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

Abstract

Improving feed efficiency (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 definition of RFI by including animal-specific 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 unspecific efficiency in the use of feed (RFI). Expected response to alternative selection indexes involving different components is also studied. Results: Based on goodness-of-fit 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-specific needs and the traditional 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 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 efficiency, reducing FI without altering performance, was 2.5% higher when the model assumed animal-specific needs than when the traditional definition of RFI was considered. Conclusions: Expected response in overall efficiency, by reducing FI without altering performance, is slightly better with a model that assumes animal-specific needs instead of batch-specific needs to correct FI. The relatively small difference between the traditional RFI model and our model is due to random intercepts (unspecific use of feed) accounting for the majority of variability in FI. Overall, a model that accounts for animal-specific needs for MW, WG and FG is statistically superior and allows for the possibility to act differentially on FE components.