Abstract

Piscirickettsia salmonis is a pathogenic agent causing the disease called salmonid rickettsial septicaemia (SRS), which generate considerable economic losses in salmon aquaculture. The bacteria affects coho salmon (Oncorhynchus kisutch), Atlantic salmon (Salmo salar) and rainbow trout (Onchorhyncus mykiss) in several countries including Norway, Canada, Scotland, Ireland and Chile. In this study we used Bayesian genome-wide association studies (GWAS) to investigate the genetic architecture of resistance against P. salmonis in coho salmon, rainbow trout and Atlantic salmon farmed populations by using dense SNP panels. Fish were independently challenged with P. salmonis. Resistance to SRS was defined as the number of days to death (DD) and as binary survival (BS; 1 for died and 0 to survived). A total of 828 CS, 2.130 RT and 2.601 AS were genotyped used ten ddRAD libraries, 57K SNP Affymetrix® Axiom® and 50K Affymetrix® SNP, respectively. GWAs was performed using the Bayes C approach by means of the Gibbs sampling algorithm implemented in GS3 software. For coho salmon and rainbow trout, both traits presented evidence for oligogenic control with few moderate-large effect loci and a large-unknown number of loci each having a small effect on the traits, while in Atlantic salmon both traits showed evidence of polygenic control. Differences in distribution of effects for P. salmonis resistance among the three salmonid species may be due to different genetic factors and mechanisms involved in the variation of the trait across species. From the practical perspective, the differential genetic architecture of P. salmonis resistance may have an impact on differences in genomic prediction accuracy of this trait in the coho salmon, rainbow trout and Atlantic salmon. Keywords: Oncorhynchus mykiss, Oncorhynchus kisutch, Salmo salar, genome-wide association study

Grazyella Yoshida, Roberto Carvalheiro, Jean Paul Lhorente, Katharina Correa, Agustin Barria, Rene Figueroa, José Manuel Yáñez

Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Biology - Disease Resistance 3, , 721, 2018
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