Bayesian genomic models boost prediction accuracy for survival to Streptococcus agalactiae infection in Nile tilapia (Oreochromus nilioticus).

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.

Economic appraisal of using genetics to control Streptococcus agalactiae in Nile tilapia under cage and pond farming system in Malaysia.

Disease outbreaks have been seen as the major threat to sustainable aquaculture worldwide. Injectable vaccines have been one of the few strategies available to control the diseases, however, the adoption of this technology globally is limited. Genetic selection for disease resistance has been proposed as the alternative strategy in livestock and aquaculture. Economic analysis for such strategies is lacking and this study assesses the economic worth of using tilapia fingerlings resistant to Streptococcosis in both cage and pond production systems. The paper also assesses the profitability of paying the higher price for such fingerlings. Partial-budgeting was used to develop a stochastic simulation model that considers the benefits and costs associated with the adoption of tilapia fingerlings resistant to Streptococcosis at the farm level, in one production cycle. In both ponds and cage production systems, the use of genetically selected Streptococcus resistant tilapia fingerlings was found to be profitable where Streptococcus infection is prevalent. In the cages and ponds where Streptococcus related mortality was ≥ 10%, the Nile tilapia aquaculture was found to be profitable even if the amount paid for genetically selected Streptococcus resistant tilapia fingerlings was 100% higher than the amount paid for standard fingerlings.

Development and Validation of 58K SNP-Array and High-Density Linkage Map in Nile Tilapia (O. niloticus).

Despite being the second most important aquaculture species in the world accounting for 7.4% of global production in 2015, tilapia aquaculture has lacked genomic tools like SNP-arrays and high-density linkage maps to improve selection accuracy and accelerate genetic progress. In this paper, we describe the development of a genotyping array containing more than 58,000 SNPs for Nile tilapia (Oreochromis niloticus). SNPs were identified from whole genome resequencing of 32 individuals from the commercial population of the Genomar strain, and were selected for the SNP-array based on polymorphic information content and physical distribution across the genome using the Orenil1.1 genome assembly as reference sequence. SNP-performance was evaluated by genotyping 4991 individuals, including 689 offspring belonging to 41 full-sib families, which revealed high-quality genotype data for 43,588 SNPs. A preliminary genetic linkage map was constructed using Lepmap2 which in turn was integrated with information from the O_niloticus_UMD1 genome assembly to produce an integrated physical and genetic linkage map comprising 40,186 SNPs distributed across 22 linkage groups (LGs). Around one-third of the LGs showed a different recombination rate between sexes, with the female being greater than the male map by a factor of 1.2 (1632.9 to 1359.6 cM, respectively), with most LGs displaying a sigmoid recombination profile. Finally, the sex-determining locus was mapped to position 40.53 cM on LG23, in the vicinity of the anti-Müllerian hormone (amh) gene. These new resources has the potential to greatly influence and improve the genetic gain when applying genomic selection and surpass the difficulties of efficient selection for invasively measured traits in Nile tilapia.