Abstract
A numerator relationship matrix (Ac) due to sires and dams was compared with a numerator relationship matrix (Ai) due to sires and maternal grandsires in a multiple-trait-reduced animal model (MT-RAM). Best linear unbiased predictors of estimated breeding values (EBV) for 200-d weight (WW) and postweaning gain (PG) (gain from 200 to 365 d of age) were estimated from data simulating a beef cattle population. As expected, mean EBV and bias (EBV-BV) for both traits were not significantly affected by different relationship matrices. The mean variances of EBV with Ac were larger than those with Ai for both traits. The mean EBV variances were closer to mean BV variances with Ac compared to Ai, which is consistent with increased precision of EBV. Product-moment correlations of EBV and BV (accuracy of prediction) were not equal (P < 0.01) for Ac compared to Ai with WW or PG. The EBV using Ac were more accurate than EBV using Ai. The increased precision and accuracy of EBV from a MT-RAM with Ac would result in greater genetic progress in the population. Key words: Relationship matrices, estimated breeding values, MT-RAM
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