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.

中文翻译：

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使用少量热带荷斯坦奶牛参考种群评估牛奶脂肪酸预测的准确性

脂肪酸 (FA) 与人类健康、乳制品的感官质量和保质期、奶牛的健康和甲烷排放有关。然而，尽管它们很重要，但尚未在所有奶牛群中定期测量它们，这可能会影响这些性状的估计育种值 (EBV) 的准确性。在这种情况下，另一种方法是使用基因组选择。因此，目的是评估基因组信息在热带荷斯坦种群牛奶性状遗传评估中的使用。使用了 4,203 头奶牛的牛奶 FA 百分比、每日产奶量和质量特性的月度记录 (n = 36,457) 以及这些奶牛中 755 头的 57,368 个单核苷酸多态性 (SNP) 的基因型。应用多基因和基因组-多基因模型进行 EBV 预测，并且通过从预测误差和 EBV 排名之间的 Spearman 相关性计算的 EBV 准确度来比较两种模型。通过使用交叉验证来评估预测准确性。在这种情况下，准确性是作为 SNP 效应总和获得的基因组育种值 (GEBV) 与每个验证组中多基因模型中获得的 EBV 之间的相关性。对于所有性状，使用基因组多基因模型并没有改变动物的排名，相关性高于 0.87。尽管如此，通过该模型，与多基因模型相比，准确率从 1.5% 提高到 6.8%。GEBV 和EBV 之间的相关性从0.52 到0.68 不等。所以，