SOLUTION OF MULTIPLE TRAIT ANIMAL-MODELS WITH MISSING DATA ON SOME TRAITS

In multiple trait animal model evaluations, several transformations (canonical, triangular, combined) can be used to decrease the size of the mixed model equations or to increase their sparsity. A simple method based on an EM argument and re lacing missing records by their expectation given the current parameters allows the use of the canonical transformation in situations where data are missing and selection is ignorable. The convergence behavior of these different approaches were compared on real data sets simulating different missing patterns. The combined transformation is always better than the triangular transformation. The extended version of the canonical transformation clearly leads to the fastest convergence, in terms of overall CPU time, especially when a reduced animal model is used (2 to 3 times faster than the triangular transformation) and when more traits are considered simultaneously.