Most current genotype imputation methods are reference-based, which posed several challenges to users, such as high computational costs and reference panel inaccessibility. Thus, deep learning models are expected to create reference-free imputation methods performing with higher accuracy and shortening the running time. We proposed a imputation method using recurrent neural networks integrating with an additional discriminator network, namely GRUD. This method was applied to datasets from genotyping chips and Low-Pass Whole Genome Sequencing (LP-WGS) with the reference panels from The 1000 Genomes Project (1KGP) phase 3, the dataset of 4810 Singaporeans (SG10K), and The 1000 Vietnamese Genome Project (VN1K). Our model performed more accurately than other existing methods on multiple datasets, especially with common variants with large minor allele frequency, and shrank running time and memory usage. In summary, these results indicated that GRUD can be implemented in genomic analyses to improve the accuracy and running-time of genotype imputation.

Transcriptome data can provide information on signaling pathways active in cancers, but new computational tools are needed to more accurately quantify pathway activity and identify tissue-specific pathway features. We developed a computational method called “BioTarget” that incorporates ChIP-seq data into cellular pathway analysis. This tool relates the expression of transcription factor TF target genes (based on ChIP-seq data) with the status of upstream signaling components for an accurate quantification of pathway activity. This analysis also reveals TF targets expressed in specific contexts/tissues. We applied BioTarget to assess the activity of TBX21 and GATA3 pathways in cancers. TBX21 and GATA3 are TF regulators that control the differentiation of T cells into Th1 and Th2 helper cells that mediate cell-based and humoral immune responses, respectively. Since tumor immune responses can impact cancer progression, the significance of our pathway scores should be revealed by effective patient stratification. We found that low Th1/Th2 activity ratios were associated with a significantly poorer survival of stomach and breast cancer patients, whereas an unbalanced Th1/Th2 response was correlated with poorer survival of colon cancer patients. Lung adenocarcinoma and lung squamous cell carcinoma patients had the lowest survival rates when both Th1 and Th2 responses were high. Our method also identified context-specific target genes for TBX21 and GATA3. Applying the BioTarget tool to BCL6, a TF associated with germinal center lymphocytes, we observed that patients with an active BCL6 pathway had significantly improved survival for breast, colon, and stomach cancer. Our findings support the effectiveness of the BioTarget tool for transcriptome analysis and point to interesting associations between some immune-response pathways and cancer progression.

(1) Background: Individuals with BRCA1/2 gene mutations are at increased risk of breast and ovarian cancer. The prevalence of BRCA1/2 mutations varies by race and ethnicity, and the prevalence and the risks associated with most BRCA1/2 mutations has not been unknown in the Vietnamese population. We herein screen the entire BRCA1 and BRCA2 genes for breast and ovarian cancer patients with a family history of breast cancer and ovarian cancer, thereby, suggesting a risk score associated with carrier status and history for aiding personalized treatment; (2) Methods: Between December 2017 and December 2019, Vietnamese patients who had a pathological diagnosis of breast and epithelial ovarian cancer were followed up, prospectively, after treatment from two large institutions in Vietnam. Blood samples from 33 Vietnamese patients with hereditary breast and ovarian cancers (HBOC) syndrome were collected and analyzed using Next Generation Sequencing; (3) Results: Eleven types of mutations in both BRCA1 (in nine patients) and BRCA2 (in three patients) were detected, two of which (BRCA1:p.Tyr1666Ter and BRCA2:p.Ser1341Ter) have not been previously documented in the literature. Seven out of 19 patient’s relatives had BRCA1/2 gene mutations. All selected patients were counselled about the likelihood of cancer rising and prophylactic screening and procedures. The study established a risk score associated with the cohorts based on carrier status and family history; (4) Conclusions: Our findings suggested the implications for the planning of a screening programme for BRCA1 and BRCA2 genes testing in breast and ovarian cancer patients and genetic screening in their relatives. BRCA1/2 mutation carriers without cancer should have early and regular cancer screening, and prophylactic measures. This study could be beneficial for a diverse group in a large population-specific cohort, related to HBOC Syndrome.

