Genome-scale comparative analysis for host resistance against sea lice between Atlantic salmon and rainbow trout.

Sea lice (Caligus rogercresseyi) is an ectoparasite which causes major production losses in the salmon aquaculture industry worldwide. Atlantic salmon (Salmo salar) and rainbow trout (Oncorhynchus mykiss) are two of the most susceptible salmonid species to sea lice infestation. The objectives of this study were to: (1) identify genomic regions associated with resistance to Caligus rogercresseyi in Atlantic salmon and rainbow trout by performing single-step Genome-Wide Association studies (ssGWAS), and (2) identify candidate genes related to trait variation based on exploring orthologous genes within the associated regions across species. A total of 2626 Atlantic salmon and 2643 rainbow trout were challenged and genotyped with 50 K and 57 K SNP panels, respectively. We ran two independent ssGWAS for sea lice resistance on each species and identified 7 and 13 regions explaining more than 1% of the genetic variance for the trait, with the most important regions explaining 3% and 2.7% for Atlantic salmon and rainbow trout, respectively. We identified genes associated with immune response, cytoskeleton function, and cell migration when focusing on important genomic regions for each species. Moreover, we found 15 common orthogroups which were present in more than one associated genomic region, within- or between-species; however, only one orthogroup showed a clear potential biological relevance in the response against sea lice. For instance, dual-specificity protein phosphatase 10-like (dusp10) and dual-specificity protein phosphatase 8 (dusp8) were found in genomic regions associated with lice density in Atlantic salmon and rainbow trout, respectively. Dusp10 and dusp8 are modulators of the MAPK pathway and might be involved in the differences of the inflammation response between lice resistant and susceptible fish from both species. Our results provide further knowledge on candidate genes related to sea lice resistance and may help establish better control for sea lice in fish populations.

High-Throughput Single Nucleotide Polymorphism (SNP) Discovery and Validation Through Whole-Genome Resequencing in Nile Tilapia (Oreochromis niloticus).

Nile tilapia (Oreochromis niloticus) is the second most important farmed fish in the world and a sustainable source of protein for human consumption. Several genetic improvement programs have been established for this species in the world. Currently, the estimation of genetic merit of breeders is typically based on genealogical and phenotypic information. Genome-wide information can be exploited to efficiently incorporate traits that are difficult to measure into the breeding goal. Thus, single nucleotide polymorphisms (SNPs) are required to investigate phenotype-genotype associations and determine the genomic basis of economically important traits. We performed de novo SNP discovery in three different populations of farmed Nile tilapia. A total of 29.9 million non-redundant SNPs were identified through Illumina (HiSeq 2500) whole-genome resequencing of 326 individual samples. After applying several filtering steps, including removing SNP based on genotype and site quality, presence of Mendelian errors, and non-unique position in the genome, a total of 50,000 high-quality SNPs were selected for the development of a custom Illumina BeadChip SNP panel. These SNPs were highly informative in the three populations analyzed showing between 43,869 (94%) and 46,139 (99%) SNPs in Hardy-Weinberg Equilibrium; 37,843 (76%) and 45,171(90%) SNPs with a minor allele frequency (MAF) higher than 0.05; and 43,450 (87%) and 46,570 (93%) SNPs with a MAF higher than 0.01. The 50K SNP panel developed in the current work will be useful for the dissection of economically relevant traits, enhancing breeding programs through genomic selection, as well as supporting genetic studies in farmed populations of Nile tilapia using dense genome-wide information.

Single-Step Genome-Wide Association Study for Resistance to Piscirickettsia salmonis in Rainbow Trout (Oncorhynchus mykiss).

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.

Genome-Wide Patterns of Population Structure and Linkage Disequilibrium in Farmed Nile Tilapia (Oreochromis niloticus).

