Accuracy of Genomic Evaluations of Juvenile Growth Rate in Common Carp (Cyprinus carpio) Using Genotyping by Sequencing

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.

Effects of common full-sib families on accuracy of genomic prediction for tagging weight in striped catfish Pangasianodon hypophthalmus

Common full-sib families (c ² ) make up a substantial proportion of total phenotypic variation in traits of commercial importance in aquaculture species and omission or inclusion of the c ² resulted in possible changes in genetic parameter estimates and re-ranking of estimated breeding values. However, the impacts of common full-sib families on accuracy of genomic prediction for commercial traits of economic importance are not well known in many species, including aquatic animals. This research explored the impacts of common full-sib families on accuracy of genomic prediction for tagging weight in a population of striped catfish comprising 11,918 fish traced back to the base population (four generations), in which 560 individuals had genotype records of 14,154 SNPs. Our single step genomic best linear unbiased prediction (ssGLBUP) showed that the accuracy of genomic prediction for tagging weight was reduced by 96.5%-130.3% when the common full-sib families were included in statistical models. The reduction in the prediction accuracy was to a smaller extent in multivariate analysis than in univariate models. Imputation of missing genotypes somewhat reduced the upward biases in the prediction accuracy for tagging weight. It is therefore suggested that genomic evaluation models for traits recorded during the early phase of growth development should account for the common full-sib families to minimise possible biases in the accuracy of genomic prediction and hence, selection response.

Increased accuracy of genomic predictions for growth under chronic thermal stress in rainbow trout by prioritizing variants from GWAS using imputed sequence data

Through imputation of genotypes, genome-wide association study (GWAS) and genomic prediction (GP) using whole-genome sequencing (WGS) data are cost-efficient and feasible in aquaculture breeding schemes. The objective was to dissect the genetic architecture of growth traits under chronic heat stress in rainbow trout (Oncorhynchus mykiss) and to assess the accuracy of GP based on imputed WGS and different preselected single nucleotide polymorphism (SNP) arrays. A total of 192 and 764 fish challenged to a heat stress experiment for 62 days were genotyped using a customized 1 K and 26 K SNP panels, respectively, and then, genotype imputation was performed from a low-density chip to WGS using 102 parents (36 males and 66 females) as the reference population. Imputed WGS data were used to perform GWAS and test GP accuracy under different preselected SNP scenarios. Heritability was estimated for body weight (BW), body length (BL) and average daily gain (ADG). Estimates using imputed WGS data ranged from 0.33 ± 0.05 to 0.55 ± 0.05 for growth traits under chronic heat stress. GWAS revealed that the top five cumulatively SNPs explained a maximum of 0.94%, 0.86% and 0.51% of genetic variance for BW, BL and ADG, respectively. Some important functional candidate genes associated with growth-related traits were found among the most important SNPs, including signal transducer and activator of transcription 5B and 3 (STAT5B and STAT3, respectively) and cytokine-inducible SH2-containing protein (CISH). WGS data resulted in a slight increase in prediction accuracy compared with pedigree-based method, whereas preselected SNPs based on the top GWAS hits improved prediction accuracies, with values ranging from 1.2 to 13.3%. Our results support the evidence of the polygenic nature of growth traits when measured under heat stress. The accuracies of GP can be improved using preselected variants from GWAS, and the use of WGS marginally increases prediction accuracy.

Genomic Predictions of Phenotypes and Pseudo-Phenotypes for Viral Nervous Necrosis Resistance, Cortisol Concentration, Antibody Titer and Body Weight in European Sea Bass

Simple Summary

Selective breeding programs based on genomic data are still not a common practice in aquaculture, although genomic selection has been widely demonstrated to be advantageous when trait phenotyping is a difficult task. In this study, we investigated the accuracy of predicting the phenotype and the estimated breeding value (EBV) of three Bayesian models and a Random Forest algorithm exploiting the information of a genome-wide SNP panel for European sea bass. The genomic predictions were developed for mortality caused by viral nervous necrosis, post-stress cortisol concentration, antibody titer against nervous necrosis virus and body weight. Selective breeding based on genomic data is a possible option for improving these traits while overcoming difficulties related to individual phenotyping of the investigated traits. Our results evidenced that the EBV used as a pseudo-phenotype enhances the predictive performances of genomic models, and that EBV can be predicted with satisfactory accuracy. The genomic prediction of the EBV for mortality might also be used to classify the phenotype for the same trait.

Abstract

In European sea bass (Dicentrarchus labrax L.), the viral nervous necrosis mortality (MORT), post-stress cortisol concentration (HC), antibody titer (AT) against nervous necrosis virus and body weight (BW) show significant heritability, which makes selective breeding a possible option for their improvement. An experimental population (N = 650) generated by a commercial broodstock was phenotyped for the aforementioned traits and genotyped with a genome-wide SNP panel (16,075 markers). We compared the predictive accuracies of three Bayesian models (Bayes B, Bayes C and Bayesian Ridge Regression) and a machine-learning method (Random Forest). The prediction accuracy of the EBV for MORT was approximately 0.90, whereas the prediction accuracies of the EBV and the phenotype were 0.86 and 0.21 for HC, 0.79 and 0.26 for AT and 0.71 and 0.38 for BW. The genomic prediction of the EBV for MORT used to classify the phenotype for the same trait showed moderate classification performance. Genome-wide association studies confirmed the polygenic nature of MORT and demonstrated a complex genetic structure for HC and AT. Genomic predictions of the EBV for MORT could potentially be used to classify the phenotype of the same trait, though further investigations on a larger experimental population are needed.

