Evaluación genómica en un solo paso para rasgos de producción y tipo en el Búfalo Mediterráneo Italiano

Single-step GBLUP (ssGBLUP) is becoming the most used method to predict breeding values in livestock, offering several advantages in terms of computational efficiency and simplifying the genetic evaluation process by integrating genomic, pedigree, and phenotypic information in a single step. Genomic information is now available for the Italian Mediterranean Buffalo (IMB), and its inclusion in the genetic evaluation system could increase both evaluation accuracy and genetic progress of the breeding objectives. The aim of this study was to test the feasibility of ssGBLUP and to present the first results of the implementation of a genomic evaluation for IMB. Phenotypic information on production traits (milk yield adjusted to 270 d, fat and protein yield and content, and cheese yield) and morphology traits (feet and legs scores and udder teat scores) were used in this study. Production records included 792,200 lactations from 293,633 buffalo cows born from 1984 to 2021. Morphological traits were from 99,609 buffalo cows from 2004 to 2023. Regarding the genotypes, a total of 3,647 genotyped animals were used. Data were analyzed fitting 2 multitrait animal models, a 6-trait model for production data, and a 2-trait model for morphology data. Breeding values (BV) were estimated with BLUP and ssGBLUP models, both considering unknown parent groups. The methods were compared in terms of correlation between BV and genetic trends. Results were also validated with the linear regression (LR) method. Three different scenarios were used according to the cut-off year used to create the partial datasets, namely T2013, T2016, and T2018. The genomic and nongenomic BV were strongly correlated, and genetic trends for each trait were similar. The average increase in accuracy moving from BLUP to ssGBLUP across traits ranged from +3% to +12%. The LR method statistics confirmed the effectiveness of the ssGBLUP method. The average validation correlation across production traits and scenarios by female/bull groups was 0.54/0.47 and 0.63/0.52 for BLUP and ssGBLUP, respectively. Accuracies were also higher with ssGBLUP (0.62/0.55) compared with BLUP (0.53/0.51). The best dispersion values (i.e., closer to 1) were observed for ssGBLUP (T2013, T2016). The ssGBLUP method provided better results across genotyped and nongenotyped animals, particularly in terms of a relative increase in accuracy associated with the inclusion of phenotypes. These results showed that implementing ssGBLUP in the breeding program can generate more accurate predictions for production and morphological traits in dairy IMB.