Use of whole-genome sequence data for fine mapping and genomic prediction of sea louse resistance in Atlantic salmon.

Sea louse (Lepeophtheirus salmonis) infestation of Atlantic salmon (Salmo salar) is a significant challenge in aquaculture. Over the years, this parasite has developed immunity to medicinal control compounds, and non-medicinal control methods have been proven to be stressful, hence the need to study the genomic architecture of salmon resistance to sea lice. Thus, this research used whole-genome sequence (WGS) data to study the genetic basis of the trait since most research using fewer SNPs did not identify significant quantitative trait loci. Mowi Genetics AS provided the genotype (50 k SNPs) and phenotype data for this research after conducting a sea lice challenge test on 3,185 salmon smolts belonging to 191 full-sib families. The 50 k SNP genotype was imputed to WGS using the information from 197 closely related individuals with sequence data. The WGS and 50 k SNPs of the challenged population were then used to estimate genetic parameters, perform a genome-wide association study (GWAS), predict genomic breeding values, and estimate its accuracy for host resistance to sea lice. The heritability of host resistance to sea lice was estimated to be 0.21 and 0.22, while the accuracy of genomic prediction was estimated to be 0.65 and 0.64 for array and WGS data, respectively. In addition, the association test using both array and WGS data did not identify any marker associated with sea lice resistance at the genome-wide level. We conclude that sea lice resistance is a polygenic trait that is moderately heritable. The genomic predictions using medium-density SNP genotyping array were equally good or better than those based on WGS data.

Use of DNA pools of a reference population for genomic selection of a binary trait in Atlantic salmon.

Genomic selection has a great potential in aquaculture breeding since many important traits are not directly measured on the candidates themselves. However, its implementation has been hindered by staggering genotyping costs because of many individual genotypes. In this study, we explored the potential of DNA pooling for creating a reference population as a tool for genomic selection of a binary trait. Two datasets from the SalmoBreed population challenged with salmonid alphavirus, which causes pancreas disease, were used. Dataset-1, that includes 855 individuals (478 survivors and 377 dead), was used to develop four DNA pool samples (i.e., 2 pools each for dead and survival). Dataset-2 includes 914 individuals (435 survivors and 479 dead) belonging to 65 full-sibling families and was used to develop in-silico DNA pools. SNP effects from the pool data were calculated based on allele frequencies estimated from the pools and used to calculate genomic breeding values (GEBVs). The correlation between SNP effects estimated based on individual genotypes and pooled data increased from 0.3 to 0.912 when the number of pools increased from 1 to 200. A similar trend was also observed for the correlation between GEBVs, which increased from 0.84 to 0.976, as the number of pools per phenotype increased from 1 to 200. For dataset-1, the accuracy of prediction was 0.71 and 0.70 when the DNA pools were sequenced in 40× and 20×, respectively, compared to an accuracy of 0.73 for the SNP chip genotypes. For dataset-2, the accuracy of prediction increased from 0.574 to 0.691 when the number of in-silico DNA pools increased from 1 to 200. For this dataset, the accuracy of prediction using individual genotypes was 0.712. A limited effect of sequencing depth on the correlation of GEBVs and prediction accuracy was observed. Results showed that a large number of pools are required to achieve as good prediction as individual genotypes; however, alternative effective pooling strategies should be studied to reduce the number of pools without reducing the prediction power. Nevertheless, it is demonstrated that pooling of a reference population can be used as a tool to optimize between cost and accuracy of selection.

Genomic and Phenotypic Agreement Defines the Use of Microwave Dielectric Spectroscopy for Recording Muscle Lipid Content in European Seabass (Dicentrarchus labrax).

Recording the fillet lipid percentage in European seabass is crucial to control lipid deposition as a means toward improving production efficiency and product quality. The reference method for recording lipid content is solvent lipid extraction and is the most accurate and precise method available. However, it is costly, requires sacrificing the fish and grinding the fillet sample which limits the scope of applications, for example grading of fillets, recording live fish or selective breeding of fish with own phenotypes are all limited. We tested a rapid, cost effective and non-destructive handheld microwave dielectric spectrometer (namely the Distell fat meter) against the reference method by recording both methods on 313 European seabass (Dicentrarchus labrax). The total method agreement between the dielectric spectrometer and the reference method was assessed by Lin's concordance correlation coefficient (CCC), which was low to moderate CCC = 0.36-0.63. We detected a significant underestimation in accuracy of lipid percentage 22-26% by the dielectric spectrometer and increased imprecision resulting in the coefficient of variation (CV) doubling for dielectric spectrometer CV = 40.7-46% as compared to the reference method 27-31%. Substantial genetic variation for fillet lipid percentage was found for both the reference method (h 2 = 0.59) and dielectric spectroscopy (h 2 = 0.38-0.58), demonstrating that selective breeding is a promising method for controlling fillet lipid content. Importantly, the genetic correlation (r g) between the dielectric spectrometer and the reference method was positive and close to unity (r g = 0.96), demonstrating the dielectric spectrometer captures practically all the genetic variation in the reference method. These findings form the basis of defining the scope of applications and experimental design for using dielectric spectroscopy for recording fillet lipid content in European seabass and validate its use for selective breeding.

Genetic Variation, GWAS and Accuracy of Prediction for Host Resistance to Sparicotyle chrysophrii in Farmed Gilthead Sea Bream (Sparus aurata).

Gilthead sea bream (Sparus aurata) belongs to a group of teleost which has high importance in Mediterranean aquaculture industry. However, industrial production is increasingly compromised by an elevated outbreak of diseases in sea cages, especially a disease caused by monogeneans parasite Sparicotyle chrysophrii. This parasite mainly colonizes gill tissues of host and causes considerable economical losses with mortality and reduction in growth. The aim of current study was to explore the genetics of host resistance against S. chrysophrii and investigate the potential for genomic selection to possibly accelerate genetic progress. To achieve the desired goals, a test population derived from the breeding nucleus of Andromeda Group was produced. This experimental population was established by crossing of parents mated in partial factorial crosses of ∼8 × 8 using 58 sires and 62 dams. The progeny obtained from this mating design was challenged with S. chrysophrii using a controllable cohabitation infection model. At the end of the challenge, fish were recorded for parasite count, and all the recorded fish were tissue sampled for genotyping by sequencing using 2b-RAD methodology. The initial (before challenge test) and the final body weight (after challenge test) of the fish were also recorded. The results obtained through the analysis of phenotypic records (n = 615) and the genotypic data (n = 841, 724 offspring and 117 parents) revealed that the resistance against this parasite is lowly heritable (h 2 = 0.147 with pedigree and 0.137 with genomic information). We observed moderately favorable genetic correlation (R g = -0.549 to -0.807) between production traits (i.e., body weight and specific growth rate) and parasite count, which signals a possibility of indirect selection. A locus at linkage group 17 was identified that surpassed chromosome-wide Bonferroni threshold which explained 22.68% of the total genetic variance, and might be playing role in producing genetic variation. The accuracy of prediction was improved by 8% with genomic information compared to pedigree.