CONSIDERATIONS ON MEASURES OF PRECISION AND CONNECTEDNESS IN MIXED LINEAR-MODELS OF GENETIC EVALUATION

Three criteria for the quality of a genetic evaluation are compared: t he prediction error variance (PEV); the loss of precision due to the e stimation of the fixed effects (degree of connectedness) (IC); and a c riterion related to the information brought by the evaluation in terms of generalized coefficient and determination (CD) (precision). These criteria are introduced through simple examples based on an animal mod el. The main differences between them are the choice of the matrix stu died (CD vs PEV, IC), the method used to account for the relationships (CD vs PEV), the use of a reference matrix or model (PEV vs CD, IC), and the data design (IC vs PEV, CD). IC is shown to favor designs with limited information provided by the data and another index is suggest ed, which minimizes this drawback. The behavior of IC and CD is studie d in a hypothetical 'herd + sire' model. The precision criteria set a balance between connectedness level and information provided by the da ta, whereas the connectedness criteria favor the model with minimum in formation and maximum connectedness level. Genetic relationships betwe en animals decrease both PEV and genetic variability. PEV considers on ly the favorable effects on PEV; CD accounts for both effects. CD sets a balance between the design and the information brought by the data, the PEV and the genetic variability and is thus a method of choice fo r studying the quality of a genetic evaluation.