Genomic heritabilities have previously been reported for the commercial traits in Nile tilapia [26,27], but these studies fail to report the predictive abilities of the genomic 3 and pedigree based models. In another study, increase in prediction accuracies was 3 indeed reported for Nile tilapia [28], based on univariate single-step GBLUP models.3 Thus, to the best of our knowledge this is the first report comparing prediction 3 accuracy using both univariate and multivariate approaches with GBLUP models and 3 pedigree-based models in Nile tilapia. Thereby, these are the first reports on 3 heritabilities and correlations using multivariate genomic models. 3 Genomic selection increases prediction accuracy in Nile tilapia 3The increase in the prediction accuracy using GBLUP models, is due to the more 3 accurate construction of the relationship matrices with better estimation of the 3 Mendelian sampling effects using genomics (Figure 4). Using PBLUP models all full-3 sibs (without own phenotype) have identical EBVs, which is the parental average. 3 Whereas, GBLUP can capture the Mendelian segregation among the full-sibs and 3 the putatively best (unphenotyped) candidates within a full-sib family can be 3 identified. This explains the very low accuracy (near to 0) in across-family cross-3 validation methods using PBLUP. Thus, the benefit of using genomics to predict the 3 breeding values is very significant for invasive traits, where the breeding values of 3 the animals in different full-sib families might have to be predicted based on 3 phenotypes on other full-sib families. For example, disease challenge test in a 3 handful of full-sib families due to expensive phenotype measurement.

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Background 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. Methods 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. Results 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. Conclusions 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.

Streptococcosis due to Streptococcus agalactiae is a major bacterial disease in Nile tilapia, and development of the resistant genetic strains can be a sustainable approach towards combating this problematic disease. Thus, a controlled disease trial was performed on 120 full-sib families to i) quantify and characterize the potential of genomic selection for S. agalactiae resistance in Nile tilapia and to ii) select the best genomic model and optimal SNP-chip for this trait.In total, 40 fish per family (15 fish intraperitoneally injected and 25 fish as cohabitants) were selected for the challenge test and mortalities recorded every 3 hours, until no mortalities occurred for a period of 3 consecutive days. Genotypes (50,690 SNPs) and phenotypes (0 for dead and 1 for alive) for 2472 cohabitant fish were available. The pedigree-based analysis utilized a deep pedigree, going 17 generations back in time. Genetic parameters were obtained using various genomic selection models (GBLUP, BayesB, BayesC, BayesR and BayesS) and traditional pedigree-based model (PBLUP). The genomic models were further analyzed using 10 different subsets of SNP-densities for optimum marker density selection. Prediction accuracy and bias were evaluated using 5 replicates of 10-fold cross-validation.Using an appropriate Bayesian genomic selection model and optimising it for SNP density increased prediction accuracy up to ∼71%, compared to a pedigree-based model. This result is encouraging for practical implementation of genomic selection for S. agalactiae resistance in Nile tilapia breeding programs.

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