Genetic parameters and expected responses to selection for components of feed efficiency in a Duroc pig line

Abstract

Abstract Background Improving feed efficiency ( $${\text{FE}}$$ FE ) is a key factor for any pig breeding company. Although this can be achieved by selection on an index of multi-trait best linear unbiased prediction of breeding values with optimal economic weights, considering deviations of feed intake from actual needs ( $${\text{RFI}}$$ RFI ) should be of value for further research on biological aspects of $${\text{FE}}$$ FE . Here, we present a random regression model that extends the classical definition of $${\text{RFI}}$$ RFI by including animal-specific needs in the model. Using this model, we explore the genetic determinism of several $${\text{FE}}$$ FE components: use of feed for growth ( $${\text{WG}}$$ WG ), use of feed for backfat deposition ( $${\text{FG}}$$ FG ), use of feed for maintenance ( $${\text{MW}}$$ MW ), and unspecific efficiency in the use of feed ( $${\text{RFI}}$$ RFI ). Expected response to alternative selection indexes involving different components is also studied. Results Based on goodness-of-fit to the available feed intake ( $${\text{FI}}$$ FI ) data, the model that assumes individual (genetic and permanent) variation in the use of feed for maintenance, $${\text{WG}}$$ WG and $${\text{FG}}$$ FG showed the best performance. Joint individual variation in feed allocation to maintenance, growth and backfat deposition comprised 37% of the individual variation of $${\text{FI}}$$ FI . The estimated heritabilities of $${\text{RFI}}$$ RFI using the model that accounts for animal-specific needs and the traditional $${\text{RFI}}$$ RFI model were 0.12 and 0.18, respectively. The estimated heritabilities for the regression coefficients were 0.44, 0.39 and 0.55 for $${\text{MW}}$$ MW , $${\text{WG}}$$ WG and $${\text{FG}}$$ FG , respectively. Estimates of genetic correlations of $${\text{RFI}}$$ RFI were positive with amount of feed used for $${\text{WG}}$$ WG and $${\text{FG}}$$ FG but negative for $${\text{MW}}$$ MW . Expected response in overall efficiency, reducing $${\text{FI}}$$ FI without altering performance, was 2.5% higher when the model assumed animal-specific needs than when the traditional definition of $${\text{RFI}}$$ RFI was considered. Conclusions Expected response in overall efficiency, by reducing $${\text{FI}}$$ FI without altering performance, is slightly better with a model that assumes animal-specific needs instead of batch-specific needs to correct $${\text{FI}}$$ FI . The relatively small difference between the traditional $${\text{RFI}}$$ RFI model and our model is due to random intercepts (unspecific use of feed) accounting for the majority of variability in $${\text{FI}}$$ FI . Overall, a model that accounts for animal-specific needs for $${\text{MW}}$$ MW , $${\text{WG}}$$ WG and $${\text{FG}}$$ FG is statistically superior and allows for the possibility to act differentially on $${\text{FE}}$$ FE components.