Comparison of Bayesian models to estimate direct genomic values in multi-breed commercial beef cattle.

While several studies have examined the accuracy of direct genomic breeding values (DGV) within and across purebred cattle populations, the accuracy of DGV in crossbred or multi-breed cattle populations has been less well examined. Interest in the use of genomic tools for both selection and management has increased within the hybrid seedstock and commercial cattle sectors and research is needed to determine their efficacy. We predicted DGV for six traits using training populations of various sizes and alternative Bayesian models for a population of 3240 crossbred animals. Our objective was to compare alternate models with different assumptions regarding the distributions of single nucleotide polymorphism (SNP) effects to determine the optimal model for enhancing feasibility of multi-breed DGV prediction for the commercial beef industry. Realized accuracies ranged from 0.40 to 0.78. Randomly assigning 60 to 70% of animals to training (n ≈ 2000 records) yielded DGV accuracies with the smallest coefficients of variation. Mixture models (BayesB95, BayesCπ) and models that allow SNP effects to be sampled from distributions with unequal variances (BayesA, BayesB95) were advantageous for traits that appear or are known to be influenced by large-effect genes. For other traits, models differed little in prediction accuracy (~0.3 to 0.6%), suggesting that they are mainly controlled by small-effect loci. The proportion (60 to 70%) of data allocated to training that optimized DGV accuracy and minimized the coefficient of variation of accuracy was similar to large dairy populations. Larger effects were estimated for some SNPs using BayesA and BayesB95 models because they allow unequal SNP variances. This substantially increased DGV accuracy for Warner-Bratzler Shear Force, for which large-effect quantitative trait loci (QTL) are known, while no loss in accuracy was observed for traits that appear to follow the infinitesimal model. Large decreases in accuracy (up to 0.07) occurred when SNPs that presumably tag large-effect QTL were over-regressed towards the mean in BayesC0 analyses. The DGV accuracies achieved here indicate that genomic selection has predictive utility in the commercial beef industry and that using models that reflect the genomic architecture of the trait can have predictive advantages in multi-breed populations.

Rolf MM ，Garrick DJ ，Fountain T ，Ramey HR ，Weaber RL ，Decker JE ，Pollak EJ ，Schnabel RD ，Taylor JF ... -  《-》

Genomic prediction of simulated multibreed and purebred performance using observed fifty thousand single nucleotide polymorphism genotypes.

Genomic prediction involves characterization of chromosome fragments in a training population to predict merit. Confidence in the predictions relies on their evaluation in a validation population. Many commercial animals are multibreed (MB) or crossbred, but seedstock populations tend to be purebred (PB). Training in MB allows selection of PB for crossbred performance. Training in PB to predict MB performance quantifies the potential for across-breed genomic prediction. Efficiency of genomic selection was evaluated for a trait with heritability 0.5 simulated using actual SNP genotypes. The PB population had 1,086 Angus animals, and the MB population had 924 individuals from 8 sire breeds. Phenotypic values were simulated for scenarios including 50, 100, 250, or 500 additive QTL randomly selected from 50K SNP panels. Panels containing various numbers of SNP, including or excluding the QTL, were used in the analysis. A Bayesian model averaging method was used to simultaneously estimate the effects of all markers on the panels in MB (or PB) training populations. Estimated effects were utilized to predict genomic merit of animals in PB (or MB) validation populations. Correlations between predicted and simulated genomic merit in the validation population was used to reflect predictive ability. Panels that included QTL were able to account for 50% or more of the within-breed genetic variance when the trait was influenced by 50 QTL. The predictive power eroded as the number of QTL increased. Panels that composed the QTL and no other markers were able to account for 50% or more genetic variance even with 500 QTL. Panels that included genomic markers as well as QTL had less predictive power as the number of markers on the panel was increased. Panels that excluded the QTL and relied on markers in linkage disequilibrium (LD) to predict QTL effects performed more poorly than marker panels with QTL. Real-life situations with 50K panels that excluded the QTL could account for no more than 20% genetic variation for 50 QTL and less than 10% for 500 QTL. The difference between panels that included and excluded QTL indicates that the predictive ability of existing panels is limited by their LD. Training in PB to predict MB tended to be more predictive than training in MB to predict PB due to greater average levels of LD in PB than in MB populations. Improved across breed prediction of genomic merit will require panels with more than 50,000 markers.

Kizilkaya K ，Fernando RL ，Garrick DJ 《-》

Accuracies of direct genomic breeding values in Hereford beef cattle using national or international training populations.

