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

Vu, Nguyen Thanh; Ha, Tran Viet; Thuy, Vo Thi Bich; Trang, Vu Thi; Nguyen, Nguyen Hong

doi:10.3390/jmse8060471

Cited by 8 publications

(5 citation statements)

References 59 publications

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“…Wild marine fish species Pangasianodon Hypophthalmus and Oreochromis niloticus have previously shown genomewide SNP heterozygosity of 0.04-0.60. In this study, the SNP heterozygosity of the wild population was found to be 0.335, which is comparable to that of the wild O. niloticus population [40,41]. However, Li et al showed that the average heterozygosity of SNP in wild roughskin sculpin was 0.121, unlike the results of this study [7].…”

Section: Discussioncontrasting

confidence: 69%

Genetic Diversity and Signatures of Selection in the Roughskin Sculpin (Trachidermus fasciatus) Revealed by Whole Genome Sequencing

San,

He,

Liu

et al. 2023

Biology

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The roughskin sculpin (Trachidermus fasciatus) is an endangered fish species in China. In recent years, artificial breeding technology has made significant progress, and the population of roughskin sculpin has recovered in the natural environment through enhancement programs and the release of juveniles. However, the effects of released roughskin sculpin on the genetic structure and diversity of wild populations remain unclear. Studies on genetic diversity analysis based on different types and numbers of molecular markers have yielded inconsistent results. In this study, we obtained 2,610,157 high-quality SNPs and 494,698 InDels through whole-genome resequencing of two farmed populations and one wild population. Both farmed populations showed consistent levels of genomic polymorphism and a slight increase in linkage compared with wild populations. The population structure of the two farmed populations was distinct from that of the wild population, but the degree of genetic differentiation was low (overall average Fst = 0.015). Selective sweep analysis showed that 523,529 genes were selected in the two farmed populations, and KEGG enrichment analysis showed that the selected genes were related to amino acid metabolism, which might be caused by artificial feeding. The findings of this study provide valuable additions to the existing genomic resources to help conserve roughskin sculpin populations.

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Section: Discussioncontrasting

confidence: 69%

Genetic Diversity and Signatures of Selection in the Roughskin Sculpin (Trachidermus fasciatus) Revealed by Whole Genome Sequencing

San,

He,

Liu

et al. 2023

Biology

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“…The DArTseq TM was based on the genome complexity reduction method in combination with high throughput next-generation sequencing using Illumina platform. The sequencing protocol including choice of restricted enzymes and library preparation was optimized for striped catfish (Pangasianodon DarTseq 1.0) using 96 independent DNA samples of striped catfish in our earlier study ( Vu et al 2020 ). Briefly, the PstI-SphI method ( Kilian et al 2012 ) was used and the compatible adaptors were designed to include Illumina flow-cell attachment sequence, sequencing primer sequence, and capturing variant length of barcode regions ( Elshire et al 2011 ).…”

Section: Methodsmentioning

confidence: 99%

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

Phuc

Oanh

et al. 2021

G3 Genes|Genomes|Genetics

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Assessments of genomic prediction accuracies using artificial intelligence (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 non-linear 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 to 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 Edwardsiella 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.

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“…DArTseq™ represents a combination of genome complexity reduction methods and high throughput sequencing platforms ( Kilian et al, 2012 ). A detailed description regarding selections of restricted enzymes, PCR reactions, library preparations and sequencing is given in our earlier studies ( Nguyen et al, 2018a ; Nguyen et al, 2018b ; Nguyen et al, 2020b ; Vu et al, 2020 ). Briefly, sequences generated from each lane were processed using proprietary DArTseq pipelines.…”

Section: Methodsmentioning

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

Cited by 8 publications

References 59 publications

Genetic Diversity and Signatures of Selection in the Roughskin Sculpin (Trachidermus fasciatus) Revealed by Whole Genome Sequencing

Genetic Diversity and Signatures of Selection in the Roughskin Sculpin (Trachidermus fasciatus) Revealed by Whole Genome Sequencing

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

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

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