Genome-wide association and genomic prediction of resistance to Flavobacterium columnare in a farmed rainbow trout population

Columnaris disease is an emerging disease affecting farmed rainbow trout (Oncorhynchus mykiss) globally. In aquaculture breeding, genomic selection has been increasingly used to improve traits that are difficult to measure on candidate fish (such as disease resistance traits). Following a natural outbreak of columnaris disease, 3054 exposed fish and their 81 parents (33 dams and 48 sires) were genotyped with the 57 K SNP AxiomTM trout genotyping array. Genetic parameters of host resistance (measured as a binary survival trait) were estimated, a genome wide association study was performed, and the accuracy of pedigree-based and genomic prediction was estimated. After quality controls, 2874 challenged fish (1403 dead fish and 1471 alive fish) and 78 parents genotyped for 27,907 SNPs remained. Pedigree based heritability was estimated to be 0.18 and 0.35 on the observed and underlying scale, respectively. Genomic heritability was estimated to be 0.21 and 0.43 on the observed and underlying scale, respectively. A quantitative trait loci (QTL) was detected on chromosome Omy3, significant at the genome-wide level, along with several suggestive QTLs on two other chromosomes. The additive effect on mortality proportion of the peak SNP from Omy3 was estimated to be 0.11 (0.018; se), implying approximately 22% difference in survival between alternate homozygous fish at the QTL. Pedigree-based prediction accuracy was 0.59, and the use of genomic evaluation increased the prediction accuracy by at least 13.6%. Using the second iteration of a weighted genomic-based evaluation increased the prediction accuracy by 18.6% compared to the pedigree-based model. These results confirm that resistance to columnaris disease is a suitable target trait for genetic improvement by selective breeding, that a natural outbreak of columnaris disease in a farm environment can be used to select for fish that are more resistant and that genomic selection is a useful approach to speed up this process.