Location: Cool and Cold Water Aquaculture ResearchTitle: Evaluation of genome-enabled selection for bacterial cold water disease resistance using progeny performance data in Rainbow Trout: Insights on genotyping methods and genomic prediction models
|Leeds, Timothy - Tim|
|FRAGOMENI, BRENO - University Of Georgia|
|HERNANDEZ, ALVARO - University Of Illinois|
|MISZTAL, IGNACY - University Of Georgia|
|Welch, Timothy - Tim|
|Wiens, Gregory - Greg|
Submitted to: Frontiers in Livestock Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/13/2016
Publication Date: 5/27/2016
Citation: Vallejo, R.L., Leeds, T.D., Fragomeni, B.O., Gao, G., Hernandez, A.G., Misztal, I., Welch, T.J., Wiens, G.D., Palti, Y. 2016. Evaluation of genome-enabled selection for bacterial cold water disease resistance using progeny performance data in Rainbow Trout: Insights on genotyping methods and genomic prediction models. Frontiers in Livestock Genomics. doi: 10.3389/fgene.2016.00096.
Interpretive Summary: Bacterial cold water disease (BCWD) causes significant economic losses in salmonid aquaculture, and traditional family-based breeding programs aimed at improving BCWD resistance have been limited to exploiting only between-family variation. Genomic-based selection is a recently developed breeding method that has revolutionized agricultural animal breeding, and to date has primarily been shown to be very effective for dairy cattle breeding. In this study we used genomic selection models to predict genomic breeding values for BCWD resistance in 10 historic archived families from the first generation of our BCWD resistance breeding line; compared the accuracy of the genome-based models to predict the genetic merit of breeders to the traditional pedigree-based model; and compared the impact of two different genotyping methods on the accuracy of the genome-based predictions. The overall accuracy of the genome-based method in this study was low to moderate, likely due to the small sample size we had to use because of the limited availability of historic archived samples. In this study we explored how different aspects of the genome-based selection method impact the accuracy of predicting the best breeders for cold water disease resistance in rainbow trout aquaculture and it provides the basis for further investigation on the implementation of genome-based selection in commercial rainbow trout breeding programs.
Technical Abstract: Bacterial cold water disease (BCWD) causes significant economic losses in salmonid aquaculture, and traditional family-based breeding programs aimed at improving BCWD resistance have been limited to exploiting only between-family variation. We used genomic selection (GS) models to predict genomic breeding values (GEBVs) for BCWD resistance in 10 families from the first generation of the NCCCWA BCWD resistance breeding line, compared the predictive ability (PA) of GEBVs to pedigree-based estimated breeding values (EBVs), and compared the impact of two SNP genotyping methods on the accuracy of GEBV predictions. The BCWD phenotypes survival days (DAYS) and survival status (STATUS) had been recorded in training fish (n = 583) subjected to experimental BCWD challenge. Training fish, and their full sibs without phenotypic data that were used as parents of the subsequent generation, were genotyped using two methods: restriction-site associated DNA (RAD) sequencing and the Rainbow Trout Axiom® 57K SNP array (Chip). The GEBVs were estimated using four GS models: BayesB, BayesC, single-step GBLUP (ssGBLUP) and weighted ssGBLUP (wssGBLUP). The EBVs were estimated using pedigree and phenotype data in the training fish only. The PA of GEBVs and EBVs was assessed by correlating mean progeny phenotype (MPP) with mid-parent EBV (family-specific) or GEBV (animal-specific). The best GEBV predictions were similar to EBV with PA values of 0.49 and 0.46 vs. 0.50 and 0.41 for DAYS and STATUS, respectively. Among the GEBV prediction methods, ssGBLUP consistently had the highest PA. The RAD genotyping platform had GEBVs with similar PA to those of GEBVs from the Chip platform. The PA of ssGBLUP and wssGBLUP methods was higher with the Chip, but for BayesB and BayesC methods it was higher with the RAD platform. The overall GEBV accuracy in this study was low to moderate, likely due to the small training sample used. This study explored the potential of GS for improving resistance to BCWD in rainbow trout using, for the first time, progeny testing data to assess the accuracy of GEBVs, and it provides the basis for further investigation on the implementation of GS in commercial rainbow trout populations.