Breeding for improved resistance to Edwardsiella ictaluri in striped catfish (Pangasianodon hypophthalmus): Quantitative genetic parameters

Genetics and Genomics of Infectious Diseases in Key Aquaculture Species

Diseases pose a significant and pressing concern for the sustainable development of the aquaculture sector, particularly as their impact continues to grow due to climatic shifts such as rising water temperatures. While various approaches, ranging from biosecurity measures to vaccines, have been devised to combat infectious diseases, their efficacy is disease and species specific and contingent upon a multitude of factors. The fields of genetics and genomics offer effective tools to control and prevent disease outbreaks in aquatic animal species. In this study, we present the key findings from our recent research, focusing on the genetic resistance to three specific diseases: White Spot Syndrome Virus (WSSV) in white shrimp, Bacterial Necrotic Pancreatitis (BNP) in striped catfish, and skin fluke (a parasitic ailment) in yellowtail kingfish. Our investigations reveal that all three species possess substantial heritable genetic components for disease-resistant traits, indicating their potential responsiveness to artificial selection in genetic improvement programs tailored to combat these diseases. Also, we observed a high genetic association between disease traits and survival rates. Through selective breeding aimed at enhancing resistance to these pathogens, we achieved substantial genetic gains, averaging 10% per generation. These selection programs also contributed positively to the overall production performance and productivity of these species. Although the effects of selection on immunological traits or immune responses were not significant in white shrimp, they yielded favorable results in striped catfish. Furthermore, our genomic analyses, including shallow genome sequencing of pedigreed populations, enriched our understanding of the genomic architecture underlying disease resistance traits. These traits are primarily governed by a polygenic nature, with numerous genes or genetic variants, each with small effects. Leveraging a range of advanced statistical methods, from mixed models to machine and deep learning, we developed prediction models that demonstrated moderate-to-high levels of accuracy in forecasting these disease-related traits. In addition to genomics, our RNA-seq experiments identified several genes that undergo upregulation in response to infection or viral loads within the populations. Preliminary microbiome data, while offering limited predictive accuracy for disease traits in one of our studied species, underscore the potential for combining such data with genome sequence information to enhance predictive power for disease traits in our populations. Lastly, this paper briefly discusses the roles of precision agriculture systems and AI algorithms and outlines the path for future research to expedite the development of disease-resistant genetic lines tailored to our target species. In conclusion, our study underscores the critical role of genetics and genomics in fortifying the aquaculture sector against the threats posed by diseases, paving the way for more sustainable and resilient aquaculture development.

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.

Animal board invited review: Widespread adoption of genetic technologies is key to sustainable expansion of global aquaculture

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The extent of application of genetic technologies to aquaculture production varies widely by species and geography.

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Achieving a more universal application of seed derived from scientifically based breeding programmes is an important goal in order to meet increasing global demands for seafood production.

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This article reviews the status of genetic technologies across the world’s top 10 highly produced species.

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Opportunities and barriers to achieving broad-scale uptake of genetic technologies in global aquaculture are discussed.

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A future outlook for potential disruptive genetic technologies and how they might affect global aquaculture production is given.

Aquaculture production comprises a diverse range of species, geographies, and farming systems. The application of genetics and breeding technologies towards improved production is highly variable, ranging from the use of wild-sourced seed through to advanced family breeding programmes augmented by genomic techniques. This technical variation exists across some of the most highly produced species globally, with several of the top ten global species by volume generally lacking well-managed breeding programmes. Given the well-documented incremental and cumulative benefits of genetic improvement on production, this is a major missed opportunity. This short review focusses on (i) the status of application of selective breeding in the world’s most produced aquaculture species, (ii) the range of genetic technologies available and the opportunities they present, and (iii) a future outlook towards realising the potential contribution of genetic technologies to aquaculture sustainability and global food security.

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.

Case study development of a challenge test against Edwardsiella ictaluri in Mekong striped catfish (Pangasianodon hypophthalmus), for use in breeding: Estimates of the genetic correlation between susceptibilities in replicated tanks

Bacillary necrosis is a problematic disease in farming of Mekong striped catfish (Pangasianodon hypophthalmus). The pathogenic bacterium is Edwardsiella ictaluri, causing numerous white spots in swelled liver, kidney and spleen. An alternative to antibiotic treatment and vaccine is to select for improved genetic resistance to the disease that requires to establish a proper challenge test. Here, four challenge tests of Mekong striped catfish against E. ictaluri are reported proposing 3 days of acclimatization of test fish prior to the challenge, with restricted water level in the test, keeping a temperature of 26°C. In the challenge, cohabitant shedders should be released directly into the test tank and make up around ⅓ of the fish, and bacteria should be added directly to water. The last two experiments, with the highest mortality, suggest that any factor involving the dead cohabitants should be removed and that additional experimentation should focus on bacteria (density) and timing for addition of bacteria to water. Genetic analyses revealed that resistance to bacillary necrosis tested in replicated tanks in the same experiment can be considered the same genetic trait.

