Cai Wentao, Hu Jian, Fan Wenlei, Xu Yaxi, Tang Jing, Xie Ming, Zhang Yunsheng, Guo Zhanbao, Zhou Zhengkui, Hou Shuisheng
Institute of Animal Science Chinese Academy of Agricultural Sciences Beijing China.
College of Animal Science and Technology Qingdao Agricultural University Qingdao China.
Evol Appl. 2024 Feb 7;17(2):e13638. doi: 10.1111/eva.13638. eCollection 2024 Feb.
Genomic selection (GS) has great potential to increase genetic gain in poultry breeding. However, the performance of genomic prediction in duck growth and breast morphological (BM) traits remains largely unknown. The objective of this study was to evaluate the benefits of genomic prediction for duck growth and BM traits using methods such as GBLUP, single-step GBLUP, Bayesian models, and different marker densities. This study collected phenotypic data for 14 growth and BM traits in a crossbreed population of 1893 Pekin duck × mallard, which included 941 genotyped ducks. The estimation of genetic parameters indicated high heritabilities for body weight (0.54-0.72), whereas moderate-to-high heritabilities for average daily gain (0.21-0.57) traits. The heritabilities of BM traits ranged from low to moderate (0.18-0.39). The prediction ability of GS on growth and BM traits increased by 7.6% on average compared to the pedigree-based BLUP method. The single-step GBLUP outperformed GBLUP in most traits with an average of 0.3% higher reliability in our study. Most of the Bayesian models had better performance on predictive reliability, except for BayesR. BayesN emerged as the top-performing model for genomic prediction of both growth and BM traits, exhibiting an average increase in reliability of 3.0% compared to GBLUP. The permutation studies revealed that 50 K markers had achieved ideal prediction reliability, while 3 K markers still achieved 90.8% predictive capability would further reduce the cost for duck growth and BM traits. This study provides promising evidence for the application of GS in improving duck growth and BM traits. Our findings offer some useful strategies for optimizing the predictive ability of GS in growth and BM traits and provide theoretical foundations for designing a low-density panel in ducks.
基因组选择（GS）在提高家禽育种的遗传进展方面具有巨大潜力。然而，基因组预测在鸭生长和胸肌形态（BM）性状上的表现仍 largely 未知。本研究的目的是使用诸如基因组最佳线性无偏预测（GBLUP）、一步法 GBLUP、贝叶斯模型和不同标记密度等方法，评估基因组预测对鸭生长和 BM 性状的益处。本研究收集了 1893 只北京鸭×绿头鸭杂交群体中 14 个生长和 BM 性状的表型数据，其中包括 941 只基因分型鸭。遗传参数估计表明体重的遗传力较高（0.54 - 0.72），而平均日增重性状的遗传力为中等至高（0.21 - 0.57）。BM 性状的遗传力范围从低到中等（0.18 - 0.39）。与基于系谱的最佳线性无偏预测（BLUP）方法相比，GS 对生长和 BM 性状的预测能力平均提高了 7.6%。在我们的研究中，一步法 GBLUP 在大多数性状上优于 GBLUP，可靠性平均高 0.3%。除了贝叶斯 R（BayesR）外，大多数贝叶斯模型在预测可靠性方面表现更好。贝叶斯 N（BayesN）成为生长和 BM 性状基因组预测的最佳模型，与 GBLUP 相比，可靠性平均提高了 3.0%。排列研究表明，50K 标记已实现理想的预测可靠性，而 3K 标记仍实现了 90.8%的预测能力，这将进一步降低鸭生长和 BM 性状的成本。本研究为 GS 在改善鸭生长和 BM 性状方面的应用提供了有前景的证据。我们的研究结果为优化 GS 在生长和 BM 性状上的预测能力提供了一些有用的策略，并为设计鸭的低密度基因分型芯片提供了理论基础。
