Human Genomics最新文献

Background: Premarital screening is a preventive public health measure to identify genetic, infectious, and chronic conditions affecting the health of couples and their future offspring. It plays a crucial role in reducing the prevalence of common hereditary disorders, particularly in regions with high consanguinity rates, like the Middle East. In the United Arab Emirates, where approximately half of the marriages are consanguineous, premarital screening has become a cornerstone of genetic healthcare, helping to mitigate the increased risk of genetic disorders. Despite its importance, gaps remain in healthcare professionals' awareness and training regarding the implementation of premarital genetic screening, highlighting the need for educational interventions to ensure its effective integration into routine practice. This study evaluates the knowledge enhancement and attitude shift among physicians participating in health education workshops on expanded premarital screening, addressing gaps in their awareness, training, and perspectives on its implementation and ethical considerations.

Results: A cross-sectional study was conducted between May 2023 and June 2024 among physicians participating in three premarital genetic screening workshops. Differences in knowledge scores before and after the training were assessed using paired t-tests. The study surveyed 60 physicians, predominantly females (85%) and Emiratis (67%). Only 25% received formal training or education on premarital genetic screening. The study observed a significant increase in knowledge scores after the workshop, with overall scores rising from a mean of 45% (SD = 15) to 77% (SD = 12), showing a mean difference of 32% (p < 0.001). This improvement was significant across different age groups, genders, and regardless of prior formal training. While the belief that expanded premarital screening should be obligatory decreased (90% to 76%), the proportion of physicians who strongly disagreed that it breaks personal privacy increased significantly (10% to 42%).

Conclusion: These findings suggest that targeted educational interventions can significantly enhance healthcare professionals' knowledge and attitudes regarding genetic screening practices. To maximize impact, these interventions should be sustainable and reinforced through regular refresher courses. Continuous education ensures that physicians remain updated on the latest guidelines, advancements in genetic screening, and best practices, ultimately improving the quality of patient care and counseling services.

Background: Ribosomal DNA (rDNA), the most abundantly expressed locus in the human genome, is represented by hundreds of units per cell. Each unit includes a 13 kb long region (47S rDNA) containing the genes of ribosomal particles, and a 30 kb long intergenic spacer (IGS). The 47S rDNA is transcribed with varying intensity in different units, some of which remain permanently silent. A key intermediator of this silencing is the promoter-associated RNA (pRNA) produced from a 2 kb long gene situated upstream of the rDNA transcription start site. Recent studies, including ours, suggest that the sequence variability, which normally occurs in mammalian cells, may account for the selective transcription of different rDNA units. The present work is based on the deep sequencing of a pRNA gene fragment and its RNA product and subsequent bioinformatic analysis.

Results: We found that a certain SNV, which converts the CCC motif into CCT, as well as deletions which reduce the number of (CCCT) tandem repeats, were significantly more frequent in the DNA than in the respective transcripts.

Conclusions: These findings allowed us to establish directly the inhibitory effect of DNA variants on the expression of pRNA and thus (indirectly) the promoting effect on the production of ribosomal RNA. Our results also suggest that (CCCT)_n/(GGGA)_n DNA repeats and the respective (GGGA)_n RNA repeats may form triplex structures, facilitating the function of pRNA.

Understanding fine-scale genetic and geographic ancestry in East and Southeast Asia is difficult due to complex population histories and limited high-resolution genomic data. Here, we introduce a comprehensive framework that combines ancestry-informative single nucleotide polymorphism (AISNP) panels with machine learning to jointly determine genetic ancestry and geographic origins in 1,703 individuals from 67 East and Southeast Asian groups. We developed seven nested AISNP panels, from 50 to 2,000 SNPs, and tested six classification algorithms: logistic regression, support vector machines, k-nearest neighbors, random forest, convolutional neural networks, and eXtreme Gradient Boosting (XGBoost). The best results came from the optimized XGBoost model, which achieved 95.6% accuracy and an AUC of 0.999 with 2,000 AISNPs. For geographic localization, we used the Locator model, a deep neural network that predicts latitude and longitude directly from unphased genotypes. Notably, Locator trained on just 2,000 AISNPs performed nearly as well as models built on high-density genomic data (597,569 SNPs). Overall, these findings show that carefully designed AISNP panels combined with suitable machine learning techniques can provide highly accurate and efficient ancestry inference, offering valuable insights for population genetics, forensic science, and biogeography in East and Southeast Asia.

Background: Recent studies have increasingly emphasized the poorer survival outcomes and reduced treatment responses associated with right-sided colon cancer (RCC). However, the underlying molecular mechanisms remain poorly understood. This study aimed to identify and characterize key biomarkers associated with progression and treatment response in patients with RCC.

Methods: Primary location-related genes (PLGs), including MX1, were identified through weighted gene coexpression network analysis (WGCNA) to construct a primary location signature prognostic risk score (PLriskScore) in the TCGA RCC dataset. The prognostic performance of PLriskScore was validated using four GEO datasets. Tumor microenvironment scores and chemotherapy responses across PLriskScore subgroups were assessed in bulk RNA sequencing datasets. The key target gene was then identified using machine learning algorithms. Its expression and functional roles were examined in single-cell RNA sequencing (scRNA-seq) datasets. Expression patterns were validated by quantitative real-time PCR (qRT-PCR) and immunohistochemical (IHC) staining of clinical samples, while functional characteristics were confirmed using Cell Counting Kit-8 (CCK-8) and transwell migration assays.