Genomic Selection in Aquaculture Species

To date, genomic prediction has been conducted in about 20 aquaculture species, with a preference for intra-family genomic selection (GS). For every trait under GS, the increase in accuracy obtained by genomic estimated breeding values instead of classical pedigree-based estimation of breeding values is very important in aquaculture species ranging from 15% to 89% for growth traits, and from 0% to 567% for disease resistance. Although the implementation of GS in aquaculture is of little additional investment in breeding programs already implementing sib testing on pedigree, the deployment of GS remains sparse, but could be boosted by adaptation of cost-effective imputation from low-density panels. Moreover, GS could help to anticipate the effect of climate change by improving sustainability-related traits such as production yield (e.g., carcass or fillet yields), feed efficiency or disease resistance, and by improving resistance to environmental variation (tolerance to temperature or salinity variation). This chapter synthesized the literature in applications of GS in finfish, crustaceans and molluscs aquaculture in the present and future breeding programs.

Development of Disease-Resistance-Associated Microsatellite DNA Markers for Selective Breeding of Tilapia (Oreochromis spp.) Farmed in Taiwan

There are numerous means to improve the tilapia aquaculture industry, and one is to develop disease resistance through selective breeding using molecular markers. In this study, 11 disease-resistance-associated microsatellite markers including 3 markers linked to hamp2, 4 linked to hamp1, 1 linked to pgrn2, 2 linked to pgrn1, and 1 linked to piscidin 4 (TP4) genes were established for tilapia strains farmed in Taiwan after challenge with Streptococcus inae. The correlation analysis of genotypes and survival revealed a total of 55 genotypes related to survival by the chi-square and Z-test. Although fewer markers were found in B and N2 strains compared with A strain, they performed well in terms of disease resistance. It suggested that this may be due to the low potency of some genotypes and the combinatorial arrangement between them. Therefore, a predictive model was built by the genotypes of the parental generation and the mortality rate of different combinations was calculated. The results show the same trend of predicted mortality in the offspring of three new disease-resistant strains as in the challenge experiment. The present findings is a nonkilling method without requiring the selection by challenge with bacteria or viruses and might increase the possibility of utilization of selective breeding using SSR markers in farms.

Genomic prediction for testes weight of the tiger pufferfish, Takifugu rubripes, using medium to low density SNPs

Aquaculture production is expected to increase with the help of genomic selection (GS). The possibility of performing GS using only a small number of SNPs has been examined in order to reduce genotyping costs; however, the practicality of this approach is still unclear. Here, we tested whether the effects of reducing the number of SNPs impaired the prediction accuracy of GS for standard length, body weight, and testes weight in the tiger pufferfish (Takifugu rubripes). High values for predictive ability (0.563-0.606) were obtained with 4000 SNPs for all traits under a genomic best linear unbiased predictor (GBLUP) model. These values were still within an acceptable range with 1200 SNPs (0.554-0.588). However, predictive abilities and prediction accuracies deteriorated using less than 1200 SNPs largely due to the reduced power in accurately estimating the genetic relationship among individuals; family structure could still be resolved with as few as 400 SNPs. This suggests that the SNPs informative for estimation of genetic relatedness among individuals differ from those for inference of family structure, and that non-random SNP selection based on the effects on family structure (e.g., site-F_ST, principal components, or random forest) is unlikely to increase the prediction accuracy for these traits.

Impact of genotypic errors with equal and unequal family contribution on accuracy of genomic prediction in aquaculture using simulation

Genotypic errors, conflict between recorded genotype and the true genotype, can lead to false or biased population genetic parameters. Here, the effect of genotypic errors on accuracy of genomic predictions and genomic relationship matrix are investigated using a simulation study based on population and genomic structure comparable to black tiger prawn, Penaeus monodon. Fifty full-sib families across five generations with phenotypic and genotypic information on 53 K SNPs were simulated. Ten replicates of different scenarios with three heritability estimates, equal and unequal family contributions were generated. Within each scenario, four SNP densities and three genotypic error rates in each SNP density were implemented. Results showed that family contribution did not have a substantial impact on accuracy of predictions across different datasets. In the absence of genotypic errors, 3 K SNP density was found to be efficient in estimating the accuracy, whilst increasing the SNP density from 3 to 20 K resulted in a marginal increase in accuracy of genomic predictions using the current population and genomic parameters. In addition, results showed that the presence of even 10% errors in a 10 and 20 K SNP panel might not have a severe impact on accuracy of predictions. However, below 10 K marker density, even a 5% error can result in lower accuracy of predictions.

Single Nucleotide Polymorphism Discovery and Genetic Differentiation Analysis of Geese Bred in Poland, Using Genotyping-by-Sequencing (GBS)

Poland is the largest European producer of goose, while goose breeding has become an essential and still increasing branch of the poultry industry. The most frequently bred goose is the White Kołuda^® breed, constituting 95% of the country’s population, whereas geese of regional varieties are bred in smaller, conservation flocks. However, a goose’s genetic diversity is inaccurately explored, mainly because the advantages of the most commonly used tools are strongly limited in non-model organisms. One of the most accurate used markers for population genetics is single nucleotide polymorphisms (SNP). A highly efficient strategy for genome-wide SNP detection is genotyping-by-sequencing (GBS), which has been already widely applied in many organisms. This study attempts to use GBS in 12 conservative goose breeds and the White Kołuda^® breed maintained in Poland. The GBS method allowed for the detection of 3833 common raw SNPs. Nevertheless, after filtering for read depth and alleles characters, we obtained the final markers panel used for a differentiation analysis that comprised 791 SNPs. These variants were located within 11 different genes, and one of the most diversified variants was associated with the EDAR gene, which is especially interesting as it participates in the plumage development, which plays a crucial role in goose breeding.

Toward Genome-Based Selection in Asian Seabass: What Can We Learn From Other Food Fishes and Farm Animals?

