Prediction ability of an alternative multi-trait genomic evaluation for residual feed intake.

Selection for feed efficiency is the goal for many genetic breeding programs in beef cattle. Residual feed intake has been included in genetic evaluations to reduce feed intake without compromising performance traits as liveweight, body gain or carcass traits. However, measuring feed intake is expensive, and only a small percentage of selection candidates are phenotyped. Genomic selection has become a very important tool to achieve effective genetic progress in these traits. Another effective strategy has been the implementation of multi-trait prediction using easily recordable predictor traits on both reference animals and candidates without phenotypes, and this could be another inexpensive way to increase accuracy. The objective of this work was to analyse and compare the prediction ability of two alternative different approaches to predict GEBVs for RFI. The population of inference was Hereford bulls in Uruguay that were genotyped candidates for to selection. The first model was the conventional univariate model for RFI and the second model was a multi-trait model which included a predictor trait (weaning weight, WW), in addition to the traits used in the first one (dry matter intake, metabolic mid test weight, average daily gain and ultrasound back fat) (DMI, MWT, ADG, UBF, respectively). GEBVs from the multi-trait model were combined using selection index theory to derive RFI values. All analyses were performed using ssGBLUP procedure. The prediction ability of both models was tested using two validation strategies (30 different replicates of random groups of animals and validation across 9 different feed intake tests). The prediction quality was assessed by the following parameters: bias, dispersion, ratio of accuracies and the relative increase in accuracy by adding phenotypic information. All parameters showed that the univariate model outperforms the multi-trait model, regardless of the validation strategy considered. These results indicate that including WW as a proxy trait in a multi-trait analysis does not improve the prediction ability when all animals to be predicted are genotyped.

Reproductive performance of Angus, Hereford, Salers and Nellore crossbred females: Additive and non-additive effects.

Reproductive traits in breeding herds can be improved through crossbreeding, which results in breed differences, heterosis and breed complementarity. The aim of this study was to estimate group additive genetic and dominance effects for three reproductive traits; probability of artificial insemination (AIP); calving success (CS); and days to calving (DC) for Hereford (H), Angus (A), Nellore (N) and Salers (S) breeds under grazing conditions. Data were obtained from an experiment carried out during 1992-2002 by the Faculty of Agronomy, Universidad de la Republica (UdelaR), Uruguay and Caja Notarial de Seguridad Social. The data set contained reproductive information of 1,164 females from 11 different genetic groups (GG) consisting of crosses between H, N, S and A. AIP, CS and DC were examined in first-calf heifers, while CS and DC were examined in second-calf and 3- to 7-year-old cows. Least square means for each GG and group additive genetic and dominance effects were estimated for each trait. F1 crossbreed females performed better for artificial insemination probability than purebred females. Crossbred A/H heifers had the highest AIPs and CS rates, while crossbred N/H 3- to 7-year-old cows recorded the highest averages for CS and DC. Estimates of group additive genetic effects did not differ amongst A, S, N and H; however, dominance increased the AIP and CS of the heifers.

Effects of ignoring inbreeding in model-based accuracy for BLUP and SSGBLUP.

Model-based accuracy, defined as the theoretical correlation between true and estimated breeding value, can be obtained for each individual as a function of its prediction error variance (PEV) and inbreeding coefficient F, in BLUP, GBLUP and SSGBLUP genetic evaluations. However, for computational convenience, inbreeding is often ignored in two places. First, in the computation of reliability = 1-PEV/(1 + F). Second, in the set-up, using Henderson's rules, of the inverse of the pedigree-based relationship matrix A. Both approximations have an effect in the computation of model-based accuracy and result in wrong values. In this work, first we present a reminder of the theory and extend it to SSGBLUP. Second, we quantify the error of ignoring inbreeding with real data in three scenarios: BLUP evaluation and SSGBLUP in Uruguayan dairy cattle, and BLUP evaluations in a line of rabbit closed for >40 generations with steady increase of inbreeding up to an average of 0.30. We show that ignoring inbreeding in the set-up of the A-inverse is equivalent to assume that non-inbred animals are actually inbred. This results in an increase of apparent PEV that is negligible for dairy cattle but considerable for rabbit. Ignoring inbreeding in reliability = 1-PEV/(1 + F) leads to underestimation of reliability for BLUP evaluations, and this underestimation is very large for rabbit. For SSGBLUP in dairy cattle, it leads to both underestimation and overestimation of reliability, both for genotyped and non-genotyped animals. We strongly recommend to include inbreeding both in the set-up of A-inverse and in the computation of reliability from PEVs.