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Research article2006Peer reviewedOpen access

Multiple-trait multiple-country genetic evaluations for udder health traits

Mark T, Sullivan PG


International genetic bull evaluations of somatic cell counts (SCC) from 8 different Holstein populations and clinical mastitis from 3 of these populations were inferred simultaneously using a multiple-trait-multiple-country evaluation (MT-MACE) model. This model considered effective independent weighting factors and multivariately deregressed national genetic evaluations for countries with multiple-trait national models. Predictions of genetic merit from MT-MACE and their reliabilities were compared with the corresponding results from 2 separate single-trait-multiple-country evaluations (ST-MACE) for different groups of bulls. The assumed heritabilities for clinical mastitis (h(2) = 0.02 to 0.05) were substantially lower than the heritabilities for SCC (h(2) = 0.08 to 0.27). The predictive ability of MT-MACE was essentially equal to or better than the predictive ability of ST-MACE for all country-trait combinations, but both methods yielded effectively unbiased and consistent consecutive predictions (correlation > 0.93). Both sets of predictions also agreed well with future national genetic evaluations for bulls receiving additional daughter information (correlation > 0.96), except for evaluations for which within-country correlations were utilized internationally, but not nationally (correlation = 0.86 to 0.97). The reliabilities for MT-MACE were essentially equal to or higher than reliabilities for ST-MACE, depending on the trait and group of bulls in question. Reliabilities increased most for young bulls, and for clinical mastitis in countries that did not use the within-country correlations with SCC in the national evaluation (up to a 23% increase in average reliability)

Published in

Journal of Dairy Science
2006, Volume: 89, number: 12, pages: 4874-4885

    UKÄ Subject classification

    Veterinary Science
    Animal and Dairy Science

    Publication identifier


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