Sun Huibang, Zheng Miaomiao, Wei Cun, Zhang Quanqi, Liu Jinxiang
MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences/Key Laboratory of Tropical Aquatic Germplasm of Hainan Province, Sanya Oceanographic Institution Ocean University of China Sanya China.
Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao Marine Science and Technology Center Qingdao China.
Evol Appl. 2025 Aug 26;18(8):e70147. doi: 10.1111/eva.70147. eCollection 2025 Aug.
Golden pompano () 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.
黄鳍鲷（）因其优良的生物学特性，是中国东南沿海快速发展的海水养殖品种。然而，与陆地家畜和作物相比，海水养殖品种的驯化历史相对较短，这表明其遗传潜力尚未得到充分挖掘。因此，海水养殖中的选择性育种为遗传改良提供了重大机遇。本研究旨在建立一个全面的基因组预测模型，以支持黄鳍鲷新的快速生长品种的选育。选择体重作为评估生长性状的主要指标。对692个样本进行全基因组测序，经过筛选后得到4886850个高质量单核苷酸多态性（SNP）。采用三种SNP选择策略评估基因组预测准确性，包括均匀法、基于全基因组关联研究（GWAS）的方法和随机法。我们解决了基于GWAS的方法中高估的问题。经过交叉验证，基于GWAS的方法在大多数SNP数据集上表现出更高的预测准确性。此外，评估了六种育种模型在基因组预测中的性能，基因组最佳线性无偏预测（GBLUP）显示出更高的预测能力。在SNP密度方面，我们确定通过均匀法选择的5000个SNP和通过基于GWAS的方法选择的7000个SNP代表了准确预测黄鳍鲷体重的最佳密度。这些发现为降低育种成本同时提高选择准确性提供了有价值的见解，为水产养殖育种计划中选择具有经济价值生长性状的黄鳍鲷提供了一种实用策略。
