One of the main pathogens affecting rainbow trout (Oncorhynchus mykiss) farming is the facultative intracellular bacteria Piscirickettsia salmonis. Current treatments, such as antibiotics and vaccines, have not had the expected effectiveness in field conditions. Genetic improvement by means of selection for resistance is proposed as a viable alternative for control. Genomic information can be used to identify the genomic regions associated with resistance and enhance the genetic evaluation methods to speed up the genetic improvement for the trait. The objectives of this study were to i) identify the genomic regions associated with resistance to P. salmonis; and ii) identify candidate genes associated with the trait in rainbow trout. We experimentally challenged 2,130 rainbow trout with P. salmonis and genotyped them with a 57 K single nucleotide polymorphism (SNP) array. Resistance to P. salmonis was defined as time to death (TD) and as binary survival (BS). Significant heritabilities were estimated for TD and BS (0.48 ± 0.04 and 0.34 ± 0.04, respectively). A total of 2,047 fish and 26,068 SNPs passed quality control for samples and genotypes. Using a single-step genome wide association analysis (ssGWAS) we identified four genomic regions explaining over 1% of the genetic variance for TD and three for BS. Interestingly, the same genomic region located on Omy27 was found to explain the highest proportion of genetic variance for both traits (2.4 and 1.5% for TD and BS, respectively). The identified SNP in this region is located within an exon of a gene related with actin cytoskeletal organization, a protein exploited by P. salmonis during infection. Other important candidate genes identified are related with innate immune response and oxidative stress. The moderate heritability values estimated in the present study show it is possible to improve resistance to P. salmonis through artificial selection in the rainbow trout population studied here. Furthermore, our results suggest a polygenic genetic architecture for the trait and provide novel insights into the candidate genes underpinning resistance to P. salmonis in O. mykiss.

The characterization of runs of homozygosity (ROH), using high-density single nucleotide polymorphisms (SNPs) allows inferences to be made about the past demographic history of animal populations and the genomic ROH has become a common approach to characterize the inbreeding. We aimed to analyze and characterize ROH patterns and compare different genomic and pedigree-based methods to estimate the inbreeding coefficient in two pure lines (POP A and B) and one recently admixed line (POP C) of coho salmon (Oncorhynchus kisutch) breeding nuclei, genotyped using a 200 K Affymetrix Axiom^® myDesign Custom SNP Array. A large number and greater mean length of ROH were found for the two “pure” lines and the recently admixed line (POP C) showed the lowest number and smaller mean length of ROH. The ROH analysis for different length classes suggests that all three coho salmon lines the genome is largely composed of a high number of short segments (16 Mb was found. A high variable number of ROH, mean length and inbreeding values across chromosomes; positively the consequence of artificial selection. Pedigree-based inbreeding values tended to underestimate genomic-based inbreeding levels, which in turn varied depending on the method used for estimation. The high positive correlations between different genomic-based inbreeding coefficients suggest that they are consistent and may be more accurate than pedigree-based methods, given that they capture information from past and more recent demographic events, even when there are no pedigree records available.

Dense single nucleotide polymorphism (SNP) arrays are essential tools for rapid high-throughput genotyping for many genetic analyses, including genomic selection and high-resolution population genomic assessments. We present a high-density (200 K) SNP array developed for the Eastern oyster (Crassostrea virginica), which is a species of significant aquaculture production and restoration efforts throughout its native range. SNP discovery was performed using low-coverage whole-genome sequencing of 435 F1 oysters from families from 11 founder populations in New Brunswick, Canada. An Affymetrix Axiom Custom array was created with 219,447 SNPs meeting stringent selection criteria and validated by genotyping more than 4,000 oysters across 2 generations. In total, 144,570 SNPs had a call rate >90%, most of which (96%) were polymorphic and were distributed across the Eastern oyster reference genome, with similar levels of genetic diversity observed in both generations. Linkage disequilibrium was low (maximum r² ∼0.32) and decayed moderately with increasing distance between SNP pairs. Taking advantage of our intergenerational data set, we quantified Mendelian inheritance errors to validate SNP selection. Although most of SNPs exhibited low Mendelian inheritance error rates overall, with 72% of called SNPs having an error rate of <1%, many loci had elevated Mendelian inheritance error rates, potentially indicating the presence of null alleles. This SNP panel provides a necessary tool to enable routine application of genomic approaches, including genomic selection, in C. virginica selective breeding programs. As demand for production increases, this resource will be essential for accelerating production and sustaining the Canadian oyster aquaculture industry.

Understanding the genetic architecture of economically important traits is essential for the design and implementation of efficient breeding programs. Here, we analysed 3,653 Eastern oysters (Crassostrea virginica) from 62 crossing groups and 307 full-sib families, all genotyped with a 200 K SNP array, to (i) estimate heritability and genetic correlations for six growth-related traits, (ii) detect quantitative trait loci (QTL) and candidate genes, and (iii) compare the accuracy of pedigree- (PBLUP) versus genomic-based (GBLUP) predictions across marker densities, selection strategies (physical distance (PD) versus linkage disequilibrium (LD) and phenotype-based genotyping strategies. SNP-based Heritabilities ranged from 0.33 to 0.56, and genetic correlations among traits exceeded 0.61, indicating strong pleiotropy. GWAS revealed a polygenic architecture with a shared QTL on chromosome 2; the top SNP explained ≤ 1.1% of phenotypic variance. Except for the 0.5 K LD panel—where PBLUP was more accurate—and the 1 K LD panel—where both methods performed similarly—GBLUP outperformed PBLUP in all scenarios, with the greatest advantage observed for physically spaced (PD) panels. The highest accuracy (0.80) was achieved with the full 100 K SNP set. GBLUP models required panels containing ≥ 0.5 K PD (accuracy ≥ 0.55) or ≥ 2 K LD (accuracy ≥ 0.56) to significatively surpass PBLUP predictions. Training sets built from extreme phenotypes boosted accuracy in small to intermediate sample sizes (e.g., 0.74 at n = 1,500 with 5 K PD SNPs versus 0.53 under random sampling). These results provide novel insights into not only the genomic regions controlling growth in this species but also about the utility of PD-based SNP panels and balanced sampling designs for enhancing GS accuracy in C. virginica.