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ARS Home » Northeast Area » Leetown, West Virginia » Cool and Cold Water Aquaculture Research » Research » Publications at this Location » Publication #350204

Research Project: Integrated Research to Improve On-Farm Animal Health in Salmonid Aquaculture

Location: Cool and Cold Water Aquaculture Research

Title: Accurate genomic predictions for BCWD resistance in rainbow trout are achieved using low-density SNP panels: Evidence that strong long-range LD is a major contributing factor

Author
item Vallejo, Roger
item Silva, Rafael - University Of Georgia
item Evenhuis, Jason
item Gao, Guangtu
item Liu, Sixin
item Parsons, James - Troutlodge, Inc
item Martin, Kyle - Troutlodge, Inc
item Wiens, Gregory - Greg
item Lourenco, Daniela - University Of Georgia
item Leeds, Timothy - Tim
item Palti, Yniv

Submitted to: Journal of Animal Breeding and Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/1/2018
Publication Date: 8/1/2018
Citation: Vallejo, R.L., Silva, R.M., Evenhuis, J., Gao, G., Liu, S., Parsons, J.E., Martin, K.E., Wiens, G.D., Lourenco, D.A., Leeds, T.D., Palti, Y. 2018. Accurate genomic predictions for BCWD resistance in rainbow trout are achieved using low-density SNP panels: Evidence that strong long-range LD is a major contributing factor. Journal of Animal Breeding and Genetics. 135:263–274. https://doi.org/10.1111/jbg.12335.
DOI: https://doi.org/10.1111/jbg.12335

Interpretive Summary: Using genome-enabled approaches for selective breeding for traits that cannot be measured directly in the fish used as potential breeders holds a great promise for more rapid genetic improvement compared to family-average estimates used in conventional selective breeding. Disease resistance is a prime example for a trait that cannot be measured directly on the potential breeders in rainbow trout aquaculture. Previously we have shown that we can generate highly accurate genomic selection predictions for bacterial cold water disease (BCWD) resistance in rainbow trout using a medium to high density array of 50,000 genetic markers. In the current study, we investigated the impact of lower-density marker panels on the accuracy of genomic predictions in a commercial rainbow trout breeding population. Using actual progeny testing performance data, we found that we can still have highly accurate prediction with a reduced-density panel of 3,000 markers. Furthermore, with a panel of only 500 markers we generated genomic merit prediction that was still remarkably higher than the conventional pedigree-based prediction method. Altogether, our results suggest that lower-density panels of genetic markers can be successfully used for implementing genomic selection for BCWD resistance in rainbow trout aquaculture. Using lower-density panels will make genomic selection more affordable and feasible for commercial fish breeding companies by substantially reducing the cost of markers genotyping. Overall, we found that even with more affordable genotyping methods, genomic selection can still substantially improve the genetic gains in traits that cannot be measured directly on the potential breeders in rainbow trout aquaculture. Therefore, using genomic selection will reduce the number of generations, time, labor and number of fish that are currently needed for achieving the same level of disease resistance improvement, with positive impacts on farm productivity and animal welfare, and on reducing the use of antibiotics used in rainbow trout farming.

Technical Abstract: Previously we have shown accurate genomic selection predictions for BCWD resistance in rainbow trout using a medium-density SNP array. Here, we investigated the impact of lower-density SNP panels on the accuracy of genomic predictions in a commercial rainbow trout breeding population. Using progeny testing performance data, we compared the accuracy of genome-enabled breeding values (GEBV) using 35K, 10K, 3K, 1K, 500, 300, and 200 SNP panels as well as a panel with 70 QTL-flanking SNP. The GEBVs were estimated using the Bayesian variable selection model BayesB, single-step GBLUP (ssGBBLUP) and weighted ssGBLUP (wssGBLUP). The accuracy of GEBVs remained high despite the sharp reductions in SNP density, and even with 500 SNP it was higher than the pedigree-based prediction (0.50 - 0.56 vs. 0.36). Furthermore, the prediction accuracy with the 70 SNP that flank QTL (0.65-0.72) was similar to the panel with 35K SNP (0.65 - 0.71). We also performed a genome-wide linkage disequilibrium (LD) analysis using a 32K SNP panel. We found that segments of strong LD (r2 greater than or equal to 0.25) spanned on average over 1 Mb across the rainbow trout genome. This observed long-range LD likely contributed to our ability to generate highly accurate genomic predictions with low-density SNP panels. Altogether, our results suggest that lower-cost, low-density SNP panels can be used for implementing genomic selection for BCWD resistance in rainbow trout breeding programs.