Diversity and linkage disequilibrium in farmed Tasmanian Atlantic salmon

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.

The generation of the first chromosome-level de novo genome assembly and the development and validation of a 50K SNP array for the St. John River aquaculture strain of North American Atlantic salmon

Atlantic salmon (Salmo salar) in Northeastern US and Eastern Canada has high economic value for the sport fishing and aquaculture industries. Large differences exist between the genomes of Atlantic salmon of European origin and North American (N.A.) origin. Given the genetic and genomic differences between the 2 lineages, it is crucial to develop unique genomic resources for N.A. Atlantic salmon. Here, we describe the resources that we recently developed for genomic and genetic research in N.A. Atlantic salmon aquaculture. Firstly, a new single nucleotide polymorphism (SNP) database for N.A. Atlantic salmon consisting of 3.1 million putative SNPs was generated using data from whole-genome resequencing of 80 N.A. Atlantic salmon individuals. Secondly, a high-density 50K SNP array enriched for the genic regions of the genome and containing 3 sex determination and 61 putative continent of origin markers was developed and validated. Thirdly, a genetic map composed of 27 linkage groups with 36K SNP markers was generated from 2,512 individuals in 141 full-sib families. Finally, a chromosome-level de novo genome assembly from a male N.A. Atlantic salmon from the St. John River aquaculture strain was generated using PacBio long reads. Information from Hi-C proximity ligation sequences and Bionano optical mapping was used to concatenate the contigs into scaffolds. The assembly contains 1,755 scaffolds and only 1,253 gaps, with a total length of 2.83 Gb and N50 of 17.2 Mb. A BUSCO analysis detected 96.2% of the conserved Actinopterygii genes in the assembly, and the genetic linkage information was used to guide the formation of 27 chromosome sequences. Comparative analysis with the reference genome assembly of the European Atlantic salmon confirmed that the karyotype differences between the 2 lineages are caused by a fission in chromosome Ssa01 and 3 chromosome fusions including the p arm of chromosome Ssa01 with Ssa23, Ssa08 with Ssa29, and Ssa26 with Ssa28. The genomic resources we have generated for Atlantic salmon provide a crucial boost for genetic research and for management of farmed and wild populations in this highly valued species.

Potential of low-density genotype imputation for cost-efficient genomic selection for resistance to Flavobacterium columnare in rainbow trout (Oncorhynchus mykiss)

BACKGROUND

Flavobacterium columnare is the pathogen agent of columnaris disease, a major emerging disease that affects rainbow trout aquaculture. Selective breeding using genomic selection has potential to achieve cumulative improvement of the host resistance. However, genomic selection is expensive partly because of the cost of genotyping large numbers of animals using high-density single nucleotide polymorphism (SNP) arrays. The objective of this study was to assess the efficiency of genomic selection for resistance to F. columnare using in silico low-density (LD) panels combined with imputation. After a natural outbreak of columnaris disease, 2874 challenged fish and 469 fish from the parental generation (n = 81 parents) were genotyped with 27,907 SNPs. The efficiency of genomic prediction using LD panels was assessed for 10 panels of different densities, which were created in silico using two sampling methods, random and equally spaced. All LD panels were also imputed to the full 28K HD panel using the parental generation as the reference population, and genomic predictions were re-evaluated. The potential of prioritizing SNPs that are associated with resistance to F. columnare was also tested for the six lower-density panels.

RESULTS

The accuracies of both imputation and genomic predictions were similar with random and equally-spaced sampling of SNPs. Using LD panels of at least 3000 SNPs or lower-density panels (as low as 300 SNPs) combined with imputation resulted in accuracies that were comparable to those of the 28K HD panel and were 11% higher than the pedigree-based predictions.

CONCLUSIONS

Compared to using the commercial HD panel, LD panels combined with imputation may provide a more affordable approach to genomic prediction of breeding values, which supports a more widespread adoption of genomic selection in aquaculture breeding programmes.

