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ARS Home » Northeast Area » Leetown, West Virginia » Cool and Cold Water Aquaculture Research » Research » Publications at this Location » Publication #316226
Research Project: Integrated Research Approaches for Improving Production Efficiency in Salmonids

Location: Cool and Cold Water Aquaculture Research

Title: Genomic selection for BCWD resistance in Rainbow trout using RADSNP and SNP genotyping platforms, single-step GBLUP and Bayesian variable selection models

Author
item Vallejo, Roger
item Leeds, Timothy - Tim
item Liu, Sixin
item Gao, Guangtu
item Welch, Timothy - Tim
item Wiens, Gregory - Greg
item Palti, Yniv

Submitted to: International Symposium on Genetics in Aquaculture
Publication Type: Abstract Only
Publication Acceptance Date: 4/29/2015
Publication Date: 6/21/2015
Citation: Vallejo, R.L., Leeds, T.D., Liu, S., Gao, G., Welch, T.J., Wiens, G.D., Palti, Y. 2015. Genomic selection for BCWD resistance in Rainbow trout using RADSNP and SNP genotyping platforms, single-step GBLUP and Bayesian variable selection models [abstract]. International Symposium on Genetics in Aquaculture. P05796.

Interpretive Summary:

Technical Abstract: Bacterial cold water disease (BCWD) causes significant economic losses in salmonid aquaculture. At the National Center for Cool and Cold Water Aquaculture (NCCCWA), we have pursued selective breeding to increase rainbow trout genetic resistance against BCWD and found that post-challenge survival is moderately heritable and responds to selection. Genomic selection (GS) is a recently developed methodology that is revolutionizing animal breeding and we plan to use GS in our breeding scheme to increase the accuracy of selecting best genetic value animals for BCWD resistance. In this study, we aimed to 1) predict genomic breeding value (GEBV) for BCWD resistance in the NCCCWA population; 2) compare the reliability of pedigree-based model (PED) with three GS models including BayesB and C and single-step GBLUP (ssGBLUP); and 3) compare the reliability of genomic predictions from two genotyping platforms (24K RADSNP and 49K SNP chip) when using GS models. The BCWD phenotypes survival days (DAYS) and survival status (STATUS) were recorded in the training animals (n=583). The reliability of genomic predictions was assessed using validation animals (n=53) that had PED estimated breeding value (EBV) for DAYS and STATUS from sibling and progeny testing. The reliability of GS models was assessed through predictive ability defined as the correlation between EBV and GEBV records, Then, the reliability was estimated as where is the reliability of EBVs. For STATUS, all the GS models (BayesB and C and ssGBLUP; 59 - 130% increase) outperformed the PED model in reliability of genomic predictions. For DAYS, the Bayesian methods BayesB and C (69 - 109% increase) outperformed the PED model; however, the PED model outperformed ssGBLUP (-19 - -6% decrease). Overall, BayesB estimated higher reliability GEBVs than BayesC and ssGBLUP. The 24K RADSNP is as efficient as the 49K SNP chip in genomic prediction for BCWD resistance; however, the SNP chip is preferred because it is more practical, enables high throughput genotyping and meta-analysis of GS, and is cost-effective. The accuracy of GS for BCWD resistance in rainbow trout is expected to increase with the use of larger training and validation-testing samples.