A number of traits in animal production such as body weight, body fat and muscle scores can be measured repeatedly on a trajectory. Kirkpatrick et al. (1990) proposed the use of a covariance function (CF) to describe the variance-covariance structure of such infinite dimensional traits. The continuous nature of CF in describing variance structure has a natural appeal above alternative ways to model repeated measurements, as pointed out by a several authors (e.g. Meyer, 1998 ; Van der Werf et al., 1998). A number of papers have described estimation of CF based on mixed linear random regression models (Meyer, 1998). Surprisingly, little attention has been given to optimising selection on trajectory traits, whereas most animal production systems are based on characteristics related to growth and development. Body weight gain, onset of muscle and fat, as well as age of sexual maturity and mature body weight are closely related to each other and each of these traits is of major economic importance in animal production systems. Selection on such traits at any (arbitrary) age results in some predictable change in a multivariate development trajectory. The aim of this paper is to discuss a methodology to optimise selection on trajectory traits using covariance functions.
Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume 2002. Session 16, , 16.07, 2002
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