Nile tilapia (Oreochromis niloticus) is one of the most produced farmed fish in the world and represents an important source of protein for human consumption. Farmed Nile tilapia populations are increasingly based on genetically improved stocks, which have been established from admixed populations. To date, there is scarce information about the population genomics of farmed Nile tilapia, assessed by dense single nucleotide polymorphism (SNP) panels. The patterns of linkage disequilibrium (LD) may affect the success of genome-wide association studies (GWAS) and genomic selection (GS), and also provide key information about demographic history of farmed Nile tilapia populations. The objectives of this study were to provide further knowledge about the population structure and LD patterns, as well as, estimate the effective population size (N e ) for three farmed Nile tilapia populations, one from Brazil (POP A) and two from Costa Rica (POP B and POP C). A total of 55 individuals from each population, were genotyped using a 50K SNP panel selected from a whole-genome sequencing (WGS) experiment. The first two principal components explained about 20% of the total variation and clearly differentiated between the three populations. Population genetic structure analysis showed evidence of admixture, especially for POP C. The contemporary N e estimated, based on LD values, ranged from 78 to 159. No differences were observed in the LD decay among populations, with a rapid decrease of r 2 with increasing inter-marker distance. Average r 2 between adjacent SNP pairs ranged from 0.19 to 0.03 for both POP A and C, and 0.20 to 0.03 f or POP B. Based on the number of independent chromosome segments in the Nile tilapia genome, at least 9.4, 7.6, and 4.6K SNPs for POP A, POP B, and POP C respectively, are required for the implementation of GS in the present farmed Nile tilapia populations.

Fine Mapping Using Whole-Genome Sequencing Confirms Anti-Müllerian Hormone as a Major Gene for Sex Determination in Farmed Nile Tilapia (Oreochromis niloticus L.).

Nile tilapia (Oreochromis niloticus) is one of the most cultivated and economically important species in world aquaculture. Intensive production promotes the use of monosex animals, due to an important dimorphism that favors male growth. Currently, the main mechanism to obtain all-male populations is the use of hormones in feeding during larval and fry phases. Identifying genomic regions associated with sex determination in Nile tilapia is a research topic of great interest. The objective of this study was to identify genomic variants associated with sex determination in three commercial populations of Nile tilapia. Whole-genome sequencing of 326 individuals was performed, and a total of 2.4 million high-quality bi-allelic single nucleotide polymorphisms (SNPs) were identified after quality control. A genome-wide association study (GWAS) was conducted to identify markers associated with the binary sex trait (males = 1; females = 0). A mixed logistic regression GWAS model was fitted and a genome-wide significant signal comprising 36 SNPs, spanning a genomic region of 536 kb in chromosome 23 was identified. Ten out of these 36 genetic variants intercept the anti-Müllerian (Amh) hormone gene. Other significant SNPs were located in the neighboring Amh gene region. This gene has been strongly associated with sex determination in several vertebrate species, playing an essential role in the differentiation of male and female reproductive tissue in early stages of development. This finding provides useful information to better understand the genetic mechanisms underlying sex determination in Nile tilapia.

Comparative Genomic Analysis of Three Salmonid Species Identifies Functional Candidate Genes Involved in Resistance to the Intracellular Bacterium Piscirickettsia salmonis.

Piscirickettsia salmonis is the etiologic agent of salmon rickettsial syndrome (SRS) and is responsible for considerable economic losses in salmon aquaculture. The bacterium affects coho salmon (CS; Oncorhynchus kisutch), Atlantic salmon (AS; Salmo salar), and rainbow trout (RT; Oncorhynchus mykiss) in several countries, including Norway, Canada, Scotland, Ireland, and Chile. We used Bayesian genome-wide association study analyses to investigate the genetic architecture of resistance to P. salmonis in farmed populations of these species. Resistance to SRS was defined as the number of days to death and as binary survival (BS). A total of 828 CS, 2130 RT, and 2601 AS individuals were phenotyped and then genotyped using double-digest restriction site-associated DNA sequencing and 57K and 50K Affymetrix® Axiom® single nucleotide polymorphism (SNP) panels, respectively. Both traits of SRS resistance in CS and RT appeared to be under oligogenic control. In AS, there was evidence of polygenic control of SRS resistance. To identify candidate genes associated with resistance, we applied a comparative genomics approach in which we systematically explored the complete set of genes adjacent to SNPs, which explained more than 1% of the genetic variance of resistance in each salmonid species (533 genes in total). Thus, genes were classified based on the following criteria: i) shared function of their protein domains among species, ii) shared orthology among species, iii) proximity to the SNP explaining the highest proportion of the genetic variance, and iv) presence in more than one genomic region explaining more than 1% of the genetic variance within species. Our results allowed us to identify 120 candidate genes belonging to at least one of the four criteria described above. Of these, 21 of them were part of at least two of the criteria defined above and are suggested to be strong functional candidates influencing P. salmonis resistance. These genes are related to diverse biological processes, such as kinase activity, GTP hydrolysis, helicase activity, lipid metabolism, cytoskeletal dynamics, inflammation, and innate immune response, which seem essential in the host response against P. salmonis infection. These results provide fundamental knowledge on the potential functional genes underpinning resistance against P. salmonis in three salmonid species.

