Genetic parameters and genomic prediction of growth and breast morphological traits in a crossbreed duck population

文献类型： 外文期刊

第一作者： Cai, Wentao

作者： Cai, Wentao;Hu, Jian;Fan, Wenlei;Tang, Jing;Xie, Ming;Zhang, Yunsheng;Guo, Zhanbao;Zhou, Zhengkui;Hou, Shuisheng;Fan, Wenlei;Xu, Yaxi

作者机构：

关键词： Bayesian models; genetic correlation; genomic selection; heritability; single-step

期刊名称：EVOLUTIONARY APPLICATIONS （ 影响因子：4.1； 五年影响因子：4.3 ）

ISSN： 1752-4571

年卷期： 2024 年 17 卷 2 期

页码：

收录情况： SCI

摘要： 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 x 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.

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