In beef production systems, carcass value is the main revenue source, and it is determined by both meat quantity and quality. Acceptable carcass and meat quality characteristics are of great importance in the beef industry, as consumers are willing to pay more for higher-quality products [1
]. Hanwoo, the predominant beef cattle of Korea, is well known for its rapid growth rate and quality features such as the bountiful marbling, tenderness, texture, flavor, and juiciness of its beef [2
]. Recently, there has been growing interest in including not only traits directly related to profitability, like back fat thickness (BFT), carcass weight (CW), eye muscle area (EMA), and marbling score (MS) [3
], but also traits related to the composition of the carcass in the Hanwoo breeding program. Thus, cattle breeders need to address carcass composition and quality traits, which determine the attainability of premium prices and consumer acceptance of meat. Primal cut traits have been proposed to characterize the carcass composition in beef cattle [4
]. These traits as a potential indicator of cutability have previously shown moderate-to-high heritability estimates in beef cattle [4
] and are known to be genetically associated with carcass merit [5
]. Thus, the improvement of primal cut yields is particularly important in Hanwoo because the evaluation of animals via these traits can be an alternative to facilitate decision-making regarding selection for desirable carcass characteristics. However, it is a challenge to improve these traits using traditional breeding approaches, since they are expensive and difficult to measure or it requires the selection candidates to be slaughtered.
The availability of genome-wide marker panels and an increased number of genotyped individuals have enabled improvements in the accuracy of the estimated breeding value (EBV). Recently, the single-step genomic best linear unbiased prediction (ssGBLUP) method has been widely applied in routine genomic evaluation [7
]. Single-step genomic BLUP provides the most comprehensive information for genomic evaluation where the phenotypes of genotyped and non-genotyped individuals, pedigree, and genotype data can be considered simultaneously in one step. Several studies have reported that the use of ssGBLUP improves genomic prediction accuracy relative to pedigree-based BLUP or genomic BLUP (GBLUP) for carcass traits [11
] and linear body measurements in Hanwoo cattle [16
], as well as in other beef cattle [17
]. Nonetheless, utilizing primal cut traits to further increase the accuracy of genomic prediction in breeding programs of Hanwoo cattle has not yet been investigated. Therefore, the present study aimed to assess the accuracy, bias, and dispersion of breeding values for primal cut traits using pedigree-based BLUP (PBLUP) and single-step genomic BLUP (ssGBLUP) methods with a linear regression (LR) method [20
]; this can provide valuable information for further implementation of genomic selection for the traits under study in Hanwoo cattle.
The current study is the first attempt to compare pedigree-based BLUP (PBLUP) and single-step genomic BLUP (ssGBLUP) methods for prediction accuracy regarding primal cut yields in Hanwoo cattle. To maximize the profitability of the beef cattle industry, selection based on genomic information has become an effective tool in breeding programs. According to our results, primal cut yields are moderately to highly heritable traits, comparable to those estimated in Hanwoo cattle [5
], Simmental cattle [27
], Chianina cattle [28
], Irish cattle [4
], and U.K. beef cattle [29
]. For instance, compared to our results, Choi et al. [5
] estimated higher heritability for bottom round (0.66), rib (0.35), sirloin (0.60), striploin (0.64), tenderloin (0.41), and top round (0.62); lower heritability for brisket (0.21), flank (0.21), and shank (0.35); and slightly similar heritability for chuck (0.19) in Hanwoo cattle. This difference might result from the discrepancy in the number of records measured (3467 vs. 920 records) and the units of measurement data (kilograms vs. percent of carcass weight). Furthermore, the existence of sufficient genetic variation indicates that improvements in these traits through genetic selection are feasible. In a recent study, we observed the favorable and moderate to high genetic correlations between the primal cut yields and three composite traits of primal cuts based on their retail value (high-value cuts, medium-value cuts, and low-value cuts) with carcass traits available in the Hanwoo selection index, indicate that these traits can be considered as a selection objective in the Hanwoo breeding scheme [30
]. A similar conclusion was drawn by Pabiou et al. [31
], who reported that four groups of wholesale cut weights predicted using video image analyses (i.e., very high value cuts, high value cuts, medium value cuts, and low value cuts) had moderate genetic correlations with carcass weight in Irish steers. Given that primal cuts yields in beef cattle are known to be heritable traits, evidence exists, although based on a limited study population [4
], which shows that these traits could be useful in multi-trait genetic evaluations targeting the improvement in the accuracy of selection for primal carcass cut weights and, by extension, overall carcass merit.
