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The growing adoption of crossbred populations by dairy farmers has sparked increased interest in multibreed genomic evaluation, which offers promising opportunities to accelerate genetic progress and refine breeding strategies. This study assessed the feasibility of applying single-step genomic best linear unbiased prediction (ssGBLUP) for multibreed genomic evaluation of cow wellness traits in Holstein (HO), Jersey (JE) and their crosses (HO × JE). The traits considered were abortion (ABRT), cystic ovaries (CYST), displaced abomasum (DA), ketosis (KETO), lameness (LAME), mastitis (MAST), metritis (METR), milk fever (MFEV), respiratory illness (RESP), retained placenta (RETP) and twinning (TWIN). The number of phenotypic records ranged from 1,176,935 for RESP to 7,703,872 for MAST. Traits were analysed using a linear model within a multi-trait framework. Variance components were estimated using Gibbs sampling, incorporating the effects of inbreeding, retained heterosis, trait-specific systematic effects and random effects of additive direct and permanent environmental effects. The algorithm for Proven and Young was used by randomly selecting a core set of 30,000 animals. Two scenarios were developed based on the number of genotyped HO animals in the evaluation: (1) including all available genotyped individuals, and (2) including only a subset of relevant genotyped HOs. Genomic predictions were compared against commercially available single-breed evaluations to assess consistency and potential improvements in predictive performance. Heritability estimates ranged from 0.003 for MFEV and CYST to 0.057 for MAST. Spearman and Pearson correlations between multibreed and single-breed evaluations for cow wellness ranged from 0.36 and 0.33 (METR) to 0.88 and 0.89 (MAST) for JE, and from 0.79 (MFEV) to 0.97 and 0.96 (METR) for HO. These findings suggest that a single-step approach can produce comparable results and accurate genomic predictions, demonstrating the feasibility of ssGBLUP for multibreed genomic evaluation in dairy populations.
Vargas et al. (Thu,) studied this question.