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ARS Home » Northeast Area » Leetown, West Virginia » Cool and Cold Water Aquaculture Research » Research » Publications at this Location » Publication #339097
Research Project: Integrated Research Approaches for Improving Production Efficiency in Salmonids

Location: Cool and Cold Water Aquaculture Research

Title: Genome-enabled selection doubles the accuracy of predicted breeding values for bacterial cold water disease resistance compared to traditional family-based selection in rainbow trout aquaculture

Author
item Vallejo, Roger
item Leeds, Timothy - Tim
item Gao, Guangtu
item PARSONS, JAMES - Troutlodge, Inc
item KYLE, MARTIN - Troutlodge, Inc
item Evenhuis, Jason
item FRAGOMENI, BRENO - University Of Georgia
item Wiens, Gregory - Greg
item Palti, Yniv

Submitted to: Genetics in Aquaculture Symposium
Publication Type: Proceedings
Publication Acceptance Date: 3/31/2017
Publication Date: 11/1/2017
Citation: Vallejo, R.L., Leeds, T.D., Gao, G., Parsons, J.E., Kyle, M.E., Evenhuis, J., Fragomeni, B.O., Wiens, G.D., Palti, Y. 2017. Genome-enabled selection doubles the accuracy of predicted breeding values for bacterial cold water disease resistance compared to traditional family-based selection in rainbow trout aquaculture. Genetics in Aquaculture Symposium. Proceedings of the 44th U.S.-Japan Aquaculture Panel Symposium:33-43.

Interpretive Summary:

Technical Abstract: We have shown previously that bacterial cold water disease (BCWD) resistance in rainbow trout can be improved using traditional family-based selection, but progress has been limited to exploiting only between-family genetic variation. Genomic selection (GS) is a new alternative enabling exploitation of within-family genetic variation. We compared three GS models to predict genomic-enabled breeding values (GEBVs) for BCWD resistance in a commercial rainbow trout population, and compared the accuracy of GEBVs to traditional breeding values (EBVs) estimated with a pedigree-based BLUP model. For these comparisons, we used BCWD survival phenotypes recorded on 7893 training fish from 102 families, from which 1473 fish from 50 families had genotypes (57K SNP array). Naïve siblings of the training fish (n = 930 testing fish) were genotyped to predict their GEBVs, of which 193 were mated to produce 138 progeny testing families (PTFs). In the following generation, 9968 progeny from the PTFs were BCWD phenotyped to empirically assess the accuracy of GEBV predictions made on their non-phenotyped parents. The accuracy of GEBVs from all three GS models were substantially higher than the BLUP model EBVs, with the increase in accuracy ranging from 83.3% to 108.8% depending on the GS model and survival phenotype used. Reducing the training sample size to n = ~1000 had no negative impact on the accuracy (0.67-0.72), but with n = ~500 the accuracy dropped to 0.53-0.61 if the training and testing fish were full-sibs, and even substantially lower to 0.22-0.25 when they were not full-sibs. Thus using progeny performance data, we have shown that the accuracy of genomic predictions with GS models (0.63-0.71) is substantially higher than the traditional pedigree-based BLUP model (0.34-0.36). We also found that using a much smaller training sample size compared to similar studies in livestock, GS can substantially improve the selection accuracy and genetic gains for BCWD resistance in commercial rainbow trout aquaculture.