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The multistage carcinogenic process is in all probability a mixture of genetic and epigenetic processes. Ionizing radiation, in addition to producing mutations mainly by gene deletion and gross chromosomal damage, can also induce epigenetic effects4. Residential radon exposure has been associated with DNA-repair gene polymorphisms in adults (XpG gene Asp1104His, ADPRT gene Val762Ala, and NBS1 gene Glu185Gln polymorphisms)16 and partly replicated in children (XpD gene Lys751Gln, XpG gene Asp1104His and ADPRT gene Val762Ala polymorphisms)17, with the latter study also reporting double-strand break repair gene polymorphisms. Epigenetics describe heritable chemical modifications of DNA and chromatin affecting gene expression, and include DNA methylation, histone modifications and microRNAs which can act in concert to regulate gene expression18. In addition, the ‘bystander effect’, in which cells that are not directly irradiated, but are in the neighbourhood of cells that have, also exhibit phenotypic features of genomic instability that is considered to be epigenetic in nature4. DNA methylation is the most stable and most readily quantifiable epigenetic marker and is sensitive to pre- and post-natal exogenous influences19. Although the mechanisms of radiation-induced changes in DNA methylation remain largely unknown, the most plausible mechanism that has been proposed describes the effects of radiation on DNA methyltransferases20, while it has further been suggested that low dose radiation can increase DNA methylation at least in part through the generation of Reactive Oxygen Species (ROS)21,22. Ionising radiation exposure has been shown to affect DNA methylation in in vivo studies, and which has the potential to be transmitted via the germline to subsequent generations23
This study aims to explore whether there is evidence of DNA methylation from residential radon exposure in the general population and assesses whether any methylation varies across the lifecourse.
Methods
Data
This study used data from the Avon Longitudinal Study of Parents and Children (ALSPAC)
A sub-sample of 1,018 ALSPAC mother– child pairs had DNA methylation measured using the Infinium HumanMethylation450 BeadChip (Illumina, Inc.)28 as part of the Accessible Resource for Integrated Epigenomic Studies (ARIES) project29. For this study DNA methylation data generated from cord blood, venous blood samples at age 7 years and again at age 15 or 17, and additionally from the mothers during pregnancy and at middle age were used. All DNA methylation analyses were performed at the University of Bristol as part of the ARIES project and has been described in detail previously29.
Ethical approval for this study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees (Reference B2805).
DNA methylation
DNA methylation profiles for ALSPAC children were generated using the Illumina Infinium HumanMethylation450 BeadChip as part of the Accessible Resource for Integrated Epigenomic Studies (ARIES)29. DNA was bisulphite-converted using the Zymo EZ DNA MethylationTM kit (Zymo,
Irvine, CA). Infinium HumanMethylation450 BeadChips (Illumina, Inc.) and used to measure genome-wide DNA methylation levels at over 485,000 CpG sites. The arrays were scanned using an Illumina iScan, with initial quality review using GenomeStudio (version 2011.1). This assay detects methylation of cytosine at CpG islands using one probe to detect the methylated and one to detect the unmethylated loci. Single-base extension of the probes incorporated a labelled chain-terminating ddNTP, which was then stained with a fluorescence reagent. The ratio of fluorescent signals from the methylated site versus the unmethylated site determines the level of methylation at the locus.
Quality control and normalization of the profiles was performed using the meffil R package (version 1.1.0) as previously described30. The level of methylation is expressed as a percentage (β-value) ranging from 0 (no cytosine methylation) to 1 (complete cytosine methylation). Finally, to reduce influence of outliers in statistical models, normalized β-values were 90%-Winsorized.
Radon exposure
Potential residential radon exposure is available from the Health Protection Agency (HPA; now Public Health England) – British Geological Survey (BGS) ‘radon potential dataset for Great Britain’, and was obtained for the Avon area (which includes the original ALSPAC catchment area) from BGS after a data sharing agreement was agreed by BGS and the PI’s Institute. Estimates of potential radon exposure were based on long-term radon measurements from 479,000 homes across Great Britain and provided with a spatial resolution of 75-metre buffers as the percentage of dwellings exceeding the 200 Bq m-3 Radon Action Level in 6 classes: 1 (0-1%), 2 (1-3%), 3 (>3-5%), 4 (>5-10%), 5 (>10-30%) and 6 (>30-100%). More information is available at: http://www.bgs.ac.uk/radon/hpa-bgs.html. To assess measurement error, we also linked the ARIES dataset to estimates from the freely available radon ‘indicative atlas’31, which is based on the ‘potential dataset’, but provides the estimates in 1-km-size squares.
Residential histories of mothers and children were geocoded to postcode centroid level, and were linked to average potential radon exposure using ArcGIS software (version 10.6)32 within the ALSPAC Data Safe Haven. This resulted in at least one address match for 986 mothers and 1001 young people (including two sets of twins). Once each residential address had a radon potential exposure class assigned, time spent at each address was calculated. This was merged with ARIES sample prevision dates, allowing time-weighted average potential radon exposures to be calculated up to the ‘mothers at middle age’, ‘children at 7’ and ‘children at 15/17’ sample extraction time points. For the cord and antenatal sample extractions, radon exposure potential of address at date of birth or closest address (temporally) to sample time point were assigned respectively.
These data were then linked to ALSPAC self-reported data selected to test for potential confounding (described below). After the linked exposure data were processed to minimise the risk of participant disclosure, the linked methylation-radon data were used for statistical analyses.
Statistical methods
For these analyses we only use participants with complete data. In the primary analyses average potential radon exposure was analysed as a continuous variable (range 1–6) to assess linear exposure-response associations. In addition, we also analysed associations based on binary exposure classifications (≤5% vs >5%).
Associations were tested using linear models using the limma R package (version 3.32.10)33. Associations were tested in (1) univariate analyses but with adjustment for the surrogate variables34 to handle batch effects, sex differences, cell count heterogeneity and possible unknown confounders35, and (2) additionally with adjustment for potential confounding factors maternal age at birth, maternal BMI, smoking during pregnancy, partner smoking during pregnancy, AHRR CpG site that detects own smoking nearly as accurately as self-report36, mother alcohol intake in early pregnancy, equivalized income, parental occupation, and parental education, and (3) all factors of models 1 and 2 and additionally for damp problems, central heating, boiler location, gas cooking, time windows open in the summer/winter day/night, and heavy traffic.
Because of the exploratory nature of this study, associations at false discovery rate (FDR) less than 20% calculated using the q method37 are reported instead of a more traditional 10% or 5% threshold. Where associations were positive, these sites were defined as “hypermethylated” and conversely when for inverse associations, these were defined as “hypomethylated”.
Results
Participants and location
The results were based on 786 to 980 participants with complete information, depending on the time analysed. A graphical overview of the geographical study area and the distribution of potential radon exposure classes, as well as the distribution of addresses in each class, is shown graphically in Figure 1, and indicates that 79% of addresses are in areas with low probability (class 1 and 2) of exposure >200 Bq m-3.
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