**Article title:** Association of childhood neighborhood disadvantage with DNA methylation **Authors:** Aaron Reuben, M.E.M.,1 Karen Sugden, Ph.D.,1 Louise Arseneault, Ph.D.,2 David L. Corcoran, Ph.D.,3 Andrea Danese, M.D., Ph.D.,2,4,5 Helen L. Fisher, Ph.D.,2 Terrie E. Moffitt, Ph.D,1,2,3,6 Joanne B. Newbury, Ph.D.,2 Candice Odgers, Ph.D.,7,8 Joey Prinz, B.A.,3 Line J.H. Rasmussen, Ph.D.,1,9 Ben Williams, B.Sc.,1 Jonathan Mill, Ph.D.,10 and Avshalom Caspi, Ph.D.,1,2,3,6 1Department of Psychology and Neuroscience, Duke University, Durham, NC, USA 2Social, Genetic, & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, & Neuroscience, King’s College London, London, UK 3Center for Genomic and Computational Biology, Duke University, Durham, NC, USA 4Department of Child & Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK 5National and Specialist CAMHS Clinic for Trauma, Anxiety, and Depression, South London and Maudsley NHS Foundation Trust, London, UK 6Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA 7Sanford School of Public Policy, Duke University, Durham, NC, USA. 8Department of Psychological Science, University of California Irvine, Irvine, CA, USA 9Clinical Research Centre, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark 10University of Exeter Medical School, University of Exeter, Exeter, UK. **Abstract:** Importance: DNA methylation has been proposed as an epigenetic mechanism by which the childhood neighborhood environment may influence the genome to compromise adult health. Objective: To determine whether childhood neighborhood disadvantage is associated with differences in DNA methylation by age 18 years. Design: Longitudinal-prospective study of a 1994-95 birth cohort, followed to age 18 years (until September, 2014; 93% retention). Data analysis was performed from March to June 2019. Setting: United Kingdom. Participants: The nationally representative Environmental-Risk Longitudinal Study (N=2,232). Exposures: High-resolution neighborhood data (indexing deprivation, dilapidation, disconnection, and dangerousness) collected across childhood. Main Outcomes and Measures: DNA methylation in whole blood was drawn at age 18. Neighborhood-to-methylation associations were tested using three prespecified approaches: (1) testing probes annotated to candidate genes involved in biological responses to growing up in disadvantaged neighborhoods and investigated in previous epigenetic research (i.e., stress-reactivity and inflammation-related genes), (2) polyepigenetic scores indexing differential methylation in phenotypes associated with growing up in disadvantaged neighborhoods (i.e., obesity, inflammation, and smoking), and (3) a theory-free Epigenome-Wide Association Study (EWAS). Results: 1,619 participants (72.5% of cohort, 806[50%] female) had complete neighborhood and DNA methylation data. Children raised in disadvantaged neighborhoods exhibited differential DNA methylation in genes related to inflammation, exposure to tobacco-smoke, and metabolism of hydrocarbons. Neighborhood-to-methylation associations were small but robust to family-level socioeconomic effects and to individual-level tobacco smoking. Exploratory follow-up tests implicated exposure to air pollutants in neighborhood-linked epigenetic differences. Conclusions and Relevance: Children raised in disadvantaged neighborhoods enter young-adulthood epigenetically distinct from their more advantaged peers. This may be one mechanism by which the childhood neighborhood environment influences adult health. Key words: Childhood, young adulthood, neighborhood socioeconomic disadvantage, DNA methylation, epigenome, Developmental Origins of Health and Disease, air pollution **Supplemental file:** Within the ‘Files’ section is a supplemental results file named ‘Reuben_Neighborhood_Disadvantage_and_Methylation_data_supplement.xlsx’. This file is not available with the published article and must be accessed from here. The file lists the results of analyses of childhood neighborhood disadvantage and young adult DNA methylation. Summary statistics across all ~430,000 methylation probes under 2 different models are included (including estimates with and without adjustment for smoking behavior). To interact with the file effectively, we recommend downloading and opening in Microsoft Excel (WARNING: file size is ~210MB and might take a long time to download on slow internet connections). Once open in excel, we have included filter/sort options for each column; the option is available via the filter button included in each column header. To apply a filter option, select the button in the appropriate column and select a filter or sort criterion from the drop-down menu (e.g. increasing P-value sort, text string filter for gene name). To deselect a currently applied filter, select ‘clear filter’ option from the drop-down menu.