1508-1515.
Thompson R. and Meyer K. (1986). A review of theoretical aspects in the estimation of breeding values for multi-trait selection. Livest. Prod. Sci. 15, 299-313.
Tyasi T.L., Ng'ambi J. and Mogashoa S. (2022). Breeding practices and trait preferences of goat keepers at Lepelle-Nkumpi Local Municipality, South Africa: Implication for the design of breeding programmes. Trop. Anim. Health Prod. 54, 68-79.
Van Grevenhof E.M., Van Arendonk J.A. and Bijma P. (2012). Response to genomic selection: the Bulmer effect and the potential of genomic selection when the number of phenotypic records is limiting. Genet. Sel. Evol. 44, 26-35.
Vandenplas J., Eding H., Bosmans M. and Calus M.P.L. (2020). Computational strategies for the preconditioned conjugate gradient method applied to ssSNPBLUP, with an application to a multivariate maternal model. Genet. Sel. Evol. 52, 24-36.
Villanueva B., Wray N.R. and Thompson R. (1993). Prediction of asymptotic rates of response from selection on multiple traits using univariate and multivariate best linear unbiased predictors. Anim. Sci. 57, 1-13.
Wellmann R. (2019). Optimum contribution selection for animal breeding and conservation: the R package optiSel. BMC Bioinform. 20, 25-37.
Xu S. (2022). Methods of multiple trait selection. Pp. 283-305 in Quantitative Genetics. S. Xu Ed., Springer International Publishing, Cham.