Genomics-driven prophylactic measures to increase streptococcosis resistance in tilapia

Streptococcosis caused by Streptococcus agalactiae and S. iniae is a significant problem that affects the success of tilapia aquaculture industries worldwide. In this critical review, we summarize the applicable practical strategies which may effectively enhance the world tilapia aquaculture development. Recently, the effect of vaccination and selective breeding programmes has been recognized as valuable tools to control the target disease and other consequent negative impacts caused by chemical and drug application. Advances in sequencing and molecular technologies are vital helpful factors with which to develop robust vaccines and increase the selective breeding programme's precision against streptococcosis. The genomic selection for streptococcosis-resistant tilapia strains and crucial genomic application for genomics' contribution to the development of novel Streptococcus vaccine, comparative genomics approach identifying vaccine candidates by reverse vaccinology, and next-generation vaccine design were described. Information from our review is encouraging for practical implementation of the development of vaccination and genomic selection in tilapia for streptococcosis resistance, which may be vital factors to sustain the world tilapia aquaculture industry effectively.

Evaluation of low-density SNP panels and imputation for cost-effective genomic selection in four aquaculture species

Genomic selection can accelerate genetic progress in aquaculture breeding programmes, particularly for traits measured on siblings of selection candidates. However, it is not widely implemented in most aquaculture species, and remains expensive due to high genotyping costs. Genotype imputation is a promising strategy that can reduce genotyping costs and facilitate the broader uptake of genomic selection in aquaculture breeding programmes. Genotype imputation can predict ungenotyped SNPs in populations genotyped at a low-density (LD), using a reference population genotyped at a high-density (HD). In this study, we used datasets of four aquaculture species (Atlantic salmon, turbot, common carp and Pacific oyster), phenotyped for different traits, to investigate the efficacy of genotype imputation for cost-effective genomic selection. The four datasets had been genotyped at HD, and eight LD panels (300-6,000 SNPs) were generated in silico. SNPs were selected to be: i) evenly distributed according to physical position ii) selected to minimise the linkage disequilibrium between adjacent SNPs or iii) randomly selected. Imputation was performed with three different software packages (AlphaImpute2, FImpute v.3 and findhap v.4). The results revealed that FImpute v.3 was faster and achieved higher imputation accuracies. Imputation accuracy increased with increasing panel density for both SNP selection methods, reaching correlations greater than 0.95 in the three fish species and 0.80 in Pacific oyster. In terms of genomic prediction accuracy, the LD and the imputed panels performed similarly, reaching values very close to the HD panels, except in the pacific oyster dataset, where the LD panel performed better than the imputed panel. In the fish species, when LD panels were used for genomic prediction without imputation, selection of markers based on either physical or genetic distance (instead of randomly) resulted in a high prediction accuracy, whereas imputation achieved near maximal prediction accuracy independently of the LD panel, showing higher reliability. Our results suggests that, in fish species, well-selected LD panels may achieve near maximal genomic selection prediction accuracy, and that the addition of imputation will result in maximal accuracy independently of the LD panel. These strategies represent effective and affordable methods to incorporate genomic selection into most aquaculture settings.

Development of a High-Density 665 K SNP Array for Rainbow Trout Genome-Wide Genotyping

Single nucleotide polymorphism (SNP) arrays, also named « SNP chips », enable very large numbers of individuals to be genotyped at a targeted set of thousands of genome-wide identified markers. We used preexisting variant datasets from USDA, a French commercial line and 30X-coverage whole genome sequencing of INRAE isogenic lines to develop an Affymetrix 665 K SNP array (HD chip) for rainbow trout. In total, we identified 32,372,492 SNPs that were polymorphic in the USDA or INRAE databases. A subset of identified SNPs were selected for inclusion on the chip, prioritizing SNPs whose flanking sequence uniquely aligned to the Swanson reference genome, with homogenous repartition over the genome and the highest Minimum Allele Frequency in both USDA and French databases. Of the 664,531 SNPs which passed the Affymetrix quality filters and were manufactured on the HD chip, 65.3% and 60.9% passed filtering metrics and were polymorphic in two other distinct French commercial populations in which, respectively, 288 and 175 sampled fish were genotyped. Only 576,118 SNPs mapped uniquely on both Swanson and Arlee reference genomes, and 12,071 SNPs did not map at all on the Arlee reference genome. Among those 576,118 SNPs, 38,948 SNPs were kept from the commercially available medium-density 57 K SNP chip. We demonstrate the utility of the HD chip by describing the high rates of linkage disequilibrium at 2–10 kb in the rainbow trout genome in comparison to the linkage disequilibrium observed at 50–100 kb which are usual distances between markers of the medium-density chip.

