02203naa a2200241 a 450000100080000000500110000800800410001910000220006024501270008226000090020952014940021865000180171265000210173065000180175165300290176965300180179865300240181670000210184070000140186170000230187570000180189877300450191621342472021-12-29       2021    bl uuuu     u00u1 u    #d1 aDALTRO, D. dos S.  aHomoscedastic or heteroscedastic inference in the genetic evaluation of a multi-breed dairy cattle.h[electronic resource]  c2021  aThe objective was to identify the best multi-breed model for the genetic evaluation of test-day milk yield (TDMY) in a Girolando population. The observations were measured from 2000 to 2014, totaling 122,507 observations of 13,544 first-lactation cows and a complete kinship matrix of 66,384 animals. Four different methodologies were evaluated: homoscedastic or heteroscedastic multi-breed model with Gaussian destruction (MAM-G-Ho or MAMG-He) and homoscedastic or heteroscedastic multi-breed model with Student?s t-distribution (MAM-T-Ho or MAM-T-He). The criteria for choosing the best model were the deviance information criterion (DIC), deviance based on the Conditional Predictive Ordinate (DCPO), and deviance based on the Bayes factors (DBF). According to the selection criteria, the MAM-T-He presented the lowest values of DIC, DCPO, and DBF, indicating a better quality of fit for the population in question. Heritability estimates were of medium magnitude for TDMY, varying from 0.27±0.02 to 0.39±0.03 in the different models. Spearman?s correlations based on estimated breeding values used to compare the ranking of animals between Student?s t and Gaussian models were low (0.63 to 0.72) when considering all sires or when considering 10, 20, or 30% of the best sires. These results support the implementation of robust models that account for sources of heteroscedasticity to increase the accuracy of genetic evaluations in the multi-breed population of the Girolando breed.  aGado Leiteiro  aGenética Animal  aVaca Leiteira  aCorrelação de Spearman  aHerdabilidade  aModelo multi-raças1 aAMBROSINI, D. P.1 aNEGRI, R.1 aSILVA, M. V. G. B.1 aCOBUCI, J. A.  tLivestock Sciencegv. 250, 104567, 2021.