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

Transcriptome analysis of Fenneropenaeus merguiensis in response to Vibrio proteolyticus infection

In recent years, due to the destruction of the culture environment and serious ecological pressure, especially in the process of culture, residual bait, faeces and fishery drug abuse will lead to the accumulation of harmful metabolites such as ammonia nitrogen and nitrite, and biological denitrification is the most economical and effective method to remove the single. Therefore, in this study, a nitrite removal strain XA19 was isolated and screened from a shrimp biofloc culture pond. This strain was identified as a clade of Vibrio proteolyticus because the homology between XA19 and V. proteolyticus WDVP was as high as 99.86% by using 16S rDNA gene sequence analysis and NCBI database comparison. Scanning electron microscopy images showed that V. proteolyticus is short-rod-shaped with a curved body and no budding spores, pods and flagella. Antimicrobial susceptibility test proved that V. proteolyticus was resistant to ampicillin, oxacillin, penicillin, vancomycin and clindamycin. In the median lethal concentration 50 (LC50 ) test, at 7-day post-infection (dpi), LC50 of V. proteolyticus for Fenneropenaeus merguiensis was 1.69 × 104  CFU/mL. Transcriptome sequencing analysis was carried out on hepatopancreas of F. merguiensis at 24 and 48 hpi. A total of 176 differentially expressed genes (DEGs) were screened at 24 hpi, including 104 up-regulated DEGs and 72 down-regulated DEGs, and a total of 52 DEGs were screened at 48 hpi, including 32 up-regulated DEGs and 20 down-regulated DEGs. In the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs, many immune-related signalling pathways were significantly enriched, including Hippo signalling pathway, phagosome, Toll and Imd signalling pathways and Wnt signalling pathway. In addition, some pathways related to Warburg effect were also enriched, including Glycolysis/Gluconeogenesis, Biosynthesis of amino acids, amino sugar and nucleotide sugar metabolism and so on. In this study, the toxicity and drug sensitivity of V. proteolyticus were systematically studied, and the immune response of hepatopancreas of F. merguiensis to V. proteolyticus infection was preliminarily revealed from the molecular level. The results may provide a reference for the prevention and control of V. proteolyticus.

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.

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.

Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms

Assessments of genomic prediction accuracies using artificial intelligent (AI) algorithms (i.e., machine and deep learning methods) are currently not available or very limited in aquaculture species. The principal aim of this study was to examine the predictive performance of these new methods for disease resistance to Edwardsiella ictaluri in a population of striped catfish Pangasianodon hypophthalmus and to make comparisons with four common methods, i.e., pedigree-based best linear unbiased prediction (PBLUP), genomic-based best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP) and a nonlinear Bayesian approach (notably BayesR). Our analyses using machine learning (i.e., ML-KAML) and deep learning (i.e., DL-MLP and DL-CNN) together with the four common methods (PBLUP, GBLUP, ssGBLUP, and BayesR) were conducted for two main disease resistance traits (i.e., survival status coded as 0 and 1 and survival time, i.e., days that the animals were still alive after the challenge test) in a pedigree consisting of 560 individual animals (490 offspring and 70 parents) genotyped for 14,154 single nucleotide polymorphism (SNPs). The results using 6,470 SNPs after quality control showed that machine learning methods outperformed PBLUP, GBLUP, and ssGBLUP, with the increases in the prediction accuracies for both traits by 9.1–15.4%. However, the prediction accuracies obtained from machine learning methods were comparable to those estimated using BayesR. Imputation of missing genotypes using AlphaFamImpute increased the prediction accuracies by 5.3–19.2% in all the methods and data used. On the other hand, there were insignificant decreases (0.3–5.6%) in the prediction accuracies for both survival status and survival time when multivariate models were used in comparison to univariate analyses. Interestingly, the genomic prediction accuracies based on only highly significant SNPs (P < 0.00001, 318–400 SNPs for survival status and 1,362–1,589 SNPs for survival time) were somewhat lower (0.3–15.6%) than those obtained from the whole set of 6,470 SNPs. In most of our analyses, the accuracies of genomic prediction were somewhat higher for survival time than survival status (0/1 data). It is concluded that although there are prospects for the application of genomic selection to increase disease resistance to E. ictaluri in striped catfish breeding programs, further evaluation of these methods should be made in independent families/populations when more data are accumulated in future generations to avoid possible biases in the genetic parameters estimates and prediction accuracies for the disease-resistant traits studied in this population of striped catfish P. hypophthalmus.

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.

