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Estimates of the genetic parameters of turkey body weight using random regression analysis

Published online by Cambridge University Press:  03 June 2011

S. A. Rafat ,
P. Namavar ,
D. J. Shodja ,
H. Janmohammadi ,
H. Z. Khosroshahi  and
I. David
S. A. Rafat*
Affiliation:
Animal Science Department, Faculty of Agriculture, University of Tabriz, Tabriz, P.C. 51666-16471, Iran
P. Namavar
Affiliation:
Animal Science Department, University of Tabriz, Tabriz, P.C. 51666-16471, Iran
D. J. Shodja
Affiliation:
Animal Science Department, Faculty of Agriculture, University of Tabriz, Tabriz, P.C. 51666-16471, Iran
H. Janmohammadi
Affiliation:
Animal Science Department, Faculty of Agriculture, University of Tabriz, Tabriz, P.C. 51666-16471, Iran
H. Z. Khosroshahi
Affiliation:
East Azerbaijan Research Centre for Agriculture and Natural Resources, Tabriz, Iran
I. David*
Affiliation:
UR631, INRA SAGA, 31320 Castanet-Tolosan, France
*
†E-mail: [email protected], [email protected]
†E-mail: [email protected], [email protected]
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Abstract

Random regression (RR) analysis has been recommended to estimate the genetic parameters of longitudinal data. The objective of this study was to evaluate the growth of turkeys using RR models. Data were collected from 957 turkeys and included 15 478 individual body weight recorded during the first week of life and between weeks 2 and 32 by 2-week intervals. To take into account the repeated measurements of weight for each animal, a specific overall growth curve was modelled using a cubic smoothing spline. Animal deviation to this curve was also modelled using an RR function. All data were analysed with the ASReml package. The results showed an increase in heritability estimates over the trajectory and peaked at 0.60 around 20 to 32 weeks of age. Genetic correlations showed that turkeys could be selected at earlier time points, at 12 weeks of age, in order to increase the growth rate. In general, genetic correlation estimates were higher among adjacent ages, decreasing markedly with the increase of distance between ages. Negative genetic correlations were observed between ages.

Keywords

repeated measurementsgrowthheritabilitysmoothing spline function
Type
Full Paper
Information
animal , Volume 5 , Issue 11 , 26 September 2011 , pp. 1699 - 1704
Copyright
Copyright © The Animal Consortium 2011

