Evaluation of machine learning method in genomic selection for growth traits of Pacific white shrimp | |
Luo, Zheng1,3; Yu, Yang3; Bao, Zhenning1,3; Li, Fuhua2,3 | |
刊名 | AQUACULTURE |
2024-02-25 | |
卷号 | 581页码:9 |
关键词 | Growth traits Genomic selection Auto-machine learning Prediction accuracy Litopeneaus vannamei |
ISSN号 | 0044-8486 |
DOI | 10.1016/j.aquaculture.2023.740376 |
通讯作者 | Yu, Yang(yuyang@qdio.ac.cn) |
英文摘要 | 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. |
资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences[XDA24030105] ; National Key R & D Program of China[2022YFD2400203] ; Shandong Provincial Natural Science Foundation[ZR2020MC191] ; Taishan Scholars Program ; Key Research and Development Program of Shandong[2021LZGC029] ; Earmarked fund for CARS-48 |
WOS关键词 | LITOPENAEUS-VANNAMEI ; BODY-WEIGHT ; PREDICTION ; HERITABILITY ; RESISTANCE ; SIZE |
WOS研究方向 | Fisheries ; Marine & Freshwater Biology |
语种 | 英语 |
出版者 | ELSEVIER |
WOS记录号 | WOS:001127025700001 |
内容类型 | 期刊论文 |
源URL | [http://ir.qdio.ac.cn/handle/337002/184182] |
专题 | 海洋研究所_实验海洋生物学重点实验室 |
通讯作者 | Yu, Yang |
作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Innovat Seed Design, Wuhan 430072, Peoples R China 3.Chinese Acad Sci, Inst Oceanol, CAS & Shandong Prov Key Lab Expt Marine Biol, Qingdao 266071, Peoples R China |
推荐引用方式 GB/T 7714 | Luo, Zheng,Yu, Yang,Bao, Zhenning,et al. Evaluation of machine learning method in genomic selection for growth traits of Pacific white shrimp[J]. AQUACULTURE,2024,581:9. |
APA | Luo, Zheng,Yu, Yang,Bao, Zhenning,&Li, Fuhua.(2024).Evaluation of machine learning method in genomic selection for growth traits of Pacific white shrimp.AQUACULTURE,581,9. |
MLA | Luo, Zheng,et al."Evaluation of machine learning method in genomic selection for growth traits of Pacific white shrimp".AQUACULTURE 581(2024):9. |
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