| Title: | Multi-omic characterization of air pollution effects: Applications of AirSigOmniTWP Hub |
| Journal: | Ecotoxicology and Environmental Safety |
| Published: | 26 Aug 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39191137/ |
| DOI: | https://doi.org/10.1016/j.ecoenv.2024.116939 |
| URL: | http://dx.doi.org/10.1016/j.ecoenv.2024.116939 |
Publication 13171
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Abstract
Air pollution, particularly fine particulate matter and gaseous pollutants including NO2 and NOx, presents significant public health challenges. While the harmful effects of these pollutants are well-documented, the molecular mechanisms underlying their impact on health remain incompletely understood. In this study, we utilized genome-wide association study (GWAS) data from the UK Biobank, expression quantitative trait loci (eQTL) data from the Genotype-Tissue Expression (GTEx) project, and protein quantitative trait loci (pQTL) data from the Atherosclerosis Risk in Communities (ARIC) study to conduct comprehensive analyses using the Unified Test for Molecular Signatures (UTMOST), Transcriptome-wide Association Studies (TWAS), and Proteome-wide Association Studies (PWAS). To integrate and synthesize these analyses, we developed the AirSigOmniTWP Hub, a specialized platform designed to consolidate and interpret the results from UTMOST, TWAS, and PWAS. TWAS analysis identified a significant association between PM10 exposure and the gene INO80E in females (P = 4.37×10⁻⁵, FDR = 0.0383), suggesting a potential role in chromatin remodeling. PWAS analysis revealed a significant association between NOx exposure and the gene PIP in females (P = 2.28×10⁻⁵, FDR = 0.0299), implicating its involvement in inflammatory pathways. Additionally, UTMOST analyses uncovered significant associations between various pollutants and genes including NCOA4P3 and SPATS2L with PM2.5 exposure, indicating potential mechanisms related to transcriptional regulation and gene-environment interactions.</p>
11 Authors
- Wei Liu
- Tong Liu
- Xinxin Si
- Jiaxing Liang
- Xia Yan
- Juexin Zhang
- Bing Pang
- Wenmin Luo
- Junhong Liu
- Huazhe Yang
- Peng Shi
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