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Association of Neighborhood Disadvantage in Childhood With DNA Methylation in Young Adulthood

This cohort study traces the biological responses and associated phenotypes of an upbringing in a socially and economically disadvantaged environment.

Neighborhood disadvantage was associated with significantly higher MEG3 methylation after controlling for maternal race/ethnicity, gender of newborn, mother's years of education, maternal household income, pre-pregnancy body-mass index, cigarette smoking, and use of antibiotics during pregnancy (β = 0.76, p = 0.002). A one standard deviation increase in prenatal neighborhood disadvantage also predicted higher MEG3 methylation (p < 0.05). vs. 20.7% live in "hard-pressed" neighborhoods. E-Risk underrepresents "urban prosperity" neighborhoods because such households are likely to be childless. Figure S1 shows E-Risk families' addresses are a near-perfect match to the deciles of the UK Dilapidation was measured from resident ratings of problems in their neighborhood (e.g. litter, vandalized public spaces, vacant storefronts) and independent raters' assessments of these same problems based on the "virtual walk-through" using Google Street View.

©2020 Reuben A et al. JAMA Network Open
Disconnection was measured from resident ratings assessing neighborhood collective efficacy and social connectedness. Neighborhood collective efficacy was assessed via the resident survey using a previously validated 10-item measure of social control and social cohesion 12 . Residents were asked about the likelihood that their neighbors could be counted on to intervene in various ways if, for example: "children were skipping school and hanging out on a street corner," "children were spray-painting graffiti on a local building." They were also asked how strongly they agreed that, for example: "people around here are willing to help their neighbors," "this is a close-knit neighborhood" (item responses: 0-4). Social connectedness was assessed based on indicators of intergenerational closure ("If any of your neighbors' children did anything that upset you would you feel that you could speak to their parents about it?"), reciprocated exchange (e.g., "Would you be happy to leave your keys with a neighbor if you went away on holiday?") and friendship ties (e.g., "Do you have any close friends that live in your neighborhood") among neighbors 13 .
Dangerousness was measured from police records of crime incidence, from neighborhood residents' ratings of how much they feared for their safety and whether they had been victimized, and from independent raters' assessments of neighborhood safety based on the "virtual walk-through" using Google Street View.

eAppendix 3. Additional Details on the Measurement of DNA Methylation
Our epigenetic study used DNA from a single tissue: blood. At age 18, whole blood was collected from 82% (N=1700) of the participants in 10mL K2EDTA tubes.
DNA was extracted from the buffy coat using a Flexigene DNA extraction kit (Qiagen, Hilden, Germany) following manufacturer's instructions. Study members who did not provide blood provided buccal swabs, but these were not included in our methylation analysis to avoid tissue-source confounds. These data have been previously described 14 and are accessible from the Gene Expression Omnibus (accession code: GSE105018).

eAppendix 4. Additional Details on the Selection of Probes for the Candidate Genes Analysis
We interrogated candidate genes hypothesized to be involved in inflammation and stress reactivity by identifying probes on the array that were annotated to prespecified genes in these domains. We chose probes annotated to these genes because they have been studied in the most detailed report about neighborhood disadvantage and DNA methylation. 6 Seven stress-reactivity-related genes and 11 inflammation-related genes were selected, with 3 to 66 probes annotated to each. Probe sequences for the Infinium HumanMethylation450 BeadChip kit were aligned to the hg19 version of the human genome using the BLAT 21 alignment algorithm. Probe sequences that mapped to multiple genomic loci were assigned to the genomic location provided by Illumina. Probe sequences that did not match any region of the genome with at least 94% identity were also assigned to the genomic location provided by Illumina. Each probe was then assigned to its nearest gene based on the GRCh37v75 ENSEMBL 22

Inflammation
Inflammation Methylation Polygenic Score (ImPEGS). ImPEGS were calculated by averaging the product of CpG probe intensities in our data and estimated coefficients across each of the 215 available CpG probes out of 218 identified as epigenome-wide significant in a meta-analysis of inflammation (C-reactive protein, CRP, level). 26 To permit control for technical variation, variation due to blood cell composition and variation due to ethnicity, scores were residualised for methylation BeadChip-specific PCs , white blood cell counts and PCs computed from genome-wide SNP data. Scores were finally standardized to M=0 and SD=1.
The ImPEGS was validated in our study by correlating it with an inflammation phenotype (plasma C-reactive protein, CRP, level). Venous blood was collected from participants in EDTA tubes at age 18 years. Tubes were spun at 2,500 x g for 10 min, and plasma drawn off. Samples were stored at -80°C. Plasma was available for 1,448 participants. Plasma CRP (high-sensitivity CRP) was measured using Quantikine ELISA Kit DCRP00 (R&D Systems, Minneapolis, MN) following the manufacturer's protocol.
The coefficient of variation (CV) was 5.6%. Plasma CRP level was log-transformed to improve normality of distribution, as is commonly done. 27 Further information on the inflammation phenotype is available in Rasmussen et al.

Smoking
Smoking Methylation Polygenic Score (SmPEGS). SmPEGS were constructed as described in Sugden et al. 29 Scores were calculated by averaging the product of CpG probe intensities in our data and estimated coefficients across each of the 2,480 available CpG probes out of 2,623 identified as epigenome-wide significant in a meta-analysis of current smoking. 30 To permit control for technical variation, variation due to blood cell composition and variation due to ethnicity, scores were residualised for methylation BeadChip-specific PCs, white blood cell counts and PCs computed from genome-wide SNP data. Scores were finally standardized to M=0 and SD=1. Notes. All associations are adjusted for the following additional covariates: sex, methylation-array control probe principal components indexing technical variation, and cell-type proportion estimates.

eAppendix 6. Additional Details on the Measurement of Air Pollution Exposure
Exposure to nitrogen oxides (NOx), a regulated gaseous pollutant composed of NO2 and nitric oxide, and PM2.5, a regulated aerosol pollutant with suspended solid and liquid particles smaller than 2.5 microns in diameter, was estimated for Study members at age 17 years based on pollution exposure estimates linked to the latitude-longitude coordinates of participants' residential addresses at 18 years of age (or where the participant spent most of their time) plus 2 additional addresses where the participants reported spending their time. 31 The most common locations were home, school, work, and shops. Creation of the pollution exposure estimates has been previously described 32 .
Pollution exposure estimates were modeled using the local-scale Community Multiscale Air Quality (CMAQ-urban) Modeling System, which is a coupled regional chemical  Note. All associations are adjusted for sex, methylation-array control probe principal components indexing technical variation, and cell-type proportion estimates.