Genome-wide association mapping and accuracy of predictions for amoebic gill disease in Atlantic salmon (Salmo salar).

Amoebic gill disease (AGD) is a parasitic disease caused by the amoeba Paramoeba perurans, which colonizes the gill tissues and causes distress for the host. AGD can cause high morbidity and mortalities in salmonid and non-salmonid fish species. To understand the genetic basis of AGD and improve health status of farmed A. salmon, a population of ~ 6,100 individuals belonging to 150 full-sib families was monitored for development of AGD in the sea of Ireland. The population was followed for two rounds of AGD infections, and fish were gill scored to identify severity of disease in first (N = 3,663) and the second (N = 3,511) infection with freshwater treatment after the first gill-scoring. A subset of this gill-scored population (N = 1,141) from 119 full-sib families were genotyped with 57,184 SNPs using custom-made Affymetrix SNP-chip. GWAS analyses were performed which resulted in five significantly associated SNP variants distributed over chromosome 1, 2 and 5. Three candidate genes; c4, tnxb and slc44a4 were found within QTL region of chromosome 2. The tnxb and c4 genes are known to be a part of innate immune system, and may play a role in resistance to AGD. The gain in prediction accuracy obtained by involving genomic information was 9-17% higher than using traditional pedigree information.

Multiple Selection Signatures in Farmed Atlantic Salmon Adapted to Different Environments Across Hemispheres.

Domestication of Atlantic salmon started approximately 40 years ago, using artificial selection through genetic improvement programs. Selection is likely to have imposed distinctive signatures on the salmon genome, which are often characterized by high genetic differentiation across population and/or reduction in genetic diversity in regions associated to traits under selection. The identification of such selection signatures may give insights into the candidate genomic regions of biological and commercial interest. Here, we used three complementary statistics to detect selection signatures, two haplotype-based (iHS and XP-EHH), and one FST-based method (BayeScan) among four populations of Atlantic salmon with a common genetic origin. Several regions were identified for these techniques that harbored genes, such as kind1 and chp2, which have been associated with growth-related traits or the kcnb2 gene related to immune system in Atlantic salmon, making them particularly relevant in the context of aquaculture. Our results provide candidate genes to inform the evolutionary and biological mechanisms controlling complex selected traits in Atlantic salmon.

QTLs Associated with Resistance to Cardiomyopathy Syndrome in Atlantic Salmon.

Cardiomyopathy syndrome (CMS) caused by piscine myocarditis virus is a major disease affecting the Norwegian Atlantic salmon industry. Three different populations of Atlantic salmon from the Mowi breeding program were used in this study. The first 2 populations (population 1 and 2) were naturally infected in a field outbreak, while the third population (population 3) went through a controlled challenged test. The aim of the study was to estimate the heritability, the genetic correlation between populations and perform genome-wide association analysis for resistance to this disease. Survival data from population 1 and 2 and heart atrium histology score data from population 3 was analyzed. A total of 571, 4312, and 901 fish from population 1, 2, and 3, respectively were genotyped with a noncommercial 55,735 Affymetrix marker panel. Genomic heritability ranged from 0.12 to 0.46 and the highest estimate was obtained from the challenge test dataset. The genetic correlation between populations was moderate (0.51-0.61). Two chromosomal regions (SSA27 and SSA12) contained single nucleotide polymorphisms associated with resistance to CMS. The highest association signal (P = 6.9751 × 10-27) was found on chromosome 27. Four genes with functional roles affecting viral resistance (magi1, pi4kb, bnip2, and ha1f) were found to map closely to the identified quantitative trait loci (QTLs). In conclusion, genetic variation for resistance to CMS was observed in all 3 populations. Two important quantitative trait loci were detected which together explain half of the total genetic variance, suggesting strong potential application for marker-assisted selection and genomic predictions to improve CMS resistance.