Genomic Prediction for Growth-Related Traits in Golden Pompano (Trachinotus ovatus)

Abstract

Golden pompano (Trachinotus ovatus) is a rapidly growing marine aquaculture species along the southeast coast of China due to its favorable biological traits. However, the relatively short domestication history of marine species compared to terrestrial livestock and crops indicates untapped genetic potential. Therefore, selective breeding in marine aquaculture presents a significant opportunity for genetic improvement. This study aimed to establish a comprehensive genomic prediction to support the selection of new fast-growing varieties of golden pompano. Body weight was selected as the primary trait for evaluating growth traits. Whole-genome sequencing was performed on 692 samples, resulting in 4,886,850 high-quality SNPs after filtering. Three SNP selection strategies were used for evaluating the genomic prediction accuracy, including the Evenly method, GWAS-based method, and Random method. We addressed the issue of overestimation in the GWAS-based method. After implementing cross-validation, the GWAS-based method demonstrated superior predictive accuracy across most SNP sets. Additionally, six breeding models were evaluated for their performance in genomic prediction, with GBLUP showing higher predictive ability. In terms of SNP density, we determined that 5000 SNPs selected via the Evenly method and 7000 SNPs selected via the GWAS-based method represent optimal densities for accurately predicting body weight in golden pompano. These findings provide valuable insights for reducing breeding costs while improving selection accuracy, providing a practical strategy for the selection of golden pompano with economically valuable growth traits in aquaculture breeding programs.