Accuracy of predicting genomic breeding values for carcass merit traits in Angus and Charolais beef cattle.

Accuracy of predicting genomic breeding values for carcass merit traits including hot carcass weight, longissimus muscle area (REA), carcass average backfat thickness (AFAT), lean meat yield (LMY) and carcass marbling score (CMAR) was evaluated based on 543 Angus and 400 Charolais steers genotyped on the Illumina BovineSNP50 Beadchip. For the genomic prediction within Angus, the average accuracy was 0.35 with a range from 0.32 (LMY) to 0.37 (CMAR) across different training/validation data-splitting strategies and statistical methods. The within-breed genomic prediction for Charolais yielded an average accuracy of 0.36 with a range from 0.24 (REA) to 0.46 (AFAT). The across-breed prediction had the lowest accuracy, which was on average near zero. When the data from the two breeds were combined to predict the breeding values of either breed, the prediction accuracy averaged 0.35 for Angus with a range from 0.33 (REA) to 0.39 (CMAR) and averaged 0.33 for Charolais with a range from 0.18 (REA) to 0.46 (AFAT). The prediction accuracy was slightly higher on average when the data were split by animal's birth year than when the data were split by sire family. These results demonstrate that the genetic relationship or relatedness of selection candidates with the training population has a great impact on the accuracy of predicting genomic breeding values under the density of the marker panel used in this study.

Chen L ，Vinsky M ，Li C 《-》

Accuracy of predicting genomic breeding values for residual feed intake in Angus and Charolais beef cattle.

In beef cattle, phenotypic data that are difficult and/or costly to measure, such as feed efficiency, and DNA marker genotypes are usually available on a small number of animals of different breeds or populations. To achieve a maximal accuracy of genomic prediction using the phenotype and genotype data, strategies for forming a training population to predict genomic breeding values (GEBV) of the selection candidates need to be evaluated. In this study, we examined the accuracy of predicting GEBV for residual feed intake (RFI) based on 522 Angus and 395 Charolais steers genotyped on SNP with the Illumina Bovine SNP50 Beadchip for 3 training population forming strategies: within breed, across breed, and by pooling data from the 2 breeds (i.e., combined). Two other scenarios with the training and validation data split by birth year and by sire family within a breed were also investigated to assess the impact of genetic relationships on the accuracy of genomic prediction. Three statistical methods including the best linear unbiased prediction with the relationship matrix defined based on the pedigree (PBLUP), based on the SNP genotypes (GBLUP), and a Bayesian method (BayesB) were used to predict the GEBV. The results showed that the accuracy of the GEBV prediction was the highest when the prediction was within breed and when the validation population had greater genetic relationships with the training population, with a maximum of 0.58 for Angus and 0.64 for Charolais. The within-breed prediction accuracies dropped to 0.29 and 0.38, respectively, when the validation populations had a minimal pedigree link with the training population. When the training population of a different breed was used to predict the GEBV of the validation population, that is, across-breed genomic prediction, the accuracies were further reduced to 0.10 to 0.22, depending on the prediction method used. Pooling data from the 2 breeds to form the training population resulted in accuracies increased to 0.31 and 0.43, respectively, for the Angus and Charolais validation populations. The results suggested that the genetic relationship of selection candidates with the training population has a greater impact on the accuracy of GEBV using the Illumina Bovine SNP50 Beadchip. Pooling data from different breeds to form the training population will improve the accuracy of across breed genomic prediction for RFI in beef cattle.

Chen L ，Schenkel F ，Vinsky M ，Crews DH Jr ，Li C ... -  《-》

Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle.

The aim of this study was to assess the accuracy of genomic predictions for 19 traits including feed efficiency, growth, and carcass and meat quality traits in beef cattle. The 10,181 cattle in our study had real or imputed genotypes for 729,068 SNP although not all cattle were measured for all traits. Animals included Bos taurus, Brahman, composite, and crossbred animals. Genomic EBV (GEBV) were calculated using 2 methods of genomic prediction [BayesR and genomic BLUP (GBLUP)] either using a common training dataset for all breeds or using a training dataset comprising only animals of the same breed. Accuracies of GEBV were assessed using 5-fold cross-validation. The accuracy of genomic prediction varied by trait and by method. Traits with a large number of recorded and genotyped animals and with high heritability gave the greatest accuracy of GEBV. Using GBLUP, the average accuracy was 0.27 across traits and breeds, but the accuracies between breeds and between traits varied widely. When the training population was restricted to animals from the same breed as the validation population, GBLUP accuracies declined by an average of 0.04. The greatest decline in accuracy was found for the 4 composite breeds. The BayesR accuracies were greater by an average of 0.03 than GBLUP accuracies, particularly for traits with known genes of moderate to large effect mutations segregating. The accuracies of 0.43 to 0.48 for IGF-I traits were among the greatest in the study. Although accuracies are low compared with those observed in dairy cattle, genomic selection would still be beneficial for traits that are hard to improve by conventional selection, such as tenderness and residual feed intake. BayesR identified many of the same quantitative trait loci as a genomewide association study but appeared to map them more precisely. All traits appear to be highly polygenic with thousands of SNP independently associated with each trait.

