Use of orthogonal functions in random regression models in describing genetic variance in Nellore cattle

Thiago Bruno Ribeiro Da Silva,Cláudio Vieira De Araújo,Thereza Cristina Borio Dos Santos Calmon De Bittencourt Bittencourt,Luiz Antônio Framartino Bezerra,Simone Inoe Araújo,Delvan Alves Da Silva,Raysildo Barbosa Lôbo,Alessandra Alves Da Silva

doi:10.1590/s1516-35982013000400004

Abstract

A total of 204,912 records of birth weights up to 550 days of age, of 24,890 Nellore cattle, offspring of 375 sires and 16,917 dams from five herds in the state of Mato Grosso, Brazil, were used in order to describe the variability of the weight development by random regression models. The model evaluated as the most suitable used the covariance function of fourth order to describe the variability of the effects of additive genetic, animal permanent environmental and maternal effects of third order to describe the maternal genetic effect, with four classes of residual variance. Heritability estimates ranged from 0.18 to 0.46 from the beginning of trajectory to 210 days of age, from 0.45 to 0.48 post-weaning to 365 days of age and from 0.47 to 0.57 at later ages. The values of additive genetic correlations for different ages showed higher estimates between the closest ages, while birth weight was not very related to the weights at older ages. The body weight performance of the animals has additive genetic variation to respond to selection.

Highlights

The state of Mato Grosso, Brazil, has beef cattle as one of the strongest point of its economy, holding the largest effective herd among Brazilian states (IBGE, 2010).Even with the numerical expression of Mato Grosso in the Brazilian livestock, there are few studies on the estimation of variance components or parameters for traits related to the performance of body weight in beef cattle, and neither are there studies on the adoption of random regression models involving only records of weight performance of Mato Grosso, so little is known about the behavior of the genetic variability of Nellore cattle in that state.Traits correlated with the performance of body weight such as live weight at different ages may be considered with continuous variation over time
The model evaluated as the most suitable used the covariance function of fourth order to describe the variability of the effects of additive genetic, animal permanent environmental and maternal effects of third order to describe the maternal genetic effect, with four classes of residual variance
The results for the values of the Akaike Information Criterion ranged from 357,221.90 to 298,482.40 for the model considered the most appropriate to describe the genetic and environmental variability of the line of weight development of the animals, i.e., the model that used covariance function of fourth polynomial order to describe the variability of direct genetic random, animal permanent environmental and maternal permanent effects and with function of covariance of the third polynomial order to describe the variability of the maternal genetic effect and residual variance from four classes

Summary

Introduction

Even with the numerical expression of Mato Grosso in the Brazilian livestock, there are few studies on the estimation of variance components or parameters for traits related to the performance of body weight in beef cattle, and neither are there studies on the adoption of random regression models involving only records of weight performance of Mato Grosso, so little is known about the behavior of the genetic variability of Nellore cattle in that state. Traits correlated with the performance of body weight such as live weight at different ages may be considered with continuous variation over time In this situation, some interest in the use of random regression models has been observed because these models describe the behavior of variance components along the growth line; they estimate and predict the parameters and genetic values anywhere on the growth line, in addition to identifying the phases of growth of the animal where there is the greatest genetic variability to trigger changes in the growth curve of the animals. The use of random regression models in records of beef cattle is present in Cyrillo (2003), Mercadante et al (2002), Nobre et al (2003) and Sakaguti (2003) in the Nellore breed and by Dias et al (2006) in Tabapuã animals, all employing different degrees of adjustment for the covariance function in the description of the random effects of the models

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Conclusion