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

BACKGROUND: Improving feed efficiency ([Formula: see text] ) 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 ([Formula: see text] ) should be of value for further research on biological aspects of [Formula: see text] . Here, we present a random regression model that extends the classical definition of [Formula: see text] by including animal-specific needs in the model. Using this model, we explore the genetic determinism of several [Formula: see text] components: use of feed for growth ([Formula: see text] ), use of feed for backfat deposition ([Formula: see text] ), use of feed for maintenance ([Formula: see text] ), and unspecific efficiency in the use of feed ([Formula: see text] ). Expected response to alternative selection indexes involving different components is also studied. RESULTS: Based on goodness-of-fit to the available feed intake ([Formula: see text] ) data, the model that assumes individual (genetic and permanent) variation in the use of feed for maintenance, [Formula: see text] and [Formula: see text] showed the best performance. Joint individual variation in feed allocation to maintenance, growth and backfat deposition comprised 37% of the individual variation of [Formula: see text] . The estimated heritabilities of [Formula: see text] using the model that accounts for animal-specific needs and the traditional [Formula: see text] model were 0.12 and 0.18, respectively. The estimated heritabilities for the regression coefficients were 0.44, 0.39 and 0.55 for [Formula: see text] , [Formula: see text] and [Formula: see text] , respectively. Estimates of genetic correlations of [Formula: see text] were positive with amount of feed used for [Formula: see text] and [Formula: see text] but negative for [Formula: see text] . Expected response in overall efficiency, reducing [Formula: see text] without altering performance, was 2.5% higher when the model assumed animal-specific needs than when the traditional definition of [Formula: see text] was considered. CONCLUSIONS: Expected response in overall efficiency, by reducing [Formula: see text] without altering performance, is slightly better with a model that assumes animal-specific needs instead of batch-specific needs to correct [Formula: see text] . The relatively small difference between the traditional [Formula: see text] model and our model is due to random intercepts (unspecific use of feed) accounting for the majority of variability in [Formula: see text] . Overall, a model that accounts for animal-specific needs for [Formula: see text] , [Formula: see text] and [Formula: see text] is statistically superior and allows for the possibility to act differentially on [Formula: see text] components.