Function-on-Function Regression for the Identification of Epigenetic Regions Exhibiting Windows of Susceptibility to Environmental Exposures

TL;DR

This paper introduces a function-on-function regression model to identify specific prenatal periods when air pollution exposure affects DNA methylation, improving detection power over existing methods.

Contribution

The study presents a novel function-on-function regression approach that accounts for covariance structures in methylation and exposure data, enhancing the identification of susceptibility windows.

Findings

Identified prenatal susceptibility windows during the third trimester.

Demonstrated improved detection power over traditional methods.

Applied the model to real cohort data with biologically relevant results.

Abstract

The ability to identify time periods when individuals are most susceptible to exposures, as well as the biological mechanisms through which these exposures act, is of great public health interest. Growing evidence supports an association between prenatal exposure to air pollution and epigenetic marks, such as DNA methylation, but the timing and gene-specific effects of these epigenetic changes are not well understood. Here, we present the first study that aims to identify prenatal windows of susceptibility to air pollution exposures in cord blood DNA methylation. In particular, we propose a function-on-function regression model that leverages data from nearby DNA methylation probes to identify epigenetic regions that exhibit windows of susceptibility to ambient particulate matter less than 2.5 microns (PM$_{2.5}$). By incorporating the covariance structure among both the multivariate DNA methylation outcome and the time-varying exposure under study, this framework yields greater power to detect windows of susceptibility and greater control of false discoveries than methods that model probes independently. We compare our method to a distributed lag model approach that models DNA methylation in a probe-by-probe manner, both in simulation and by application to motivating data from the Project Viva birth cohort. In two epigenetic regions selected based on prior studies of air pollution effects on epigenome-wide methylation, we identify windows of susceptibility to PM$_{2.5}$ exposure near the beginning and middle of the third trimester of pregnancy.