Genomic predictions for fillet yield and firmness in rainbow trout using reduced-density SNP panels

BackgroundOne of the most important goals for the rainbow trout aquaculture industry is to improve fillet yield and fillet quality. Previously, we showed that a 50K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with fillet yield and fillet firmness. In this study, data from 1568 fish genotyped for the 50K transcribed-SNP chip and similar to 774 fish phenotyped for fillet yield and fillet firmness 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).ResultsThe genomic predictions outperformed the traditional EBV by 35% for fillet yield and 42% for fillet firmness. The predictive ability for fillet yield and fillet firmness 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.ConclusionThese results suggest that genomic evaluation is a feasible strategy to identify and select fish with superior genetic merit within rainbow trout families, even with low-density SNP panels.

Automated summary

Genomic evaluation is a feasible strategy to improve fillet yield and quality in rainbow trout, outperforming traditional family-based breeding values. Furthermore, even with low-density SNP panels, genomic predictions show higher predictive abilities than traditional methods.