Genomic selection using a subset of SNPs identified by genome-wide association analysis for disease resistance traits in aquaculture species

Abstract Genomic selection (GS) has been proved to be a useful method for selective breeding. However, the cost for genotyping a large amount of SNPs is too expensive, especially for aquaculture species with a low individual commodity value. In this study, we proposed a strategy for GS using a subset of markers selected by genome wide association studies (GWAS), and evaluated the prediction accuracy in different aquaculture species. The results showed that the prediction accuracy using SNPs selected by GWAS was higher than using total SNPs, and the BayesB model presented a better performance than GBLUP model. The optimal SNP numbers used for GS were varied for different traits and species. The proposed strategy of GS in the present study could not only reduce the genotyping cost, but also improve the prediction accuracy of GS, which will be very helpful to accelerate the application of GS for disease resistance in aquaculture species.