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

Abstract

BackgroundImproving feed efficiency ({text{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}}) should be of value for further research on biological aspects of {text{FE}}. Here, we present a random regression model that extends the classical definition of {text{RFI}} by including animal-specific needs in the model. Using this model, we explore the genetic determinism of several {text{FE}} components: use of feed for growth ({text{WG}}), use of feed for backfat deposition ({text{FG}}), use of feed for maintenance ({text{MW}}), and unspecific efficiency in the use of feed ({text{RFI}}). Expected response to alternative selection indexes involving different components is also studied.ResultsBased on goodness-of-fit to the available feed intake ({text{FI}}) data, the model that assumes individual (genetic and permanent) variation in the use of feed for maintenance, {text{WG}} and {text{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}}. The estimated heritabilities of {text{RFI}} using the model that accounts for animal-specific needs and the traditional {text{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}}, {text{WG}} and {text{FG}}, respectively. Estimates of genetic correlations of {text{RFI}} were positive with amount of feed used for {text{WG}} and {text{FG}} but negative for {text{MW}}. Expected response in overall efficiency, reducing {text{FI}} without altering performance, was 2.5% higher when the model assumed animal-specific needs than when the traditional definition of {text{RFI}} was considered.ConclusionsExpected response in overall efficiency, by reducing {text{FI}} without altering performance, is slightly better with a model that assumes animal-specific needs instead of batch-specific needs to correct {text{FI}}. The relatively small difference between the traditional {text{RFI}} model and our model is due to random intercepts (unspecific use of feed) accounting for the majority of variability in {text{FI}}. Overall, a model that accounts for animal-specific needs for {text{MW}}, {text{WG}} and {text{FG}} is statistically superior and allows for the possibility to act differentially on {text{FE}} components.