Streptococcosis is a major bacterial disease in Nile tilapia that is caused by Streptococcus agalactiae infection, and development of resistant strains of Nile tilapia represents a sustainable approach towards combating this disease. In this study, we performed a controlled disease trial on 120 full-sib families to (i) quantify and characterize the potential of genomic selection for survival to S. agalactiae infection in Nile tilapia, and (ii) identify the best genomic model and the optimal density of single nucleotide polymorphisms (SNPs) for this trait. In total, 40 fish per family (15 fish intraperitoneally injected and 25 fish as cohabitants) were used in the challenge test. Mortalities were recorded every 3 h for 35 days. After quality control, genotypes (50,690 SNPs) and phenotypes (0 for dead and 1 for alive) for 2472 cohabitant fish were available. Genetic parameters were obtained using various genomic selection models (genomic best linear unbiased prediction (GBLUP), BayesB, BayesC, BayesR and BayesS) and a traditional pedigree-based model (PBLUP). The pedigree-based analysis used a deep 17-generation pedigree. Prediction accuracy and bias were evaluated using five replicates of tenfold cross-validation. The genomic models were further analyzed using 10 subsets of SNPs at different densities to explore the effect of pruning and SNP density on predictive accuracy. Moderate estimates of heritabilities ranging from 0.15 ± 0.03 to 0.26 ± 0.05 were obtained with the different models. Compared to a pedigree-based model, GBLUP (using all the SNPs) increased prediction accuracy by 15.4%. Furthermore, use of the most appropriate Bayesian genomic selection model and SNP density increased the prediction accuracy up to 71%. The 40 to 50 SNPs with non-zero effects were consistent for all BayesB, BayesC and BayesS models with respect to marker id and/or marker locations. These results demonstrate the potential of genomic selection for survival to S. agalactiae infection in Nile tilapia. Compared to the PBLUP and GBLUP models, Bayesian genomic models were found to boost the prediction accuracy significantly.

中文翻译：

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贝叶斯基因组模型提高了尼罗罗非鱼（Oreochromus nilioticus）对无乳链球菌感染存活率的预测准确性

链球菌病是尼罗罗非鱼的主要细菌性疾病，由无乳链球菌感染引起，尼罗罗非鱼耐药菌株的开发代表了对抗这种疾病的可持续方法。在这项研究中，我们对 120 个全同胞家族进行了一项受控疾病试验，以 (i) 量化和表征基因组选择对尼罗罗非鱼无乳链球菌感染存活的潜力，以及 (ii) 确定最佳基因组模型和此性状的最佳单核苷酸多态性 (SNP) 密度。在挑战测试中，每个家庭总共使用 40 条鱼（15 条腹腔注射鱼和 25 条鱼作为同居者）。在 35 天内每 3 小时记录一次死亡率。质量控制后，基因型（50, 690 个 SNP）和 2472 条同居鱼的表型（0 表示死亡，1 表示活着）。使用各种基因组选择模型（基因组最佳线性无偏预测 (GBLUP)、BayesB、BayesC、BayesR 和 BayesS）和传统的基于谱系的模型 (PBLUP) 获得遗传参数。基于谱系的分析使用了深度的 17 代谱系。使用十倍交叉验证的五次重复评估预测准确性和偏差。使用 10 个不同密度的 SNP 子集进一步分析了基因组模型，以探索修剪和 SNP 密度对预测准确性的影响。使用不同模型获得了介于 0.15 ± 0.03 至 0.26 ± 0.05 之间的中等遗传力估计值。与基于谱系的模型相比，GBLUP（使用所有 SNP）将预测准确度提高了 15.4%。此外，使用最合适的贝叶斯基因组选择模型和 SNP 密度将预测准确度提高到 71%。具有非零效应的 40 到 50 个 SNP 对于所有 BayesB、BayesC 和 BayesS 模型在标记 id 和/或标记位置方面是一致的。这些结果证明了基因组选择对于尼罗罗非鱼中无乳链球菌感染存活的潜力。与 PBLUP 和 GBLUP 模型相比，发现贝叶斯基因组模型显着提高了预测精度。尼罗罗非鱼的无乳杆菌感染。与 PBLUP 和 GBLUP 模型相比，发现贝叶斯基因组模型显着提高了预测精度。尼罗罗非鱼的无乳杆菌感染。与 PBLUP 和 GBLUP 模型相比，发现贝叶斯基因组模型显着提高了预测精度。