4.7 Article

Association between DNA methylation variability and self-reported exposure to heavy metals

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SCIENTIFIC REPORTS
卷 12, 期 1, 页码 -

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NATURE PORTFOLIO
DOI: 10.1038/s41598-022-13892-w

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  1. Australian National Health and Medical Research Council Enabling Grant [402783]
  2. National Health and Medical Research Council [1078901, 1083187, 1113400, 1121962]

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This study evaluated the utility of using lifetime-long self-reported exposures for identifying differential DNA methylation in an amyotrophic lateral sclerosis cases-control cohort. The results indicate the potential for using self-reported exposure histories in detecting DNA methylation changes in response to the environment, but also highlight the confounded nature of environmental exposure in cohort studies.
Individuals encounter varying environmental exposures throughout their lifetimes. Some exposures such as smoking are readily observed and have high personal recall; others are more indirect or sporadic and might only be inferred from long occupational histories or lifestyles. We evaluated the utility of using lifetime-long self-reported exposures for identifying differential methylation in an amyotrophic lateral sclerosis cases-control cohort of 855 individuals. Individuals submitted paper-based surveys on exposure and occupational histories as well as whole blood samples. Genome-wide DNA methylation levels were quantified using the Illumina Infinium Human Methylation450 array. We analyzed 15 environmental exposures using the OSCA software linear and MOA models, where we regressed exposures individually by methylation adjusted for batch effects and disease status as well as predicted scores for age, sex, cell count, and smoking status. We also regressed on the first principal components on clustered environmental exposures to detect DNA methylation changes associated with a more generalised definition of environmental exposure. Five DNA methylation probes across three environmental exposures (cadmium, mercury and metalwork) were significantly associated using the MOA models and seven through the linear models, with one additionally across a principal component representing chemical exposures. Methylome-wide significance for four of these markers was driven by extreme hyper/hypo-methylation in small numbers of individuals. The results indicate the potential for using self-reported exposure histories in detecting DNA methylation changes in response to the environment, but also highlight the confounded nature of environmental exposure in cohort studies.

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