Multiple trait genetic analysis of underlying biological variables of production functions

A Bayesian procedure to analyze performance data from production functions is presented. The method implies the consideration of each parameter of the production function as a different trait. The systematic effects and genetic relationship between animals are taken into account in the adjustment of production for each animal. The method weights all possible sources of information. In this sense, it allows estimation of the parameters of production functions with only one data along the production cycle and to reduce the influence of outliers. Under analysis, marginal posterior distributions of the function parameters, breeding values, systematic effects and (co)variance components were obtained using the Gibbs sampling algorithm. The procedure requires the definition of the joint posterior distribution to be generalized to any production function. From this joint posterior distribution, the full set of posterior conditional distributions needed for the Gibbs sampling algorithm can be easily obtained. (C) 1997 Published by Elsevier Science B.V.