Genomic prediction ability and genetic parameters for residual feed intake calculated using different approaches in Nellore beef cattle

Abstract This study aimed to estimate genetic parameters and the prediction ability and bias of the genomic values for residual feed intake (RFI) using genotypic and phenotypic records from 8,354 Nellore animals. The RFI was calculated using a regression equation for each test (RFItest) and for the whole population (RFIpop). An analysis of variance (ANOVA) using the same dataset and number of records 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 (ssGBLUP) under single and two-trait animal model analyses. The ssGBLUP method was used for the genomic prediction and the cross-validation method as a validation strategy. The prediction ability and bias were estimated to compare the RFItest and RFIpop genomic breeding values (GEBVs). 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 that obtained for the RFIpop. The method of calculating the RFI influences the decomposition and estimation of variance components, and genomic prediction. The genetic correlations between RFIpop and RFItest were high, implying that changes in sires ranking and genetic response are expected using one or the other. 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.