Estimates of recent and historical effective population size in turbot, seabream, seabass and carp selective breeding programmes

Background

The high fecundity of fish species allows intense selection to be practised and therefore leads to fast genetic gains. Based on this, numerous selective breeding programmes have been started in Europe in the last decades, but in general, little is known about how the base populations of breeders have been built. Such knowledge is important because base populations can be created from very few individuals, which can lead to small effective population sizes and associated reductions in genetic variability. In this study, we used genomic information that was recently made available for turbot (Scophthalmus maximus), gilthead seabream (Sparus aurata), European seabass (Dicentrarchus labrax) and common carp (Cyprinus carpio) to obtain accurate estimates of the effective size for commercial populations.

Methods

Restriction-site associated DNA sequencing data were used to estimate current and historical effective population sizes. We used a novel method that considers the linkage disequilibrium spectrum for the whole range of genetic distances between all pairs of single nucleotide polymorphisms (SNPs), and thus accounts for potential fluctuations in population size over time.

Results

Our results show that the current effective population size for these populations is small (equal to or less than 50 fish), potentially putting the sustainability of the breeding programmes at risk. We have also detected important drops in effective population size about five to nine generations ago, most likely as a result of domestication and the start of selective breeding programmes for these species in Europe.

Conclusions

Our findings highlight the need to broaden the genetic composition of the base populations from which selection programmes start, and suggest that measures designed to increase effective population size within all farmed populations analysed here should be implemented in order to manage genetic variability and ensure the sustainability of the breeding programmes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-021-00680-9.

Optimum mating designs for exploiting dominance in genomic selection schemes for aquaculture species

Background

In commercial fish, dominance effects could be exploited by predicting production abilities of the offspring that would be generated by different mating pairs and choosing those pairs that maximise the average offspring phenotype. Consequently, matings would be performed to reduce inbreeding depression. This can be achieved by applying mate selection (MS) that combines selection and mating decisions in a single step. An alternative strategy to MS would be to apply minimum coancestry mating (MCM) after selection based on estimated breeding values. The objective of this study was to evaluate, by computer simulations, the potential benefits that can be obtained by implementing MS or MCM based on genomic data for exploiting dominance effects when creating commercial fish populations that are derived from a breeding nucleus.

Methods

The selected trait was determined by a variable number of loci with additive and dominance effects. The population consisted of 50 full-sib families with 30 offspring each. Males and females with the highest estimated genomic breeding values were selected in the nucleus and paired using the MCM strategy. Both MCM and MS were used to create the commercial population.

Results

For a moderate number of SNPs, equal or even higher mean phenotypic values are obtained by selecting on genomic breeding values and then applying MCM than by using MS when the trait exhibited substantial inbreeding depression. This could be because MCM leads to high levels of heterozygosity across the whole genome, even for loci affecting the trait that are in linkage equilibrium with the SNPs. In contrast, MS specifically promotes heterozygosity for SNPs for which a dominance effect has been detected.

Conclusions

In most scenarios, for the management of aquaculture breeding programs it seems advisable to follow the MCM strategy when creating the commercial population, especially for traits with large inbreeding depression. Moreover, MCM has the appealing property of reducing inbreeding levels, with a corresponding reduction in inbreeding depression for traits beyond those included in the selection objective.

Genomic predictions for fillet yield and firmness in rainbow trout using reduced-density SNP panels

Background

One of the most important goals for the rainbow trout aquaculture industry is to improve fillet yield and fillet quality. Previously, we showed that a 50 K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with fillet yield and fillet firmness. In this study, data from 1568 fish genotyped for the 50 K transcribed-SNP chip and ~ 774 fish phenotyped for fillet yield and fillet firmness were used in a single-step genomic BLUP (ssGBLUP) model to compute the genomic estimated breeding values (GEBV). In addition, pedigree-based best linear unbiased prediction (PBLUP) was used to calculate traditional, family-based estimated breeding values (EBV).

Results

The genomic predictions outperformed the traditional EBV by 35% for fillet yield and 42% for fillet firmness. The predictive ability for fillet yield and fillet firmness was 0.19–0.20 with PBLUP, and 0.27 with ssGBLUP. Additionally, reducing SNP panel densities indicated that using 500–800 SNPs in genomic predictions still provides predictive abilities higher than PBLUP.

