Bayesian genomic models boost prediction accuracy for resistance againstStreptococcus agalactiaein Nile tilapia (Oreochromus nilioticus)

Abstract

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.

Experimental validation of genetic selection for resistance againstStreptococcus agalactiaevia different routes of infection in the commercial Nile tilapia breeding programme

“…Although intraperitoneal route bypassed the natural immune barriers, it demonstrated higher additive genetic variation and heritability, compared to the cohabitation route of infection. The heritability for S. agalactiae resistance in Nile tilapia has been estimated previously by various authors using both pedigree (LaFrentz et al., 2016; Lin, 2016; Craig A Shoemaker, et al., 2017; Sukhavachana et al., 2019) and genomics (Joshi et al., 2020). These estimates vary from 0.13 to 0.32 and provide enough evidence for the possibility of genetic selection for Streptococcus resistance, but the genetic correlation of ~0 between S. agalactiae and S. iniae (Shoemaker, et al., 2017) calls for specialised challenge tests for each strain.…”

“…The study was conducted using two lines of the commercial GenoMar Supreme Tilapia (GST®) breeding nucleus in the Philippines. The “selected line” comprised the fish selected for Streptococcus agalactiae [Ib serotype] (Joshi et al., 2020) resistance. Top animals were selected based on estimated breeding values (EBVs) for S. agalactiae resistance from the breeding nucleus for one generation (Lin, 2016) and randomly mated for 2 generations to produce the selected line.…”

Optimizing genomic prediction using low‐density marker panels for streptococcosis resistance in red tilapia (Oreochromis spp.)

“…Although prediction models such as GBLUP and Bayesian alphabet have been considered for genomic prediction of streptococcosis resistance in Nile tilapia (Joshi et al . 2020; Lu et al . 2020), the genetic architecture may not be the same in red tilapia.…”

Genomics to accelerate genetic improvement in tilapia

“…a heritability of 0.27 was estimated by Sukhavachana et al (2019). Joshi et al, (2020b) reported genomic heritabilities for resistance against S. agalactiae in Nile tilapia ranging from 0.15 to 0.26. Significant genetic variation has also been detected for resistance against Flavobacterium columnare (Wonmongkol et al, 2018) and Streptococcus iniae (LaFrentz et al, 2016;Shoemaker et al, 2017), indicating the potential for improving survival against bacterial outbreaks by using artificial selection.…”

“…Furthermore, univariate and multivariate genomic selection approaches have also been tested using dense SNP genotypes for growth-and disease resistancerelated traits (e.g. Streptococcus resistance), showing an increase in prediction accuracy, when compared with conventional pedigree-based genetic evaluation methods (Joshi et al, 2020c(Joshi et al, , 2019. These results indicate the benefits of genomic evaluations for the genetic improvement of these populations.…”