The objective of this study was to estimate accuracies of direct genomic breeding values (DGV) for nationally evaluated traits of 1,081 American (AMH), 100 Argentine (ARH), 75 Canadian (CAH), and 395 Uruguayan (URH) Hereford animals genotyped using the Illumina BovineSNP50 BeadChip. Deregressed EBV (DEBV) were used as observations in a weighted analysis to derive DGV using BayesB and BayesC methods. The AMH animals were clustered into 4 groups, using either K-means or random clustering. Cross validation was performed with the group not used in training providing validation of the accuracies of estimated DGV. Genomic predictions were also evaluated for AMH animals by training on older animals and validating on younger animals. Bivariate animal models were used for each trait to estimate genetic correlations between DEBV and DGV. Genomic predictions were separately evaluated for foreign animals from each country using marker estimates from training on AMH or pooled international data. Pedigree estimated breeding values were developed for AMH animals, using traditional, pedigree-based BLUP (PBLUP) for comparison purposes. Using BayesB (BayesC) method, the average simple correlations between DGV and DEBV in AMH animals was 0.24 (0.21), 0.39 (0.36), and 0.32 (0.30) when training and validation sets were formed by K-means clustering, random allocation or year of birth of the animals, respectively. Genetic correlations between DEBV and DGV ranged from 0.20 (0.18) to 0.52 (0.45) in AMH animals. The DGV from BayesB were more accurate than from BayesC for most traits in AMH animals. Genomic predictions for foreign animals were less accurate than those obtained in AMH animals. Among foreign animals, genomic predictions were more accurate for CAH animals, which reflect the greater use of AMH sires in CAH in comparison with ARH and URH populations. Small changes in accuracies of DGV were observed for foreign animals by using admixed training populations. On average, genomic predictions across countries were more accurate for CAH and URH animals using BayesB. On average, accuracies of genomic predictions using BayesB (BayesC) method were 66% (55%) greater than those obtained from PBLUP. These results demonstrate the feasibility of developing DGV for American Hereford beef cattle. However, foreign breeders, especially South American Hereford breeders, need to genotype more animals to obtain more accurate genomic predictions.

Saatchi M ，Ward J ，Garrick DJ 《-》

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 ... -  《-》

Accuracy of genomic breeding values in multibreed beef cattle populations derived from deregressed breeding values and phenotypes.

Genomic selection involves the assessment of genetic merit through prediction equations that allocate genetic variation with dense marker genotypes. It has the potential to provide accurate breeding values for selection candidates at an early age and facilitate selection for expensive or difficult to measure traits. Accurate across-breed prediction would allow genomic selection to be applied on a larger scale in the beef industry, but the limited availability of large populations for the development of prediction equations has delayed researchers from providing genomic predictions that are accurate across multiple beef breeds. In this study, the accuracy of genomic predictions for 6 growth and carcass traits were derived and evaluated using 2 multibreed beef cattle populations: 3,358 crossbred cattle of the U.S. Meat Animal Research Center Germplasm Evaluation Program (USMARC_GPE) and 1,834 high accuracy bull sires of the 2,000 Bull Project (2000_BULL) representing influential breeds in the U.S. beef cattle industry. The 2000_BULL EPD were deregressed, scaled, and weighted to adjust for between- and within-breed heterogeneous variance before use in training and validation. Molecular breeding values (MBV) trained in each multibreed population and in Angus and Hereford purebred sires of 2000_BULL were derived using the GenSel BayesCπ function (Fernando and Garrick, 2009) and cross-validated. Less than 10% of large effect loci were shared between prediction equations trained on (USMARC_GPE) relative to 2000_BULL although locus effects were moderately to highly correlated for most traits and the traits themselves were highly correlated between populations. Prediction of MBV accuracy was low and variable between populations. For growth traits, MBV accounted for up to 18% of genetic variation in a pooled, multibreed analysis and up to 28% in single breeds. For carcass traits, MBV explained up to 8% of genetic variation in a pooled, multibreed analysis and up to 42% in single breeds. Prediction equations trained in multibreed populations were more accurate for Angus and Hereford subpopulations because those were the breeds most highly represented in the training populations. Accuracies were less for prediction equations trained in a single breed due to the smaller number of records derived from a single breed in the training populations.

Weber KL ，Thallman RM ，Keele JW ，Snelling WM ，Bennett GL ，Smith TP ，McDaneld TG ，Allan MF ，Van Eenennaam AL ，Kuehn LA ... -  《-》