In this study, estimates of the accuracy, bias, and dispersion of primal cut yields were obtained via evaluation models that used only pedigree or a combination of pedigree and genomic relationship matrices using the LR method. Our results demonstrated that the ssGBLUP model outperformed PBLUP for all 10 traits in either a partial or whole dataset. A shift in the average accuracy from 0.26 to 0.46 was obtained by changing from a PBLUP to ssGBLUP method across the studied traits using the partial dataset. Similarly, the use of the whole dataset in analyses led to an average increase in accuracy from 0.62 to 0.71 by moving the model from PBLUP to ssGBLUP for all the primal cut yields. In other words, when genomic information was included in the analyses, the average improvements in the accuracy were + 20 and + 9 percentage points using partial and whole datasets, respectively, compared with pedigree-based BLUP due to the capture of variation in Mendelian sampling [32
]. Furthermore, the increase in accuracy in genomic prediction depends upon the quality and veracity of the available information, complete or incorrect pedigree, number of phenotyped/genotyped animals, and heritability of traits [34
]. It has also been shown that the use of all phenotyped animals, pedigree, and genomic information simultaneously in the ssGBLUP model provides better predictability compared to the pedigree-based model, which could also be explained by better relationships [24
]. Hence, our results showed that using genomic prediction is relatively more beneficial for all evaluated traits, thereby enabling a significant increase in the rate of genetic gain [36
Among the studied traits, ssGBLUP provided a substantially higher prediction accuracy than the PBLUP method for bottom round, shank, top round, and brisket compared with the other traits. It is reasonable to assume that this higher accuracy could be due to the heritability of the traits of interest, which were 0.50, 0.50, 0.52, and 0.51 herein for bottom round, shank, top round, and brisket, respectively (Table 2
). The importance of heritability on the accuracies of estimated breeding values is shown in Figure S2
, where the regression coefficients of accuracy (in the partial data) on the heritabilities were significantly positive regardless of the method. As expected, the superiority of ssGBLUP over PBLUP was more obvious for the traits with higher heritability, suggesting that heritability can be used as a measure to evaluate the accuracy of the target traits in genomic evaluation for a breeding program. Similar findings were found by Bolormaa et al. [37
], who reported that the most accurate predictions were observed for the traits with highest heritability. In addition, another study showed that higher prediction accuracies were obtained for ADG and DMI, which had the highest heritabilities (h2
= 0.39 and h2
= 0.43, respectively) among the feed efficiency traits in Nellore cattle [38
Concerning the ratio of the superiority of ssGBLUP over the pedigree-based method, the performance of the estimation of the ratio was improved by the added genomic information in ssGBLUP compared with the PBLUP method across all traits. The estimated value of the ratio indicates the increase in accuracy from pedigree to genomic predictions in both partial and whole datasets. The results indicate that the relative gain in the accuracy of GEBV with ssGBLUP was 65% using the partial dataset, whereas the ssGBLUP method was superior to PBLUP when the whole dataset was used, with an average increase or relative gain of 7% across all traits. In fact, these findings indicate the positive impact of genomic information on the genetic evaluations when no phenotypic data are available for the candidate animals or even when phenotypes are available on individuals; the addition of genomic information in the analysis can considerably improve the accuracy of prediction relative to pedigree-based models.