Due to the steadily increasing need for seafood and the plateauing output of fisheries, more fish need to be produced by aquaculture production. In parallel with the improvement of farming methods, elite food fish lines with superior traits for production must be generated by selection programs that utilize cutting-edge tools of genomics. The purpose of this review is to provide a historical overview and status report of a selection program performed on a catadromous predator, the Asian seabass (Lates calcarifer, Bloch 1790) that can change its sex during its lifetime. We describe the practices of wet lab, farm and lab in detail by focusing onto the foundations and achievements of the program. In addition to the approaches used for selection, our review also provides an inventory of genetic/genomic platforms and technologies developed to (i) provide current and future support for the selection process; and (ii) improve our understanding of the biology of the species. Approaches used for the improvement of terrestrial farm animals are used as examples and references, as those processes are far ahead of the ones used in aquaculture and thus they might help those working on fish to select the best possible options and avoid potential pitfalls.

Development of a 38 K single nucleotide polymorphism array and application in genomic selection for resistance against Vibrio harveyi in Chinese tongue sole, Cynoglossus semilaevis

Based on 1572 re-sequenced Chinese tongue sole (Cynoglossus semilaevis), we investigated the accuracy of four genomic methods at predicting genomic estimated breeding values (GEBVs) of Vibrio harveyi resistance in C. semilaevis when SNPs varying from 500 to 500 k. All methods outperformed the pedigree-based best linear unbiased prediction when SNPs reached 50 k or more. Then, we developed an SNP array "Solechip No.1" for C. semilaevis breeding using the Affymetrix Axiom technology. This array contains 38,295 SNPs with an average of 10.5 kb inter-spacing between two adjacent SNPs. We selected 44 candidates as the parents of 23 families and genotyped them by the array. The challenge survival rates of offspring families had a correlation of 0.706 with the mid-parental GEBVs. This SNP array is a convenient and reliable tool in genotyping, which could be used for improving V. harveyi resistance in C. semilaevis coupled with the genomic selection methods.

First genomic prediction and genome‐wide association for complex growth‐related traits in Rock Bream (Oplegnathus fasciatus)

Rock Bream (Oplegnathus fasciatus) is an important aquaculture species for offshore cage aquaculture and fish stocking of marine ranching in East Asia. Genomic selection has the potential to expedite genetic gain for the key target traits of a breeding program, but has not yet been evaluated in Oplegnathus. The purposes of the present study were to explore the performance of genomic selection to improve breeding value accuracy through real data analyses using six statistical models and to carry out genome‐wide association studies (GWAS) to dissect the genetic architecture of economically vital growth‐related traits (body weight, total length, and body depth) in the O. fasciatus population. After quality control, genotypes for 16,162 SNPs were acquired for 455 fish. Heritability was estimated to be moderate for the three traits (0.38 for BW, 0.33 for TL, and 0.24 for BD), and results of GWAS indicated that the underlying genetic architecture was polygenic. Six statistic models (GBLUP, BayesA, BayesB, BayesC, Bayesian Ridge‐Regression, and Bayesian LASSO) showed similar performance for the predictability of genomic estimated breeding value (GEBV). The low SNP density (around 1 K selected SNP based on GWAS) is sufficient for accurate prediction on the breeding value for the three growth‐related traits in the current studied population, which will provide a good compromise between genotyping costs and predictability in such standard breeding populations advanced. These consequences illustrate that the employment of genomic selection in O. fasciatus breeding could provide advantages for the selection of breeding candidates to facilitate complex economic growth traits.

Prediction Accuracies of Genomic Selection for Nine Commercially Important Traits in the Portuguese Oyster (Crassostrea angulata) Using DArT-Seq Technology

Genomic selection has been widely used in terrestrial animals but has had limited application in aquaculture due to relatively high genotyping costs. Genomic information has an important role in improving the prediction accuracy of breeding values, especially for traits that are difficult or expensive to measure. The purposes of this study were to (i) further evaluate the use of genomic information to improve prediction accuracies of breeding values from, (ii) compare different prediction methods (BayesA, BayesCπ and GBLUP) on prediction accuracies in our field data, and (iii) investigate the effects of different SNP marker densities on prediction accuracies of traits in the Portuguese oyster (Crassostrea angulata). The traits studied are all of economic importance and included morphometric traits (shell length, shell width, shell depth, shell weight), edibility traits (tenderness, taste, moisture content), and disease traits (Polydora sp. and Marteilioides chungmuensis). A total of 18,849 single nucleotide polymorphisms were obtained from genotyping by sequencing and used to estimate genetic parameters (heritability and genetic correlation) and the prediction accuracy of genomic selection for these traits. Multi-locus mixed model analysis indicated high estimates of heritability for edibility traits; 0.44 for moisture content, 0.59 for taste, and 0.72 for tenderness. The morphometric traits, shell length, shell width, shell depth and shell weight had estimated genomic heritabilities ranging from 0.28 to 0.55. The genomic heritabilities were relatively low for the disease related traits: Polydora sp. prevalence (0.11) and M. chungmuensis (0.10). Genomic correlations between whole weight and other morphometric traits were from moderate to high and positive (0.58–0.90). However, unfavourably positive genomic correlations were observed between whole weight and the disease traits (0.35–0.37). The genomic best linear unbiased prediction method (GBLUP) showed slightly higher accuracy for the traits studied (0.240–0.794) compared with both BayesA and BayesCπ methods but these differences were not significant. In addition, there is a large potential for using low-density SNP markers for genomic selection in this population at a number of 3000 SNPs. Therefore, there is the prospect to improve morphometric, edibility and disease related traits using genomic information in this species.

