Search Results (4)

Asthma, a chronic respiratory condition characterized by airway inflammation, affects millions of individuals worldwide. Challenges remain in asthma prediction and diagnosis from its complex etiology involving genetic and environmental factors. Here, we investigated the relationship between genome-wide DNA methylation and genetic risk for asthma quantified via polygenic scores in two cohorts from the Netherlands Twin Register; one enriched with asthmatic families measured on the Illumina EPIC array (n = 526) and a general population cohort measured on the Illumina HM450K array (n = 2680). We performed epigenome-wide association studies of asthma polygenic scores in each cohort with results combined through meta-analysis (total samples = 3206). The EWAS meta-analysis identified 63 significantly associated CpGs, (following Bonferroni correction, α = 0.05/358,316). An investigation of previous mQTL associations identified 48 mQTL associations between 24 unique CpGs and 48 SNPs, of which two SNPs have previous associations with asthma. Enrichment analysis using the 63 significant CpGs highlighted previous associations with ancestry, smoking, and air pollution. A dizygotic twin within-pair analysis of the 63 CpGs revealed similar directional effects between the two cohorts in 33 of the 63 CpGs. These findings further characterize the intricate relationship between DNA methylation and genetics relative to asthma. Full article

With the increasing environmental impacts of human activities, the problem of polygenic multipollutants in groundwater has attracted the attention of researchers. Identifying the hydrobiogeochemical characteristics of the surface sewage that replenishes groundwater is crucial to addressing this problem. The input of polygenic multipollutants into groundwater leads to not only the mechanical superposition of pollutants but also the formation of secondary pollutant types. The evolution of polygenic multipollutants is influenced by aquifer characteristics, carbon sources, microbial abundance, etc. Therefore, this study took a sewage leakage point in Northwest China as the research object, carried out a controlled laboratory experiment on the impact of sewage discharge on groundwater, and, combined with long-term field monitoring results, determined the main hydrobiogeochemical processes of polygenic multipollutants and their secondary pollutants. The results showed that the redox environment and the gradient change in pH were identified as the most critical controlling factors. In oxidative groundwater during the early stage of vertical infiltration, sewage carries a substantial amount of NH₄⁺, which is oxidized to form the secondary pollutant NO₃⁻. As O₂ is consumed, the reduction intensifies, and secondary pollutants NO₃⁻, Mn (IV), and Fe(III) minerals are successively reduced. Compared with the natural conditions of rainwater vertical infiltration, the reaction rates and intensities of various reactions significantly increase during sewage vertical infiltration. However, there is a notable difference in the groundwater pH between sewage and rainwater vertical infiltration. In O₂ and secondary pollutant NO₃⁻ reduction, a large amount of CO₂ is rapidly generated. Excessive CO₂ dissolves to produce a substantial amount of H⁺, promoting the acidic dissolution of Mn (II) minerals and generation of Mn²⁺. Sewage provides a higher carbon load, enhancing Mn (II) acidic dissolution and stimulating the activity of dissimilatory nitrate reduction to ammonium, which exhibits a higher contribution to NO₃⁻ reduction. This results in a portion of NO₃⁻ converted from NH₄⁺ being reduced back to NH₄⁺ and retained in the groundwater, reducing the denitrification’s capacity to remove secondary NO₃⁻. This has important implications for pollution management and groundwater remediation, particularly monitored natural attenuation. Full article

Polygenic susceptibility likely influences individual responses to air pollutants and the risk of asthma. We compared the role of polygenic susceptibility on air pollution-associated asthma between German and Japanese women. We investigated women that were enrolled in the German SALIA cohort (n = 771, mean age = 73 years) and the Japanese Shika cohort (n = 847, mean age = 67 years) with known asthma status. Adjusted logistic regression models were used to assess the associations between (1) particulate matter with a median aerodynamic diameter ≤ 2.5μm (PM_2.5) and nitrogen dioxide (NO₂), (2) polygenic risk scores (PRS), and (3) gene-environment interactions (G × E) with asthma. We found an increased risk of asthma in Japanese women after exposure to low pollutant levels (PM_2.5: median = 12.7µg/m³, p-value < 0.001, NO₂: median = 8.5µg/m³, p-value < 0.001) and in German women protective polygenic effects (p-value = 0.008). While we found no significant G × E effects, the direction in both groups was that the PRS increased the effect of PM_2.5 and decreased the effect of NO₂ on asthma. Our study confirms that exposure to low air pollution levels increases the risk of asthma in Japanese women and indicates polygenic effects in German women; however, there was no evidence of G × E effects. Future genome-wide G × E studies should further explore the role of ethnic-specific polygenic susceptibility to asthma. Full article

For the personalization of polygenic/omics-based health care, the purpose of this study was to examine the gene–environment interactions and predictors of colorectal cancer (CRC) by including five key genes in the one-carbon metabolism pathways. In this proof-of-concept study, we included a total of 54 families and 108 participants, 54 CRC cases and 54 matched family friends representing four major racial ethnic groups in southern California (White, Asian, Hispanics, and Black). We used three phases of data analytics, including exploratory, family-based analyses adjusting for the dependence within the family for sharing genetic heritage, the ensemble method, and generalized regression models for predictive modeling with a machine learning validation procedure to validate the results for enhanced prediction and reproducibility. The results revealed that despite the family members sharing genetic heritage, the CRC group had greater combined gene polymorphism rates than the family controls (p < 0.05), on MTHFR C677T, MTR A2756G, MTRR A66G, and DHFR 19 bp except MTHFR A1298C. Four racial groups presented different polymorphism rates for four genes (all p < 0.05) except MTHFR A1298C. Following the ensemble method, the most influential factors were identified, and the best predictive models were generated by using the generalized regression models, with Akaike’s information criterion and leave-one-out cross validation methods. Body mass index (BMI) and gender were consistent predictors of CRC for both models when individual genes versus total polymorphism counts were used, and alcohol use was interactive with BMI status. Body mass index status was also interactive with both gender and MTHFR C677T gene polymorphism, and the exposure to environmental pollutants was an additional predictor. These results point to the important roles of environmental and modifiable factors in relation to gene–environment interactions in the prevention of CRC. Full article