Genomic Prediction for Whole Weight, Body Shape, Meat Yield, and Color Traits in the Portuguese Oyster Crassostrea angulata

Genetic improvement for quality traits, especially color and meat yield, has been limited in aquaculture because the assessment of these traits requires that the animals be slaughtered first. Genotyping technologies do, however, provide an opportunity to improve the selection efficiency for these traits. The main purpose of this study is to assess the potential for using genomic information to improve meat yield (soft tissue weight and condition index), body shape (cup and fan ratios), color (shell and mantle), and whole weight traits at harvest in the Portuguese oyster, Crassostrea angulata. The study consisted of 647 oysters: 188 oysters from 57 full-sib families from the first generation and 459 oysters from 33 full-sib families from the second generation. The number per family ranged from two to eight oysters for the first and 12–15 oysters for the second generation. After quality control, a set of 13,048 markers were analyzed to estimate the genetic parameters (heritability and genetic correlation) and predictive accuracy of the genomic selection for these traits. The multi-locus mixed model analysis indicated high estimates of heritability for meat yield traits: 0.43 for soft tissue weight and 0.77 for condition index. The estimated genomic heritabilities were 0.45 for whole weight, 0.24 for cup ratio, and 0.33 for fan ratio and ranged from 0.14 to 0.54 for color traits. The genetic correlations among whole weight, meat yield, and body shape traits were favorably positive, suggesting that the selection for whole weight would have beneficial effects on meat yield and body shape traits. Of paramount importance is the fact that the genomic prediction showed moderate to high accuracy for the traits studied (0.38–0.92). Therefore, there are good prospects to improve whole weight, meat yield, body shape, and color traits using genomic information. A multi-trait selection program using the genomic information can boost the genetic gain and minimize inbreeding in the long-term for this population.

Genome-Wide Marker Analysis for Traits of Economic Importance in Asian Seabass Lates calcarifer

To date, it is not known whether animal breeding values in Asian seabass (Lates calcarifer) can be estimated using single nucleotide polymorphisms (SNPs) generated from new high-throughput genotyping by sequencing platforms. The principal aim of the present study was to assess the genomic prediction accuracy for growth traits, survival, cannibalism, and disease resistance against Streptococcus iniae in this species L. calcarifer. Additionally, this study attempted to identify markers associated with the five traits studied as well as to understand if the genotype data can be used to estimate genetic parameters for these complex traits. The genomic best linear unbiased prediction (gBLUP) method was used to analyze 11,084 SNPs and showed that the prediction accuracies for growth traits (weight and length) were high (0.67–0.75). By contrast, these estimates for survival were low (0.25). Multi-locus mixed model analyses identified four SNPs significantly associated with body weight (p < 5 × 10−8 or −log10 p ≥ 5). There were, however, no significant associations detected for other traits. Similarly, the SNP heritability was moderate, while the estimates for other traits were approximated to zero and not significant. Genetic correlations between body weight and standard length were close to unity. Collectively, the results obtained from this study suggest that genotyping by sequencing platforms can provide informative DNA markers to conduct genome-wide association analysis, estimation of genetic parameters, and evaluation of genomic prediction accuracy for complex traits in Asian seabass.

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.

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.

Prospects for Genetic Improvement in Objective Measurements of Body Colour in Pacific Whiteleg Shrimp (Litopenaeus vannamei)

Body colour, together with growth and survival, are traits of commercial importance in Pacific whiteleg shrimp (Litopenaeus vannamei). However, heritability estimates for objective measurements of body colour are not available in Whiteleg shrimp species, including L. vannamei. Further, the effect of genotype by environment interactions (G × E) on this trait (i.e., the objective measures of body colour) and its genetic associations with growth are not known in this species. The present study presented the first attempt at understanding the genetic architecture of this complex character (body colour) that is of economic significance to the shrimp aquaculture sector world-wide. Specifically, we investigated the quantitative genetic basis of shrimp colour, while using the measurement tool (colorimeter) for a Whiteleg shrimp population reared in two contrasting environments. A total of 5464 shrimp had the objective measurements of body colour (lightness, yellowness, and redness) and growth trait records (weight, length and width). They were the offspring of 204 dams and 197 sires. The restricted maximum likelihood mixed model analysis showed that there were heritable additive genetic components for all of the measurements of shrimp colour, with the heritability (h2) ranging from 0.11–0.55. The h2 estimates for redness and yellowness traits differed between the two environments (h2 = 0.66–0.82 in Khanhhoa vs. 0.00–0.03 in Haiphong). However, the heritability for colour traits was moderate (0.11–0.55) when the two environments were combined. There is existence of (co)-genetic variances between the studied traits. The genetic correlations of body traits with redness or yellowness colour of the shrimp were moderate and positive (a*: 0.13–0.32 for redness and b*: 0.19–0.40 for yellowness). The effect of G × E interactions on shrimp colours could be important, as the genetic correlations for these traits between the two environments were low (−0.41 to 0.16). Our results showed that the genetic improvement for body colour can be achieved through direct selection and the increased redness colour is also expected to have favorable impacts on growth traits. Breeding programs to improve shrimp colour should account for the effects of environmental factors.