Genomic selection models substantially improve the accuracy of genetic merit predictions for fillet yield and body weight in rainbow trout using a multi-trait model and multi-generation progeny testing

Authors: L. S. GARCIA, ANDRE - University Of Georgia; TSURUTA, SHOGO - University Of Georgia; Gao, Guangtu; Palti, Yniv; LOURENCO, TIM - University Of Georgia; Leeds, Timothy - Tim

Submitted to: Genetics Selection Evolution
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/16/2023
Publication Date: 2/9/2023
Citation: L. S. Garcia, A., Tsuruta, S., Gao, G., Palti, Y., Lourenco, T., Leeds, T.D. Genomic selection models substantially improve the accuracy of genetic merit predictions for fillet yield and body weight in rainbow trout using a multi-trait model and multi-generation progeny testing. Genetics Selection Evolution. 55:11 (2013). https://doi.org/10.1186/s12711-023-00782-6.
DOI: https://doi.org/10.1186/s12711-023-00782-6

Interpretive Summary: Increasing the proportion of a whole fish that is edible fillet, or fillet yield, is a means to improve production efficiency, and thus is of interest to fish farmers and consumers. However, fillet yield poses a challenge to animal breeders aiming to improve this trait through selective breeding because it cannot be directly measured in breeding candidates. The continued development of rainbow trout genomics resources now allow breeders and scientists to utilize genomic selection whereby the genetic merit of breeding candidates can be uniquely estimated, despite the absence of phenotypic information for fillet yield, based on analysis of the breeding candidate's DNA. In this study, we compared the accuracy of genetic merit predictions from genomic selection models to those derived from a traditional pedigree-based model. The results suggest that accuracy of genetic merit predictions can be increased up to 50% when using genomic selection models. In addition, this study confirmed that fillet yield in this population is under polygenic control as no genomic regions were identified as having large effect on the trait. This study demonstrates that use of genomic selection can increase the rate of genetic improvement of fillet yield in rainbow trout populations.

Technical Abstract: In aquaculture, the proportion of edible meat (FY= fillet yield) is of major economic importance, and breeding animals of superior genetic merit for this trait can improve efficiency and profitability. Achieving genetic gains for fillet yield is possible using pedigree-based best linear unbiased prediction (BLUP) model with direct and indirect selection. To investigate the feasibility of using genomic selection (GS) to improve FY and body weight (BW) in rainbow trout, the prediction accuracy of GS models was compared to that of BLUP. Additionally, a genome-wide association study (GWAS) was conducted to identify QTL markers for the traits. All analyses were performed using a two-trait model with FY and BW, and variance components, heritability, and genetic correlations were estimated without genomic information. The data used included 14,165 fish in the pedigree, of which 2,742 and 12,890 had FY and BW phenotypic records, respectively, and 2,484 had genotypes from the 57K SNP array. The heritabilities were moderate, at 0.41 and 0.33 for FY and BW, respectively. Both traits were lowly but positively correlated (r = 0.24), which suggests the possibility for favorable correlated genetic gains. GS models increased prediction accuracy compared to BLUP by up to 50% for FY and 28% for BW. Biases were present in the evaluations but were reduced when using genomic information using 0.5 allele frequency to build the genomic relationship matrix. No significant QTL were found for either trait, indicating that both traits are polygenic and that marker-assisted selection will not be helpful for those traits in this population.