Genome-Wide Association Analysis for Resistance to Infectious Pancreatic Necrosis Virus Identifies Candidate Genes Involved in Viral Replication and Immune Response in Rainbow Trout (Oncorhynchus mykiss).

Infectious pancreatic necrosis (IPN) is a viral disease with considerable negative impact on the rainbow trout (Oncorhynchus mykiss) aquaculture industry. The aim of the present work was to detect genomic regions that explain resistance to infectious pancreatic necrosis virus (IPNV) in rainbow trout. A total of 2,278 fish from 58 full-sib families were challenged with IPNV and 768 individuals were genotyped (488 resistant and 280 susceptible), using a 57K SNP panel Axiom, Affymetrix. A genome-wide association study (GWAS) was performed using the phenotypes time to death (TD) and binary survival (BS), along with the genotypes of the challenged fish using a Bayesian model (Bayes C). Heritabilities for resistance to IPNV estimated using genomic information, were 0.53 and 0.82 for TD and BS, respectively. The Bayesian GWAS detected a SNP located on chromosome 5 explaining 19% of the genetic variance for TD. The proximity of Sentrin-specific protease 5 (SENP5) to this SNP makes it a candidate gene for resistance against IPNV. In case of BS, a SNP located on chromosome 23 was detected explaining 9% of the genetic variance. However, the moderate-low proportion of variance explained by the detected marker leads to the conclusion that the incorporation of all genomic information, through genomic selection, would be the most appropriate approach to accelerate genetic progress for the improvement of resistance against IPNV in rainbow trout.

Genome-Wide Association Study and Cost-Efficient Genomic Predictions for Growth and Fillet Yield in Nile Tilapia (Oreochromis niloticus).

Fillet yield (FY) and harvest weight (HW) are economically important traits in Nile tilapia production. Genetic improvement of these traits, especially for FY, are lacking, due to the absence of efficient methods to measure the traits without sacrificing fish and the use of information from relatives to selection. However, genomic information could be used by genomic selection to improve traits that are difficult to measure directly in selection candidates, as in the case of FY. The objectives of this study were: (i) to perform genome-wide association studies (GWAS) to dissect the genetic architecture of FY and HW, (ii) to evaluate the accuracy of genotype imputation and (iii) to assess the accuracy of genomic selection using true and imputed low-density (LD) single nucleotide polymorphism (SNP) panels to determine a cost-effective strategy for practical implementation of genomic information in tilapia breeding programs. The data set consisted of 5,866 phenotyped animals and 1,238 genotyped animals (108 parents and 1,130 offspring) using a 50K SNP panel. The GWAS were performed using all genotyped and phenotyped animals. The genotyped imputation was performed from LD panels (LD0.5K, LD1K and LD3K) to high-density panel (HD), using information from parents and 20% of offspring in the reference set and the remaining 80% in the validation set. In addition, we tested the accuracy of genomic selection using true and imputed genotypes comparing the accuracy obtained from pedigree-based best linear unbiased prediction (PBLUP) and genomic predictions. The results from GWAS supports evidence of the polygenic nature of FY and HW. The accuracy of imputation ranged from 0.90 to 0.98 for LD0.5K and LD3K, respectively. The accuracy of genomic prediction outperformed the estimated breeding value from PBLUP. The use of imputation for genomic selection resulted in an increased relative accuracy independent of the trait and LD panel analyzed. The present results suggest that genotype imputation could be a cost-effective strategy for genomic selection in Nile tilapia breeding programs.

Whole Genome Linkage Disequilibrium and Effective Population Size in a Coho Salmon (Oncorhynchus kisutch) Breeding Population Using a High-Density SNP Array.

