Comparison of Different Variance Component Estimation Approaches for MACE: Direct and Bottom-up PC

Abstract

Multiple-trait across country evaluation (MACE) is used for international genetic evaluation of dairy bulls. MACE treats records in different countries as different traits. Thus, a sire will get a breeding value for each participating country. Whenever a country makes changes to their national evaluation model, the genetic variance-covariance (VCV) matrix needs to be re-estimated. Estimation of the VCV matrix is a different task. For the Holstein production evaluation, which includes 26 traits, it is not possible to estimate the VCV matrix in a single analysis with the currently available estimation methods and the given time constraints. Hence, the complete matrix is built from analyses of sub-sets. This readily results in a non-positive matrix and a bending procedure (Jorjani et al., 2003) needs to be applied to obtain a positive definite matrix. In addition, the VCV matrix is usually over-parameterized as genetic correlations between countries are generally high.