Vietnam is an important crossroads within Mainland Southeast Asia (MSEA) and a gateway to Island Southeast Asia, and as such exhibits high levels of ethnolinguistic diversity. However, comparatively few studies have been undertaken of the genetic diversity of Vietnamese populations. In order to gain comprehensive insights into MSEA mtDNA phylogeography, we sequenced 609 complete mtDNA genomes from individuals belonging to five language families (Austroasiatic, Tai-Kadai, Hmong-Mien, Sino-Tibetan and Austronesian) and analyzed them in comparison with sequences from other MSEA countries and Taiwan. Within Vietnam, we identified 399 haplotypes belonging to 135 haplogroups; among the five language families, the sequences from Austronesian groups differ the most from the other groups. Phylogenetic analysis revealed 111 novel Vietnamese mtDNA lineages. Bayesian estimates of coalescence times and associated 95% HPD for these show a peak of mtDNA diversification around 2.5–3 kya, which coincides with the Dong Son culture, and thus may be associated with the agriculturally-driven expansion of this culture. Networks of major MSEA haplogroups emphasize the overall distinctiveness of sequences from Taiwan, in keeping with previous studies that suggested at most a minor impact of the Austronesian expansion from Taiwan on MSEA. We also see evidence for population expansions across MSEA geographic regions and language families.

Since the emergence and rapid transmission of SARS-CoV-2, numerous scientific reports have searched for the association of host genetic variants with COVID-19, but the data are mostly acquired from Europe. In the current work, we explored the link between host genes (SARS-CoV-2 entry and immune system related to COVID-19 sensitivity/severity) and ABO blood types with COVID-19 from whole-exome data of 200 COVID-19 patients and 100 controls in Vietnam. The O blood type was found to be a protective factor that weakens the worst outcomes of infected individuals. For SARS-CoV-2 susceptibility, rs2229207 (TC genotype, allele C) and rs17860118 (allele T) of IFNAR2 increased the risk of infection, but rs139940581 (CT genotype, allele T) of SLC6A20 reduced virus sensitivity. For COVID-19 progress, the frequencies of rs4622692 (TG genotype) and rs1048610 (TC genotype) of ADAM17 were significantly higher in the moderate group than in the severe/fatal group. The variant rs12329760 (AA genotype) of TMPRSS2 was significantly associated with asymptomatic/mild symptoms. Additionally, rs2304255 (CT genotype, allele T) of TYK2 and rs2277735 (AG genotype) of DPP9 were associated with severe/fatal outcomes. Studies on different populations will give better insights into the pathogenesis, which is ethnic-dependent, and thus decipher the genetic factor’s contribution to mechanisms that predispose people to being more vulnerable to COVID-19.

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.

Assessments of genomic prediction accuracies using 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., KAML) and deep learning (i.e., DeepGP) 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 showed that KAML outperformed GBLUP and ssGBLUP, with the increases in the prediction accuracies for both traits by 5.1 - 47.7%. However, the prediction accuracies obtained from KAML were comparable to those estimated using BayesR. Imputation of missing genotypes using AlphaFamImpute increased the prediction accuracies by 0.2 33.2% in all the methods used. On the other hand, there were no significant increases 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) were not largely different from those obtained from the whole set of 14,154 SNPs. In all our analyses, the accuracies of genomic prediction were somewhat higher for survival time than survival status (0/1 data). It is concluded that there are prospects for the application of genomic selection to increase disease resistance to Edwardsiella ictaluri in striped catfish breeding programs

Striped catfish (Pangasianodon hypophthalmus) is an economically important fish in Vietnam. The catfish fillets contain high fatty acid composition. The FABP family is involved in lipid transport and metabolism as well as in the regulation of gene expression and cell development. In this study, the catfish genome database was searched for fabp gene family; then, gene structure, classification and phylogenetic relationships were analyzed. In striped catfish genome, we found 10 fabp genes that are homologous to other fish species and other 5 novel fabp genes that have not been clearly annotated. These newly identified fabp genes cluster separately from the known members of the fabp family on the phylogenetic tree, and further studies are needed to understand their roles and functions. We examined transcriptional gene expression of fabp3, fabp7 and fabp10a genes in muscle, liver and brain tissues of the stripped catfish. The results showed that fabp10a gene was not strongly expressed in all 3 types of tissues; fabp3 gene was most strongly expressed in liver tissue and fabp7 was highly up-regulated in brain tissue. The results of this study provide a resource for further research on the function of fabp genes and their genetic diversity in striped catfish.