Assessing the accuracy of prediction for milk fatty acids by using a small reference population of tropical Holstein cows

Fatty acids (FA) have been related to effects on human health, sensory quality and shelf life of dairy products, cow's health and methane emission. However, despite their importance, they are not regularly measured in all dairy herds yet, which can affect the accuracy of estimated breeding values (EBV) for these traits. In this case, an alternative is to use genomic selection. Thus, the aim was to assess the use of genomic information in the genetic evaluation for milk traits in a tropical Holstein population. Monthly records (n = 36,457) of milk FA percentage, daily milk yield and quality traits from 4,203 cows as well as the genotypes of 755 of these cows for 57,368 single nucleotide polymorphisms (SNP) were used. Polygenic and genomic-polygenic models were applied for EBV prediction, and both models were compared through the EBV accuracy calculated from the prediction error and Spearman's correlation among EBV rankings. Prediction accuracy was assessed by using cross-validation. In this case, the accuracy was the correlation between the genomic breeding values (GEBV) obtained as the sum of SNP effects and the EBV obtained in the polygenic model in each validation group. For all traits, the use of the genomic-polygenic model did not alter the animals' ranking, with correlations higher than 0.87. Nevertheless, through this model, the accuracy increased from 1.5% to 6.8% compared to the polygenic model. The correlations between GEBV and EBV varied from 0.52 to 0.68. Therefore, the use of a small group of genotyped cows in the genetic evaluation can increase the accuracy of EBV for milk FA and other traditional milk traits.