We compared emBBI with the established FASTmrEMMA method in both simulation experiments and real data analysis. In the first simulation, the power of emBBI and FASTmrEMMA method in the detection of the fixed QTN are almost $100\%$ under three polygenic background, which indicates that these two methods easily capture the information. FASTmrEMMA has the fast computational speed, which is nearly two times more than the FASTmrEMMA algorithm. The accuracy of emBBI is slightly better than FASTmrEMMA under different polygenic background. In the second simulation, the average power of emBBI in the detection of the random position QTNs is $91\%,$ FASTmrEMMA is slightly well performance $93\%,$ both of them are weaker than simulation I. The computational time and accuracy the similar tendency with simulation I. The gap becomes increasingly high along with the stronger polygenic background. In the real data analysis, FASTmrEMMA method detects $11$ known genes for SDV$,$ which is less than emBBI $(12$ known genes$).$ Most genes are located in different sequences. The main reason is: although both emBBI and FASTmrEMMA method have polygenic correction stage to white the noise, the following algorithms are different. FASTmrEMMA is a combination of single$-$locus and multi$-$locus analysis, while emBBI employs the least squares method to reduce the dimensions and then estimates and tests by emBayesB and LOD test, respectively. We have revised this part and added more details in discussion section.