Abstract
Infection by Haemonchus contortus is the gastrointestinal parasitosis of greatest economic and social importance in the production of goats, being the leading cause of mortality in the Brazilian goat industry. Genetic selection for resistance to parasites in Anglo-Nubian goats has proven to be a viable alternative to traditional methods of controlling gastrointestinal nematodes, producing animals genetically superior in resistance to parasites. This study aimed to evaluate the impact of including genomic information on the estimation of variance components, genetic parameters, and predictive accuracy of genetic values for the new trait Parasitic Resistance (PR) in Anglo-Nubian goats. The PR is generated by measuring Body Condition Score, FAMACHA chart (ranging from 1 to 5), and Fecal Egg Count on a regular scale. These three traits were used as input variables in the CAPRIOVI software, which uses fuzzy logic to create the PR score, ranging from 0 to 10. Animals with lower scores are more likely to be parasitized and require treatment, while animals with higher scores are less likely to be infected. We used information from 352 animals, totaling 3,855 repeated measurements of PR over the years (2001 to 2020) and 1,500 animals in the pedigree. Animals with less than three measurements for any of the three input traits were removed from the dataset. Contemporary groups were built from information from the year of birth, data collection season, birth season, and physiological stage of the animal, considered as fixed effects, and age at the time of data collection as a covariate in an animal repeatability model. A total of 89 animals were genotyped using the Bead Chip Goats SNP50. After quality control, the remaining 38,659 SNPs (Table 1) were used to build the hybrid matrix (H). The variance components (VC) were estimated using the Bayesian Gibbs Sampling method, and breeding values were predicted using the Best Linear Unbiased Prediction (BLUP) and single-step Best Linear Unbiased Prediction (ssGBLUP) methods. The additive genetic variance and heritability estimation ranged from 0.13 to 0.14 and 0.04 to 0.05, respectively (Table 2). The accuracies obtained for PR with BLUP and ssGBLUP were 0.12 and 0.14, respectively, representing a gain of 16.7%. The genomic information (25 % of all animals with phenotypes) with the repeated records improved the predictive model capability based on accuracy. It also improved the VC estimation for PR in Anglo-Nubian goats raised in the Brazilian semi-arid region. These results highlight the positive impact of genetic evaluation of goats for PR, even with few phenotypes and genotyped animals. In addition to elucidating the additive genetic influence of parasite resistance, it can be a great source of information on the selection and production of Anglo-Nubian goats.