| Title: | Genetic sensitivity moderates the effects of joint exposure to air pollution on depression risk: a genome-wide by environment interaction analysis |
| Journal: | Environmental Pollution |
| Published: | 20 Sep 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40983175/ |
| DOI: | https://doi.org/10.1016/j.envpol.2025.127145 |
Publication 19453
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Abstract
How ambient air pollutants and genetic factors interact to influence depression risk remains unclear. This study aims to investigate how genetic factors regulate the relationship between air pollution and depression. Using UK Biobank data, an air pollution index was constructed from NO2, NOx, PM2.5, PM2.5-10 and PM10 to assess joint exposure. A genome-wide interaction analysis identified 189 air pollution-sensitive SNPs (APS-SNPs) enriched in neurodevelopment. Polygenic scores (PGS) constructed by APS-SNPs showed significant interaction with the air pollution index, NO2, NOx, PM2.5 and PM10 on depression risk (all P < 0.05). Individuals with high PGS had a greater depression risk from air pollution index (OR = 3.17, 95 %CI: 2.63-3.83). Interaction effects between PGS and NO2 and PM2.5 were validated in the independent sample. PGS also interacted with greenspace percentage, natural environment percentage, and water percentage. Individuals with high PGS had a significantly lower risk of depression when exposing to more greenspace (buffer 300m, OR = 0.91, 95 %CI: 0.83-0.99; buffer 1000m, OR = 0.90, 95 %CI: 0.83-0.98). Our findings identified that joint exposure to various air pollutants increases depression risk, moderated by genetic factors, and modifying life environments could be an effective intervention for those genetically sensitive to environmental factors.</p>
12 Keywords
- Air Pollutants
- Air Pollution
- Depression
- Environmental Exposure
- Female
- Gene-Environment Interaction
- Genome-Wide Association Study
- Humans
- Male
- Middle Aged
- Particulate Matter
- Polymorphism, Single Nucleotide
15 Authors
- Yaoyao Sun
- Xue Lv
- Wenjian Bi
- Yuyanan Zhang
- Zhe Lu
- Jing Guo
- Zhewei Kang
- Xiaoyang Feng
- Yundan Liao
- Guorui Zhao
- Junyuan Sun
- Yang Yang
- Yunqing Zhu
- Rui Yuan
- Weihua Yue
1 Application
| Application ID | Title |
|---|---|
| 78795 | Regression methods for phenome-wide association analysis on large-scale biobank data |
Enabling scientific discoveries that improve human health