PSXII-3 - Including non-additive genetic effects in genomic prediction and estimation of variance components for performance and heat stress traits in pigs

Abstract: Non-additive genetic effects may play important roles in the phenotypic expression of performance and adaptation traits in livestock. Therefore, we aimed to evaluate the inclusion of non-additive genetic effects in genomic prediction models and variance component estimation of performance traits in a purebred pig population and heat tolerance indicators in a crossbred pig population. The first dataset consisted of 3,534 individuals with genotypes for 52,843 SNPs and five pre-adjusted phenotypes from a public database of a purebred pig line. Twelve models fitting or not dominance and/or epistasis effects (additive-by-additive, additive-by-dominance, and dominance-by-dominance) and inbreeding were used to estimate variance components. Prediction ability was assessed based on 10-fold cross-validation and the bias, dispersion, and accuracy estimates were computed based on the LR method. We also evaluated the impact of including non-additive genetic effects on the ranking of the animals’ breeding value and in the proportion of commonly selected individuals. The second dataset consisted of records from 1,645 lactating sows (Large White x Landrace cross) genotyped for 50,703 SNPs and traits related to heat stress response: skin temperature (ear, shoulder, rump, and tail), vaginal temperature measured every 10 minutes, and the average of the six records per hour corresponding to 08:00, 12:00, 16:00, and 20:00 hours during four days; respiration rate, panting score (PS; score scale from 0 to 3), and hair density (HD, score scale from 0 to 2). Four models including or not inbreeding and the effect of dominance and additive-by-additive epistasis were used to estimate variance components. There was no effect on residual variance estimates due to the inclusion of non-additive genetic effects in the models for most traits. However, non-additive genetic effects reduced additive variance estimates, especially when additive-by-additive epistasis was fitted. Including non-additive genetic effects in the model did not improve the prediction accuracy of breeding values for purebreds, but there was a substantial change in the animals’ ranking and in the proportion of commonly selected individuals. For the crossbred traits, small non-additive genetic variance with large standard error estimates were obtained. Nevertheless, PS and HD presented notable additive-by-additive epistatic variance. PS presented h²_aa estimates of 0.15 and additive-by-additive epistasis corresponding to 86.76% of the total genetic variance, while HD presented additive-by-additive epistatic heritability (h²_aa) estimates from 0.46 to 0.49 and the proportion of total genetic variance explained by additive-by-additive epistasis ranged from 66.91% to 71.87%. In conclusion, including non-additive genetic effects did not improve the accuracy of prediction of breeding values for purebreds, but it changed the ranking of animals and selection decisions. Although PS and HD had large additive-by-additive epistasis effects, most of the traits related to heat stress in the crossbred population did not present relevant non-additive genetic effects.