In this study a Bayesian method for estimating genetic parameters for linear (residual feed intake, RFI) and ratio trait (feed conversion ratio, FCR) of feed efficiency in farm animals is presented. The Bayesian method integrates over all unknown model parameters including “fixed” and random effects and properly handles ratio traits that do not have standard distributions. A Bayesian procedure for the analysis of response to selection on linear versus ratio traits were developed and applied in pigs to examine different selection criteria for feed efficiency. The Bayesian methodology allowed prediction of breeding values for ratio and linear definitions of feed efficiency from the same multi-variate model for the traits measured without the need for approximations to handle ratio traits. RFI is defined directly in the model without the need for a two-step estimation procedure. Two different definitions of RFI, differing in whether genetic or phenotypic correlations between RFI and production traits are constrained to be zero, are extended to Bayesian analysis. Due to the unknown distributions the genetic parameters of FCR cannot be estimated directly. Instead we develop the posterior multivariate distribution of additive genetic (co)variance available for selection. The example show that inference based on this measure is very similar to the estimates of additive genetic (co)variance and, therefore, can be used to investigate the posterior distribution of additive genetic variance in the ratio trait of FCR as well. Finally we explore the posterior distribution of the genetic superiority of the selected group when selecting on different definitions of feed efficiency or on production traits. The current study shows that direct selection against FCR results in disproportional selection pressure on its component traits and also on lean meat percentage (LMP). However, direct selection against genetic RFI allows for selection on the proportion of average daily feed intake (ADFI) that is independent of production. In addition, as genetic RFI has zero genetic correlation with other production traits in the breeding program, an RFI index that is independent of production traits is easier to communicate with farmers/advisors. Selection for improved feed efficiency is likely best achieved through multiple-trait selection on genetic RFI and production traits. Keywords: Bayesian analysis, feed efficiency, genetic parameters, response to selection
Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Biology - Feed Intake and Efficiency 2, , 481, 2018
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