Genomics to accelerate genetic improvement in tilapia

Selective breeding of tilapia populations started in the early 1990s and over the past three decades tilapia has become one of the most important farmed freshwater species, being produced in more than 125 countries around the globe. Although genome assemblies have been available since 2011, most of the tilapia industry still depends on classical selection techniques using mass spawning or pedigree information to select for growth traits with reported genetic gains of up to 20% per generation. The involvement of international breeding companies and research institutions has resulted in the rapid development and application of genomic resources in the last few years. GWAS and genomic selection are expected to contribute to uncovering the genetic variants involved in economically relevant traits and increasing the genetic gain in selective breeding programs, respectively. Developments over the next few years will probably focus on achieving a deep understanding of genetic architecture of complex traits, as well as accelerating genetic progress in the selection for growth-, quality- and robustness-related traits. Novel phenotyping technologies (i.e. phenomics), lower-cost whole-genome sequencing approaches, functional genomics and gene editing tools will be crucial in future developments for the improvement of tilapia aquaculture.

Harnessing genomics to fast-track genetic improvement in aquaculture

Aquaculture is the fastest-growing farmed food sector and will soon become the primary source of fish and shellfish for human diets. In contrast to crop and livestock production, aquaculture production is derived from numerous, exceptionally diverse species that are typically in the early stages of domestication. Genetic improvement of production traits via well-designed, managed breeding programmes has great potential to help meet the rising seafood demand driven by human population growth. Supported by continuous advances in sequencing and bioinformatics, genomics is increasingly being applied across the broad range of aquaculture species and at all stages of the domestication process to optimize selective breeding. In the future, combining genomic selection with biotechnological innovations, such as genome editing and surrogate broodstock technologies, may further expedite genetic improvement in aquaculture.

Characterising the mechanisms underlying genetic resistance to amoebic gill disease in Atlantic salmon using RNA sequencing

Background

Gill health is one of the main concerns for Atlantic salmon aquaculture, and Amoebic Gill Disease (AGD), attributable to infection by the amoeba Neoparamoeba perurans, is a frequent cause of morbidity. In the absence of preventive measures, increasing genetic resistance of salmon to AGD via selective breeding can reduce the incidence of the disease and mitigate gill damage. Understanding the mechanisms leading to AGD resistance and the underlying causative genomic features can aid in this effort, while also providing critical information for the development of other control strategies. AGD resistance is considered to be moderately heritable, and several putative QTL have been identified. The aim of the current study was to improve understanding of the mechanisms underlying AGD resistance, and to identify putative causative genomic factors underlying the QTL. To achieve this, RNA was extracted from the gill and head kidney of AGD resistant and susceptible animals following a challenge with N. perurans, and sequenced.

Results

Comparison between resistant and susceptible animals primarily highlighted differences mainly in the local immune response in the gill, involving red blood cell genes and genes related to immune function and cell adhesion. Differentially expressed immune genes pointed to a contrast in Th2 and Th17 responses, which is consistent with the increased heritability observed after successive challenges with the amoeba. Five QTL-region candidate genes showed differential expression, including a gene connected to interferon responses (GVINP1), a gene involved in systemic inflammation (MAP4K4), and a positive regulator of apoptosis (TRIM39). Analyses of allele-specific expression highlighted a gene in the QTL region on chromosome 17, cellular repressor of E1A-stimulated genes 1 (CREG1), showing allelic differential expression suggestive of a cis-acting regulatory variant.

Conclusions

In summary, this study provides new insights into the mechanisms of resistance to AGD in Atlantic salmon, and highlights candidate genes for further functional studies that can further elucidate the genomic mechanisms leading to resistance and contribute to enhancing salmon health via improved genomic selection.

Fine-Scale Population Genetic Structure and Parapatric Cryptic Species of Kuruma Shrimp (Marsupenaeus japonicus), Along the Northwestern Pacific Coast of China

The kuruma shrimp (Marsupenaeus japonicus) includes two cryptic species, which are distributed mostly allopatrically but co-occur in the northern South China Sea (from Huilai to Beihai). To obtain a better understanding of the fine-scale genetic structure and parapatric diversification of these two varieties in the northwestern Pacific region, we used a genotyping-by-sequencing (GBS) and comparative transcriptomics approach to establish their phylogenetic relationships. Using the GBS technique, we genotyped 28891 SNPs in 160 individuals in the Northwest Pacific. The results supported two highly diverged evolutionary lineages of kuruma shrimp (var. I and II). The ND and XM populations showed complex genetic patterns, which might be affected by the complex environment of the Taiwan Strait. In addition, the migration rates and inbreeding coefficients of XM and BH were much lower than those of the other populations, which might be related to the land-sea changes and complex ocean currents in the Taiwan Strait and Qiongzhou Strait. Based on the synonymous substitution rates (ds) of 2,491 candidate orthologs, we estimated that the divergence time between the two varieties was 0.26~0.69 Mya. Choice and no-choice interbreeding experiments provided support for the biological species concept, by showing the existence of reproductive isolation or incompatibility. In view of these differences between the two Marsupenaeus species, we believe that it is essential and urgent to establish a genetic database for each and reevaluate their ecological suitable conditions in order to improve species-specific culturing techniques. Moreover, this research can serve as a case study for future research on speciation and hybridization.

Potential of genomic selection for improvement of resistance to ostreid herpesvirus in Pacific oyster (Crassostrea gigas)

In genomic selection (GS), genome-wide SNP markers are used to generate genomic estimated breeding values for selection candidates. The application of GS in shellfish looks promising and has the potential to help in dealing with one of the main issues currently affecting Pacific oyster production worldwide, which is the 'summer mortality syndrome'. This causes periodic mass mortality in farms worldwide and has mainly been attributed to a specific variant of the ostreid herpesvirus (OsHV-1). In the current study, we evaluated the potential of genomic selection for host resistance to OsHV-1 in Pacific oysters, and compared it with pedigree-based approaches. An OsHV-1 disease challenge was performed using an immersion-based virus exposure treatment for oysters for 7 days. A total of 768 samples were genotyped using the medium-density SNP array for oysters. A GWAS was performed for the survival trait using a GBLUP approach in blupf90 software. Heritability ranged from 0.25 ± 0.05 to 0.37 ± 0.05 (mean ± SE) based on pedigree and genomic information respectively. Genomic prediction was more accurate than pedigree prediction, and SNP density reduction had little impact on prediction accuracy until marker densities dropped below approximately 500 SNPs. This demonstrates the potential for GS in Pacific oyster breeding programmes, and importantly, demonstrates that a low number of SNPs might suffice to obtain accurate genomic estimated breeding values, thus potentially making the implementation of GS more cost effective.

