The Accuracy Of Genomic Predictions For Bacterial Cold Water Disease Resistance Remains Higher Than The Pedigree-Based Model One Generation After Model Training In A Commercial Rainbow Trout Breeding Population

Bacterial cold water disease (BCWD) causes significant mortality and economic losses in salmonid aquaculture. Previously, we reported high genomic prediction accuracy of 0.72 for BCWD resistance in rainbow trout using the 57 K Axiom SNP array. Due to the high cost of phenotyping and genotyping in rainbow trout breeding programs, it is paramount to know if acceptable genomic prediction accuracy can be obtained in the subsequent generation without retraining the prediction model. In the current study, we found that the accuracy of genomic prediction without model retraining in the subsequent generation was reduced from 0.65 and 0.61 to 0.56 and 0.53 using a lower density array of 10 K SNPs and a panel of 49 QTL-linked SNPs, respectively. Although markedly lower than the genomic prediction with model retraining, the prediction accuracy without retraining was better than the pedigree-based model (PBLUP) with retraining (0.48) and substantially higher than the PBLUP model accuracy without retraining (0.22). We conclude that genomic selection provides better prediction accuracy than the traditional PBLUP model even when 'skipping' one generation of model retraining. The weighted single-step GBLUP (wssGBLUP) and single-step Bayesian multiple regression BayesB (ssBMR-BayesB) had higher genomic prediction accuracy than single-step GBLUP (ssGBLUP), which is consistent with the oligogenic inheritance of BCWD resistance in this population. Imputation from the 10 K array genotypes back to the 57 K array genotypes did not improve the accuracy of genomic prediction, likely due to the high linkage disequilibrium in rainbow trout aquaculture breeding populations and the oligogenic architecture of BCWD resistance.