Conclusion

These results suggest that genomic evaluation is a feasible strategy to identify and select fish with superior genetic merit within rainbow trout families, even with low-density SNP panels.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07404-9.

Marker genotyping error effects on genomic predictions under different genetic architectures

This study aimed to determine the effect of different rates of marker genotyping error on the accuracy of genomic prediction that was examined under distinct marker and quantitative trait loci (QTL) densities and different heritability estimates using a stochastic simulation approach. For each scenario of simulation, a reference population with phenotypic and genotypic records and a validation population with only genotypic records were considered. Marker effects were estimated in the reference population, and then their genotypic records were used to predict genomic breeding values in the validation population. The prediction accuracy was calculated as the correlation between estimated and true breeding values. The prediction bias was examined by computing the regression of true genomic breeding value on estimated genomic breeding value. The accuracy of the genomic evaluation was the highest in a scenario with no marker genotyping error and varied from 0.731 to 0.934. The accuracy of the genomic evaluation was the lowest in a scenario with marker genotyping error equal to 20% and changed from 0.517 to 0.762. The unbiased regression coefficients of true genomic breeding value on estimated genomic breeding value were obtained in the reference and validation populations when the rate of marker genotyping error was equal to zero. The results showed that marker genotyping error can reduce the accuracy of genomic evaluations. Moreover, marker genotyping error can provide biased estimates of genomic breeding values. Therefore, for obtaining accurate results it is recommended to minimize the marker genotyping errors to zero in genomic evaluation programs.

Development and Validation of an Open Access SNP Array for Nile Tilapia (Oreochromis niloticus)

Tilapia are among the most important farmed fish species worldwide, and are fundamental for the food security of many developing countries. Several genetically improved Nile tilapia (Oreochromis niloticus) strains exist, such as the iconic Genetically Improved Farmed Tilapia (GIFT), and breeding programs typically follow classical pedigree-based selection. The use of genome-wide single-nucleotide polymorphism (SNP) data can enable an understanding of the genetic architecture of economically important traits and the acceleration of genetic gain via genomic selection. Due to the global importance and diversity of Nile tilapia, an open access SNP array would be beneficial for aquaculture research and production. In the current study, a ∼65K SNP array was designed based on SNPs discovered from whole-genome sequence data from a GIFT breeding nucleus population and the overlap with SNP datasets from wild fish populations and several other farmed Nile tilapia strains. The SNP array was applied to clearly distinguish between different tilapia populations across Asia and Africa, with at least ∼30,000 SNPs segregating in each of the diverse population samples tested. It is anticipated that this SNP array will be an enabling tool for population genetics and tilapia breeding research, facilitating consistency and comparison of results across studies.

Changed Patterns of Genomic Variation Following Recent Domestication: Selection Sweeps in Farmed Atlantic Salmon

The introduction of wild Atlantic salmon into captivity, and their subsequent artificial selection for production traits, has caused phenotypic differences between domesticated fish and their wild counterparts. Identification of regions of the genome underling these changes offers the promise of characterizing the early biological consequences of domestication. In the current study, we sequenced a population of farmed European Atlantic salmon and compared the observed patterns of SNP variation to those found in conspecific wild populations. This identified 139 genomic regions that contained significantly elevated SNP homozygosity in farmed fish when compared to their wild counterparts. The most extreme was adjacent to versican, a gene involved in control of neural crest cell migration. To control for false positive signals, a second and independent dataset of farmed and wild European Atlantic salmon was assessed using the same methodology. A total of 81 outlier regions detected in the first dataset showed significantly reduced homozygosity within the second one, strongly suggesting the genomic regions identified are enriched for true selection sweeps. Examination of the associated genes identified a number previously characterized as targets of selection in other domestic species and that have roles in development, behavior and olfactory system. These include arcvf, sema6, errb4, id2-like, and 6n1-like genes. Finally, we searched for evidence of parallel sweeps using a farmed population of North American origin. This failed to detect a convincing overlap to the putative sweeps present in European populations, suggesting the factors that drive patterns of variation under domestication and early artificial selection were largely independent. This is the first analysis on domestication of aquaculture species exploiting whole-genome sequence data and resulted in the identification of sweeps common to multiple independent populations of farmed European Atlantic salmon.

