Multivariate genomic prediction for commercial traits of economic importance in Banana shrimp Fenneropenaeus merguiensis

Nguyen, Nguyen Hong; Vu, Nguyen Thanh; Patil, Shruti S.; Sandhu, Karansher S.

doi:10.1016/j.aquaculture.2022.738229

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…A summary of the prediction accuracy for disease resistance traits, including survival rate and time to death, employing various statistical methods and algorithms, is shown in Table 4. Across our studied species, a consistent trend emerged indicating that multivariate analysis increased the precision of genomic prediction for disease-related traits [59,60]. The imputation of missing genotypes also contributed to an enhanced predictive capacity by 5-18% [41].…”

Section: Genomic Prediction To Enable Genome-based Selectionsupporting

confidence: 59%

“…However, Bayes R marginally outperformed GBLUP and other Bayesian techniques. Both deep learning and machine learning approaches surpassed GBLUP and Bayes R to some extent; nevertheless, the advantages of AI algorithms are contingent upon the specific populations and traits under consideration [41,60]. Their benefits become more pronounced when modelling intricate interaction networks and variables (Nguyen et al, unpublished).…”

Section: Genomic Prediction To Enable Genome-based Selectionmentioning

confidence: 99%

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Genetics and Genomics of Infectious Diseases in Key Aquaculture Species

Nguyen

2023

Preprint

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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 field 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 favourable 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, 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.

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Section: Genomic Prediction To Enable Genome-based Selectionsupporting

confidence: 59%

Section: Genomic Prediction To Enable Genome-based Selectionmentioning

confidence: 99%

Genetics and Genomics of Infectious Diseases in Key Aquaculture Species

Nguyen

2023

Preprint

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“…Results from these studies showed that the accuracies of genomic predictions were not improved for fillet weight and fillet yield in Nile tilapia O. niloticus ( Joshi et al, 2020 ) or for survival status and survival time in striped catfish P. hypophthalmus ( Vu et al, 2021 ), likely because the high heritability of these two traits and their high genetic correlations; hence, adding one trait did not improve the prediction accuracy of the other. In yellowtail kingfish, Nguyen et al (2022) also showed that the benefits of multi- vs. univariate analysis depend on statistical methods used and genomic architecture of traits. Hence, molecular dissection of the genomic architecture of traits (e.g., identifying pleotropic loci) can help further understand the impacts of multi-trait analysis on the prediction accuracy for tagging weight and disease resistance examined in this population.…”

Section: Discussionmentioning

confidence: 99%

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

Phuc

Nguyen

et al. 2023

Front. Genet.

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Common full-sib families (c2) make up a substantial proportion of total phenotypic variation in traits of commercial importance in aquaculture species and omission or inclusion of the c2 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.

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“…The grid search CV process allocated 80% of the training data to hyperparameter optimization, with the remaining data reserved for validation, achieved through the independent split function of ‘Keras’ [ 27 ]. Further insights into the hyperparameter optimization process and the libraries employed are available in prior publications [ 28 , 63 , 64 ].…”

Section: Methodsmentioning

confidence: 99%

Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers

Lozada,

Sandhu,

Bhatta

2023

BMC Genom Data

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Background Genomewide prediction estimates the genomic breeding values of selection candidates which can be utilized for population improvement and cultivar development. Ridge regression and deep learning-based selection models were implemented for yield and agronomic traits of 204 chile pepper genotypes evaluated in multi-environment trials in New Mexico, USA. Results Accuracy of prediction differed across different models under ten-fold cross-validations, where high prediction accuracy was observed for highly heritable traits such as plant height and plant width. No model was superior across traits using 14,922 SNP markers for genomewide selection. Bayesian ridge regression had the highest average accuracy for first pod date (0.77) and total yield per plant (0.33). Multilayer perceptron (MLP) was the most superior for flowering time (0.76) and plant height (0.73), whereas the genomic BLUP model had the highest accuracy for plant width (0.62). Using a subset of 7,690 SNP loci resulting from grouping markers based on linkage disequilibrium coefficients resulted in improved accuracy for first pod date, ten pod weight, and total yield per plant, even under a relatively small training population size for MLP and random forest models. Genomic and ridge regression BLUP models were sufficient for optimal prediction accuracies for small training population size. Combining phenotypic selection and genomewide selection resulted in improved selection response for yield-related traits, indicating that integrated approaches can result in improved gains achieved through selection. Conclusions Accuracy values for ridge regression and deep learning prediction models demonstrate the potential of implementing genomewide selection for genetic improvement in chile pepper breeding programs. Ultimately, a large training data is relevant for improved genomic selection accuracy for the deep learning models.

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Multivariate genomic prediction for commercial traits of economic importance in Banana shrimp Fenneropenaeus merguiensis

Cited by 8 publications

References 27 publications

Genetics and Genomics of Infectious Diseases in Key Aquaculture Species

Genetics and Genomics of Infectious Diseases in Key Aquaculture Species

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

Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers

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