LAUSR

Abstract Streptococcosis is a major disease that causes severe mortality in tilapia aquaculture worldwide. Although the conventional BLUP family selection to enhance disease resistance in a commercial red tilapia stock was successful, the response was low due to the low heritability of the traits. An alternative strategy is the utilization of genomic information to identify the best performing candidates within families. In this study, we performed genome-wide association studies for red tilapia resistance to Streptococcus agalactiae using 11,480 SNPs within 110 families represented by 1020 fish. Nineteen SNP markers were found to explain ~10% of the genetic variation. We compared the accuracies of genomic prediction using the pedigree-based (PBLUP), marker-based (GBLUP), and Bayesian models. The prediction accuracy was assessed by performing ten replicates of five-fold cross-validation. In each replicate, approximately 80% of the data (n~816) were sampled for the training set and the remaining data (n~204) were used for the validation. The BayesB model yielded the highest accuracies (0.31 and 0.20) followed by GBLUP (0.25 and 0.15) and PBLUP (0.15 and 0.06) for days to death and binary trait.