Estimates of Genetic Parameters for Body Weight in Turkish Holstein Bulls using Random Regression Model

Year 2019, Volume: 25 Issue: 3, 328 - 333, 05.09.2019

Aşkın Galiç , Çiğdem Takma

https://doi.org/10.15832/ankutbd.417422

Cited By: 1

Abstract

The objective of this study was to estimate genetic parameters for the body weights of Turkish Holstein bulls using the random regression model. The data set consists of 1475 body weight records from 395 Holstein bulls raised in the same herd. Body weight records of bulls aged between 32 and 725 days old were collected at approximately two-month intervals from December 2013 to October 2014. In the study body weight measurements made on the same day were accepted as a group and the bulls were grouped into 10 different groups according to their age. The additive genetic and permanent environmental effects were estimated using DFREML algorithm by third order Legendre polynomials. The additive genetic variance estimates ranged from 10.73 to 4867.07, the phenotypic variance estimates ranged from 382.84 to 5514.86 and permanent environmental variance estimates ranged from 0.33 to 63.27. The heritability values were estimated between 0.03 to 0.90. The phenotypic and additive genetic correlations between body weights were positively estimated between 0.085 to 0.89 and 0.53 to 0.94, respectively. It was concluded that use of body weight at an earlier age will give advantage in breeding studies for body weight at slaughter.

Keywords

Holstein; Heritability; Meat; Random regression model

References

• Albuquerque L G & Meyer K (2001). Estimates of covariance functions for growth from birth to 630 days of age in Nellore cattle. Journal of Animal Science 79, 2776-2789.
• Anonymous (2015). Red Meat Strategy. Republic of Turkey Ministry of Food, Agriculture and Livestock, General Directorate of Livestock. http://www.tarim.gov.tr/HAYGEM/Belgeler/Hayvancık%20Et%20Stratejisi.pdf (16.12.2016)
• Anonymous (2016). Food and Agricultural Organization of the United Nations. http://www.fao.org/faostat/en/#data (16.12.2016)
• Arango J A, Cundiff L V & Van Vleck L D (2004). Covariance functions and random regression models for cow weight in beef cattle. Journal of Animal Science 82, 54–67.
• Baldi F, Albuquerque L G, Cyrillo J N S G, Branco R H, de Oliveira B C & Mercadante M E Z (2012). Genetic parameter estimates for live weight and daily live weight gain obtained for Nellore bulls in a test station using different models. Livestock Science 144, 148-156.
• Bohmanova J, Misztal I & Bertrand J K (2005). Studies on multiple trait and random regression models for genetic evaluation of beef cattle for growth. Journal of Animal Science 83, 62-67.
• Boligon A A, Mercadante M E Z, Forni S, Lobo R B & Albuquerque L G (2010). Covariance functions for body weight from birth to maturity in Nellore cows. Journal of Animal Science 88, 849–859.
• Ferraz J B S & Eler J P (2010). Public x private partnership in the development of animal breeding research. Revista Brasileira de Zootecnia 39, 216–222.Jaffrezic F, White I M S, Thompson R & Visscher P M (2002). Contrasting models for lactation curve analysis. Journal of Dairy Science 85, 968-975.
• Liinamo A E & Van Arendonk J A M (1999). Combining selection for carcass quality, body weight, and milk traits in dairy cattle. Journal of Dairy Science 82, 802–809.
• Lopes F B, Magnabosco C U, Paulini F, Silva M C, Miyagi E S & Lobo R B (2012). Analysis of longitudinal data of Nellore cattle from performance test at pasture using random regression model. Springerplus 1, 49-55.
• Martinez C A, Elzo M A, Manrique C & Jimenez A (2012). Genetic parameters and breeding values for live weight using random regression models in a Bos taurus-Bos indicus multibreed cattle population in Colombia. Revista Colombiana de Ciencias Pecuarias 25, 548-565.
• Menéndez-Buxadera A, Carleos C, Baro J A, Villa A & Cañón J (2008). Multi-trait and random regression approaches for addressing the wide range of weaning ages in Asturiana de los Valles beef cattle for genetic parameter estimation. Journal of Animal Science 86, 278–286.
• Meyer K (1998). DFREML 3.0β Program Package and User Notes. Animal Genetics and Breeding Unit, Univ. New England, Armidale, New South Wales, Australia.
• Nephawe K A, Maiawashe A & Theron H E (2006). The effect of herd of origin by year on post-weaning traits of young beef bulls at centralized testing centres in South Africa. South African Journal of Animal Science 36, 33–39.
• Nobre P R C, Misztal I, Tsuruta S, Bertrand J K, Silva L O C & Lopes P S (2003). Analyses of growth curves of Nellore cattle by multiple-trait and random regression models. Journal of Animal Science 81, 918-926.
• Parkkonen P, Liinamo A E & Ojala M (2000). Estimates of genetic parameters for carcass traits in Finnish Ayrshire and Holstein-Friesian. Livestock Production Science 64, 203–213.
• Razook A G, Figueiredo L A, Nardon R F, Gonçalves J N S & Ruggieri A C (2001). Breed and selection for post-weaning weight effects on feedlot and carcass traits for the 15th progeny of Sertãozinho (SP-Brazil) Zebu and Caracu herds. Revista Brasileira de Zootecnia 30, 115–124.
• Schenkel F S, Miller S P, Jamrozik J & Wilton J W (2002). Two-step and random regression analyses of weight gain of station-tested beef bulls. Journal of Animal Science 80, 1497–1507.
• Silva T B R, Araújo C V, Bittencourt T C B S C, Araújo S I, Lôbo R B, Bezerra L A F & Silva A A (2013). Use of orthogonal functions in random regression models in describing genetic variance in Nellore cattle. Revista Brasileira de Zootecnia 42, 254-258.
• Valente B D, Silva M A, Silva L O C, Bergman J A G, Pereira J C C, Fridrich A B, Ferreira I C & Correa G S S (2008). Covariance structure of body weight in function of age for Nellore animals from Southeast and Center West of Brazil. Arquivo Brasileiro de Medicina Veterinária e Zootecnia 60, 389-400.