Optimizing Low-Cost Genotyping and Imputation Strategies for Genomic Selection in Atlantic Salmon

Genomic selection enables cumulative genetic gains in key production traits such as disease resistance, playing an important role in the economic and environmental sustainability of aquaculture production. However, it requires genome-wide genetic marker data on large populations, which can be prohibitively expensive. Genotype imputation is a cost-effective method for obtaining high-density genotypes, but its value in aquaculture breeding programs which are characterized by large full-sibling families has yet to be fully assessed. The aim of this study was to optimize the use of low-density genotypes and evaluate genotype imputation strategies for cost-effective genomic prediction. Phenotypes and genotypes (78,362 SNPs) were obtained for 610 individuals from a Scottish Atlantic salmon breeding program population (Landcatch, UK) challenged with sea lice, Lepeophtheirus salmonis The genomic prediction accuracy of genomic selection was calculated using GBLUP approaches and compared across SNP panels of varying densities and composition, with and without imputation. Imputation was tested when parents were genotyped for the optimal SNP panel, and offspring were genotyped for a range of lower density imputation panels. Reducing SNP density had little impact on prediction accuracy until 5,000 SNPs, below which the accuracy dropped. Imputation accuracy increased with increasing imputation panel density. Genomic prediction accuracy when offspring were genotyped for just 200 SNPs, and parents for 5,000 SNPs, was 0.53. This accuracy was similar to the full high density and optimal density dataset, and markedly higher than using 200 SNPs without imputation. These results suggest that imputation from very low to medium density can be a cost-effective tool for genomic selection in Atlantic salmon breeding programs.

Evaluation of Genomic Selection for Seven Economic Traits in Yellow Drum (Nibea albiflora)

Yellow drum (Nibea albiflora) is an important maricultural fish in China, and genetic improvement is necessary for this species. This research evaluated the application of genomic selection methods to predict the genetic values of seven economic traits for yellow drum. Using genome-wide single-nucleotide polymorphisms (SNPs), we estimated the genetic parameters for seven traits, including body length (BL), swimming bladder index (SBI), swimming bladder weight (SBW), body thickness (BT), body height (BH), body length/body height ratio (LHR), and gonad weight index (GWI). The heritability estimates ranged from 0.309 to 0.843. We evaluated the prediction performance of various statistical methods, and no one method provided the highest predictive ability for all traits. We then evaluated and compared the use of genome-wide association study (GWAS)-informative SNPs and random SNPs for prediction and found that GWAS-informative SNPs obviously increased. It only needed 5 and 100 informative SNPs for LHR and BT to achieve almost the same predictive abilities as using genome-wide SNPs, and for BL, SBI, SBW, BH, and GWI, about 1000 to 3000 informative SNPs were needed to achieve whole-genome level predictive abilities. It can be concluded from the test results that breeders can use fewer SNPs to save the breeding costs of genomic selection for some traits.

Genomic prediction for disease resistance to Hepatopancreatic parvovirus and growth, carcass and quality traits in Banana shrimp Fenneropenaeus merguiensis

Conventional genetic improvement of disease resistance in aquatic animal species involves challenge tests or using qPCR to quantify viral load that is costly, time-consuming and causing biosecurity concerns. Recent developments in high throughput next generation genome sequencing platforms such as genotyping by sequencing (GBS) have opened new possibilities for improving disease traits based on DNA information. The principal aim of this study was thus to examine potential application of genomic selection to improve resistance to hepatopancreatic parvovirus (HPV) in banana shrimp Fenneropenaeus merguiensis. Specifically, we used a total of 9472 single nucleotide polymorphisms (SNPs) developed de novo from GBS platforms to assess accuracy of genomic prediction for HPV resistance and growth, carcass and quality-related traits in this white shrimp species. Our multi-locus mixed model analysis showed moderate heritabilities for HPV resistance (h² = 0.46) and other traits studied (0.10 to 0.55). Genetic correlations of HPV titre with growth and carcass traits, estimated using SNPs markers, were negative (i.e., favourable), suggesting that selection for improved growth and carcass traits may have increased HPV resistance (i.e., reduced HPV titre). More importantly, our gBLUP model demonstrated that the accuracy of gBLUP prediction was moderate for HPV disease resistance (0.46). The genomic prediction accuracy was somewhat greater for growth and carcass related traits especially for body weight (0.76) and meat or tail weight (0.77). On the other hand, the prediction accuracy was from 0.25 to 0.41 for quality traits (raw and cooked colour and flesh streaks). Collectively, it is concluded that there are prospects to apply genomic selection in the genetic improvement for increased disease resistance, carcass and quality-related traits in this population of banana shrimp F. merguiensis.

