Abstract
It was used 4,313 weight records from birth to 196 days of age from 946 Anglo-nubiana breed goats, progenies from 43 sires and 279 dams, controlled in the period from 1980 to 2005, with the objective of estimating covariance functions and genetic parameters of animals by using random regression models. It was evaluated 12 random regression models, with degrees ranging from 1 to 7 for direct additive genetic and maternal and animal permanent environment effect and residual variance adjusted by using animal age ordinary polynomial of third order. Models were compared by using likelihood ratio test and by Bayesian information criterion of Schwarz and Akaike information criterion. The model selected based on Bayesian information criterion was the one that considered the maternal and direct additive genetic effect adjusted by a quadratic polynomial and the animal permanent environmental effect adjusted by a cubic polynomial (M334). Heritability estimates for direct effect were lower in the beginning and at the end of the studied period and maternal heritability estimates were higher at 196 days of age in comparison to the other growth phases. Genetic correlation ranged from moderate to high and they decreased as the distance between ages increased. Higher efficiency in selection for weight can be obtained by considering weights close to weaning, which is a period when the highest estimates of genetic variance and heritability are obtained.
Highlights
In Brazil, most goat herds (92%) are located in the northeastern region and they are greatly influenced by Anglo Nubian breed, which was introduced in the country to genetically improve goat meat and milk production of native breeds by crosses among breeds and for maintenance of the breed
Kirkpatrick & Heckman (1989) described some advantages of the covariance function models in relation to multi-trait model which are the following: they allow to predict the covariance structure in any point of a continuous time scale, gives more flexibility in the use of measure recorded in any moment of the growth trajectory without the need of adjusting the record for standard age; they allow to obtain eigen values and eigen vectors that gives information about the direction in which the average curve has higher chance to be modified by selection, in function of higher genetic variance; they allow to estimate a continuous gradient selection function considering the effect of selection in all points of the growth curve, and they allow to accurately predict the selection response
Several scientific papers have been using covariance functions by means of random regression models applied to animal genetic improvement (Sakaguti et al, 2003; Arango et al, 2004; Albuquerque & Meyer, 2005; Santoro et al, 2005; Dias et al, 2006; Sousa et al, 2008)
Summary
In Brazil, most goat herds (92%) are located in the northeastern region and they are greatly influenced by Anglo Nubian breed, which was introduced in the country to genetically improve goat meat and milk production of native breeds by crosses among breeds and for maintenance of the breed.For meat type goat, weights recorded during the growth period of the animals in structured population are the mainKirkpatrick & Heckman (1989) described some advantages of the covariance function models in relation to multi-trait model which are the following: they allow to predict the covariance structure in any point of a continuous time scale, gives more flexibility in the use of measure recorded in any moment of the growth trajectory without the need of adjusting the record for standard age; they allow to obtain eigen values and eigen vectors that gives information about the direction in which the average curve has higher chance to be modified by selection, in function of higher genetic variance; they allow to estimate a continuous gradient selection function considering the effect of selection in all points of the growth curve, and they allow to accurately predict the selection response.The use of random regression on model longitudinal data is not recent in linear model analyses and it was introduced by Henderson Júnior (1982). In Brazil, most goat herds (92%) are located in the northeastern region and they are greatly influenced by Anglo Nubian breed, which was introduced in the country to genetically improve goat meat and milk production of native breeds by crosses among breeds and for maintenance of the breed. Several scientific papers have been using covariance functions by means of random regression models applied to animal genetic improvement (Sakaguti et al, 2003; Arango et al, 2004; Albuquerque & Meyer, 2005; Santoro et al, 2005; Dias et al, 2006; Sousa et al, 2008). The objective of this paper was to evaluate the use of covariance function analyses using random regression models in genetic evaluation of goat growth curve of Anglo-Nubian breed
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