Bayesian estimation of genetic variance and response to selection on linear or ratio traits of feed efficiency in dairy cattle

Abstract

This study aimed to estimate genetic parameters of the linear trait genetic residual feed intake (RFI) and the ratio traits feed conversion ratio (FCR) and feed conversion efficiency (FCE) along with dry matter intake (DMI) and energy sink traits such as energy-corrected milk (ECM), body weight (BW), body condition score (BCS), and BW change (BWC) across different weeks in the first lactation of Danish Holstein cows. A second objective was to conduct a Bayesian analysis of direct and correlated superiority of the selected group when selecting on genetic RFI, FCR, or FCE. Feed intake and energy sink traits were recorded during wk 1 to 44 of lactation on 847 primiparous Danish Holstein cows. A Bayesian multivariate random regression animal model was used to analyze DMI, ECM, BW, and BCS in different weeks of lactation. Genetic RFI was obtained by conditioning DMI on ECM, BW, BCS, and BWC using genetic partial regression coefficients. The posterior distribution of the breeding values for FCR and FCE was derived from the posterior distribution of functions of "fixed" environmental effects and random additive genetic effects on DMI and ECM. Genetic superiority of the selected group was defined as the difference in additive genetic mean of the selected top individuals expected to be potential parents, and the total population after integrating genetic trends out of the posterior distribution of selection responses. Posterior means of heritability of genetic RFI ranged from 0.10 to 0.15, genetic variance of FCR and FCE ranged from 2.13 × 10 −3 to 3.2 × 10 −3 (kg 2 DMI/kg 2 ECM) and 6.11 × 10 −3 to 2.4 × 10 −2 (kg 2 ECM/kg 2 DMI), respectively. Selection against RFI showed a direct response of −1.01 to −2.23 kg/d RFI and correlated responses of −0.031 to −0.056 kg/kg for FCR, 0.104 to 0.160 kg/kg for FCE, and −0.316 to −1.057 kg/d for DMI in different weeks of lactation. Selection against RFI had no significant effect on production traits but selection for ratio traits reduced BW and BCS. Posterior means of genetic correlation between DMI and ratio traits were low. In conclusion, the Bayesian procedure allowed us to estimate genetic RFI without the need for separate multiple regression analysis and considered the nonnormal posterior distribution of ratio traits. Selection against genetic RFI might be an effective means to improve feed efficiency compared with ratio traits for feed efficiency in dairy cattle.

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

“… 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.…”

An alternative interpretation of residual feed intake by phenotypic recursive relationships in dairy cattle

“…In reality, however, phenotypes are subject to measurement errors. Multiple-trait models have been proposed which obtain RFI genetic values indirectly through follow-up partial regression (Kennedy et al, 1993;Lu et al, 2015;Islam et al, 2020;Tempelman and Lu, 2020). Dependent variables include DMI and energy sinks, but noting that some researchers (e.g., Lu et al, 2015) recommended treating 𝛥BW as a covariate because its heritability was low.…”

“…In the first stage, DMI is taken to be a linear function of variables (i.e., energy sinks) to account for body tissue mobilization. In dairy cattle, energy sinks often include metabolic body weight (MBW), milk net energy (MILKNE), and changes in BW (ΔBW) (e.g., Pryce et al, 2015;Tempelman et al, 2015;Løvendahl et al, 2018;Islam et al, 2020). In the second stage model, the computed RFI phenotypes are fitted by a mixed-effects model to estimate RFI genetic values and relevant genetic parameters.…”

“…In reality, however, phenotypes are subject to measurement errors. Multiple-trait models have been proposed that obtain RFI genetic values indirectly through followup partial regression (Kennedy et al, 1993;Lu et al, 2015;Islam et al, 2020;Tempelman and Lu, 2020). With various methods available, the biological implications for the computed RFI remain to be exploited (Martin et al, 2021).…”