Genomic analysis for feed efficiency traits in beef cattle and algorithms to approximate accuracies for single-step genomic best linear unbiased predictor
Authors:   , Ramos Pedro Vital Brasil 
Published: December 2023

Feed efficiency plays a major role in the overall profitability and sustainability of the beef cattle industry, as it is directly related to the reduction of the animals' input requirements. Traditionally, the average daily feed intake and weight gain are used to calculate feed efficiency traits. The phenotypes are collected in feedlot systems with electronic feed bunks, requiring high financial costs, which limits the data collection. Therefore, new methodologies capable of predicting breeding values with high accuracy for these traits become crucial for selecting efficient animals. In this sense, feed efficiency traits can be analyzed longitudinally using random regression models (RRM), which allow fitting random genetic and environmental effects over time by considering the covariance pattern between the daily records. RRM can be a feasible alternative to evaluate genetic parameters as a function of the evaluation period and increase the accuracy of estimated breeding values (EBV) for feed efficiency. The accuracy of EBV is calculated to help make selection decisions, representing the correlation between the true breeding value and EBV. The theoretical accuracy can be calculated based on the prediction error variances obtained from the diagonal of the inverse of the left-hand side (LHS) of the mixed model equations (MME). However, inverting the LHS is not computationally feasible for an extensive system of equations, especially if genomic information is available. Thus, different algorithms to approximate accuracies have been proposed. Therefore, the first objective of this study was to propose genomic evaluations for dry matter intake (DMI), body weight gain (BWG), residual feed intake (RFI), and residual weight gain (RWG) data collected during an 84-day feedlot test period via random regression models and evaluate the genetic parameters behavior for feed efficiency traits and their implication for new selection strategies. Genetic parameters and genomic breeding values (GEBV) were estimated by random regression models under ssGBLUP for Nellore cattle using orthogonal Legendre polynomials and B-spline. The random regression model using linear B-splines proved a feasible alternative for the genomic evaluation of feed efficiency. Heritability estimates of DMI, RFI, BWG, and RWG indicate enough additive genetic variance to achieve a moderate response to selection. Based on genetic correlations and ranking comparisons between the test days, a new selection strategy can be adopted by reducing the performance test to 56 days for DMI and RFI selection and 77 days for BWG and RWG selection. The second objective of this study was to compare the approximated accuracies from two algorithms implemented in the BLUPF90 suite of programs and compare the approximated accuracies from the two algorithms against the exact accuracy based on the inversion of the LHS of MME. Algorithm 1 approximates accuracies based on the diagonal of the genomic relationship matrix (G). In turn, Algorithm 2 uses block sparse inversion of 𝐆−𝟏 . The Data were provided by the American Angus Association and included three datasets of growth, carcass, and marbling traits. For the genomic evaluations, a multi-trait model was applied to the datasets. To ensure the feasibility of inverting the LHS of the MME, a subset of data under single-trait models was used to compare approximated and exact accuracies. Accuracies from Algorithm 2 presented a higher correlation with the exact accuracies than from Algorithm 1. Additionally, Algorithm 2's accuracies were, in general, closer to the exact accuracies according to the mean square error and demonstrated similar changes in behavior when adding new genotyped animals to the analysis. In summary, the random regression model is a feasible alternative for the genomic evaluation of feed efficiency in Nellore cattle and the Algorithm 2 is more suitable for approximating accuracies of GEBV and should be used for routine ssGBLUP evaluations. Keywords: Feed intake; Longitudinal data; B-splines; Accuracy approximation; Genomic evaluation.