- Volume 27 Issue 10
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Genetic Relationships of Carcass Traits with Retail Cut Productivity of Hanwoo Cattle
- Koh, Daeyoung (College of Animal Resource Science, Kangwon National University) ;
- Lee, Jeongkoo (College of Animal Resource Science, Kangwon National University) ;
- Won, Seunggun (College of Animal Resource Science, Kangwon National University) ;
- Lee, Chaeyoung (Department of Bioinfomatics and Life Science, Soongsil University) ;
- Kim, Jongbok (College of Animal Resource Science, Kangwon National University)
- Received : 2014.03.05
- Accepted : 2014.06.21
- Published : 2014.10.01
This study aimed to estimate genetic correlation between carcass grading and retail productivity traits and to estimate the correlated response on retail productivity traits through selection for carcass grading traits in order to assess the efficacy of indirect selection. Genetic parameters were estimated with the data from 4240 Hanwoo steers using mixed models, and phenotypes included carcass weight (CWT), back fat thickness (BFT), eye muscle area (EMA), marbling (MAR), and estimated lean yield percentage (ELP) as the carcass grading traits, and weight and portion of retail cuts (RCW and RCP), trimmed fats (TFW and TFP) and trimmed bones (TBW and TBP) as the lean productivity traits. The CWT had positive genetic correlations with RCW (0.95) and TFW (0.73), but its genetic correlation with RCP was negligible (0.02). The BFT was negatively correlated with RCP (-0.63), but positively correlated with TFW and TFP (0.77 and 0.70). Genetic correlations of MAR with TFW and TFP were low. Among the carcass grading traits, only EMA was positively correlated with both RCW (0.60) and RCP (0.72). The EMA had a relatively strong negative genetic correlation with TFW (-0.64). The genetic correlation coefficients of ELP with RCP, TFW, and TFP were 0.76, -0.90, and -0.82, respectively. These correlation coefficients suggested that the ELP and EMA might be favorable traits in regulating lean productivity of carcass.
Correlated Response;Indirect Selection;Genetic Parameters;Mixed Model
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