Resistance to pathogens in North American Atlantic salmon: Genome wide association analyses for bacterial kidney disease, salmon lice, and infectious salmon anemia

Resistance to pathogens is an economically important trait to the Atlantic salmon aquaculture industry. Many diseases that are common at sea cages, where product is reared until harvest, can cause high levels of mortality or cause product downgrading due to reduced growth or appearance. This profit loss has prompted many breeding programs in the industry to investigate methods to breed for pathogen resistance, taking advantage of the naturally existing genetic variation within their broodstock candidates. In this PhD thesis, I exposed Atlantic salmon from the Saint John River strain to three pathogens: Renibacterium salmoninarum causing bacterial kidney disease (BKD), the salmon louse, and infectious salmon anemia (ISA) virus. Each of these disease challenges allowed for the recording of disease phenotypes representing resistance such as survival, time to death, or lice load, to be recorded on siblings of the broodstock candidates from Kelly Cove Salmon Ltd.’s breeding nucleus. High density SNP genotyping was performed on representatives from all families challenged, where the phenotypic extremes from each family were selected. Using the combination of genotypes and phenotypes, I performed genome wide association analyses to determine the genetic architecture of the disease resistance traits and identify regions of the genome associated with disease resistance. Resistance to BKD and salmon lice showed a polygenic trait inheritance while ISA resistance appeared to have oligogenic inheritance, and the regions of the genomes associated with resistance mostly differed for these traits. The exception was Ssa14 where two SNPs were within 1 Mb of each other, one associated with ISA resistance and the other associated with salmon lice resistance. For each disease resistance trait, I detected chromosome wide and/or genome wide significant SNPs that each explained between 4-9% of the phenotypic variation in the trait. These SNPs can be implemented as a subset of SNPs into a genomic selection protocol to estimate genomic estimated breeding values for related individuals. The continuation of disease challenges will allow for greater datasets representing more of the genetic variation within the population and increasing the sample size will allow for greater power of detection of SNPs truly associated with the resistance traits.