A global pandemic has been declared for coronavirus disease 2019 (COVID-19), which has serious impacts on human health and healthcare systems in the affected areas, including Vietnam. None of the previous studies have a framework to provide summary statistics of the virus variants and assess the severity associated with virus proteins and host cells in COVID-19 patients in Vietnam. In this paper, we comprehensively investigated SARS-CoV-2 variants and immune responses in COVID-19 patients. We provided summary statistics of target sequences of SARS-CoV-2 in Vietnam and other countries for data scientists to use in downstream analysis for therapeutic targets. For host cells, we proposed a predictive model of the severity of COVID-19 based on public datasets of hospitalization status in Vietnam, incorporating a polygenic risk score. This score uses immunogenic SNP biomarkers as indicators of COVID-19 severity. We identified that the Delta variant of SARS-CoV-2 is most prevalent in southern areas of Vietnam and it is different from other areas in the world using various data sources. Our predictive models of COVID-19 severity had high accuracy (Random Forest AUC = 0.81, Elastic Net AUC = 0.7, and SVM AUC = 0.69) and showed that the use of polygenic risk scores increased the models’ predictive capabilities. We provided a comprehensive analysis for COVID-19 severity in Vietnam. This investigation is not only helpful for COVID-19 treatment in therapeutic target studies, but also could influence further research on the disease progression and personalized clinical outcomes.

Despite the raised awareness of the role of pharmacogenomic (PGx) in personalized medicines for COVID-19, data for COVID-19 drugs is extremely scarce and not even a publication on this topic for post-COVID-19 medications to date. In the current study, we investigated the genetic variations associated with COVID-19 and post-COVID-19 therapies by using whole genome sequencing data of the 1000 Vietnamese Genomes Project (1KVG) in comparison with other populations retrieved from the 1000 Genomes Project Phase 3 (1KGP3) and the Genome Aggregation Database (gnomAD). Moreover, we also evaluated the risk of drug interactions in comorbid COVID-19 and post-COVID-19 patients based on pharmacogenomic profiles of drugs using a computational approach. For COVID-19 therapies, variants related to the response of two causal treatment agents (tolicizumab and ritonavir) and antithrombotic drugs are common in the Vietnamese cohort. Regarding post-COVID-19, drugs for mental manipulations possess the highest number of clinical annotated variants carried by Vietnamese individuals. Among the superpopulations, East Asian populations shared the most similar genetic structure with the Vietnamese population, whereas the African population showed the most difference. Comorbid patients are at an increased drug-drug interaction (DDI) risk when suffering from COVID-19 and after recovering as well due to a large number of potential DDIs which have been identified. Our results presented the population-specific understanding of the pharmacogenomic aspect of COVID-19 and post-COVID-19 therapy to optimize therapeutic outcomes and promote personalized medicine strategy. We also partly clarified the higher risk in COVID-19 patients with underlying conditions by assessing the potential drug interactions.

Vietnam, the 16th most populated nation, remains profoundly underrepresented in global genomic databases. Here, we present VN1K, a first-ever comprehensive and well-curated resource of multi-omics data with a wide-range of phenotypic information of 1,011 unrelated Vietnamese individuals. High-depth short-read whole-genome sequencing data were generated for all samples along with various - omic data, including microarray, long-read whole-genome sequencing, and RNA sequencing. Using a high-sensitivity variant detection pipeline, which included a pangenome graph reference and a deep-learning framework, we identified nearly 40 million variants of which 8.5 million are novel with nearly 900 thousand short insertions/deletions and 39 thousand structural variants. Specifically, VN1K featured a first-ever whole-genome methylation profile based on long read sequencing. A genotype imputation panel was also created with the highest accuracy on the Vietnamese population. Variants with significantly different allele frequencies in the Vietnamese population compared to others were found to be functionally significant, especially in genes associated with immune diseases ( HLA-B, KIR3DL3, KIR2DL1, KIR2DL4 ) or drug responses ( CYP2C19, CYP2D6, VKORC1, CYP2B6 ). We were also able to map various loci related to hepatitis B virus infection as well as six disease traits, including triglyceride levels, LDL-C, serum glucose levels, HbA1c, and levels of two liver enzymes (ALT and AST). VN1K dataset is accessible via genome.vinbigdata.org, an integrated platform with both linear and graph-based genome browser for facilitating data exploration, research, and applications in precision medicine.