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

Abstract

BackgroundThis 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.ResultsPosterior 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.ConclusionsThe 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.

Investigating the potential for genetic improvement of nitrogen and phosphorus efficiency in a Swiss large white pig population using chemical analysis

“…It is also possible that the prior of the Bayesian animal model overly influenced the estimate. In contrast to other studies that reported heritabilities of ADG and FCR in pigs ranging from 0.05 to 0.48 and 0.22 to 0.40 for ADG and FCR, respectively (Kavlak & Uimari, 2019; Nascimento, Nascimento, Dekkers, & Serão, 2019; Saintilan et al, 2013; Shirali, Varley, & Jensen, 2018; Silva, Lopes, Lopes, & Gasparino, 2019), our heritability estimate of ADG was close to zero and the one of FCR was rather low. Several QTLs were identified for ADG (Shirali et al, 2013; Silva et al, 2019) and FCR (Reyer et al, 2017; Silva et al, 2019), indicating a genetic basis of these traits.…”

Use of group records of feed intake to select for feed efficiency in rabbit

“…However, expected direct response would be lower than that of selection based on phenotypic regression (i.e., RFI). Implementation of this definition of FE has been performed using multiple‐trait models for individual records of FI (Shirali, Varley, & Jensen, ; Strathe et al, ). Only Shirali, Nielsen, Moller, and Jensen () used group records of FI to estimate genetic parameters of the classical definition of RFI using a single‐trait model with different (but correlated) genetic and permanent effects for each cage mate, which could be considered a different approach.…”

First Evidence of Realized Selection Response on Fillet Yield in Rainbow Trout Oncorhynchus mykiss, Using Sib Selection or Based on Correlated Ultrasound Measurements

“…To further complicate things, fillet yield is a ratio trait, and ratios have long been recognized as difficult traits to improve, because of their non-normality and because a given change in the ratio may be the result of different changes in its component traits. The expected genetic gain from truncation selection on ratio traits is difficult to compute using selection index theory, particularly when the component traits are correlated and have different means (Shirali et al, 2018). This is the case for the component traits of fillet yield, fillet weight, and body weight, which are very highly phenotypically and genetically correlated (r P = 0.89-0.99, r G = 0.93-0.99, see Fraslin et al, 2018 for a review).…”