Results: Seven PLGs (MX1, MLLT6, PEDS1, STK4, TOMM34, SLC35A2, and LZTS3) were used to establish the PLriskScore. A higher PLriskScore was significantly associated with poorer prognosis, advanced TNM stages, and increased stromal scores. Notably, the VEGF pathway was enriched in the high-PLriskScore group. Machine learning algorithms identified MX1 as a novel key target, closely related to chemoresistance and tumor microenvironment dysregulation. scRNA-seq analyses revealed that MX1 was predominantly expressed in epithelial cells and promoted angiogenesis through interactions with endothelial cells via the VEGFA-VEGFR2 pathway. Finally, in vitro experiments demonstrated that MX1 was highly expressed in RCC and enhanced proliferation and migration in two CRC cell lines.

Conclusion: A prognostic model based on primary location-related genes was developed to predict prognosis and chemotherapy response in RCC. MX1 was identified as a pivotal target that promotes angiogenesis and chemoresistance, thereby providing a potential therapeutic strategy for patients with RCC.

Background/aims: The advent of genetic biobanking has powered gene-environment interaction (GxE) studies in various disease contexts. Therefore, we aimed to discover novel GxE effects that address hot spring residency as a risk to inconspicuous disease association.

Methods: A complete genetic and demographic registry comprising 129,451 individuals was obtained from Taiwan Biobank (TWB). After geographical disease prevalence was analyzed to identify putative disease association with hot-spring residency, multivariable regression and logistic regression were rechecked to exclude socioeconomic confounders in geographical-disease association. Genome-wide association study (GWAS), gene ontology (GO), and protein-protein interaction (PPI) analysis identified predisposing genetic factors among hotspring-associated diseases. Lastly, a polygenic risk score (PRS) model was formulated to stratify environmental susceptibility in accord with their genetic predisposition.

Results: After socioeconomic covariate adjustment, prevalence of dry eye disease (DED) was significantly associated with hot spring distribution. Through single nucleotide polymorphisms (SNPs) discovery and subsequent PPI pathway aggregation, CDKL2 kinase pathways were significantly enriched in hot-spring specific DED functional SNPs. Notably, PRS predicted disease well in hot spring regions (AUC = 0.9168). Hot spring and discovered SNPs contributed to crossover GxE effect on DED (relative risk (RR)_G+E-= 0.99; RR_G-E+ = 0.35; RR_G+E+ = 2.04).

Conclusion: We identified hot-spring exposure as a modifiable risk in the PRS-predicted GxE context of DED.

Introduction: Dabigatran is a direct oral anticoagulant associated with a high incidence of gastrointestinal bleeding, which presents a significant clinical concern. Genetic polymorphisms in the enzymes responsible for drug absorption (ABCB1) and activation (CES1) may influence dabigatran's pharmacokinetics, potentially altering drug concentration and therapeutic response. The current systematic review and meta-analysis aim to identify genetic variants correlated with dabigatran exposure and evaluate their importance.

Methods: We systematically searched PubMed, Web of Science, Scopus, Cochrane Library, and Embase to identify studies on dabigatran pharmacogenomics. The review included observational and clinical studies that met eligibility criteria. RevMan 5.4 was used to conduct the meta-analysis. Quality assessment was done using ROB 2.0 and NOS tools.

Results: Out of 1336 records retrieved, 1008 were screened, resulting in 16 studies included in the systematic review and 9 in the meta-analysis. Data from 3834 participants (61.8% males) were reviewed. For the ABCB1 polymorphism rs4148738, both CT and TT genotypes decreased C _trough compared to CC genotype (MD = - 9.82, 95% CI [- 17.65, - 1.99], P = 0.01) and (MD= - 7.69, 95% CI [- 15.54, 0.16], P = 0.05), respectively. While CES1 rs8192935 GG increased C _max compared to AA (MD = 22.66, 95% CI [5.04, 40.27], P = 0.01). For CES1 rs2244613 AA, both C _max and C _trough exhibited higher levels compared to GG (MD = 13.58, 95% CI [- 0.08,27.25], P = 0.05) and (MD = 13.41, 95% CI [8.05,18.77], P < 0.01), respectively. Also, compared to GG the heterozygote type GA increased the C _max (MD = 32.02, 95%CI [16.54,47.5], P < 0.01). Bleeding risk did not significantly differ across ABCB1 rs1045642, ABCB1 rs4148738 and CES1 rs8192935 polymorphisms. Only CES1 rs2244613 T allele showed significant effect on bleeding (OR = 2.43, P = 0.002). Stroke incidence did not differ across ABCB1 rs4148738, CES1 rs2244613, and CES1 rs8192935 genotypes.

Conclusion: ABCB1 rs4148738 T allele reduced dabigatran trough levels, while CES1 rs8192935 GG increased peak levels. CES1 rs2244613 TT raised both peak and trough levels, and its T allele was linked to higher bleeding risk. No consistent associations were found for other variants. These findings highlight CES1 rs2244613 as a key contributor to variability in dabigatran response, warranting further large-scale studies to confirm its role in personalized anticoagulation therapy.