Genetic Parameter Estimates of Carcass Traits under National Scale Breeding Scheme for Beef Cattle

Abstract

Carcass and price traits of 72,969 Hanwoo cows, bulls and steers aged 16 to 80 months at slaughter collected from 2002 to 2013 at 75 beef packing plants in Korea were analyzed to determine heritability, correlation and breeding value using the Multi-Trait restricted maximum likelihood (REML) animal model procedure. The traits included carcass measurements, scores and grades at 24 h postmortem and bid prices at auction. Relatively high heritability was found for maturity (0.41±0.031), while moderate heritability estimates were obtained for backfat thickness (0.20±0.018), longissimus muscle (LM) area (0.23±0.020), carcass weight (0.28±0.019), yield index (0.20±0.018), yield grade (0.16±0.017), marbling (0.28±0.021), texture (0.14±0.016), quality grade (0.26±0.016) and price/kg (0.24±0.025). Relatively low heritability estimates were observed for meat color (0.06±0.013) and fat color (0.06±0.012). Heritability estimates for most traits were lower than those in the literature. Genetic correlations of carcass measurements with characteristic scores or quality grade of carcass ranged from −0.27 to +0.21. Genetic correlations of yield grade with backfat thickness, LM area and carcass weight were 0.91, −0.43, and −0.09, respectively. Genetic correlations of quality grade with scores of marbling, meat color, fat color and texture were −0.99, 0.48, 0.47, and 0.98, respectively. Genetic correlations of price/kg with LM area, carcass weight, marbling, meat color, texture and maturity were 0.57, 0.64, 0.76, −0.41, −0.79, and −0.42, respectively. Genetic correlations of carcass price with LM area, carcass weight, marbling and texture were 0.61, 0.57, 0.64, and −0.73, respectively, with standard errors ranging from ±0.047 to ±0.058. The mean carcass weight breeding values increased by more than 8 kg, whereas the mean marbling scores decreased by approximately 0.2 from 2000 through 2009. Overall, the results suggest that genetic improvement of productivity and carcass quality could be obtained under the national scale breeding scheme of Korea for Hanwoo and that continuous efforts to improve the breeding scheme should be made to increase genetic progress.

A Gene-Set Enrichment and Protein–Protein Interaction Network-Based GWAS with Regulatory SNPs Identifies Candidate Genes and Pathways Associated with Carcass Traits in Hanwoo Cattle

“…MS was highly positively correlated with EMA, both phenotypically and genetically. The high positive correlation between CWT and EMA, both genetically and phenotypically, is consistent with previous reports in Hanwoo [29,50,52,53]; similar correlations have been reported in Brahman and Japanese Brown cattle [54,55]. Kim et al [51] had also reported a medium negative correlation between EMA and BFT.…”

“…The mean values of CWT, BFT, EMA, and MS measured at a similar slaughter age were consistent with those of Roh et al [48] but differed marginally with [49]. Several previous studies have reported lower estimates for these traits [29,50], which could be attributed to differences in the number of animals investigated or nutritional status during finishing period or due to differences in the methods employed for measuring the phenotypes. The lower phenotypic CV for CWT and EMA (12% and 13%) and higher estimates for BFT and MS (35% and 31%), are consistent with previous studies [29,51].…”

Effect of errors in pedigree on the accuracy of estimated breeding value for carcass traits in Korean Hanwoo cattle

“…Park et al [23] and Yoon et al [24] reported similar results for carcass traits previously in Hanwoo Korean cattle. More recently, Do et al [25] reported slightly higher values for car cass traits in Hanwoo cattle compared with our study. On the other hand, lesser values in some of the carcass traits have been observed [26] in Japanese black (Wagyu).…”

Estimation of Genetic Parameters by Single-Trait and Multi-Trait Models for Carcass Traits in Hanwoo Cattle

“…An increase in heritability for the multi-trait model as compared to the single-trait model may be due to the incorporation of information occurring by correlation among the traits in the multi-trait model. It can be observed that genetic correlation among traits reached higher values than published in literature [18,22] with approximately similar phenotypic correlation between BFT-CWT, CWT-EMA, and BFT-MS. Further, the high genetic correlation between traits is advantageous in the multi-trait model for low heritability traits, as it helps in understanding how closely or distantly traits are associated with each other [23,24]. A strong correlation was observed between EMA-MS (pedigree = 0.63, genomic = 0.51).…”