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En los sistemas de producción de leche de trópico alto en el departamento de Antioquia (Colombia), la utilización de la raza Holstein ha permitido obtener altos niveles de producción. No obstante, la implementación de cruces con razas criollas es una opción para introducir genes que aporten adaptabilidad al medio. El objetivo de este trabajo es comparar parámetros productivos entre ganado Holstein y sus cruces con Blanco-Orejinegro, en diferentes proporciones de ambas razas. Se utilizaron 125 lactancias pertenecientes a 48 vacas de un hato antioqueño. Se ajustaron modelos mixtos de medidas repetidas, incluyendo efectos fijos de número de parto, grupo genético y grupo contemporáneo. Se usaron modelos de regresión lineal para evaluar el efecto del porcentaje de la raza criolla sobre los parámetros productivos. El grupo genético tuvo un efecto significativo sobre la producción al pico, la producción total y la producción para el día 150. El Holstein puro presentó un mayor desempeño que los animales cruzados para la producción de leche (x=6.853,48 L), la producción al pico (x=34,6 L) y la producción al día 150 (x=19,97 L). El análisis de regresión lineal mostró una tendencia leve a la disminución de productividad a medida que aumenta la proporción de Blanco-Orejinegro. Si bien utilizar animales cruzados en sistemas de lechería especializada podría disminuir los parámetros productivos del hato, también permite la introducción de genes que aporten adaptación al medioambiente y resistencia a enfermedades.

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The cattle breed Blanco Orejinegro (BON) is an important animal genetic resource in Colombia that needs to be studied to identify its productive benefits for Colombian livestock. The aim of this work was to establish the best linear model that explains the variability for birth weight, weaning weight and yearling weight, and estimate the effect of the serological status for Bovine Viral Diarrhea (BVD) and Bovine Leukosis (BL) in BON cattle in Colombia. The information on animal weighings belonging to 14 BON cattle herds, was collected and cleansed. Using the least squares method, 12 linear models were evaluated including as fixed effects, factors such as herd, sex, weighing month, birth order, season (rainy or dry), birth year, and contemporary group (formed by the concatenation of the factors herd, sex, and birth year). The weighing age was included as a covariate for weaning and yearling weights. For the selection of a model that best describes each parameter, the criteria for choosing models, such as Akaike information criterion (AIC), Bayesian Information criterion (BIC), coefficient of determination (R2), and the sum of squares of the error (SSE), were considered. The model that included the contemporary group showed the best fit, being also the best to describe the three parameters evaluated, since, of the four criteria considered for its evaluation, AIC and SSE showed the lowest values and the highest R2. With this model, weight predictions with higher precision were able to be performed. Employing indirect ELISA screening tests of blood plasma, the serological status of each animal was estimated for BVD and BL viral infections. The serological status of these two viral infections was included in the best-fit model. There was no significant effect of the serological status on the parameters evaluated, so there are indications that animals that came in contact with the virus were not affected during growth.

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Context In beef cattle populations, there is little evidence regarding the minimum number of genetic markers needed to obtain reliable genomic prediction and imputed genotypes. Aims This study aimed to evaluate the impact of single nucleotide polymorphism (SNP) marker density and minor allele frequency (MAF), on genomic predictions and imputation performance for high and low heritability traits using the single-step genomic Best Linear Unbiased Prediction methodology (ssGBLUP) in a simulated beef cattle population. Methods The simulated genomic and phenotypic data were obtained through QMsim software. 735 293 SNPs markers and 7000 quantitative trait loci (QTL) were randomly simulated. The mutation rate (10−5), QTL effects distribution (gamma distribution with shape parameter = 0.4) and minor allele frequency (MAF ≥ 0.02) of markers were used for quality control. A total of 335k SNPs (high density, HD) and 1000 QTLs were finally considered. Densities of 33 500 (35k), 16 750 (16k), 4186 (4k) and 2093 (2k) SNPs were customised through windows of 10, 20, 80 and 160 SNPs by chromosome, respectively. Three marker selection criteria were used within windows: (1) informative markers with MAF values close to 0.5 (HI); (2) less informative markers with the lowest MAF values (LI); (3) markers evenly distributed (ED). We evaluated the prediction of the high-density array and of 12 scenarios of customised SNP arrays, further the imputation performance of them. The genomic predictions and imputed genotypes were obtained with Blupf90 and FImpute software, respectively, and statistics parameters were applied to evaluate the accuracy of genotypes imputed. The Pearson’s correlation, the coefficient of regression, and the difference between genomic predictions and true breeding values were used to evaluate the prediction ability (PA), inflation (b), and bias (d), respectively. Key results Densities above 16k SNPs using HI and ED criteria displayed lower b, higher PA and higher imputation accuracy. Consequently, similar values of PA, b and d were observed with the use of imputed genotypes. The LI criterion with densities higher than 35k SNPs, showed higher PA and similar predictions using imputed genotypes, however lower b and quality of imputed genotypes were observed. Conclusion The results obtained showed that at least 5% of HI or ED SNPs available in the HD array are necessary to obtain reliable genomic predictions and imputed genotypes. Implications The development of low-density customised arrays based on criteria of MAF and even distribution of SNPs, might be a cost-effective and feasible approach to implement genomic selection in beef cattle.