According to our results, the increase in accuracy from partial to whole dataset (
) was over two times for the pedigree-based evaluation compared to the genomic-based method (Figure 3
). This indicates that the accuracy of predictions for the genomic method compared with the pedigree-based method is less affected by the use of the whole dataset, which is in agreement with the literature [20
]. For instance, Granado-Tajada et al. [40
] showed that adding phenotypes increased accuracy by double and 81% using the pedigree evaluation for two strains of dairy sheep, namely Latxa Cara Rubia (LCR) and Latxa Cara Negra from Euskadi (LCNEUS), respectively, while this estimator was 96% and 78% using a genomic method for LCR and LCNEUS, respectively. Moreover, in another study on dairy sheep (Manech Tête Rousse breed), Macedo et al. [41
] exhibited an increase in accuracy of 78% for the PBLUP method and 51% for the ssGBLUP method by adding phenotypic information of focal individuals to the partial dataset. Similarly, Bermann et al. [39
] reported that the accuracies based on the LR method were 0.45 for BLUP and 0.76 for ssGBLUP in the simulated data, whereas those obtained with the real data (chicken mortality) were 0.41 and 0.47 for BLUP and ssGBLUP, respectively. These authors also demonstrated that moving from the partial to the whole dataset resulted in a 17% and 20.4% gain in accuracy based on the pedigree evaluation and a 14% and 9.8% increase for the genomic-based evaluation in real and simulated data, respectively.
These findings were also in concordance with those reported by Mehrban et al. [14
], who applied the LR method to evaluate carcass traits in Hanwoo cattle using four evaluation models and showed that using genomic information and additional phenotypic information from highly genetically correlated traits is a suitable tool to improve the prediction accuracy.
Consistent with our results, several previous studies have proved the superiority of ssGBLUP over traditional BLUP approaches in various livestock since it was proposed [7
]. Moreover, in Hanwoo cattle studies, the accuracies obtained from single-step models were found to be higher than those from pedigree-based and multi-step methods for carcass traits [11
] and body measurement traits [16
This estimator was proposed by Legarra and Reverter [20
], and it is an estimator of the ratio between accuracies based on pedigree and genomic evaluations in partial and whole datasets. They also highlighted that the correlation between predictions obtained with partial and whole datasets is not a measure of accuracy, but an estimator of the ratio between accuracies. In fact, the advantage of this method is the simplicity of its application, comparing the EBV/GEBV estimated from less information (partial dataset) with the EBV/GEBV from data containing more information (whole dataset) for the same individuals in different evaluations.
In addition to the prediction accuracy, the bias and estimator of dispersion are other important indicators when comparing different prediction methods in breeding programs. Bias, for which the desired value is zero, refers to the difference between the mean of EBV in partial and whole dataset. In general, predictions obtained via the single-step approach by adding genomic information showed less bias compared with those obtained from the pedigree-based method across the studied traits, except for rib. The dispersion can be defined as the value of the slope of the regression of breeding values estimated with whole dataset on breeding values estimated using partial dataset, and it has an expected value of 1. Dispersion estimates less than or greater than 1 indicate an overestimation (inflation) or underestimation (deflation), respectively. The results showed that the average absolute deviation of dispersion from 1 was less for ssGBLUP than for the PBLUP method. Therefore, the inclusion of all information available in the evaluation simultaneously reduced the overestimation and underestimation of the breeding values [52
], and the single-step genomic approach is able to partially account for pre-selection, as expected [53
]. It was previously shown that inflation or deflation of predictions could result in incorrect comparisons between animals of different generations and inaccurate genetic trend estimates [54
Consequently, the results of the current study provide a comprehensive analysis of accuracy, bias, and dispersion for primal cut yields using the LR method, which can be applied to any model and any data structure [20
]. Our results demonstrated that ssGBLUP generally provided a higher accuracy than the PBLUP method for primal cut yields in Hanwoo cattle that could have a practical applicability for breeding programs.