Genetic parameters and multi-trait genomic prediction for hemoparasites infection levels in cattle

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Babesiosis and anaplasmosis are tick-borne diseases that substantially affect the economic outcomes of livestock production in tropical countries. This study aimed to evaluate the genetics of resistance to infection caused by these parasites through the estimation of heritabilities and genetic correlations of infection levels among Babesia bigemina (BigIL), B. bovis (BovIL), and Anaplasma marginale (AmIL), and tick counts (TC). The predictive ability of single and multi-trait genomic prediction models was evaluated through various combinations of these traits. To our knowledge, this is the first genomic study to examine BigIL and AmIL. Infection levels of BigIL (n = 1,882), BovIL (n = 1,858), and AmIL (n = 1,523) were estimated from blood samples using real-time PCR. TC phenotypes (n = 5,867) were obtained by counting the number of parasites larger than 4.5 mm from the right-hand side of each animal. Genotypic data were available for 3,977 animals which were then imputed up to ∼777,000 SNP and, after quality control, 502,398 SNP remained for downstream analyses. Variance components for BigIL and AmIL and the genetic correlations between traits were estimated using a Bayesian approach. The single-step best linear unbiased prediction was used to estimate genomic breeding values (GBV). The heritability estimates for BigIL and AmIL were low at 0.094 and 0.090, respectively, suggesting high environmental influence levels for both traits. The genetic correlations between tick count and infection levels for BigIL (0.239), BovIL (0.160), and AmIL (-0.019) were low, as well as the correlation between AmIL and BovIL (0.043). The genetic correlations between BigIL and BovIL (0.524) and BigIL and AmIL (0.793) were high, which contributed to improved GBV accuracies when these traits were combined in multi-trait models in comparison to single-trait models. These results suggested that multi-trait genomic prediction models of infection levels for tick-borne diseases are preferable to single-trait models. Additionally, our results indicated that the TC data and the GBV based on them are not useful for predicting infection levels of BigIL, BovIL, and AmIL.