Evaluation of genomic prediction of resistance to visceral white-spot disease in large yellow croaker (Larimichthys crocea)

• Published in: Aquaculture Vol. 599; p. 742114
• Main Authors: Yuan, Yingbo, Zhou, Meng, Li, Zeyu, Jiang, Dan, Wang, Zhiyong, Fang, Ming
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.04.2025
• Subjects: Genomic selection; Large yellow croaker; Visceral white-nodules disease; Genomic selection; Visceral white-nodules disease; Large yellow croaker
• ISSN: 0044-8486
• DOI: 10.1016/j.aquaculture.2024.742114

Visceral white-nodules disease (VWND) causes significant economic losses in L. crocea farming. Selection and breeding for disease resistance is a useful tool in preventing or reducing disease outbreaks, and genomic selection (GS) is an effective method that uses genome-wide markers and phenotype information to predict genomic estimated breeding values (GEBVs) of parent fish. In this study, we evaluated the effect of the genomic selection with 600 L. crocea challenged with Pseudomonas plecoglossicida based on binary survival (BS) and time to death (DT) phenotypes as well as 10,586,121 high quality SNPs. Four statistical models (BayesA, BayesB, BayesCπ, and GBLUP) were used to explore the feasibility of genomic selection for genetic improvement of VWND in L. crocea. The accuracy of the different GS methods was assessed using a five-fold cross-validation scheme with different schemes, including SNP densities, sample sizes, and case-control ratio. The results showed that the heritability estimates ranged from 0.46 to 0.69; the four models performed similarly in predicting the genomic estimated breeding value (GEBV) when the SNP density reached 50 K, and the prediction accuracy reached stability (0.22–0.32) and BayesB was more sensitive to decreasing SNP density than the other three models. We also found that the case-control ratio had a significant impact on the design of the case-control experiment, and the prediction accuracy of GEBV was highest when the number of cases was equal to the control ratio (0.24–0.27). We systemically assessed the accuracy of GEBV and explored the potential of GS in improving resistance to VWND in L. crocea, which will lay the foundation for optimizing experiment design for GS of VWND in L. crocea. •BayesB was more sensitive to decreasing SNP density than the other three models.•The prediction accuracy of GEBV was highest when the number of cases was equal to the control ratio.•The result of present study will theoretical guide the application of using GS for genetic improvement of VWND in Larimichthys crocea.