Han Chengming, Zhu Linxi, Wang Mengdie, Hu Jian, Yang Qinglei, Liu Zhenlin, Zhou Zhengkui, Li Cong, Hou Shuisheng, Cai Wentao
Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; College of Animal Science and Technology, Northwest A&F University, 712100, China.
Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; College of Animal Science and Technology, Shanxi Agricultural University, 030031, China.
Poult Sci. 2025 Jul 3;104(10):105510. doi: 10.1016/j.psj.2025.105510.
Egg production traits are critical in duck breeding. Although genomic selection (GS) has been widely applied in livestock breeding, limited research has focused on predicting duck egg production traits, particularly across different physiological stages. In this study, we systematically explored the genetic parameters of egg production traits and evaluated the performance of GS in a commercial Pekin duck population. Analysis of 8,455 laying ducks showed that the heritability of cumulative egg production at 210, 280, and 300 days was 0.35, 0.37, and 0.48, respectively. Heritability during the early, peak, and post stages of cumulative egg production was 0.32, 0.15, and 0.24. The heritability of the egg laying rate from 25 to 60 weeks was 0.25. The cumulative egg production traits exhibited very strong mutual genetic correlations (≥ 0.95), while genetic correlations among egg laying stage traits ranged from 0.03 to 0.74. In the comparison between the pedigree-based best linear unbiased prediction (BLUP) and the genomic BLUP (GBLUP), using five-fold cross-validation, GBLUP outperformed the traditional pedigree BLUP model, with an average predictive reliability of 0.154, which was 0.029 higher than the predictive reliability of BLUP. In forward prediction, GBLUP also outperformed BLUP for all traits, with an average reliability of 0.097, which was 0.111 higher than the predictive reliability of BLUP. We also assessed the impact of linkage disequilibrium (LD) filtering on predictive reliability, which improved predictive reliability by 0.022 when the LD threshold was set to 0.14. In the comparison between GBLUP and Bayesian models utilizing a genotype with an LD pruning threshold of 0.14, GBLUP showed higher reliability than BayesB and BayesN in five-fold cross-validation, but lower than BayesCπ. In forward prediction, GBLUP demonstrated more robust performance, outperforming BayesB, BayesCπ, and BayesN, with improvements of 0.03, 0.019, and 0.05, respectively. This study provides a foundation for the application of GS in duck egg production and offers practical insights for improving predictive reliability in egg production.
产蛋性状在鸭育种中至关重要。尽管基因组选择(GS)已在畜禽育种中广泛应用,但针对鸭蛋生产性状预测的研究有限,尤其是跨不同生理阶段的预测。在本研究中,我们系统地探究了产蛋性状的遗传参数,并评估了GS在一个商业北京鸭群体中的表现。对8455只产蛋鸭的分析表明,210天、280天和300天累计产蛋量的遗传力分别为0.35、0.37和0.48。累计产蛋早期、高峰期和后期的遗传力分别为0.32、0.15和0.24。25至60周龄产蛋率的遗传力为0.25。累计产蛋性状表现出非常强的相互遗传相关性(≥0.95),而产蛋阶段性状之间的遗传相关性范围为0.03至0.74。在基于系谱的最佳线性无偏预测(BLUP)和基因组BLUP(GBLUP)的比较中,采用五倍交叉验证,GBLUP的表现优于传统的系谱BLUP模型,平均预测可靠性为0.154,比BLUP的预测可靠性高0.029。在向前预测中,GBLUP在所有性状上也优于BLUP,平均可靠性为0.097,比BLUP的预测可靠性高0.111。我们还评估了连锁不平衡(LD)过滤对预测可靠性的影响,当将LD阈值设置为0.14时,预测可靠性提高了0.022。在使用LD修剪阈值为0.14的基因型的GBLUP和贝叶斯模型的比较中,在五倍交叉验证中,GBLUP的可靠性高于BayesB和BayesN,但低于BayesCπ。在向前预测中,GBLUP表现出更强的性能,优于BayesB、BayesCπ和BayesN,分别提高了0.03、0.019和0.05。本研究为GS在鸭蛋生产中的应用提供了基础,并为提高产蛋预测可靠性提供了实用见解。