The estimation of linkage disequilibrium between molecular markers within a population is critical when establishing the minimum number of markers required for association studies, genomic selection, and inferring historical events influencing different populations. This work aimed to evaluate the extent and decay of linkage disequilibrium in a coho salmon breeding population using a high-density SNP array. Linkage disequilibrium was estimated between a total of 93,502 SNPs found in 64 individuals (33 dams and 31 sires) from the breeding population. The markers encompass all 30 coho salmon chromosomes and comprise 1,684.62 Mb of the genome. The average density of markers per chromosome ranged from 48.31 to 66 per 1 Mb. The minor allele frequency averaged 0.26 (with a range from 0.22 to 0.27). The overall average linkage disequilibrium among SNPs pairs measured as r 2 was 0.10. The Average r 2 value decreased with increasing physical distance, with values ranging from 0.21 to 0.07 at a distance lower than 1 kb and up to 10 Mb, respectively. An r 2 threshold of 0.2 was reached at distance of approximately 40 Kb. Chromosomes Okis05, Okis15 and Okis28 showed high levels of linkage disequilibrium (>0.20 at distances lower than 1 Mb). Average r 2 values were lower than 0.15 for all chromosomes at distances greater than 4 Mb. An effective population size of 43 was estimated for the population 10 generations ago, and 325, for 139 generations ago. Based on the effective number of chromosome segments, we suggest that at least 74,000 SNPs would be necessary for an association mapping study and genomic predictions. Therefore, the SNP panel used allowed us to capture high-resolution information in the farmed coho salmon population. Furthermore, based on the contemporary N e, a new mate allocation strategy is suggested to increase the effective population size.

Discovery and Functional Annotation of Quantitative Trait Loci Affecting Resistance to Sea Lice in Atlantic Salmon.

Sea lice (Caligus rogercresseyi) are ectoparasitic copepods which have a large negative economic and welfare impact in Atlantic salmon (Salmo salar) aquaculture, particularly in Chile. A multi-faceted prevention and control strategy is required to tackle lice, and selective breeding contributes via cumulative improvement of host resistance to the parasite. While host resistance has been shown to be heritable, little is yet known about the individual loci that contribute to this resistance, the potential underlying genes, and their mechanisms of action. In this study we took a multifaceted approach to identify and characterize quantitative trait loci (QTL) affecting host resistance in a population of 2,688 Caligus-challenged Atlantic salmon post-smolts from a commercial breeding program. We used low and medium density genotyping with imputation to collect genome-wide SNP marker data for all animals. Moderate heritability estimates of 0.28 and 0.24 were obtained for lice density (as a measure of host resistance) and growth during infestation, respectively. Three QTL explaining between 7 and 13% of the genetic variation in resistance to sea lice (as represented by the traits of lice density) were detected on chromosomes 3, 18, and 21. Characterisation of these QTL regions was undertaken using RNA sequencing and pooled whole genome sequencing data. This resulted in the identification of a shortlist of potential underlying causative genes, and candidate functional mutations for further study. For example, candidates within the chromosome 3 QTL include a putative premature stop mutation in TOB1 (an anti-proliferative transcription factor involved in T cell regulation) and an uncharacterized protein which showed significant differential allelic expression (implying the existence of a cis-acting regulatory mutation). While host resistance to sea lice is polygenic in nature, the results of this study highlight significant QTL regions together explaining between 7 and 13 % of the heritability of the trait. Future investigation of these QTL may enable improved knowledge of the functional mechanisms of host resistance to sea lice, and incorporation of functional variants to improve genomic selection accuracy.

Single-step genomic evaluation improves accuracy of breeding value predictions for resistance to infectious pancreatic necrosis virus in rainbow trout.

The aim of this study was to compare the accuracy of breeding values (EBVs) predicted using the traditional pedigree based Best Linear Unbiased Prediction (PBLUP) and the single-step genomic Best Linear Unbiased Prediction (ssGBLUP) for resistance against infectious pancreatic necrosis virus (IPNV) in rainbow trout. A total of 2278 animals were challenged against IPNV and 768 individuals were genotyped using a 57 K single nucleotide polymorphism array for rainbow trout. Accuracies for both methods were assessed using five-fold cross-validation. The heritabilities were higher for PBLUP compared to ssGBLUP. The ssGBLUP accuracies outperformed PBLUP in 7 and 11% for days to death and binary survival, respectively. The ssGBLUP could be an alternative approach to improve the accuracy of breeding values for resistance against infectious pancreatic necrosis virus in rainbow trout, using information from genotyped and non-genotyped animals.