Assessment of genetic diversity and population structure in cultured Australian Pacific oysters

The recent development of Pacific oyster (Crassostrea gigas) SNP genotyping arrays has allowed detailed characterisation of genetic diversity and population structure within and between oyster populations. It also raises the potential of harnessing genomic selection for genetic improvement in oyster breeding programmes. The aim of this study was to characterise a breeding population of Australian oysters through genotyping and analysis of 18 027 SNPs, followed by comparison with genotypes of oyster sampled from Europe and Asia. This revealed that the Australian populations had similar population diversity (H_E ) to oysters from New Zealand, the British Isles, France and Japan. Population divergence was assessed using PCA of genetic distance and revealed that Australian oysters were distinct from all other populations tested. Australian Pacific oysters originate from planned introductions sourced from three Japanese populations. Approximately 95% of these introductions were from geographically, and potentially genetically, distinct populations from the Nagasaki oysters assessed in this study. Finally, in preparation for the application of genomic selection in oyster breeding programmes, the strength of LD was evaluated and subsets of loci were tested for their ability to accurately infer relationships. Weak LD was observed on average; however, SNP subsets were shown to accurately reconstitute a genomic relationship matrix constructed using all loci. This suggests that low-density SNP panels may have utility in the Australian population tested, and the findings represent an important first step towards the design and implementation of genomic approaches for applied breeding in Pacific oysters.

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.

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 r² was 0.10. The Average r² 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 r² 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 r² 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.

Polygenic and sex specific architecture for two maturation traits in farmed Atlantic salmon

BACKGROUND

A key developmental transformation in the life of all vertebrates is the transition to sexual maturity, whereby individuals are capable of reproducing for the first time. In the farming of Atlantic salmon, early maturation prior to harvest size has serious negative production impacts.

RESULTS

We report genome wide association studies (GWAS) using fish measured for sexual maturation in freshwater or the marine environment. Genotypic data from a custom 50 K single nucleotide polymorphism (SNP) array was used to identify 13 significantly associated SNP for freshwater maturation with the most strongly associated on chromosomes 10 and 11. A higher number of associations (48) were detected for marine maturation, and the two peak loci were found to be the same for both traits. The number and broad distribution of GWAS hits confirmed a highly polygenetic nature, and GWAS performed separately within males and females revealed sex specific genetic behaviour for loci co-located with positional candidate genes phosphatidylinositol-binding clathrin assembly protein-like (picalm) and membrane-associated guanylate kinase, WW and PDZ domain-containing protein 2 (magi2).

CONCLUSIONS

The results extend earlier work and have implications for future applied breeding strategies to delay maturation in this important aquaculture species.

Genomic Selection in Aquaculture: Application, Limitations and Opportunities With Special Reference to Marine Shrimp and Pearl Oysters

Within aquaculture industries, selection based on genomic information (genomic selection) has the profound potential to change genetic improvement programs and production systems. Genomic selection exploits the use of realized genomic relationships among individuals and information from genome-wide markers in close linkage disequilibrium with genes of biological and economic importance. We discuss the technical advances, practical requirements, and commercial applications that have made genomic selection feasible in a range of aquaculture industries, with a particular focus on molluscs (pearl oysters, Pinctada maxima) and marine shrimp (Litopenaeus vannamei and Penaeus monodon). The use of low-cost genome sequencing has enabled cost-effective genotyping on a large scale and is of particular value for species without a reference genome or access to commercial genotyping arrays. We highlight the pitfalls and offer the solutions to the genotyping by sequencing approach and the building of appropriate genetic resources to undertake genomic selection from first-hand experience. We describe the potential to capture large-scale commercial phenotypes based on image analysis and artificial intelligence through machine learning, as inputs for calculation of genomic breeding values. The application of genomic selection over traditional aquatic breeding programs offers significant advantages through being able to accurately predict complex polygenic traits including disease resistance; increasing rates of genetic gain; minimizing inbreeding; and negating potential limiting effects of genotype by environment interactions. Further practical advantages of genomic selection through the use of large-scale communal mating and rearing systems are highlighted, as well as presenting rate-limiting steps that impact on attaining maximum benefits from adopting genomic selection. Genomic selection is now at the tipping point where commercial applications can be readily adopted and offer significant short- and long-term solutions to sustainable and profitable aquaculture industries.

