Salmonid rickettsial syndrome (SRS) remains as one of the most important pathogens for salmon farming. Genetic improvement has proven to be a viable alternative to reduce mortality in breeding stock. Understanding the genetic architecture of resistance has been a matter of ongoing research aimed at establishing the most appropriate method by which genomic information can be incorporated into breeding programs. However, the genetic architecture of complex traits such as SRS resistance may vary due to genetic and environmental background. In this work, we used the genotypes of a total of 5839 Atlantic salmon from 4 different experimental challenges against Piscirickttsia salmonis, which were imputed high density (~930K SNP) to perform within-population genomic-association analyses, followed by a meta-analysis of resistance to SRS defined as binary survival and day of death. The objectives of this study were to i) uncover the genomic regions associated with resistance to SRS among multiple populations; and ii) identify candidate genes associated with each trait definition. SNP-based meta-analysis revealed a clear QTL on Ssa02 for both traits while gene-based meta-analysis revealed 16 genes in common for both traits. Our results suggest a polygenic genetic architecture and provide novel insights into the candidate genes underpinning resistance to P. salmonis in Salmo salar.

Sea lice (Caligus rogercresseyi) is an ectoparasite that causes significant production losses in the salmon aquaculture industry of the southern hemisphere. Atlantic salmon (Salmo salar) is an important salmonid for the aquaculture industry and a species highly susceptible to sea lice infestation. Genetic variation for resistance to sea lice, defined as parasite load, has been found in Atlantic salmon. In addition, sea lice load has been shown to be a polygenic trait, controlled by several quantitative trait loci (QTL) which have small to medium effect, making them difficult to map with sufficient statistical power when sample sizes are limited. The use of medium density single nucleotide polymorphisms (SNP) can also adversely affect the success of identifying genetic variants significantly associated with sea lice load. In order to improve the ability to detect QTL significantly associated with sea lice load, we combined genotype imputation from medium- to high SNP-density and performed genome-wide association studies (GWAS) across different populations of Atlantic salmon. The imputation of genotypes of 6,144 fish challenged against sea lice from four year classes was performed to increase density from 70K SNPs to 600K SNPs. A meta-GWAS was then carried out for three different traits: lice count, lice density and log-lice density. Using this approach, we detected a genomic region highly associated with sea lice load on Atlantic salmon chromosomes (ssa) 3 and 12 pronounced peaks and several other regions surpassing the significance threshold across almost all other chromosomes. We also identified important genes within the QTL regions, many of these genes are involved in tissue reparation, such as Mucin-16-like isoform X2 and Filamentous growth regulator 23-like isoform X1. The QTL region on ssa03 also contained cytoskeletal-modifying and immune response related genes such as Coronin 1A and Claudin. Our results confirm the highly polygenic architecture of sea lice load, but they also show that high experimental power can lead to the identification of candidate genes and thus to increased insight into the biology of sea lice resistance in Atlantic salmon.

Sea lice infestation is one of the major fish health problems during the grow-out phase in Atlantic salmon (Salmo salar) aquaculture. In this study, we integrated different genomic approaches, including whole-genome sequencing (WGS), genotype imputation and meta-analysis of genome-wide association studies (GWAS), to identify single-nucleotide polymorphisms (SNPs) associated with sea lice count in Atlantic salmon. Different sets of trait-associated SNPs were prioritized and compared against randomly chosen markers, based on the accuracy of genomic predictions for the trait. Lice count phenotypes and dense genotypes of five breeding populations challenged against sea lice were used. Genotype imputation was applied to increase SNP density of challenged animals to WGS level. The summary statistics from GWAS of each population were then combined in a meta-analysis to increase the sample size and improve the statistical power of associations. Eight different genotyping scenarios were considered for genomic prediction: 70K_array: 70K standard genotyping panel; 70K_priori: 70K SNPs with the highest p-values identified in the meta-analysis; 30K_priori: 30K SNPs with the highest p-values identified in the meta-analysis; WGS: SNPs imputed to whole-genome sequencing level; and the remaining four scenarios were the same SNP sets with a linkage disequilibrium (LD) pruning filter: 70K_array_LD; 70K_priori_LD; 30K_priori_LD and WGS_LD, respectively. Genomic prediction accuracy was evaluated using a five-fold cross-validation scheme in two different populations excluding them from the meta-analysis to remove possible validation-reference bias. Results showed significant genetic variation for sea lice counting in Atlantic salmon across populations, with heritabilities ranging from 0.06 to 0.24. The meta-analysis identified several SNPs associated with sea lice resistance, mainly in Ssa03 and Ssa09 chromosomes. Genomic prediction using the GWAS-based prioritized SNPs showed higher accuracy compared to using the standard SNP array in most of scenarios, achieving up to 57% increase in accuracy. Accuracy of prioritized scenarios was higher for the 70K_priori in comparison to 30K_priori. The use of WGS data in genomic prediction presented marginal or negative accuracy gain compared to the standard SNP array. The LD-pruning filter presented no benefits, reducing accuracy in most of scenarios. Overall, our study demonstrated the potential of prioritized of imputed sequence variants from multi-population GWAS meta-analysis to improve prediction accuracy for sea lice count in Atlantic salmon. The findings suggest that incorporating WGS data and prioritized SNPs from GWAS meta-analysis can accelerate the genetic progress of selection for polygenic traits in salmon aquaculture.

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