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

Abstract

Background: Improving feed efciency (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 (RFI) should be of value for further research on biological aspects of FE. Here, we present a random regression model that extends the classical defnition of RFI by including animal-specifc needs in the model. Using this model, we explore the genetic determinism of several FE components: use of feed for growth (WG), use of feed for backfat deposition (FG), use of feed for maintenance (MW), and unspecifc efciency in the use of feed (RFI). Expected response to alternative selection indexes involving diferent components is also studied. Results: Based on goodness-of-ft to the available feed intake (FI) data, the model that assumes individual (genetic and permanent) variation in the use of feed for maintenance, WG and FG showed the best performance. Joint individual variation in feed allocation to maintenance, growth and backfat deposition comprised 37% of the individual variation of FI. The estimated heritabilities of RFI using the model that accounts for animal-specifc needs and the traditional RFI model were 0.12 and 0.18, respectively. The estimated heritabilities for the regression coefcients were 0.44, 0.39 and 0.55 for MW, WG and FG, respectively. Estimates of genetic correlations of RFI were positive with amount of feed used for WG and FG but negative for MW. Expected response in overall efciency, reducing FI without altering performance, was 2.5% higher when the model assumed animal-specifc needs than when the traditional defnition of RFI was considered. Conclusions: Expected response in overall efciency, by reducing FI without altering performance, is slightly better with a model that assumes animal-specifc needs instead of batch-specifc needs to correct FI. The relatively small diference between the traditional RFI model and our model is due to random intercepts (unspecifc use of feed) accounting for the majority of variability in FI. Overall, a model that accounts for animal-specifc needs for MW, WG and FG is statistically superior and allows for the possibility to act diferentially on FE components.