Background One of the most important goals for the rainbow trout aquaculture industry is to improve muscle yield and llet quality. Previously, we showed that a 50K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with muscle yield and llet rmness. In this study, data from 1,568 sh genotyped for the 50K transcribed-SNP chip and~ 774 sh phenotyped for muscle yield and llet rmness were used in a single-step genomic BLUP (ssGBLUP) model to compute the genomic estimated breeding values (GEBV). In addition, pedigree-based best linear unbiased prediction (PBLUP) was used to calculate traditional, family-based estimated breeding values (EBV). Results The genomic predictions outperformed the traditional EBV by 35% for muscle yield and 42% for llet rmness. The predictive ability for muscle yield and llet rmness was 0.19-0.20 with PBLUP, and 0.27 with ssGBLUP. Additionally, reducing SNP panel densities indicated that using 500–800 SNPs in genomic predictions still provides predictive abilities higher than PBLUP.