Wentao Cai;Jianguo Hu;Wenlei Fan;Yaxi Xu;Jing Tang;Ming Xie;Yunsheng Zhang;Zhanbao Guo;Zhengkui Zhou;Shuisheng Ho

Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, Chin; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China;College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China;College of Animal Science and Technology, Beijing University of Agriculture, Beijing, China;Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China; Shandong New Hope Liuhe Group Co., Ltd., Qingdao, China

Bayesian model;Carcass traits;Duck;Genome prediction;Genomic relationship matrix;Mark densit

BACKGROUND: Carcass traits are crucial for broiler ducks, but carcass traits can only be measured postmortem. Genomic selection (GS) is an effective approach in animal breeding to improve selection and reduce costs. However, the performance of genomic prediction in duck carcass traits remains largely unknown. RESULTS: In this study, we estimated the genetic parameters, performed GS using different models and marker densities, and compared the estimation performance between GS and conventional BLUP on 35 carcass traits in an F(2) population of ducks. Most of the cut weight traits and intestine length traits were estimated to be high and moderate heritabilities, respectively, while the heritabilities of percentage slaughter traits were dynamic. The reliability of genome prediction using GBLUP increased by an average of 0.06 compared to the conventional BLUP method. The Permutation studies revealed that 50K markers had achieved ideal prediction reliability, while 3K markers still achieved 90.7% predictive capability would further reduce the cost for duck carcass traits. The genomic relationship matrix normalized by our true variance method instead of the widely used [Formula: see text] could achieve an increase in prediction reliability in most traits. We detected most of the bayesian models had a better performance, especially for BayesN. Compared to GBLUP, BayesN can further improve the predictive reliability with an average of 0.06 for duck carcass traits. CONCLUSION: This study demonstrates genomic selection for duck carcass traits is promising. The genomic prediction can be further improved by modifying the genomic relationship matrix using our proposed true variance method and several Bayesian models. Permutation study provides a theoretical basis for the fact that low-density arrays can be used to reduce genotype costs in duck genome selection. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40104-023-00875-8.