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<p>Enhancing the quality and yield of Korean beef relies on improving carcass traits, including carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS). This study aimed to evaluate the accuracy of genomic EBVs for these traits using the genomic BLUP method. Phenotypic data were collected from 19,153 Hanwoo steers and 6,200 Hanwoo cows, with all animals genotyped using the Illumina Bovine 50K SNP chip. The population was divided into three groups to evaluate prediction accuracy. For CWT, theoretical accuracy reached 0.76, 0.75, and 0.78 for Groups 1, 2, and 3, respectively, with realized accuracy ranging from 0.70 to 0.74, indicating a strong correlation between predicted and actual performance. For EMA, theoretical accuracy ranged from 0.74 to 0.76, while realized accuracy was lower (0.64, 0.68, 0.69), suggesting the need for improved prediction models or larger, more diverse reference populations. BF showed theoretical accuracies of 0.75, 0.75, and 0.77, with realized accuracies of 0.59, 0.62, and 0.65. MS demonstrated the highest performance, with theoretical accuracies between 0.78 and 0.81, and realized accuracies between 0.73 and 0.78, reflecting a strong genetic component in marbling traits. This study underscores the importance of building a larger, cow-specific reference population to enhance GEBV prediction accuracy and maximize genetic gains in Hanwoo cow breeding programs.</p>

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Evaluation of genomic breeding values and accuracy for carcass traits in Korean Hanwoo cows using whole-genome SNP chip panels

  • 期刊论文

  • 发表日期：

    2025年6月17日

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  • Ji-Hee Jang,
  • Han-Deul Lee,
  • Jong-Joo Kim,
  • Md Azizul Haque

摘要

Enhancing the quality and yield of Korean beef relies on improving carcass traits, including carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS). This study aimed to evaluate the accuracy of genomic EBVs for these traits using the genomic BLUP method. Phenotypic data were collected from 19,153 Hanwoo steers and 6,200 Hanwoo cows, with all animals genotyped using the Illumina Bovine 50K SNP chip. The population was divided into three groups to evaluate prediction accuracy. For CWT, theoretical accuracy reached 0.76, 0.75, and 0.78 for Groups 1, 2, and 3, respectively, with realized accuracy ranging from 0.70 to 0.74, indicating a strong correlation between predicted and actual performance. For EMA, theoretical accuracy ranged from 0.74 to 0.76, while realized accuracy was lower (0.64, 0.68, 0.69), suggesting the need for improved prediction models or larger, more diverse reference populations. BF showed theoretical accuracies of 0.75, 0.75, and 0.77, with realized accuracies of 0.59, 0.62, and 0.65. MS demonstrated the highest performance, with theoretical accuracies between 0.78 and 0.81, and realized accuracies between 0.73 and 0.78, reflecting a strong genetic component in marbling traits. This study underscores the importance of building a larger, cow-specific reference population to enhance GEBV prediction accuracy and maximize genetic gains in Hanwoo cow breeding programs.