Genetic parameters for body weight and different definitions of residual feed intake in broiler chickens

Abstract

Background The objectives of this study were to (1) simultaneously estimate genetic parameters for BW, feed intake (FI), and body weight gain (Gain) during a FI test in broiler chickens using multi-trait Bayesian analysis; (2) derive phenotypic and genetic residual feed intake (RFI) and estimate genetic parameters of the resulting traits; and (3) compute a Bayesian measure of direct and correlated superiority of a group selected on phenotypic or genetic residual feed intake. A total of 56,649 male and female broiler chickens were measured at one of two ages ($${\text{t}}$$ t or $${\text{t}} - 6$$ t - 6 days). BW, FI, and Gain of males and females at the two ages were considered as separate traits, resulting in a 12-trait model. Phenotypic RFI ($${\text{RFI}}_{\text{P}}$$ RFI P ) and genetic RFI ($${\text{RFI}}_{\text{G}}$$ RFI G ) were estimated from a conditional distribution of FI given BW and Gain using partial phenotypic and partial genetic regression coefficients, respectively. Results Posterior means of heritability for BW, FI and Gain were moderately high and estimates were significantly different between males and females at the same age for all traits. In addition, the genetic correlations between male and female traits at the same age were significantly different from 1, which suggests a sex-by-genotype interaction. Genetic correlations between $${\text{RFI}}_{\text{P}}$$ RFI P and $${\text{RFI}}_{\text{G }}$$ RFI G were significantly different from 1 at an older age but not at a younger age. Conclusions The results of the multivariate Bayesian analyses in this study showed that genetic evaluation for production and feed efficiency traits should take sex and age differences into account to increase accuracy of selection and genetic gain. Moreover, for communicating with stakeholders, it is easier to explain results from selection on $${\text{RFI}}_{\text{G}}$$ RFI G than selection on $${\text{RFI}}_{\text{P}}$$ RFI P , since $${\text{RFI}}_{\text{G}}$$ RFI G is genetically independent of production traits and it explains the efficiency of birds in nutrient utilization independently of energy requirements for production and maintenance.

New residual feed intake criterion for longitudinal data

“…In addition, it is the easiest and most common measure of RFI used in pigs and other species (e.g. cattle, sheep, chicken and even fish [ 39 – 42 ]), although some authors recommend to compute RFI from the genetic covariance matrix, such as Mebratie et al[ 8 ]. Our results obtained with the multi-SAD and the phenotypic regression models were different.…”

“…On the other hand, if genetic regression is used to derive RFI, it is then independent of the other traits at the genetic level (but not at the phenotypic level) [ 5 ]. The use of different regression coefficients for the genetic and environmental parts of the component traits (feed intake, production, body weight/composition traits), in a multiple trait approach, accounts for the multiple origins of the phenotypic correlations [ 8 , 9 ] and provides a RFI that is independent of the other traits at the genetic level.…”

Transcriptome Analysis Identifies Candidate Genes and Signaling Pathways Associated With Feed Efficiency in Xiayan Chicken

“…Feed efficiency is generally estimated using residual feed intake (RFI), which was proposed in 1963 and is considered the most functional parameter for the evaluation of feed efficiency ( Koch et al, 1963 ). At present, RFI has been applied in the artificial selection of feed efficiency in mammals and poultry ( Mebratie et al, 2019 ; Banerjee et al, 2020 ; Liu and VandeHaar, 2020 ). There is a general agreement that RFI is a moderately inherited characteristic, making it easy to improve the feed efficiency of commercial breeding companies ( Sell-Kubiak et al, 2017 ).…”

Simultaneous Bayesian estimation of genetic parameters for curves of weight, feed intake, and residual feed intake in beef cattle

“…The method presented here has been applied for the estimation of genetic parameters and prediction of response to selection for linear or ratio expressions of feed efficiency in pigs by Shirali et al (2018) . Mebratie et al (2019) used this approach for the simultaneous estimation of genetic parameters for production and feed efficiency traits for male and female broiler chickens using a multi-trait Bayesian analysis. In dairy cattle, Islam et al (2020) used a Bayesian multivariate random regression to analyze dry matter intake, energy-corrected milk, body weight, and body condition score and derived a genetic RFI from it.…”

“…This avoids the analysis of derived traits as well as the use of a two-step procedure for computing RFI so that more consistent inference can be made. The method was previously applied for the estimation of genetic parameters for feed efficiency in pigs ( Shirali et al, 2018 ), broiler chickens ( Mebratie et al, 2019 ), and dairy cattle ( Islam et al, 2020 ). Here, we illustrate the method with more theoretical background and apply it on data on growing beef bulls.…”