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.

Multi-Tissue Transcriptome Profiling of North American Derived Atlantic Salmon

The availability of a reference genome assembly for Atlantic salmon, Salmo salar, SNP genotyping platforms and low cost sequencing are enhancing the understanding of both life history and production-related traits in this important commercial species. We collected and analyzed transcriptomes from selected tissues of Atlantic salmon to inform future functional and comparative genomics studies. Messenger RNA (mRNA) was isolated from pituitary gland, brain, ovary, and liver before Illumina sequencing produced a total of 640 million 150-bp paired-end reads. Following read mapping, feature counting, and normalization, cluster analysis identified genes highly expressed in a tissue-specific manner. We identified a cluster of 508 tissue specific genes for pituitary gland, 3395 for brain, 2939 for ovary, and 539 for liver. Functional profiling identified gene clusters describing the unique functions of each tissue. Moreover, highly-expressed transcription factors (TFs) present in each tissue-specific gene cluster were identified. TFs belonging to homeobox and bhlh families were identified for pituitary gland, pou and zf-c2h2 families for brain, arid, and zf-c2h2 for ovary and rxr-like family for liver. The data and analysis presented are relevant to the emerging Functional Annotation of All Salmonid Genomes (FAASG) initiative that is seeking to develop a detailed understanding of both salmonid evolution and the genomic elements that drive gene expression and regulation.

Accurate genomic predictions for BCWD resistance in rainbow trout are achieved using low-density SNP panels: Evidence that long-range LD is a major contributing factor

Previously accurate genomic predictions for Bacterial cold water disease (BCWD) resistance in rainbow trout were obtained using a medium-density single nucleotide polymorphism (SNP) array. Here, the impact of lower-density SNP panels on the accuracy of genomic predictions was investigated in a commercial rainbow trout breeding population. Using progeny performance data, the accuracy of genomic breeding values (GEBV) using 35K, 10K, 3K, 1K, 500, 300 and 200 SNP panels as well as a panel with 70 quantitative trait loci (QTL)-flanking SNP was compared. The GEBVs were estimated using the Bayesian method BayesB, single-step GBLUP (ssGBLUP) and weighted ssGBLUP (wssGBLUP). The accuracy of GEBVs remained high despite the sharp reductions in SNP density, and even with 500 SNP accuracy was higher than the pedigree-based prediction (0.50-0.56 versus 0.36). Furthermore, the prediction accuracy with the 70 QTL-flanking SNP (0.65-0.72) was similar to the panel with 35K SNP (0.65-0.71). Genomewide linkage disequilibrium (LD) analysis revealed strong LD (r2  ≥ 0.25) spanning on average over 1 Mb across the rainbow trout genome. This long-range LD likely contributed to the accurate genomic predictions with the low-density SNP panels. Population structure analysis supported the hypothesis that long-range LD in this population may be caused by admixture. Results suggest that lower-cost, low-density SNP panels can be used for implementing genomic selection for BCWD resistance in rainbow trout breeding programs.

Evolution of Sex Determination Loci in Atlantic Salmon

Teleost fish exhibit a remarkable diversity in the control of sex determination, offering the opportunity to identify novel differentiation mechanisms and their ecological consequences. Here, we perform GWAS using 4715 fish and 46,501 SNP to map sex determination to three separate genomic locations in Atlantic salmon (Salmo salar). To characterize each, whole genome sequencing was performed to 30-fold depth of coverage using 20 fish representing each of three identified sex lineages. SNP polymorphism reveals male fish carry a single copy of the male specific region, consistent with an XX/XY or male heterogametric sex system. Haplotype analysis revealed deep divergence between the putatively ancestral locus on chromosome 2, compared with loci on chromosomes 3 and 6. Haplotypes in fish carrying either the chromosome 3 or 6 loci were nearly indistinguishable, indicating a founding event that occurred following the speciation event that defined Salmo salar from other salmonids. These findings highlight the evolutionarily fluid state of sex determination systems in salmonids, and resolve to the sequence level differences in animals with divergent sex lineages.