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References

Akbas, Y, Takma, C, Yaylak, E 2004. Genetic parameters for quail body weights using a random regression model. South African Journal of Animal Science 34, 104–109.CrossRefGoogle Scholar
Albuquerque, LG, Meyer, K 2001. Estimates of covariance functions for growth from birth to 630 days of age in Nelore cattle. Journal of Animal Science 79, 2776–2789.CrossRefGoogle ScholarPubMed
Anang, A, Mielenz, N, Schuler, L 2002. Monthly model for genetic evaluation of laying hens II. Random regression. British Poultry Science 43, 384–390.CrossRefGoogle ScholarPubMed
Banos, G, Avendaño, S, Olori, V 2006. Time dependent genetic parameters for broiler chicken body weight measured in selection and commercial environments. In proceedings of the 8th world congress on genetics applied to livestock production, CD-ROM communication no. 07–05, p. 4. Belo Horizonte, Minas Gerais, Brazil.Google Scholar
Case, L, Miller, S, Wood, B 2010. Genetic parameters of feed efficiency traits in the Turkey (Meleagris gallopavo). In proceeding of the 9th world congress on genetics applied to livestock production, p. 4. Leipzig, Germany.Google Scholar
Chapuis, H, Tixier-Boichard, M, Delabrosse, Y, Ducrocq, V 1996. Multivariate restricted maximum likelihood estimation of genetic parameters for production traits in three selected turkey strains. Genetics Selection Evolution 28, 299–317.CrossRefGoogle Scholar
Costa, CN, De Melo Rodrigues, CM, Packer, IU, Ferreira de Freitas, A, Teixeira, NM, Cobuci, JA 2008. Genetic parameters for test day milk yield of first lactation Holstein cows estimated by random regression using Legendre polynomials. Revista Brasileira de Zootecnia 37, 602–608.CrossRefGoogle Scholar
DeGroot, BJ, Keown, JF, Van Vleck, LD, Kachman, SD 2007. Estimates of genetic parameters for Holstein cows for test-day yield traits with a random regression cubic spline model. Genetics and Molecular Research 6, 434–444.Google ScholarPubMed
DioneIlo, NJL, Silva, MA, Correa, GSS 2006. Genetic evaluation of European quail by random regression analysis. In proceedings of the 8th world congress on genetics applied to livestock production, Communication 8, pp. 10–12. Belo Horizonte, Minas Gerais, Brazil.Google Scholar
El Faro, L, Cardoso, VL, Albuquerque, LGD 2008. Variance component estimates applying random regression models for test-day milk yield in Caracu heifers (Bos taurus Artiodactyla, Bovidae). Genetics and Molecular Biology 31, 665–673.CrossRefGoogle Scholar
Fischer, TM, Gilmour, AR, Van der Werf, JHJ 2004a. Computing approximate standard errors for genetic parameters derived from random regression models fitted by average information REML. Genetics Selection Evolution 36, 363–369.CrossRefGoogle ScholarPubMed
Fischer, TM, Van der Werf, JHJ, Banks, RG, Ball, AJ 2004b. Description of lamb growth using random regression on field data. Livestock Production Science 89, 175–185.CrossRefGoogle Scholar
Gilmour, AR, Gogel, BJ, Cullis, BR, Thompson, R 2006. ASReml User Guide Release 3.0 VSN International Ltd, Hemel Hempstead, HP1 1ES, UK. www.vsni.co.ukGoogle Scholar
Henderson, CR Jr 1982. Analysis of covariance in the mixed model: higher-level, nonhomogeneous, and random regressions. Biometrics 38, 623–640.CrossRefGoogle ScholarPubMed
Hu, YH, Poivey, JP, Rouvier, R, Wang, CT, Tai, C 1999. Heritabilities and genetic correlations of body weights and feather length in growing Muscovy selected in Taiwan. British Poultry Science 40, 605–612.CrossRefGoogle ScholarPubMed
Huisman, AE, Veerkamp, RF, Van Arendonk, JA 2002. Genetic parameters for various random regression models to describe the weight data of pigs. Journal of Animal Science 80, 575–582.CrossRefGoogle ScholarPubMed
Jaffrezic, F, Venot, E, Laloe, D, Vinet, A, Renand, G 2004. Use of structured antedependence models for the genetic analysis of growth curves. Journal of Animal Science 82, 3465–3473.CrossRefGoogle ScholarPubMed
Kirkpatrick, M, Heckman, N 1989. A quantitative genetic model for growth, shape, reaction norms, and other infinite-dimensional characters. Journal of Mathematical Biology 27, 429–450.CrossRefGoogle ScholarPubMed
Kranis, A, Hocking, PM, Hill, WG, Woolliams, JA 2006. Genetic parameters for a heavy female turkey line: impact of simultaneous selection for body weight and total egg number. British Poultry Science 47, 685–693.CrossRefGoogle ScholarPubMed
Kranis, A, Su, G, Sorensen, D, Woolliams, JA 2007. The application of random regression models in the genetic analysis of monthly egg production in turkeys and a comparison with alternative longitudinal models. Poultry Science 86, 470–475.CrossRefGoogle Scholar
McKay, LR, Schaeffer, LR, McMillan, I 2002. Analysis of growth curves in rainbow trout using random regression. In proceedings of the 7th world congress on genetics applied to livestock production, Communication 06-11, Paper 241, Montpellier, France.Google Scholar
Mignon-Grasteau, S, Beaumont, C, Poivey, JP, de Rochambeau, H 1998. Estimation of the genetic parameters of sexual dimorphism of body weight in ‘label’ chickens and Muscovy ducks. British Poultry Science 30, 481–491.Google Scholar
Misztal, I 2006. Properties of random regression models using linear splines. Journal of Animal Breeding and Genetics 123, 74–80.CrossRefGoogle ScholarPubMed
Molina, A, Menendez-Buxadera, A, Valera, M, Serradilla, JM 2007. Random regression model of growth during the first three months of age in Spanish Merino sheep. Journal of Animal Science 85, 2830–2839.CrossRefGoogle ScholarPubMed
National Research Council 1994. Nutrient requirements of poultry, 9th edition. National Academy Press, Washington, D.C.Google Scholar
Nestor, KE, McCartney, MG, Harvey, WR 1967. Genetics of growth and reproduction in the turkey. 1. Genetics and non-genetic variations in body weight and body measurements. Poultry Science 46, 1374–1384.CrossRefGoogle Scholar
Nestor, KE, Anderson, JW, Patterson, RA 2000. Genetics of growth and reproduction in the turkey. 14. Changes in genetic parameters over thirty generations of selection for increased body weight. Poultry Science 79, 445–452.CrossRefGoogle ScholarPubMed
Nestor, KE, Anderson, JW, Patterson, RA, Velleman, SG 2006. Genetics of growth and reproduction in the turkey. 16. Effect of repeated backcrossing of an egg line to a commercial sire line. Poultry Science 85, 1550–1554.CrossRefGoogle ScholarPubMed
Nestor, KE, Anderson, JW, Patterson, RA, Velleman, SG 2008. Genetics of growth and reproduction in the turkey. 17. Changes in genetic parameters over forty generations of selection for increased sixteen-week body weight. Poultry Science 87, 1971–1979.CrossRefGoogle ScholarPubMed
Oliveira, KAPd, Lobo, RNB, Faco, O 2010. Genetic evaluation of partial growth trajectory of Santa Inês breed using random regression models. Revista Brasileira de Zootecnia 39, 1029–1036.CrossRefGoogle Scholar
Pletcher, SD, Geyer, CJ 1999. The genetic analysis of age-dependent traits: modeling the character process. Genetics and Molecular Research 153, 825–835.Google ScholarPubMed
Szwaczkowski, T, Stanislaw, W, Stanislawska-Barczak, E, Badowski, J, Bielinska, H, Wolc, A 2007. Genetic variability of body weight in two goose strains under long-term selection. Journal of Applied Genetics 48, 253–260.CrossRefGoogle ScholarPubMed
Tholon, P, Queiroz, SA 2006. Random regression models for body weight from birth to 210 days of age in partridges (Rhynchotus rufescens), using different structures of residual variances. In XII European poultry conference, Verona, Italy.Google Scholar
Van Der Werf, JHJ, Goddard, ME, Meyer, K 1998. The use of covariance functions and random regressions for genetic evaluation of milk production based on test day records. Journal of Dairy Science 81, 3300–3308.CrossRefGoogle ScholarPubMed
Wilson, AJ, Kruuk, LEB, Coltman, DW 2005. Ontogenetic patterns in heritable variation for body size: using random regression models in a wild ungulate population. American Naturalist 166, E177–E192.CrossRefGoogle Scholar
Wolc, A, White, I, Olori, V, Hill, W 2009. Inheritance of fertility in broiler chickens. Genetics Selection Evolution 41, 47–55.CrossRefGoogle ScholarPubMed