Background In pigs, crossbreeding aims at exploiting heterosis, but heterosis is difficult to quantify. Heterozygosity at genetic markers is easier to measure and could potentially be used as an indicator of heterosis. The objective of this study was to investigate the effect of heterozygosity on various maternal and production traits in purebred and crossbred pigs. The proportion of heterozygosity at genetic markers across the genome for each individual was included in the prediction model as a fixed regression across or within breeds. Results Estimates of regression coefficients of heterozygosity showed large effects for some traits. For maternal traits, regression coefficient estimates were always in a favourable direction, while for production, meat and slaughter quality traits, they were both favourable and unfavourable. Traits with the largest estimated effects of heterozygosity were total number born, litter weight at 3 weeks, weight at 150 days, and age at 40 kg. Estimates of regression coefficients on heterozygosity differed between breeds. Traits with the largest effect of heterozygosity also showed a significant (P < 0.05) increase in prediction accuracy when heterozygosity was included in the model compared to the model without heterozygosity. Conclusions For traits with the largest estimates of regression coefficients on heterozygosity, the inclusion of heterozygosity in the model improved prediction accuracy. Using models that include heterozygosity would result in selecting different animals for breeding, which has the potential to improve genetic gain for these traits. This is most beneficial when crossbreds or several breeds are included in the estimation of breeding values and is relevant to all species, not only pigs. Thus, our results show that including heterozygosity in the model is beneficial for some traits, likely due to dominant gene action. Electronic supplementary material The online version of this article (10.1186/s12711-019-0450-1) contains supplementary material, which is available to authorized users.

In pig breeding, the final product is a crossbred (CB) animal, while selection is performed at the purebred (PB) level using mainly PB data. However, incorporating CB data in genetic evaluations is expected to result in greater genetic progress at the CB level. Currently, there is no optimal way to include CB genotypes into the genomic relationship matrix. This is because, in single-step genomic BLUP, which is the most commonly used method, genomic and pedigree relationships must refer to the same base. This may not be the case when several breeds and CB are included. An alternative to overcome this issue may be to use a genomic relationship matrix (G matrix) that accounts for both linkage disequilibrium (LD) and linkage analysis (LA), called G. The objectives of this study were to further develop the G matrix approach to utilize both PB and CB genotypes simultaneously, to investigate its performance, and the general added value of including CB genotypes in genomic evaluations. Data were available on Dutch Landrace, Large White, and the F1 cross of those breeds. In total, 7 different G matrix compositions (PB alone, PB together, each PB with the CB, all genotypes across breeds, and G) were tested on 3 maternal traits: total number born (TNB), live born (LB), and gestation length (GL). Results show that G gave the greatest prediction accuracy of all the relationship matrices tested for PB prediction, but not for CB prediction. Including CB genotypes in general increased prediction accuracy for all breeds. However, in some cases, these increases in prediction accuracy were not significant (at < 0.05). To conclude, CB genotypes increased prediction accuracy for some of the traits and breeds, but not for all. The G matrix had significantly greater prediction accuracy in PB than the other G matrix with both PB and CB genotypes, except in one case. While for CB, the G matrix with genotypes across all breeds gave the greatest accuracy, though this was not significantly different from G. Computation time was high for G, and research will be needed to reduce its computational costs to make it feasible for use in routine evaluations. The main conclusion is that inclusion of CB genotypes is beneficial for both PB and CB animals.

The backtest response of a pig gives an indication of its coping style, that is, its preferred strategy to cope with stressful situations, which may in turn be related to production traits. The objective of this study was therefore to estimate the heritability of the backtest response and estimate genetic correlations with production traits (birth weight, growth, fat depth and loin depth). The backtest was performed by placing the piglet on its back for 60 s and the number of struggles (NrS) and vocalizations (NrV), and the latency to struggle and vocalize (LV) was recorded. In total, 992 piglets were subjected to the backtest. Heritability estimates for backtest traits were statistically moderate (although high for behavioral traits), with LV having the highest heritability estimate (0.56±0.10, P<0.001) and NrS having the lowest estimate (0.37±0.09, P<0.001). Backtest traits also had high genetic correlations with each other, with vocalization traits (NrV and LV) having the highest (-0.94±0.03, P<0.001), and NrS with NrV the lowest correlation (0.70±0.09, P<0.001). No significant correlations were found between backtest traits and production traits, but correlations between NrS and birth weight (-0.38±0.25), and NrV and loin depth (-0.28±0.19) approached significance (P=0.07). More research into genotype-by-environment interactions may be needed to assess possible connections between backtest traits and production traits, as this may depend on the circumstances (environment, experiences, etc.). In conclusion, heritability estimates of backtest traits are high and it would therefore be possible to select for them. The high genetic correlations between backtest traits indicate that it may be possible to only consider one or two traits for characterization and selection purposes. There were no significant genetic correlations found between backtest traits and production traits, although some of the correlations approached significance and hence warrant further research.