Genomic Predictions and Genome-Wide Association Study of Resistance Against Piscirickettsia salmonis in Coho Salmon (Oncorhynchus kisutch) Using ddRAD Sequencing.

Piscirickettsia salmonis is one of the main infectious diseases affecting coho salmon (Oncorhynchus kisutch) farming, and current treatments have been ineffective for the control of this disease. Genetic improvement for P. salmonis resistance has been proposed as a feasible alternative for the control of this infectious disease in farmed fish. Genotyping by sequencing (GBS) strategies allow genotyping of hundreds of individuals with thousands of single nucleotide polymorphisms (SNPs), which can be used to perform genome wide association studies (GWAS) and predict genetic values using genome-wide information. We used double-digest restriction-site associated DNA (ddRAD) sequencing to dissect the genetic architecture of resistance against P. salmonis in a farmed coho salmon population and to identify molecular markers associated with the trait. We also evaluated genomic selection (GS) models in order to determine the potential to accelerate the genetic improvement of this trait by means of using genome-wide molecular information. A total of 764 individuals from 33 full-sib families (17 highly resistant and 16 highly susceptible) were experimentally challenged against P. salmonis and their genotypes were assayed using ddRAD sequencing. A total of 9,389 SNPs markers were identified in the population. These markers were used to test genomic selection models and compare different GWAS methodologies for resistance measured as day of death (DD) and binary survival (BIN). Genomic selection models showed higher accuracies than the traditional pedigree-based best linear unbiased prediction (PBLUP) method, for both DD and BIN. The models showed an improvement of up to 95% and 155% respectively over PBLUP. One SNP related with B-cell development was identified as a potential functional candidate associated with resistance to P. salmonis defined as DD.

Population Genomic Structure and Genome-Wide Linkage Disequilibrium in Farmed Atlantic Salmon (Salmo salar L.) Using Dense SNP Genotypes.

Chilean Farmed Atlantic salmon (Salmo salar) populations were established with individuals of both European and North American origins. These populations are expected to be highly genetically differentiated due to evolutionary history and poor gene flow between ancestral populations from different continents. The extent and decay of linkage disequilibrium (LD) among single nucleotide polymorphism (SNP) impacts the implementation of genome-wide association studies and genomic selection and provides relevant information about demographic processes of fish populations. We assessed the population structure and characterized the extent and decay of LD in three Chilean commercial populations of Atlantic salmon with North American (NAM), Scottish (SCO), and Norwegian (NOR) origin. A total of 123 animals were genotyped using a 159 K SNP Axiom® myDesignTM Genotyping Array. A total of 32 K SNP markers, representing the common SNPs along the three populations after quality control were used. The principal component analysis explained 78.9% of the genetic diversity between populations, clearly discriminating between populations of North American and European origin, and also between European populations. NAM had the lowest effective population size, followed by SCO and NOR. Large differences in the LD decay were observed between populations of North American and European origin. An r 2 threshold of 0.2 was estimated for marker pairs separated by 7,800, 64, and 50 kb in the NAM, SCO, and NOR populations, respectively. In this study we show that this SNP panel can be used to detect association between markers and traits of interests and also to capture high-resolution information for genome-enabled predictions. Also, we suggest the feasibility to achieve similar prediction accuracies using a smaller SNP data set for the NAM population, compared with samples with European origin which would need a higher density SNP array.

Genomic Prediction Accuracy for Resistance Against Piscirickettsia salmonis in Farmed Rainbow Trout.

