Location: Cool and Cold Water Aquaculture ResearchTitle: Genome-wide association analysis of bacterial cold water disease resistance in rainbow trout reveals the potential of a hybrid approach between genomic selection and marker assisted selection
|MARTIN, KYLE - Troutlodge, Inc|
|Wiens, Gregory - Greg|
|Leeds, Timothy - Tim|
|PARSONS, JAMES - Troutlodge, Inc|
Submitted to: Plant and Animal Genome Conference
Publication Type: Abstract Only
Publication Acceptance Date: 11/13/2016
Publication Date: 1/14/2017
Citation: Palti, Y., Vallejo, R.L., Martin, K., Evenhuis, J., Gao, G., Liu, S., Wiens, G.D., Leeds, T.D., Parsons, J. 2017. Genome-wide association analysis of bacterial cold water disease resistance in rainbow trout reveals the potential of a hybrid approach between genomic selection and marker assisted selection [abstract]. Plant and Animal Genome Conference. WO40(P1011).
Technical Abstract: Genomic selection (GS) simultaneously incorporates dense SNP marker genotypes with phenotypic data from related animals to predict animal-specific genomic breeding value (GEBV), which circumvents the need to measure the disease phenotype in potential breeders. Marker assisted selection (MAS) involves the selection of SNPs linked to positive alleles of a single locus with a major effect on the total genetic variation for the trait. Application of MAS is more economical as it requires genotyping of a much smaller number of SNPs at a lower cost, but it is only effective in traits with a single major-effect gene like IPNV resistance in Atlantic salmon. Here, using a commercial rainbow trout breeding population, we conducted GWAS (genome-wide association study) to identify loci that account for at least 1% of the genetic variation for bacterial cold water disease (BCWD) resistance in a laboratory challenge model. Combining the results from two analysis algorithms (weighted ssGBLUP (wssGBLUP), and BayesB) we have identified seven chromosomal regions with moderate to major effect of at least 1% on four different chromosomes (Omy03, 05, 08 and 25). We then evaluated the accuracy of GEBV predictions using reduced SNP densities, including a panel of 70 SNPs flanking the moderate-major QTL identified in GWAS. As expected, reducing the SNP density had a negative effect on the predictions accuracy, but the panel of the 70 QTL-flanking SNPs had an accuracy of 0.66, which is slightly lower than the accuracy of 0.71 from the full dataset of approximately 42K SNPs; equal to the accuracy from a sub-set of 10K SNPs evenly distributed across the genome; and more than doubles the accuracy of the predictions from the pedigree-based (BLUP) model.