Evaluation of machine learning method in genomic selection for growth traits of Pacific white shrimp

doi:10.1016/j.aquaculture.2023.740376

IOCAS-IR > 实验海洋生物学重点实验室

	Evaluation of machine learning method in genomic selection for growth traits of Pacific white shrimp
	Luo, Zheng1,2 ; Yu, Yang1 ; Bao, Zhenning 1,2; Li, Fuhua 1,3
	2024-02-25
发表期刊	AQUACULTURE
ISSN	0044-8486
卷号	581 页码:9
通讯作者	Yu, Yang([email protected])
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.
关键词	Growth traits Genomic selection Auto-machine learning Prediction accuracy Litopeneaus vannamei
DOI	10.1016/j.aquaculture.2023.740376
收录类别	SCI
语种	英语
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研究方向	Fisheries ; Marine & Freshwater Biology
WOS类目	Fisheries ; Marine & Freshwater Biology
WOS记录号	WOS:001127025700001
出版者	ELSEVIER
WOS关键词	LITOPENAEUS-VANNAMEI ; BODY-WEIGHT ; PREDICTION ; HERITABILITY ; RESISTANCE ; SIZE
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/184183
专题	实验海洋生物学重点实验室
通讯作者	Yu, Yang
作者单位	1.Chinese Acad Sci, Inst Oceanol, CAS & Shandong Prov Key Lab Expt Marine Biol, Qingdao 266071, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Innovat Seed Design, Wuhan 430072, 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.