Salmonid rickettsial syndrome (SRS), caused by the intracellular bacterium Piscirickettsia salmonis, is one of the main diseases affecting rainbow trout (Oncorhynchus mykiss) farming. To accelerate genetic progress, genomic selection methods can be used as an effective approach to control the disease. The aims of this study were: (i) to compare the accuracy of estimated breeding values using pedigree-based best linear unbiased prediction (PBLUP) with genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), Bayes C, and Bayesian Lasso (LASSO); and (ii) to test the accuracy of genomic prediction and PBLUP using different marker densities (0.5, 3, 10, 20, and 27 K) for resistance against P. salmonis in rainbow trout. Phenotypes were recorded as number of days to death (DD) and binary survival (BS) from 2416 fish challenged with P. salmonis A total of 1934 fish were genotyped using a 57 K single-nucleotide polymorphism (SNP) array. All genomic prediction methods achieved higher accuracies than PBLUP. The relative increase in accuracy for different genomic models ranged from 28 to 41% for both DD and BS at 27 K SNP. Between different genomic models, the highest relative increase in accuracy was obtained with Bayes C (∼40%), where 3 K SNP was enough to achieve a similar accuracy to that of the 27 K SNP for both traits. For resistance against P. salmonis in rainbow trout, we showed that genomic predictions using GBLUP, ssGBLUP, Bayes C, and LASSO can increase accuracy compared with PBLUP. Moreover, it is possible to use relatively low-density SNP panels for genomic prediction without compromising accuracy predictions for resistance against P. salmonis in rainbow trout.

Genomic predictions can accelerate selection for resistance against Piscirickettsia salmonis in Atlantic salmon (Salmo salar).

BACKGROUND: Salmon Rickettsial Syndrome (SRS) caused by Piscirickettsia salmonis is a major disease affecting the Chilean salmon industry. Genomic selection (GS) is a method wherein genome-wide markers and phenotype information of full-sibs are used to predict genomic EBV (GEBV) of selection candidates and is expected to have increased accuracy and response to selection over traditional pedigree based Best Linear Unbiased Prediction (PBLUP). Widely used GS methods such as genomic BLUP (GBLUP), SNPBLUP, Bayes C and Bayesian Lasso may perform differently with respect to accuracy of GEBV prediction. Our aim was to compare the accuracy, in terms of reliability of genome-enabled prediction, from different GS methods with PBLUP for resistance to SRS in an Atlantic salmon breeding program. Number of days to death (DAYS), binary survival status (STATUS) phenotypes, and 50 K SNP array genotypes were obtained from 2601 smolts challenged with P. salmonis. The reliability of different GS methods at different SNP densities with and without pedigree were compared to PBLUP using a five-fold cross validation scheme. RESULTS: Heritability estimated from GS methods was significantly higher than PBLUP. Pearson's correlation between predicted GEBV from PBLUP and GS models ranged from 0.79 to 0.91 and 0.79-0.95 for DAYS and STATUS, respectively. The relative increase in reliability from different GS methods for DAYS and STATUS with 50 K SNP ranged from 8 to 25% and 27-30%, respectively. All GS methods outperformed PBLUP at all marker densities. DAYS and STATUS showed superior reliability over PBLUP even at the lowest marker density of 3 K and 500 SNP, respectively. 20 K SNP showed close to maximal reliability for both traits with little improvement using higher densities. CONCLUSIONS: These results indicate that genomic predictions can accelerate genetic progress for SRS resistance in Atlantic salmon and implementation of this approach will contribute to the control of SRS in Chile. We recommend GBLUP for routine GS evaluation because this method is computationally faster and the results are very similar with other GS methods. The use of lower density SNP or the combination of low density SNP and an imputation strategy may help to reduce genotyping costs without compromising gain in reliability.

The use of genomic information increases the accuracy of breeding value predictions for sea louse (Caligus rogercresseyi) resistance in Atlantic salmon (Salmo salar).

Sea lice infestations caused by Caligus rogercresseyi are a main concern to the salmon farming industry due to associated economic losses. Resistance to this parasite was shown to have low to moderate genetic variation and its genetic architecture was suggested to be polygenic. The aim of this study was to compare accuracies of breeding value predictions obtained with pedigree-based best linear unbiased prediction (P-BLUP) methodology against different genomic prediction approaches: genomic BLUP (G-BLUP), Bayesian Lasso, and Bayes C. To achieve this, 2404 individuals from 118 families were measured for C. rogercresseyi count after a challenge and genotyped using 37 K single nucleotide polymorphisms. Accuracies were assessed using fivefold cross-validation and SNP densities of 0.5, 1, 5, 10, 25 and 37 K. Accuracy of genomic predictions increased with increasing SNP density and was higher than pedigree-based BLUP predictions by up to 22%. Both Bayesian and G-BLUP methods can predict breeding values with higher accuracies than pedigree-based BLUP, however, G-BLUP may be the preferred method because of reduced computation time and ease of implementation. A relatively low marker density (i.e. 10 K) is sufficient for maximal increase in accuracy when using G-BLUP or Bayesian methods for genomic prediction of C. rogercresseyi resistance in Atlantic salmon.

