Haque Md Azizul, Jang Eun-Bi, Lee Han-Deul, Shin Dae-Hyun, Jang Ji-Hee, Kim Jong-Joo
Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, 38541, Republic of Korea.
Trop Anim Health Prod. 2025 Jan 28;57(2):38. doi: 10.1007/s11250-025-04293-y.
To improve the quality and yield of the Korean beef industry, selection criteria often focus on estimated breeding values for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS). This study estimated genetic parameters and assessed the accuracy of genomic estimated breeding values (GEBVs) using SNP weighting methods. We compared the accuracy of these methods with the genomic best linear unbiased prediction (GBLUP) and various Bayesian approaches (BayesA, BayesB, BayesC, and BayesCPi) for the specified traits. The study used single-trait animal models, including GBLUP, weighted GBLUP (WGBLUP), and the Bayesian methods to predict genomic breeding values in a population of Hanwoo steers. A total of 19154 phenotypes were collected with all animals genotyped using the Illumina Bovine 50 K SNP chip. The average heritability for the carcass traits was 0.33 (GBLUP) and 0.35 (Bayesian), with Bayesian methods yielding heritability estimates that were on average 0.02 points (6.1%) higher than GBLUP. The accuracy of genomic predictions ranged from 0.7-0.83 (GBLUP), 0.83-0.87 (WGBLUP), and 0.81-0.87 across the Bayesian methods. WGBLUP accuracies for the carcass traits were, on average 8.97% higher than the GBLUP accuracies and 1.80% higher than the Bayesian alphabets. The Bayesian alphabet's accuracy is also, on average 6.00% higher than the GBLUP accuracy. According to these findings, the weighting GBLUP approach provides higher prediction accuracy for Hanwoo carcass traits than the Bayesian alphabet. Therefore, WGBLUP can be used for genomic selection in the Hanwoo evaluation program.
为提高韩国肉牛产业的质量和产量，选择标准通常侧重于胴体重（CWT）、眼肌面积（EMA）、背膘厚度（BF）和大理石花纹评分（MS）的估计育种值。本研究估计了遗传参数，并使用单核苷酸多态性（SNP）加权方法评估了基因组估计育种值（GEBV）的准确性。我们将这些方法的准确性与基因组最佳线性无偏预测（GBLUP）以及针对特定性状的各种贝叶斯方法（BayesA、BayesB、BayesC和BayesCPi）进行了比较。该研究使用单性状动物模型，包括GBLUP、加权GBLUP（WGBLUP）和贝叶斯方法，来预测韩牛公牛群体的基因组育种值。共收集了19154个表型数据，所有动物均使用Illumina牛50K SNP芯片进行基因分型。胴体性状的平均遗传力为0.33（GBLUP）和0.35（贝叶斯方法），贝叶斯方法得出的遗传力估计值平均比GBLUP高0.02个百分点（6.1%）。基因组预测的准确性在GBLUP方法中为0.7 - 0.83，在WGBLUP方法中为0.83 - 0.87，在贝叶斯方法中为0.81 - 0.87。胴体性状的WGBLUP准确性平均比GBLUP准确性高8.97%，比贝叶斯方法高1.80%。贝叶斯方法的准确性平均也比GBLUP准确性高6.00%。根据这些发现，加权GBLUP方法在韩牛胴体性状预测准确性方面高于贝叶斯方法。因此，WGBLUP可用于韩牛评估项目的基因组选择。
