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Polynomials to model the growth of young bulls in performance tests

Published online by Cambridge University Press:  10 January 2014

D. C. B. Scalez ,
B. O. Fragomeni ,
T. L. Passafaro ,
I. G. Pereira  and
F. L. B. Toral
D. C. B. Scalez
Affiliation:
Faculdade de Agronomia, Medicina Veterinária e Zootecnia, Universidade Federal de Mato Grosso, Avenida Fernando Correa da Costa, 2367, CEP 78060-900, Cuiabá/MT, Brazil
B. O. Fragomeni
Affiliation:
Departamento de Zootecnia, Escola de Veterinária, Universidade Federal de Minas Gerais (DZOO/EV/UFMG), Avenida Antônio Carlos, 6627, Caixa Postal 567, CEP 31270-901, Belo Horizonte/MG, Brazil.
T. L. Passafaro
Affiliation:
Departamento de Zootecnia, Escola de Veterinária, Universidade Federal de Minas Gerais (DZOO/EV/UFMG), Avenida Antônio Carlos, 6627, Caixa Postal 567, CEP 31270-901, Belo Horizonte/MG, Brazil.
I. G. Pereira
Affiliation:
Departamento de Zootecnia, Escola de Veterinária, Universidade Federal de Minas Gerais (DZOO/EV/UFMG), Avenida Antônio Carlos, 6627, Caixa Postal 567, CEP 31270-901, Belo Horizonte/MG, Brazil.
F. L. B. Toral*
Affiliation:
Departamento de Zootecnia, Escola de Veterinária, Universidade Federal de Minas Gerais (DZOO/EV/UFMG), Avenida Antônio Carlos, 6627, Caixa Postal 567, CEP 31270-901, Belo Horizonte/MG, Brazil.
*
† E-mail: [email protected]
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Abstract

The use of polynomial functions to describe the average growth trajectory and covariance functions of Nellore and MA (21/32 Charolais+11/32 Nellore) young bulls in performance tests was studied. The average growth trajectories and additive genetic and permanent environmental covariance functions were fit with Legendre (linear through quintic) and quadratic B-spline (with two to four intervals) polynomials. In general, the Legendre and quadratic B-spline models that included more covariance parameters provided a better fit with the data. When comparing models with the same number of parameters, the quadratic B-spline provided a better fit than the Legendre polynomials. The quadratic B-spline with four intervals provided the best fit for the Nellore and MA groups. The fitting of random regression models with different types of polynomials (Legendre polynomials or B-spline) affected neither the genetic parameters estimates nor the ranking of the Nellore young bulls. However, fitting different type of polynomials affected the genetic parameters estimates and the ranking of the MA young bulls. Parsimonious Legendre or quadratic B-spline models could be used for genetic evaluation of body weight of Nellore young bulls in performance tests, whereas these parsimonious models were less efficient for animals of the MA genetic group owing to limited data at the extreme ages.

Keywords

B-splinesbeef cattlelegendrerandom regression
Type
Full Paper
Information
animal , Volume 8 , Issue 3 , March 2014 , pp. 370 - 378
Copyright
© The Animal Consortium 2014 

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References

Abramowitiz, M and Stegun, IA 1965. Handbook of mathematical functions with formulas, graphs, and mathematical functions, 55th edition. National Bureau of Standards Applied Mathematics, Dover, NY, USA.Google Scholar
Albuquerque, LG and Meyer, K 2001. Estimates of covariance functions for growth from birth to 630 of agen in Nellore cattle. Journal of Animal Science 79, 2776–2789.Google Scholar
Arango, JA, Cundiff, LV and Van Vleck, LD 2004. Covariance functions and random regression models for cow weight in beef cattle. Journal of Animal Science 82, 54–67.Google Scholar
Baldi, F, Albuquerque, LG and Alencar, MM 2010a. Random regression models on Legendre polynomials to estimate genetic parameters for weights from birth to adult age in Canchim cattle. Journal of Animal Breeding and Genetics 127, 289–299.Google Scholar
Baldi, F, Alencar, MM and Albuquerque, LG 2010b. Random regression analyses using B-splines functions to model growth from birth to adult age in Canchim cattle. Journal of Animal Breeding and Genetics 127, 433–441.Google Scholar
Bohmanova, J, Misztal, I and Bertrand, JK 2005. Studies on multiple trait and random regression models for genetic evaluation of beef cattle for growth. Journal of Animal Science 83, 62–67.Google Scholar
Boligon, AA, Mercadante, MEZ, Forni, S, Lôbo, RB and Albuquerque, LG 2010. Covariance functions for body weight from birth to maturity in Nellore cows. Journal of Animal Science 88, 849–859.Google Scholar
Boligon, AA, Mercadante, MEZ, Lôbo, RB, Baldi, F and Albuquerque, LG 2012. Random regression analyses using B-spline functions to model growth of Nellore cattle. Animal 6, 212–220.Google Scholar
Bozdogan, H 1987. Model selection and Akaike’s information criterion (AIC): the general theory and its analytical extensions. Psychometrika 52, 345–370.Google Scholar
De Boor, C 2001. A practical guide to splines, 2nd edition. Springer Verlag, New York, NY, USA.Google Scholar
Josahkian, LA, Lucas, CH and Machado, CHC 2009. Manual do serviço de registro genealógico das raças zebuínas e PMGZ. Associação Brasileira dos Criadores de Zebu, Uberaba, MG, Brazil.Google Scholar
Kirkpatrick, M, Lofsvold, D and Bulmer, M 1990. Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124, 979–993.Google Scholar
Kirkpatrick, M, Hill, WG and Thompson, R 1994. Estimating the covariance structure of traits during growth and aging, illustrated with lactation in dairy cattle. Genetics Research 64, 57–69.Google Scholar
Meyer, K 1998. Estimating covariance functions for longitudinal data using a random regression model. Genetics Selection Evolution 30, 221–240.Google Scholar
Meyer, K 1999. Estimates of genetic and phenotypic covariance functions for post weaning growth and mature weight of beef cows. Journal of Animal Breeding and Genetics 116, 181–205.Google Scholar
Meyer, K 2000. Random regressions to model phenotypic variation in monthly weights of Australian beef cows. Livestock Production Science 65, 19–38.CrossRefGoogle Scholar
Meyer, K 2005. Random regression analyses using B-spline to model growth of Australian Angus cattle. Genetics Selection Evolution 37, 473–500.Google Scholar
Meyer, K 2006. ‘Wombat’ – digging deep for quantitative genetics analysis by restricted maximum likelihood. In Book of abstracts of 8th Word Congress on Genetics Applied to Livestock Production, 13–18 August 2006, Belo Horizonte, Brazil, pp. 248.Google Scholar
Riley, DG, Coleman, SW, Chase, CC Jr, Olson, TA and Hammond, AC 2007. Genetic parameters for body weight, hip height, and the ratio of weight and hip height from random regression analyses of Brahman feedlot cattle. Journal of Animal Science 85, 42–52.Google Scholar
Schaeffer, LR and Dekkers, JCM 1994. Random regressions in animal models for test-day production in dairy cattle. In Proceedings of 5th World Congress on Genetics Applied to Livestock Production, 7–12 August 1994, Guelph, Canada, pp. 443–446.Google Scholar
Wolfinger, RD 1993. Covariance structure selection in general mixed models. Communications in Statistics – Simulation and Computation 22, 1079-1106.Google Scholar