High-Density Linkage Map and Mapping for Sex and Growth-Related Traits of Largemouth Bass (Micropterus salmoides)

The largemouth bass is an important species, and its culture has risen sharply with the surge in fish aquaculture in China. Due to the lack of selective breeding technology for the largemouth bass, the growth rate and disease resistance are low, its sexual maturation is slow, and other serious problems are contributing to a sharp decline in the safety and quality of largemouth bass products in recent decades. Therefore, comprehensive breeding programs to improve the economic performance and promote the modern industrial development of largemouth bass must be considered a priority. Here, a total of 152 adult largemouth bass, including two parents and 150 progenies, were selected to produce the genetic mapping family. Then, a high-density linkage map was constructed based on restriction site–associated DNA sequencing using 6,917 single-nucleotide polymorphisms (SNPs) located in 24 linkage groups (LGs). The total genetic length of the linkage map was 1,261.96 cM, and the length of each LG varied from 24.72 cM for LG02 to 117.53 cM for LG16, with an average length of 52.58 cM and an average SNP number of 286. Thirteen significant quantitative trait loci (QTLs) for sex determination were located on LG04, LG05, LG08, LG12, LG15, LG21, and LG23. An informative QTL cluster that included six QTLs was detected on LG12. However, one notable QTL, which accounted for 71.48% of the total phenotypic variation, was located in the region of 1.85 cM on LG05. In addition, 32 identified QTLs were related to growth, including body weight, body length, body height, and head length. The QTLs for these growth-related traits are located in 13 LG regions and have little effect on phenotypic variation. This high-density genetic linkage map will enable the fine-mapping of economic traits and support the future genome assembly of the largemouth bass. Additionally, our study will be useful for future selective culture of largemouth bass and could potentially be used in molecular-assisted breeding of largemouth bass for aquaculture.

First Genome-wide Association Analysis for Growth Traits in the Largest Coral Reef-Dwelling Bony Fishes, the Giant Grouper (Epinephelus lanceolatus)

The giant grouper, Epinephelus lanceolatus, is the largest coral reef-dwelling bony fish species. However, despite extremely fast growth performance and the considerable economic importance in this species, its genetic regulation of growth remains unknown. Here, we performed the first genome-wide association study (GWAS) for five growth traits in 289 giant groupers using 42,323 single nucleotide polymorphisms (SNPs) obtained by genotyping-by-sequencing (GBS). We identified a total of 36 growth-related SNPs, of which 11 SNPs reached a genome-wide significance level. The phenotypic variance explained by these SNPs varied from 7.09% for body height to 18.42% for body length. Moreover, 22 quantitative trait loci (QTLs) for growth traits, including nine significant QTLs and 13 suggestive QTLs, were found on multiple chromosomes. Interestingly, the QTL (LG17: 6934451) was shared between body weight and body height, while two significant QTLs (LG7: 22596399 and LG15: 11877836) for body length were consistent with the associated regions of total length at the genome-wide suggestive level. Eight potential candidate genes close to the associated SNPs were selected for expression analysis, of which four genes (phosphatidylinositol transfer protein cytoplasmic 1, protein tyrosine phosphatase receptor type E, alpha/beta hydrolase domain-containing protein 17C, and vascular endothelial growth factor A-A) were differentially expressed and involved in metabolism, development, response stress, etc. This study improves our understanding of the complex genetic architecture of growth in the giant grouper. The results contribute to the selective breeding of grouper species and the conservation of coral reef fishes.

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.

Scanning of Genetic Variants and Genetic Mapping of Phenotypic Traits in Gilthead Sea Bream Through ddRAD Sequencing

Gilthead sea bream (Sparus aurata) is a teleost of considerable economic importance in Southern European aquaculture. The aquaculture industry shows a growing interest in the application of genetic methods that can locate phenotype-genotype associations with high economic impact. Through selective breeding, the aquaculture industry can exploit this information to maximize the financial yield. Here, we present a Genome Wide Association Study (GWAS) of 112 samples belonging to seven different sea bream families collected from a Greek commercial aquaculture company. Through double digest Random Amplified DNA (ddRAD) Sequencing, we generated a per-sample genetic profile consisting of 2,258 high-quality Single Nucleotide Polymorphisms (SNPs). These profiles were tested for association with four phenotypes of major financial importance: Fat, Weight, Tag Weight, and the Length to Width ratio. We applied two methods of association analysis. The first is the typical single-SNP to phenotype test, and the second is a feature selection (FS) method through two novel algorithms that are employed for the first time in aquaculture genomics and produce groups with multiple SNPs associated to a phenotype. In total, we identified 9 single SNPs and 6 groups of SNPs associated with weight-related phenotypes (Weight and Tag Weight), 2 groups associated with Fat, and 16 groups associated with the Length to Width ratio. Six identified loci (Chr4:23265532, Chr6:12617755, Chr:8:11613979, Chr13:1098152, Chr15:3260819, and Chr22:14483563) were present in genes associated with growth in other teleosts or even mammals, such as semaphorin-3A and neurotrophin-3. These loci are strong candidates for future studies that will help us unveil the genetic mechanisms underlying growth and improve the sea bream aquaculture productivity by providing genomic anchors for selection programs.

Optimizing Genomic Prediction of Host Resistance to Koi Herpesvirus Disease in Carp

Genomic selection (GS) is increasingly applied in breeding programs of major aquaculture species, enabling improved prediction accuracy and genetic gain compared to pedigree-based approaches. Koi Herpesvirus disease (KHVD) is notifiable by the World Organization for Animal Health and the European Union, causing major economic losses to carp production. GS has potential to breed carp with improved resistance to KHVD, thereby contributing to disease control. In the current study, Restriction-site Associated DNA sequencing (RAD-seq) was applied on a population of 1,425 common carp juveniles which had been challenged with Koi herpes virus, followed by sampling of survivors and mortalities. GS was tested on a wide range of scenarios by varying both SNP densities and the genetic relationships between training and validation sets. The accuracy of correctly identifying KHVD resistant animals using GS was between 8 and 18% higher than pedigree best linear unbiased predictor (pBLUP) depending on the tested scenario. Furthermore, minor decreases in prediction accuracy were observed with decreased SNP density. However, the genetic relationship between the training and validation sets was a key factor in the efficacy of genomic prediction of KHVD resistance in carp, with substantially lower prediction accuracy when the relationships between the training and validation sets did not contain close relatives.

