Genomic prediction ability and genetic parameters for residual feed intake calculated using different approaches and their associations with growth, reproductive, and carcass traits in Nellore cattle

• Main Author: Brunes, Ludmilla Costa
• Publication Date: 2023
• Other Authors: de Faria, Carina Ubirajara, Magnabosco, Cláudio Ulhoa, Lobo, Raysildo Barbosa, Peripolli, Elisa [UNESP], Aguilar, Ignacio, Baldi, Fernando [UNESP]
• Format: Article
• Language: eng
• Source: Repositório Institucional da UNESP
• Download full: http://dx.doi.org/10.1007/s13353-022-00734-8; http://hdl.handle.net/11449/246348
• topic: Accuracy; Beef cattle; Bos taurus indicus; Feed efficiency; Genomic selection; Residual feed intake equation

This study aimed to estimate prediction ability and genetic parameters for residual feed intake (RFI) calculated using a regression equation for each test (RFItest) and for the whole population (RFIpop) in Nellore beef cattle. It also aimed to evaluate the correlations between RFIpop and RFItest with growth, reproductive, and carcass traits. Genotypic and phenotypic records from 8354 animals were used. An analysis of variance (ANOVA) was performed to verify the adequacy of the regression equations applied to estimate the RFItest and RFIpop. The (co)variance components were obtained using the single-step genomic best linear unbiased prediction under single and two-trait animal model analyses. The genetic and phenotypic correlations between RFItest and RFIpop with dry matter intake, frame, growth, reproduction, and carcass-related traits were evaluated. The prediction ability and bias were estimated to compare the RFItest and RFIpop genomic breeding values (GEBV). The RFIpop ANOVA showed a higher significance level (p < 0.0001) than did the RFItest for the fixed effects. The RFIpop displayed higher additive genetic variance estimated than the RFItest, although the RFIpop and RFItest displayed similar heritabilities. Overall, the RFItest showed higher residual correlations with growth, reproductive, and carcass traits, while the RFIpop displayed higher genetic correlations with such traits. The GEBV for the RFItest was slightly biased than GEBV RFIpop. The approach to calculate the RFI influenced the decomposition and estimation of variance components and genomic prediction for RFI. The application of RFIpop would be more appropriate for genetic evaluation purpose to adjust or correct for non-genetic effects and to decrease the prediction bias for RFI.