The Japanese flounder is one of the most widely farmed economic flatfish species throughout eastern Asia including China, Korea, and Japan. Edwardsiella tarda is a major species of pathogenic bacteria that causes ascites disease and, consequently, a huge economy loss for Japanese flounder farming. After generation selection, traditional breeding methods can hardly improve the E. tarda resistance effectively. Genomic selection is an effective way to predict the breeding potential of parents and has rarely been used in aquatic breeding. In this study, we chose 931 individuals from 90 families, challenged by E. tarda from 2013 to 2015 as a reference population and 71 parents of these families as selection candidates. 1,934,475 markers were detected via genome sequencing and applied in this study. Two different methods, BayesCπ and GBLUP, were used for genomic prediction. In the reference population, two methods led to the same accuracy (0.946) and Pearson's correlation results between phenotype and genomic estimated breeding value (GEBV) of BayesCπ and GBLUP were 0.912 and 0.761, respectively. In selection candidates, GEBVs from two methods were highly similar (0.980). A comparison of GEBV with the survival rate of families that were structured by selection candidates showed correlations of 0.662 and 0.665, respectively. This study established a genomic selection method for the Japanese flounder and for the first time applied this to E. tarda resistance breeding.
Keywords: BayesCπ; Edwardsiella tarda; GBLUP; Genomic selection; Japanese flounder.