Fisheries Science Institute, Beijing Academy of Agriculture and Forestry Sciences & Beijing Key Laboratory of Fisheries Biotechnology, Beijing 100068, China.
Key Laboratory of Sturgeon Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Hangzhou 311799, China.
Int J Mol Sci. 2024 Sep 9;25(17):9756. doi: 10.3390/ijms25179756.
Caviar yield, caviar color, and body weight are crucial economic traits in sturgeon breeding. Understanding the molecular mechanisms behind these traits is essential for their genetic improvement. In this study, we performed whole-genome sequencing on 673 Russian sturgeons, renowned for their high-quality caviar. With an average sequencing depth of 13.69×, we obtained approximately 10.41 million high-quality single nucleotide polymorphisms (SNPs). Using a genome-wide association study (GWAS) with a single-marker regression model, we identified SNPs and genes associated with these traits. Our findings revealed several candidate genes for each trait: caviar yield: , , , , , , , , , and ; caviar color: , , , , , and ; body weight: , , , , , , , , , , , , , , and . Additionally, using the genomic feature BLUP (GFBLUP) method, which combines linkage disequilibrium (LD) pruning markers with GWAS prior information, we improved genomic prediction accuracy by 2%, 1.9%, and 3.1% for caviar yield, caviar color, and body weight traits, respectively, compared to the GBLUP method. In conclusion, this study enhances our understanding of the genetic mechanisms underlying caviar yield, caviar color, and body weight traits in sturgeons, providing opportunities for genetic improvement of these traits through genomic selection.
鱼子酱产量、鱼子酱颜色和体重是鲟鱼养殖中的重要经济性状。了解这些性状背后的分子机制对于它们的遗传改良至关重要。在这项研究中，我们对 673 条俄罗斯鲟鱼进行了全基因组测序，这些鲟鱼以其高品质的鱼子酱而闻名。平均测序深度为 13.69×，我们获得了大约 1041 万个高质量的单核苷酸多态性（SNP）。我们使用基于单标记回归模型的全基因组关联研究（GWAS），鉴定了与这些性状相关的 SNP 和基因。我们的研究结果揭示了每个性状的几个候选基因：鱼子酱产量： 、 、 、 、 、 、 、 、 ；鱼子酱颜色： 、 、 、 、 、 ；体重： 、 、 、 、 、 、 、 、 、 、 、 、 、 。此外，使用基因组特征 BLUP（GFBLUP）方法，该方法将连锁不平衡（LD）剔除标记与 GWAS 先验信息相结合，与 GBLUP 方法相比，我们分别将鱼子酱产量、鱼子酱颜色和体重性状的基因组预测准确性提高了 2%、1.9%和 3.1%。总之，这项研究增进了我们对鲟鱼鱼子酱产量、鱼子酱颜色和体重性状遗传机制的理解，为通过基因组选择对这些性状进行遗传改良提供了机会。
