Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods.

The aim of this study was to compare the predictive performance of the Genomic Best Linear Unbiased Predictor (GBLUP) and machine learning methods (Random Forest, RF; Support Vector Machine, SVM; Artificial Neural Network, ANN) in simulated populations presenting different levels of dominance effects. Simulated genome comprised 50k SNP and 300 QTL, both biallelic and randomly distributed across 29 autosomes. A total of six traits were simulated considering different values for the narrow and broad-sense heritability. In the purely additive scenario with low heritability (h2 = 0.10), the predictive ability obtained using GBLUP was slightly higher than the other methods whereas ANN provided the highest accuracies for scenarios with moderate heritability (h2 = 0.30). The accuracies of dominance deviations predictions varied from 0.180 to 0.350 in GBLUP extended for dominance effects (GBLUP-D), from 0.06 to 0.185 in RF and they were null using the ANN and SVM methods. Although RF has presented higher accuracies for total genetic effect predictions, the mean-squared error values in such a model were worse than those observed for GBLUP-D in scenarios with large additive and dominance variances. When applied to prescreen important regions, the RF approach detected QTL with high additive and/or dominance effects. Among machine learning methods, only the RF was capable to cover implicitly dominance effects without increasing the number of covariates in the model, resulting in higher accuracies for the total genetic and phenotypic values as the dominance ratio increases. Nevertheless, whether the interest is to infer directly on dominance effects, GBLUP-D could be a more suitable method.

Comparison of gene expression of Toll-like receptors and cytokines between Piau and Commercial line (Landrace×Large White crossbred) pigs vaccinated against Pasteurella multocida type D.

We aimed to compare Toll-like receptors (TLR) and cytokines expression in local Piau breed and a Commercial line (Landrace×Large White crossbred) pigs in response to vaccination against Pasteurella multocida type D. Seronegative gilts for Pasteurella multocida type D and Mycoplasma hyopneumoniae were used, from which peripheral blood mononuclear cells (PBMC) were collected in four time points (T0, T1, T2 and T3; before and after each vaccination dose). For bronchoalveolar lavage fluid cells (BALF), we set groups of vaccinated and unvaccinated animals for both genetic groups. Gene expression was evaluated on PBMC and BALF. In PBMC, when we analyzed time points within breeds, significant differences in expression for TLRs and cytokines, except TGFß, were observed for Commercial animals. For the Piau pigs, only TGFß showed differential expression. Comparing the expression among genetic groups, the Commercial pigs showed higher expression for TLRs after first vaccination dose, while for IL2, IL6, IL12 and IL13, higher expression was also observed in T3 and IL8 and IL10, in T1 and T3. Still comparing the breeds, the crossbred animals showed higher expression for TNFα in T1 and T2, while for TGFß only in T2. For gene expression in BALF, vaccinated Commercial pigs showed higher expression of TLR6, TLR10, IL6, IL8, IL10, TNFα and TGFß genes than vaccinated Piau pigs. The Commercial line pigs showed higher sensitivity to vaccination, while in local Piau breed lower responsiveness, which may partly explain genetic variability in immune response and will let us better understand the tolerance/susceptibility for pasteurellosis.