外文摘要：The Pacific white shrimp is one of the most important species in the aquaculture industry worldwide, and the growth is regarded as primary trait for selective breeding programmes. In this study, the heritability and genetic correlation of two growth traits, including body length (BL) and the ratio of abdomen length to cephalothorax length (AL/CL) were analyzed, and the genomic prediction based on different genomic selection models including machine learning method were evaluated. The heritabilities of BL and AL/CL were 0.25 +/- 0.04 and 0.07 +/- 0.03, respectively. The two phenotypes showed moderate negative correlations (-0.70 +/- 0.14). Com-parison of the different prediction models showed that NeuralNet had the highest prediction accuracy. The prediction accuracy of NeuralNet increased by about 10% compared to GBLUP. Furthermore, NeuralNet pre-sented the highest prediction accuracy under different marker densities, and the prediction accuracy using 1000 SNPs was similar to that estimated by total SNPs. When comparing multi-trait models (MTM) and single-trait models (STM), NeuralNet outperformed the other methods, which increased prediction accuracy by around 30%. Overall, the NeuralNet model may have better application prospects for genomic selection breeding in shrimp. These results provide a strong basis for accelerating the application of genomic selection breeding in shrimp improvement programmes.
外文关键词：genomic selection;Growth traits;Prediction accuracy;Auto-machine learning;Litopeneaus vannamei
作者：Yu, Yang;Luo, Zheng;Bao, Zhenning;Li, Fuhua
作者单位：Chinese Acad Sci;Univ Chinese Acad Sci
期刊名称：AQUACULTURE
期刊影响因子：0.0
出版年份：2024
出版刊次：581
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台