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 ... -  《-》

Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels.

Achieving accurate genomic estimated breeding values for dairy cattle requires a very large reference population of genotyped and phenotyped individuals. Assembling such reference populations has been achieved for breeds such as Holstein, but is challenging for breeds with fewer individuals. An alternative is to use a multi-breed reference population, such that smaller breeds gain some advantage in accuracy of genomic estimated breeding values (GEBV) from information from larger breeds. However, this requires that marker-quantitative trait loci associations persist across breeds. Here, we assessed the gain in accuracy of GEBV in Jersey cattle as a result of using a combined Holstein and Jersey reference population, with either 39,745 or 624,213 single nucleotide polymorphism (SNP) markers. The surrogate used for accuracy was the correlation of GEBV with daughter trait deviations in a validation population. Two methods were used to predict breeding values, either a genomic BLUP (GBLUP_mod), or a new method, BayesR, which used a mixture of normal distributions as the prior for SNP effects, including one distribution that set SNP effects to zero. The GBLUP_mod method scaled both the genomic relationship matrix and the additive relationship matrix to a base at the time the breeds diverged, and regressed the genomic relationship matrix to account for sampling errors in estimating relationship coefficients due to a finite number of markers, before combining the 2 matrices. Although these modifications did result in less biased breeding values for Jerseys compared with an unmodified genomic relationship matrix, BayesR gave the highest accuracies of GEBV for the 3 traits investigated (milk yield, fat yield, and protein yield), with an average increase in accuracy compared with GBLUP_mod across the 3 traits of 0.05 for both Jerseys and Holsteins. The advantage was limited for either Jerseys or Holsteins in using 624,213 SNP rather than 39,745 SNP (0.01 for Holsteins and 0.03 for Jerseys, averaged across traits). Even this limited and nonsignificant advantage was only observed when BayesR was used. An alternative panel, which extracted the SNP in the transcribed part of the bovine genome from the 624,213 SNP panel (to give 58,532 SNP), performed better, with an increase in accuracy of 0.03 for Jerseys across traits. This panel captures much of the increased genomic content of the 624,213 SNP panel, with the advantage of a greatly reduced number of SNP effects to estimate. Taken together, using this panel, a combined breed reference and using BayesR rather than GBLUP_mod increased the accuracy of GEBV in Jerseys from 0.43 to 0.52, averaged across the 3 traits.

Erbe M ，Hayes BJ ，Matukumalli LK ，Goswami S ，Bowman PJ ，Reich CM ，Mason BA ，Goddard ME ... -  《-》

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 ... -  《-》

Estimation of genomic breeding values for residual feed intake in a multibreed cattle population.

Residual feed intake (RFI) is a measure of the efficiency of animals in feed utilization. The accuracies of GEBV for RFI could be improved by increasing the size of the reference population. Combining RFI records of different breeds is a way to do that. The aims of this study were to 1) develop a method for calculating GEBV in a multibreed population and 2) improve the accuracies of GEBV by using SNP associated with RFI. An alternative method for calculating accuracies of GEBV using genomic BLUP (GBLUP) equations is also described and compared to cross-validation tests. The dataset included RFI records and 606,096 SNP genotypes for 5,614 Bos taurus animals including 842 Holstein heifers and 2,009 Australian and 2,763 Canadian beef cattle. A range of models were tested for combining genotype and phenotype information from different breeds and the best model included an overall effect of each SNP, an effect of each SNP specific to a breed, and a small residual polygenic effect defined by the pedigree. In this model, the Holsteins and some Angus cattle were combined into 1 "breed class" because they were the only cattle measured for RFI at an early age (6-9 mo of age) and were fed a similar diet. The average empirical accuracy (0.31), estimated by calculating the correlation between GEBV and actual phenotypes divided by the square root of estimated heritability in 5-fold cross-validation tests, was near to that expected using the GBLUP equations (0.34). The average empirical and expected accuracies were 0.30 and 0.31, respectively, when the GEBV were estimated for each breed separately. Therefore, the across-breed reference population increased the accuracy of GEBV slightly, although the gain was greater for breeds with smaller number of individuals in the reference population (0.08 in Murray Grey and 0.11 in Hereford for empirical accuracy). In a second approach, SNP that were significantly (P < 0.001) associated with RFI in the beef cattle genomewide association studies were used to create an auxiliary genomic relationship matrix for estimating GEBV in Holstein heifers. The empirical (and expected) accuracy of GEBV within Holsteins increased from 0.33 (0.35) to 0.39 (0.36) and improved even more to 0.43 (0.50) when using a multibreed reference population. Therefore, a multibreed reference population is a useful resource to find SNP with a greater than average association with RFI in 1 breed and use them to estimate GEBV in another breed.

Khansefid M ，Pryce JE ，Bolormaa S ，Miller SP ，Wang Z ，Li C ，Goddard ME ... -  《-》

Accuracy of genomic breeding values for residual feed intake in crossbred beef cattle.

The benefit of using genomic breeding values (GEBV) in predicting ADG, DMI, and residual feed intake for an admixed population was investigated. Phenotypic data consisting of individual daily feed intake measurements for 721 beef cattle steers tested over 5 yr was available for analysis. The animals used were an admixed population of spring-born steers, progeny of a cross between 3 sire breeds and a composite dam line. Training and validation data sets were defined by randomly splitting the data into training and testing data sets based on sire family so that there was no overlap of sires in the 2 sets. The random split was replicated to obtain 5 separate data sets. Two methods (BayesB and random regression BLUP) were used to estimate marker effects and to define marker panels and ultimately the GEBV. The accuracy of prediction (the correlation between the phenotypes and GEBV) was compared between SNP panels. Accuracy for all traits was low, ranging from 0.223 to 0.479 for marker panels with 200 SNP, and 0.114 to 0.246 for marker panels with 37,959 SNP, depending on the genomic selection method used. This was less than accuracies observed for polygenic EBV accuracies, which ranged from 0.504 to 0.602. The results obtained from this study demonstrate that the utility of genetic markers for genomic prediction of residual feed intake in beef cattle may be suboptimal. Differences in accuracy were observed between sire breeds when the random regression BLUP method was used, which may imply that the correlations obtained by this method were confounded by the ability of the selected SNP to trace breed differences. This may also suggest that prediction equations derived from such an admixed population may be useful only in populations of similar composition. Given the sample size used in this study, there is a need for increased feed intake testing if substantially greater accuracies are to be achieved.

Mujibi FD ，Nkrumah JD ，Durunna ON ，Stothard P ，Mah J ，Wang Z ，Basarab J ，Plastow G ，Crews DH Jr ，Moore SS ... -  《-》