Biology subjects

Coull, B.

Publications and source records attributed to Coull, B..

3 recordsLinked to original sources

bioRxiv2023

An epigenome-wide analysis of DNA methylation, racialized and economic inequities, and air pollution

ImportanceDNA methylation (DNAm) provides a plausible mechanism by which adverse exposures become embodied and contribute to health inequities, due to its role in genome regulation and responsiveness to social and biophysical exposures tied to societal context. However, scant epigenome-wide association studies (EWAS) have included structural and lifecourse measures of exposure, especially in relation to structural discrimination. ObjectiveOur study tests the hypothesis that DNAm is a mechanism by which racial discrimination, economic adversity, and air pollution become biologically embodied. DesignA series of cross-sectional EWAS, conducted in My Body My Story (MBMS, biological specimens collected 2008-2010, DNAm assayed in 2021); and the Multi Ethnic Study of Atherosclerosis (MESA; biological specimens collected 2010-2012, DNAm assayed in 2012-2013); using new georeferenced social exposure data for both studies (generated in 2022). SettingMBMS was recruited from four community health centers in Boston; MESA was recruited from four field sites in: Baltimore, MD; Forsyth County, NC; New York City, NY; and St. Paul, MN. ParticipantsTwo population-based samples of US-born Black non-Hispanic (Black NH), white non-Hispanic (white NH), and Hispanic individuals (MBMS; n=224 Black NH and 69 white NH) and (MESA; n=229 Black NH, n=555 white NH and n=191 Hispanic). ExposuresEight social exposures encompassing racial discrimination, economic adversity, and air pollution. Main outcomeGenome-wide changes in DNAm, as measured using the Illumina EPIC BeadChip (MBMS; using frozen blood spots) and Illumina 450k BeadChip (MESA; using purified monocytes). Our hypothesis was formulated after data collection. ResultsWe observed the strongest associations with traffic-related air pollution (measured via black carbon and nitrogen oxides exposure), with evidence from both studies suggesting that air pollution exposure may induce epigenetic changes related to inflammatory processes. We also found suggestive associations of DNAm variation with measures of structural racial discrimination (e.g., for Black NH participants, born in a Jim Crow state; adult exposure to racialized economic residential segregation) situated in genes with plausible links to effects on health. Conclusions and RelevanceOverall, this work suggests that DNAm is a biological mechanism through which structural racism and air pollution become embodied and may lead to health inequities. Key pointsO_ST_ABSQuestionC_ST_ABSCould DNAm be a mechanism by which adversity becomes embodied? FindingsTraffic-related air pollution exposure may induce epigenetic changes related to inflammatory processes and there are suggestive associations with measures of structural racism MeaningDNAm may be a biological mechanism through which structural racism and air pollution become biologically embodied

bioinformatics↗

bioRxiv2017

An almost nearly optimal sequential testing approach to permutation-based association testing

The following technical report describes the technical details for the implementation of a sequential testing approach to permutation-based association testing in whole-genome sequencing studies. The sequential testing approach enables to control the probability of a type 1 and type 2 error at arbitrary small pre-specified levels and approaches the theoretical minimum of expected number of required permutations as these levels go to zero.

genetics↗

bioRxiv2017

Combining Ensemble Learning Techniques and G-Computation to Investigate Chemical Mixtures in Environmental Epidemiology Studies

BackgroundAlthough biomonitoring studies demonstrate that the general population experiences exposure to multiple chemicals, most environmental epidemiology studies consider each chemical separately when assessing adverse effects of environmental exposures. Hence, the critical need for novel approaches to handle multiple correlated exposures.\n\nMethodsWe propose a novel approach using the G-formula, a maximum likelihood-based substitution estimator, combined with an ensemble learning technique (i.e. SuperLearner) to infer causal effect estimates for a multi-pollutant mixture. We simulated four continuous outcomes from real data on 5 correlated exposures under four exposure-response relationships with increasing complexity and 500 replications. The first simulated exposure-response was generated as a linear function depending on two exposures; the second was based on a univariate nonlinear exposure-response relationship; the third was generated as a linear exposure-response relationship depending on two exposures and their interaction; the fourth simulation was based on a non-linear exposure-response relationship with an effect modification by sex and a linear relationship with a second exposure. We assessed the method based on its predictive performance (Minimum Square error [MSE]), its ability to detect the true predictors and interactions (i.e. false discovery proportion, sensitivity), and its bias. We compared the method with generalized linear and additive models, elastic net, random forests, and Extreme gradient boosting. Finally, we reconstructed the exposure-response relationships and developed a toolbox for interactions visualization using individual conditional expectations.\n\nResultsThe proposed method yielded the best average MSE across all the scenarios, and was therefore able to adapt to the true underlying structure of the data. The method succeeded to detect the true predictors and interactions, and was less biased in all the scenarios. Finally, we could correctly reconstruct the exposure-response relationships in all the simulations.\n\nConclusionsThis is the first approach combining ensemble learning techniques and causal inference to unravel the effects of chemical mixtures and their interactions in epidemiological studies. Additional developments including high dimensional exposure data, and testing for detection of low to moderate associations will be carried out in future developments.

epidemiology↗