Multiple interacting QTLs affect disease challenge survival in common carp (Cyprinus carpio)

With the steady growth of the human population, food security becomes a prime challenge. Aquaculture is the fastest growing sector providing proteins from an animal source, but outbreaks of infectious diseases repeatedly hamper the production and further development of this sector. Breeding of disease-resistant strains is a desired sustainable solution to this problem. Cyprinid herpes virus-3 (CyHV-3) is a dsDNA virus damaging production of common carp, an important food and ornamental fish. Previously, we have demonstrated successful introgression of CyHV-3 resistance from a feral strain to commercial strains. Here, we used genotyping by sequencing to identify two novel quantitative trait loci (QTLs) for disease survival that map to different linkage groups than two other QTLs that we previously identified. Effects of these four QTLs were validated and further studied in 14 families with various levels of disease resistance. CyHV-3 survival was found to be a quantitative trait conditioned by mild additive QTL effects and by intricate dominant allelic and epistatic QTL-QTL interactions. Both rare feral alleles and alleles common to feral and cultured strains contributed to survival. This and other advantages of feral alleles introgression were demonstrated. These QTLs, which affected survival of individuals within families, had no significant effect on variation in cumulative family % survival, suggesting that more between family variation remains to be explored. Unraveling the underlying genetics of survival is important for enhancing the breeding of resistant strains and our knowledge of disease resistance mechanisms.

The future of parentage analysis: From microsatellites to SNPs and beyond

Parentage analysis is a cornerstone of molecular ecology that has delivered fundamental insights into behaviour, ecology and evolution. Microsatellite markers have long been the king of parentage, their hypervariable nature conferring sufficient power to correctly assign offspring to parents. However, microsatellite markers have seen a sharp decline in use with the rise of next-generation sequencing technologies, especially in the study of population genetics and local adaptation. The time is ripe to review the current state of parentage analysis and see how it stands to be affected by the emergence of next-generation sequencing approaches. We find that single nucleotide polymorphisms (SNPs), the typical next-generation sequencing marker, remain underutilized in parentage analysis but are gaining momentum, with 58 SNP-based parentage analyses published thus far. Many of these papers, particularly the earlier ones, compare the power of SNPs and microsatellites in a parentage context. In virtually every case, SNPs are at least as powerful as microsatellite markers. As few as 100-500 SNPs are sufficient to resolve parentage completely in most situations. We also provide an overview of the analytical programs that are commonly used and compatible with SNP data. As the next-generation parentage enterprise grows, a reliance on likelihood and Bayesian approaches, as opposed to strict exclusion, will become increasingly important. We discuss some of the caveats surrounding the use of next-generation sequencing data for parentage analysis and conclude that the future is bright for this important realm of molecular ecology.

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.

Disentangling Genetic Variation for Resistance and Endurance to Scuticociliatosis in Turbot Using Pedigree and Genomic Information

Selective breeding for improving host responses to infectious pathogens is a promising option for disease control. In fact, disease resilience, the ability of a host to survive or cope with infectious challenge, has become a highly desirable breeding goal. However, resilience is a complex trait composed of two different host defence mechanisms, namely resistance (the ability of a host to avoid becoming infected or diseased) and endurance (the ability of an infected host to survive the infection). While both could be targeted for genetic improvement, it is currently unknown how they contribute to survival, as reliable estimates of genetic parameters for both traits obtained simultaneously are scarce. A difficulty lies in obtaining endurance phenotypes for genetic analyses. In this study, we present the results from an innovative challenge test carried out in turbot whose design allowed disentangling the genetic basis of resistance and endurance to Philasterides dicentrarchi, a parasite causing scuticociliatosis that leads to substantial economic losses in the aquaculture industry. A noticeable characteristic of the parasite is that it causes visual signs that can be used for disentangling resistance and endurance. Our results showed the existence of genetic variation for both traits (heritability = 0.26 and 0.12 for resistance and endurance, respectively) and for the composite trait resilience (heritability = 0.15). The genetic correlation between resistance and resilience was very high (0.90) indicating that both are at a large extent the same trait, but no significant genetic correlation was found between resistance and endurance. A total of 18,125 SNPs obtained from 2b-RAD sequencing enabled genome-wide association analyses for detecting QTLs controlling the three traits. A candidate QTL region on linkage group 19 that explains 33% of the additive genetic variance was identified for resilience. The region contains relevant genes related to immune response and defence mechanisms. Although no significant associations were found for resistance, the pattern of association was the same as for resilience. For endurance, one significant association was found on linkage group 2. The accuracy of genomic breeding values was also explored for resilience, showing that it increased by 12% when compared with the accuracy of pedigree-based breeding values. To our knowledge, this is the first study in turbot disentangling the genetic basis of resistance and endurance to scuticociliatosis.

Mapping and Sequencing of a Significant Quantitative Trait Locus Affecting Resistance to Koi Herpesvirus in Common Carp

Cyprinids are the most highly produced group of fishes globally, with common carp being one of the most valuable species of the group. Koi herpesvirus (KHV) infections can result in high levels of mortality, causing major economic losses, and is listed as a notifiable disease by the World Organization for Animal Health. Selective breeding for host resistance has the potential to reduce morbidity and losses due to KHV. Therefore, improving knowledge about host resistance and methods of incorporating genomic data into breeding for resistance may contribute to a decrease in economic losses in carp farming. In the current study, a population of 1,425 carp juveniles, originating from a factorial cross between 40 sires and 20 dams was challenged with KHV. Mortalities and survivors were recorded and sampled for genotyping by sequencing using Restriction Site-Associated DNA sequencing (RADseq). Genome-wide association analyses were performed to investigate the genetic architecture of resistance to KHV. A genome-wide significant QTL affecting resistance to KHV was identified on linkage group 44, explaining approximately 7% of the additive genetic variance. Pooled whole genome resequencing of a subset of resistant (n = 60) and susceptible animals (n = 60) was performed to characterize QTL regions, including identification of putative candidate genes and functional annotation of associated polymorphisms. The TRIM25 gene was identified as a promising positional and functional candidate within the QTL region of LG 44, and a putative premature stop mutation in this gene was discovered.

