Indirect predictions based on genomic evaluation of indicine multibreed populations using the single step GBLUP method with metafounders

Abstract

The objective of this research project is to obtain indirect predictions of genotyped animals of the Brahman, Guzerá and Tabapuã using a multibreed genetic evaluation that also includes the Nelore breed. For this, phenotypic and pedigree information from the breeding programs of the four breeds will be used, from the National Association of Breeders and Researchers (ANCP), Ribeirão Preto, SP. Phenotypic information of adjusted weight to 210 (W210) and 450 (W450) days of age and scrotal circumference at 365 days of age (C365) will be employed. For the Nelore breed, records of 1,443,729, 1,237,339 and 443,044 animals for W210, W450, and C365, respectively, distributed in 294 herds will be used. For the Brahman breed, the data base will be composed for 81.414, 40.655 and 16.478 records for W210, W450, and C365, respectively, distributed in 35 herds. For the Guzerá breed, 26,261, 21,747 and 7,137 records for W210, W450, and C365, respectively, distributed in 25 herds will be used. Finally, for the Tabapuã breed, it will be utilized 9,943, 6,695 and 2,178 records for W210, W450, and C365, respectively. Genotypic data of 38,586, 3102, 1389 and 427 animals from the Nelore, Brahman, Guzerá and Tabapuã breeds, respectively. The chip ZBN Zoetis with 74,653 SNPs will be employed. Single breed and multi-breed analyzes will be performed for the three traits, based on the ssGBLUP (single breed) or using the metafounders (ssGBLUP + MF) methodology for the multibreed. Indirect predictions (direct genomic value - DGV) for the (young) genotyped animals from the small breeds with records but not progeny will be obtained, through a linear function of SNP effects and contents using a multibreed, Nelore o within- reference population. The predictive ability will be calculated as the correlation between DGV and the adjusted phenotypes for fixed effects (Y*) or the GEBV estimated using or not MF with all the data set (intra or multi-breed), in order to evaluate the impact of multibreed evaluation and MF on genetic predictions for growth and reproductive traits. (AU)