Environmental Exposure and DNA Damage

National Institute Of Environmental Health Sciences

Investigators

Linked publications, trials & patents

Abstract

In work published this year the Taylor group has created a new breast cancer risk score based on blood DNA methylation. Using a prospective study of 2774 women who were cancer-free at enrollment and followed for years after blood draw, they used machine learning to identify a set of methylation marks that predict women who would later develop breast cancer. DNA methylation plays an important role in controlling gene expression and changes with age and environmental exposure. The breast cancer risk information encoded by the methylation-based breast cancer risk score is as predictive as the risk captured by existing genetic scores based on a womans DNA sequence, and outperforms traditional breast cancer risk factors, such as a womans lifestyle factors and reproductive history. This research provides insight into how genetic and environmental factors combine to cause the most common cancer in American women. In related work using these same women we have shown that healthy diet is associated with a younger biological age as measured by DNA methylation. These results were particularly strong for methylation-based measures of biological age that are known predictors of mortality. Participants answered questions about their food consumption in the past 12 months, and their responses were used to calculate adherence to different healthy eating indexes. We examined several different measures of healthy eating all showed beneficial effects. Related publications from our group have examined the effects of alcohol consumption, body composition, and exercise on methylation-based measures of biological age. Measurement of DNA methylation is often done using array-based methods. Over the years our group has published a number of statistical methods designed to improve the processing of raw data. We recently compared our processing method pipeline to alternative published pipelines using two large datasets based on duplicate samples and laboratory mixing experiments. Our evaluations show that the ENmix pipeline performs the best with significantly higher correlation and lower absolute difference between duplicate pairs, higher intraclass correlation coefficients (ICC) and smaller deviations from expected methylation level in mixture experiments. In addition to the pipeline function, ENmix software provides an integrated set of functions for reading in raw data files from mouse and human arrays, quality control, data preprocessing, visualization, detection of differentially methylated regions (DMRs), estimation of cell type proportions, and calculation of methylation age clocks.