Jhj. Van Der Werf et al., The use of covariance functions and random regressions for genetic evaluation of milk production based on test day records, J DAIRY SCI, 81(12), 1998, pp. 3300-3308
In the analysis of test day records for dairy cattle, covariance functions
allow a continuous change of variances and covariances of test day yields o
n different lactation days. The equivalence between covariance functions as
an infinite dimensional extension of multivariate models and random regres
sion models is shown in this paper. A canonical transformation procedure is
proposed for random regression models in large-scale genetic evaluations.
Two methods were used to estimate covariance function coefficients for firs
t parity test day yields of Holsteins: 1) a two-step procedure fitting cova
riance functions to matrices with estimated genetic and residual covariance
s between predetermined periods of lactation and 2) REML directly from data
with a random regression model. The first method gave more reliable estima
tes, particularly for the periphery of the trajectory. The goodness of fit
of a random regression model based on covariables describing the shape of t
he lactation curve was nearly the same as random regression on Legendre pol
ynomials. In the latter model, two and three regression coefficients were s
ufficient to fit the covariance structure for additive genetic and permanen
t environment, respectively. The eigenfunction pattern revealed the possibi
lity of selection for persistency. Covariance functions can be usefully imp
lemented in large-scale test day models by means of random regressions.