Genetic correlations between challenge tested susceptibility to bacillary necrosis, caused by Edwardsiella ictaluri, and growth performance tested survival and harvest body weight in Mekong striped catfish (Pangasianodon hypophthalmus)

The aim was to carry out a joint genetic analysis of survival and harvest body weight, recorded in a growth performance test in Mekong striped catfish (Pangasianodon hypophthalmus), and susceptibility to bacillary necrosis (caused by Edwardsiella ictaluri), recorded in challenge tests. Data were from two challenge tested year-classes (~6,000 fish in both) that both had growth test data available for survival and body weight (~13,000 fish each year). Data were analysed with a linear tri-variate sire-dam model without the common environmental effect because otherwise genetic correlations were estimated with large standard errors. Susceptibility to bacillary necrosis was found weakly genetically correlated to both growth and survival in the growth test, while growth was found with moderate favourable genetic correlation to growth survival. To defend continued challenge testing of striped catfish in Vietnam, a strong genetic relationship needs to be established between bacillary necrosis and survival under a natural disease outbreak. This requires another field test (in addition to the growth test) with siblings, without antibiotic treatment and the cause of death continuously monitored. Meanwhile, the routine challenge testing with the aim to indirectly improve field survival through selection should continue.

Genetic analysis of resistance in Mekong striped catfish (Pangasianodon hypophthalmus) to bacillary necrosis caused by Edwardsiella ictaluri

The aim of this study was to analyse four cohabitation challenge-test experiments with Mekong striped catfish (Pangasianodon hypophthalmus) against the bacterium Edwardsiella ictaluri. The data were genetically analysed per experiment by three models: 1) a cross-sectional linear model; 2) a cross-sectional threshold model; and 3) a linear survival model, at both 50% mortality (for models 1 and 2) and at the end of the test (for all three models). In two of the experiments (3 and 4) that were carried out in two replicated tanks, the predicted family effects (sum of sire, dam and common environmental effects) in each tank were correlated with the family survival in the other replicated tank (cross-validation). The heritability estimates of resistance to E. ictaluri infection were ≤ 0.012 with the survival model, and up to 0.135 - 0.220 (50% survival) and 0.085 and 0.174 (endpoint survival) for the cross-sectional linear and threshold models, respectively. The challenge test should aim for an endpoint survival that ceases naturally at 50%. Then, genetic analysis should be carried out for survival at the endpoint (reflecting susceptibility) with a simple cross-sectional linear model.

Population Genomic Analyses of Wild and Farmed Striped Catfish Pangasianodon Hypophthalmus in the Lower Mekong River

The striped catfish Pangasianodon hypophthalmus is an important freshwater fish cultured in many countries where the collection of wild brooders is still widely practiced. Global farming development of this species makes use of significant natural resources that pose challenges for the genetic diversity of striped catfish. Hence, this study aims to conduct a systematic genetic diversity assessment of wild and farmed catfish stocks collected from four major pangasius-farming countries, using a new genotyping by sequencing platform known as DArT-seq technology. Our population genomic analyses using 7263 single-nucleotide polymorphisms (SNPs) after high-quality-control showed that there were two distinct populations of striped catfish in the lower Mekong river: (i) wild catfish from Thailand and (ii) catfish from Cambodia and Vietnam. The genetic diversity was greatest (0.363) in the wild stock from Thailand, but it was lower in farmed and wild stocks in other countries (0.049 to 0.088). The wild stocks were more genetically diverse than the farmed animals (0.103 vs. 0.064). The inbreeding coefficient ranged from 0.004 and 0.109, with the lowest value (−0.499) in the wild animals from Thailand. Molecular inference methods revealed high degree of historical effective population size (1043.9–1258.4), but there was considerable decline in the contemporary estimates in all populations (10.8 to 73.6). Our additional analyses calculating divergent times and migration patterns showed that the wild catfish from Thailand stand out as separate lineages, while those from Cambodia and Vietnam are genetically identical. Our results also indicated that the cultured stock in Bangladesh originated from the lower part of the Mekong river. These findings have significant practical implications in the context of genetic selection and conservation of striped catfish in the region. Collectively, they will contribute to the sustainable development of the striped catfish sector in these countries.

Hematological and immune genes responses in yellow catfish (Pelteobagrus fulvidraco) with septicemia induced by Edwardsiella ictaluri

Yellow catfish (Pelteobagrus fulvidraco) has been an economically important freshwater species in China because of its good meat quality. In present, the high-density breeding industry has suffered great damage from bacterial infections, in especial, the rapid illness and death of fish caused by bacterial septicemia leads to huge economic losses. Therefore, it is urgent and important to identify pathogenic bacteria and study its pathogenicity. In this study, we isolated a bacterial strain from the yellow catfish with typical septicemia and named it E. 719, then, by morphological observations, regression infection, biochemical identification, 16S rDNA sequence analysis and triple PCR identification, E. 719 was determined to be Edwardsiella ictaluri. Further, we infected yellow catfish with E. ictaluri to study its effects on mortality rate, hematological, histopathological disturbances and expression of immune genes. The mortality results showed that E. ictaluri was highly pathogenic, all infected fish died after 14 days post injection, and the distribution of bacteria in body kidney, spleen, liver, head kidney and brain of fish was continuously detected by measuring the amount of bacteria in the tissues. In addition, the number of red blood cells decreased significantly with the time of infection, while the number of white blood cells and thrombocytes increased. In particular, the number of monocytes and neutrophils increased significantly in the differential leucocyte count (DLC). Histopathologic changes observed by HE staining showed similar results, gill, intestine, spleen and head kidney showed obvious inflammation, bleeding and necrosis. Besides, checking by real time quantitative RT-PCR assays, in both spleen and head kidney tissues which were the major immune organs, mRNA expressions of immune gene IL-1β, TNF-α, and MR significantly increased in the early and middle stages of infection, which suggested that the infection of E. ictaluri caused a strong immune response in yellow catfish. This study provides a preliminary basis for the diagnosis and treatment of pathophysiology septicemia in yellow catfish induced by E. ictaluri.

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.