Genome-wide association analysis reveals loci associated with resistance against Piscirickettsia salmonis in two Atlantic salmon (Salmo salar L.) chromosomes.

BACKGROUND: Pisciricketssia salmonis is the causal agent of Salmon Rickettsial Syndrome (SRS), which affects salmon species and causes severe economic losses. Selective breeding for disease resistance represents one approach for controlling SRS in farmed Atlantic salmon. Knowledge concerning the architecture of the resistance trait is needed before deciding on the most appropriate approach to enhance artificial selection for P. salmonis resistance in Atlantic salmon. The purpose of the study was to dissect the genetic variation in the resistance to this pathogen in Atlantic salmon. METHODS: 2,601 Atlantic salmon smolts were experimentally challenged against P. salmonis by means of intra-peritoneal injection. These smolts were the progeny of 40 sires and 118 dams from a Chilean breeding population. Mortalities were recorded daily and the experiment ended at day 40 post-inoculation. Fish were genotyped using a 50K Affymetrix® Axiom® myDesignTM Single Nucleotide Polymorphism (SNP) Genotyping Array. A Genome Wide Association Analysis was performed on data from the challenged fish. Linear regression and logistic regression models were tested. RESULTS: Genome Wide Association Analysis indicated that resistance to P. salmonis is a moderately polygenic trait. There were five SNPs in chromosomes Ssa01 and Ssa17 significantly associated with the traits analysed. The proportion of the phenotypic variance explained by each marker is small, ranging from 0.007 to 0.045. Candidate genes including interleukin receptors and fucosyltransferase have been found to be physically linked with these genetic markers and may play an important role in the differential immune response against this pathogen. CONCLUSIONS: Due to the small amount of variance explained by each significant marker we conclude that genetic resistance to this pathogen can be more efficiently improved with the implementation of genetic evaluations incorporating genotype information from a dense SNP array.

The consequences of including non-additive effects on the genetic evaluation of harvest body weight in Coho salmon (Oncorhynchus kisutch).

BACKGROUND: In this study, we used different animal models to estimate genetic and environmental variance components on harvest weight in two populations of Oncorhynchus kisutch, forming two classes i.e. odd- and even-year spawners. METHODS: The models used were: additive, with and without inbreeding as a covariable (A + F and A respectively); additive plus common environmental due to full-sib families and inbreeding (A + C + F); additive plus parental dominance and inbreeding (A + D + F); and a full model (A + C + D + F). Genetic parameters and breeding values obtained by different models were compared to evaluate the consequences of including non-additive effects on genetic evaluation. RESULTS: Including inbreeding as a covariable did not affect the estimation of genetic parameters, but heritability was reduced when dominance or common environmental effects were included. A high heritability for harvest weight was estimated in both populations (even = 0.46 and odd = 0.50) when simple additive models (A + F and A) were used. Heritabilities decreased to 0.21 (even) and 0.37 (odd) when the full model was used (A + C + D + F). In this full model, the magnitude of the dominance variance was 0.19 (even) and 0.06 (odd), while the magnitude of the common environmental effect was lower than 0.01 in both populations. The correlation between breeding values estimated with different models was very high in all cases (i.e. higher than 0.98). However, ranking of the 30 best males and the 100 best females per generation changed when a high dominance variance was estimated, as was the case in one of the two populations (even). CONCLUSIONS: Dominance and common environmental variance may be important components of variance in harvest weight in O. kisutch, thus not including them may produce an overestimation of the predicted response; furthermore, genetic evaluation was seen to be partially affected, since the ranking of selected animals changed with the inclusion of non-additive effects in the animal model.