Bayesian estimation of direct and correlated responses to selection on linear or ratio expressions of feed efficiency in pigs

Abstract

Background: This study aimed at (1) deriving Bayesian methods to predict breeding values for ratio (i.e. feed conversion ratio; FCR) or linear (i.e. residual feed intake; RFI) traits; (2) estimating genetic parameters for average daily feed consumption (ADFI), average daily weight gain (ADG), lean meat percentage (LMP) along with the derived traits of RFI and FCR; and (3) deriving Bayesian estimates of direct and correlated responses to selection on RFI, FCR, ADG, ADFI, and LMP. Response to selection was defined as the difference in additive genetic mean of the selected top individuals, expected to be parents of the next generation, and the total population after integrating genetic trends out of the posterior distribution of selection responses. Inferences were based on marginal posterior distributions obtained from the Bayesian method for integration over unknown population parameters and "fixed" environmental effects and for appropriate handling of ratio traits. Terminal line pigs (n = 3724) were used for a multi-variate model for ADFI, ADG, and LMP. RFI was estimated from the conditional distribution of ADFI given ADG and LMP, using either genetic (RFIG) or phenotypic (RFIP) partial regression coefficients. The posterior distribution of the FCR's breeding values was derived from the posterior distribution of "fixed" environmental effects and additive genetic effects on ADFI and ADG. Results: Posterior means of heritability were 0.32, 0.26, 0.56, 0.20, and 0.15 for ADFI, ADG, LMP, RFIP, and RFIG, respectively. Selection against RFIG showed a direct response of - 0.16 kg/d and correlated responses of - 0.16 kg/kg for FCR and - 0.15 kg/d for ADFI, with no effect on other production traits. Selection against FCR resulted in a direct response of - 0.17 kg/kg and correlated responses of - 0.14 kg/d for RFIG, - 0.18 kg/d for ADFI, and 0.98% for LMP. Conclusions: The Bayesian methodology developed here enables prediction of breeding values for FCR and RFI from a single multi-variate model. In addition, we derived posterior distributions of direct and correlated responses to selection. Genetic parameter estimates indicated a genetic basis for the studied traits and that genetic improvement through selection was possible. Direct selection against FCR or RFIP resulted in unexpected responses in production traits.