Genome-Wide Association and Genomic Selection for Resistance to Amoebic Gill Disease in Atlantic Salmon

Amoebic gill disease (AGD) is one of the largest threats to salmon aquaculture, causing serious economic and animal welfare burden. Treatments can be expensive and environmentally damaging, hence the need for alternative strategies. Breeding for disease resistance can contribute to prevention and control of AGD, providing long-term cumulative benefits in selected stocks. The use of genomic selection can expedite selection for disease resistance due to improved accuracy compared to pedigree-based approaches. The aim of this work was to quantify and characterize genetic variation in AGD resistance in salmon, the genetic architecture of the trait, and the potential of genomic selection to contribute to disease control. An AGD challenge was performed in ∼1,500 Atlantic salmon, using gill damage and amoebic load as indicator traits for host resistance. Both traits are heritable (h2 ∼0.25-0.30) and show high positive correlation, indicating they may be good measurements of host resistance to AGD. While the genetic architecture of resistance appeared to be largely polygenic in nature, two regions on chromosome 18 showed suggestive association with both AGD resistance traits. Using a cross-validation approach, genomic prediction accuracy was up to 18% higher than that obtained using pedigree, and a reduction in marker density to ∼2,000 SNPs was sufficient to obtain accuracies similar to those obtained using the whole dataset. This study indicates that resistance to AGD is a suitable trait for genomic selection, and the addition of this trait to Atlantic salmon breeding programs can lead to more resistant stocks.

Accuracy of genomic within-family selection in aquaculture breeding programmes

In aquaculture breeding programmes, selection within families cannot be applied for traits that cannot be recorded on the candidates (e.g., disease resistance or fillet quality). However, this problem can be overcome if genomic evaluation is used. Within-family genomic evaluation has been proposed for these programmes as large family sizes are available and substantial levels of linkage disequilibrium (LD) within families can be attained with a limited number of markers even in populations in global linkage equilibrium. Here, we compare by computer simulation: (i) within-family and population-wide LD; and (ii) the accuracy of within-family genomic selection when genomic evaluations are carried out either at the population level or within families. The population simulated was composed by a varying number of families of full-sibs (half for training and half for testing). The results indicate that, to practice within-family selection, performing the genomic evaluation separately for each family using only molecular information from the family could be recommended for populations either in linkage equilibrium or with a low level of disequilibrium.

Genotype Imputation To Improve the Cost-Efficiency of Genomic Selection in Farmed Atlantic Salmon

Genomic selection uses genome-wide marker information to predict breeding values for traits of economic interest, and is more accurate than pedigree-based methods. The development of high density SNP arrays for Atlantic salmon has enabled genomic selection in selective breeding programs, alongside high-resolution association mapping of the genetic basis of complex traits. However, in sibling testing schemes typical of salmon breeding programs, trait records are available on many thousands of fish with close relationships to the selection candidates. Therefore, routine high density SNP genotyping may be prohibitively expensive. One means to reducing genotyping cost is the use of genotype imputation, where selected key animals (e.g., breeding program parents) are genotyped at high density, and the majority of individuals (e.g., performance tested fish and selection candidates) are genotyped at much lower density, followed by imputation to high density. The main objectives of the current study were to assess the feasibility and accuracy of genotype imputation in the context of a salmon breeding program. The specific aims were: (i) to measure the accuracy of genotype imputation using medium (25 K) and high (78 K) density mapped SNP panels, by masking varying proportions of the genotypes and assessing the correlation between the imputed genotypes and the true genotypes; and (ii) to assess the efficacy of imputed genotype data in genomic prediction of key performance traits (sea lice resistance and body weight). Imputation accuracies of up to 0.90 were observed using the simple two-generation pedigree dataset, and moderately high accuracy (0.83) was possible even with very low density SNP data (∼250 SNPs). The performance of genomic prediction using imputed genotype data was comparable to using true genotype data, and both were superior to pedigree-based prediction. These results demonstrate that the genotype imputation approach used in this study can provide a cost-effective method for generating robust genome-wide SNP data for genomic prediction in Atlantic salmon. Genotype imputation approaches are likely to form a critical component of cost-efficient genomic selection programs to improve economically important traits in aquaculture.