Development and Validation of 58K SNP-Array and High-Density Linkage Map in Nile Tilapia (O. niloticus)

Despite being the second most important aquaculture species in the world accounting for 7.4% of global production in 2015, tilapia aquaculture has lacked genomic tools like SNP-arrays and high-density linkage maps to improve selection accuracy and accelerate genetic progress. In this paper, we describe the development of a genotyping array containing more than 58,000 SNPs for Nile tilapia (Oreochromis niloticus). SNPs were identified from whole genome resequencing of 32 individuals from the commercial population of the Genomar strain, and were selected for the SNP-array based on polymorphic information content and physical distribution across the genome using the Orenil1.1 genome assembly as reference sequence. SNP-performance was evaluated by genotyping 4991 individuals, including 689 offspring belonging to 41 full-sib families, which revealed high-quality genotype data for 43,588 SNPs. A preliminary genetic linkage map was constructed using Lepmap2 which in turn was integrated with information from the O_niloticus_UMD1 genome assembly to produce an integrated physical and genetic linkage map comprising 40,186 SNPs distributed across 22 linkage groups (LGs). Around one-third of the LGs showed a different recombination rate between sexes, with the female being greater than the male map by a factor of 1.2 (1632.9 to 1359.6 cM, respectively), with most LGs displaying a sigmoid recombination profile. Finally, the sex-determining locus was mapped to position 40.53 cM on LG23, in the vicinity of the anti-Müllerian hormone (amh) gene. These new resources has the potential to greatly influence and improve the genetic gain when applying genomic selection and surpass the difficulties of efficient selection for invasively measured traits in Nile tilapia.

Genomic Selection for Growth Traits in Pacific Oyster (Crassostrea gigas): Potential of Low-Density Marker Panels for Breeding Value Prediction

Pacific oysters are a key aquaculture species globally, and genetic improvement via selective breeding is a major target. Genomic selection has the potential to expedite genetic gain for key target traits of a breeding program, but has not yet been evaluated in oyster. The recent development of SNP arrays for Pacific oyster (Crassostrea gigas) raises the opportunity to test genomic selection strategies for polygenic traits. In this study, a population of 820 oysters (comprising 23 full-sibling families) were genotyped using a medium density SNP array (23 K informative SNPs), and the genetic architecture of growth-related traits [shell height (SH), shell length (SL), and wet weight (WW)] was evaluated. Heritability was estimated to be moderate for the three traits (0.26 ± 0.06 for SH, 0.23 ± 0.06 for SL and 0.35 ± 0.05 for WW), and results of a GWAS indicated that the underlying genetic architecture was polygenic. Genomic prediction approaches were used to estimate breeding values for growth, and compared to pedigree based approaches. The accuracy of the genomic prediction models (GBLUP) outperformed the traditional pedigree approach (PBLUP) by ∼25% for SL and WW, and ∼30% for SH. Further, reduction in SNP marker density had little impact on prediction accuracy, even when density was reduced to a few hundred SNPs. These results suggest that the use of genomic selection in oyster breeding could offer benefits for the selection of breeding candidates to improve complex economic traits at relatively modest cost.

Genome-wide association and genomic prediction of resistance to viral nervous necrosis in European sea bass (Dicentrarchus labrax) using RAD sequencing

BACKGROUND

European sea bass (Dicentrarchus labrax) is one of the most important species for European aquaculture. Viral nervous necrosis (VNN), commonly caused by the redspotted grouper nervous necrosis virus (RGNNV), can result in high levels of morbidity and mortality, mainly during the larval and juvenile stages of cultured sea bass. In the absence of efficient therapeutic treatments, selective breeding for host resistance offers a promising strategy to control this disease. Our study aimed at investigating genetic resistance to VNN and genomic-based approaches to improve disease resistance by selective breeding. A population of 1538 sea bass juveniles from a factorial cross between 48 sires and 17 dams was challenged with RGNNV with mortalities and survivors being recorded and sampled for genotyping by the RAD sequencing approach.

RESULTS

We used genome-wide genotype data from 9195 single nucleotide polymorphisms (SNPs) for downstream analysis. Estimates of heritability of survival on the underlying scale for the pedigree and genomic relationship matrices were 0.27 (HPD interval 95%: 0.14-0.40) and 0.43 (0.29-0.57), respectively. Classical genome-wide association analysis detected genome-wide significant quantitative trait loci (QTL) for resistance to VNN on chromosomes (unassigned scaffolds in the case of 'chromosome' 25) 3, 20 and 25 (P < 1e06). Weighted genomic best linear unbiased predictor provided additional support for the QTL on chromosome 3 and suggested that it explained 4% of the additive genetic variation. Genomic prediction approaches were tested to investigate the potential of using genome-wide SNP data to estimate breeding values for resistance to VNN and showed that genomic prediction resulted in a 13% increase in successful classification of resistant and susceptible animals compared to pedigree-based methods, with Bayes A and Bayes B giving the highest predictive ability.

CONCLUSIONS

Genome-wide significant QTL were identified but each with relatively small effects on the trait. Tests of genomic prediction suggested that incorporating genome-wide SNP data is likely to result in higher accuracy of estimated breeding values for resistance to VNN. RAD sequencing is an effective method for generating such genome-wide SNPs, and our findings highlight the potential of genomic selection to breed farmed European sea bass with improved resistance to VNN.