Bolormaa S ，Pryce JE ，Kemper K ，Savin K ，Hayes BJ ，Barendse W ，Zhang Y ，Reich CM ，Mason BA ，Bunch RJ ，Harrison BE ，Reverter A ，Herd RM ，Tier B ，Graser HU ，Goddard ME ... -  《-》

Evaluation of a genomic-enhanced sorting system for feeder cattle1.

This study evaluated the use of molecular breeding values (MBVs) for carcass traits to sort steers into quality grid and lean meat yield (LMY) groups. A discovery set of 2,609 animals with genotypes and carcass phenotypes was used to predict MBVs for LMY and marbling score (MBS) for 299 Angus, 181 Charolais, and 638 Kinsella Composite steers using genomic best linear unbiased prediction. Steers were sorted in silico into four MBV groups namely Quality (with MBVs greater than the mean for LMY and MBS), Lean (with MBVs greater than the mean for LMY but less than or equal to the mean for MBS), Marbling (with MBVs greater than the mean for MBS but less than or equal to the mean for LMY), and Other (with MBVs lower than the mean for LMY and MBS). Carcass phenotypes on the steers were then collected at slaughter and evaluated for consistency with the assigned MBV groups using descriptive statistics and least square analysis. Accuracy of MBV predictions was assessed by Pearson's correlation between predicted MBV and adjusted phenotype divided by the square root of trait heritability. Genomic breed compositions were predicted for all steers to correct for possible population stratification and breed effects in the test model. The number of steers that met the expected carcass outcome was counted to produce actual percentages for each MBV group. Results showed that on average, Quality and Marbling groups had greater back-fat and more marbling across the three populations while Lean group had leaner carcasses. Carcass weights were similar across MBV groups. Within MBV groups, decreases in variability were observed for most traits suggesting improvement in carcass uniformity. Greater than 70% of the steers in Quality, Lean, and Marbling groups met the Quality Grid and Y1-LMY target for Angus and Charolais but not for Kinsella composite. The accuracy of MBV prediction ranged from 0.43 to 0.59 indicating that up to 35% of the observed carcass trait variability can be predicted, which suggests utility of MBV as a marker-assisted management tool to sort feeder cattle into uniform carcass endpoint groups under similar environmental and management conditions. Further investigation is warranted to evaluate the performance of feeder cattle sorted based on MBV and managed for different carcass endpoints as well as the cost-benefit implications for feedlot operations.

Akanno EC ，Ekine-Dzivenu C ，Chen L ，Vinsky M ，Abo-Ismail MK ，MacNeil MD ，Plastow G ，Basarab J ，Li C ，Fitzsimmons C ... -  《-》

Phenotypic and genetic relationships of feed efficiency with growth performance, ultrasound, and carcass merit traits in Angus and Charolais steers.

Feed efficiency is of particular importance to the beef industry, as feed costs represent the single largest variable cost in beef production systems. Selection for more efficient cattle will lead to reduction of feed related costs, but should not have adverse impacts on quality of the carcass. In this study, we evaluated phenotypic and genetic correlations of residual feed intake (RFI), RFI adjusted for end-of-test ultrasound backfat thickness (RFIf), and RFI adjusted for ultrasound backfat thickness and LM area (RFIfr) with growth, ultrasound, and carcass merit traits in an Angus population of 551 steers and in a Charolais population of 417 steers. In the Angus steer population, the phenotypic and genetic correlation of RFI with carcass merit traits including HCW, carcass backfat, carcass LM area, lean meat yield, and carcass marbling were not significant or weak with correlations coefficients ranging from -0.0007 ± 0.05 to 0.18 ± 0.21. In the Charolais steer population, the phenotypic and genetic correlations of RFI with the carcass merit traits were also weak, with correlation coefficients ranging from -0.07 ± 0.06 to 0.19 ± 0.18, except for the genetic correlation with carcass average backfat, which was moderate with a magnitude of 0.42 ± 0.29. Inclusion of ultrasound backfat thickness in the model to predict the expected daily DMI for maintenance explained on average an additional 0.5% variation of DMI in the Angus steers and 2.3% variation of DMI in the Charolais steer population. Inclusion of both the ultrasound backfat and LM area in the model explained only 0.7% additional variance in DMI in the Angus steer population and only 0.6% in the Charolais steer population on top of the RFIf model. We concluded that RFIf adjusted for ultrasound backfat at the end of the test will lead to decreases of both the phenotypic and genetic correlations with carcass backfat and marbling score to a greater extent for late-maturing beef breeds such as Charolais than for early-maturing beef breeds such as Angus. However, further inclusion of ultrasound LM area on top of the final ultrasound backfat in the model of calculating RFI had little effect in reducing the correlations of RFI with the carcass merit traits.

Mao F ，Chen L ，Vinsky M ，Okine E ，Wang Z ，Basarab J ，Crews DH Jr ，Li C ... -  《-》