Computational and modeling approaches for US threshold genetic evaluations of calving ease

The US dairy calving ease (CE) genetic evaluation is based on a threshold sire-maternal grandsire (SMGS) model and includes 2 genetic components: one reflecting the sire's direct genetic effect on calving, and the other capturing the maternal influence, modeled through either the dam or the maternal grandsire. This study compared 2 CE evaluation models-SMGS and sire-maternal (SMAT)-using different solving algorithms: Newton-Raphson (NR) and expectation maximization (EM). The analysis used over 24 million CE records provided by the Council on Dairy Cattle Breeding. Correlations of GEBV for phenotyped sires and maternal grandsires were highly consistent across algorithms, exceeding 0.99 within models. The NR algorithm was the most computationally efficient solver, requiring fewer iterations and less computing time than EM. Both SMGS and SMAT models are suitable for routine genetic evaluation of CE in US dairy cattle, with NR and EM offering reliable and efficient solutions for single-trait analysis.