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Revista mexicana de ciencias pecuarias

versión On-line ISSN 2448-6698versión impresa ISSN 2007-1124

Resumen

ROSERO ALPALA, Jaime Anibal; RANGEL GARCIA, Wilson David; ROJAS BARRETO, Adonai  y  ORLANDO BURGOS-PAZ, William. Contribution of genomic data in defining the breed composition of dual-purpose cattle. Rev. mex. de cienc. pecuarias [online]. 2021, vol.12, n.4, pp.1008-1024.  Epub 06-Jun-2022. ISSN 2448-6698.  https://doi.org/10.22319/rmcp.v12i4.5690.

Animal heterosis is key in obtaining more productive animals and better adapted to the tropics. However, inadequate genetic management leads to the obtaining of mosaics of breeds that includes the loss of the productive potential of the herd. The objective of this study was to define the breed composition of dual-purpose crossbred cattle in the piedmont plains, department of Meta-Colombia. A total of 126 crossbred (CRO) individuals from six herds were evaluated by a phenotypic (APP) and a genotypic (GBA) approach. For GBA, the control breeds associated with Bos taurus taurus and Bos taurus indicus were included, for this they were genotyped with a GeneSeek GGP-LD chip of 26K of SNP and analyzed by principal component analysis (PCA) and Bayesian probabilistic assignment of ADMIXTURE. The breed groups generated by APP varied with respect to GBA. Molecular analysis detected seven (k=7) genetic groups in the breed composition of the study animals. The three breeds with the highest participation in the breed composition of crossbred individuals were: Holstein, Gyr, Brahman with 23.4, 21.4, and 21 % respectively, while the remaining, Blanco Orejinegro, Brown Swiss, Normande and Jersey did not exceed 13 %. Unlike APP, the GBA approach effectively allowed the identification of the breed composition of crossbred cattle and provided key information for the development of mating programs that seek to improve productive indicators, and in turn tend to the adaptation of animals, an essential requirement for dual-purpose bovine systems.

Palabras llave : Genotyping; Crossbred; Variability; Genetics.

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