Increasing Temporal Sensitivity of Omics Association Studies with Epigenome-Wide Distributed Lag Models

Abstract Current methods for identifying temporal windows of effect for time-varying exposures in omics settings can control false discovery rates at the biomarker-level but cannot efficiently screen for timing-specific effects in high dimensions. Current approaches leverage separate models for site screening and identification of susceptible time windows, which miss associations that vary over time. We introduce the epigenome-wide distributed lag model (EWDLM), a novel approach that combines traditional false discovery rate methods with the distributed lag model (DLM) to screen for timing-specific effects in high dimensional settings. This is accomplished by marginalizing DLM effect estimates over time and correcting for multiple comparisons. In a simulation investigating timing-specific effects of ambient air pollution during pregnancy on DNA methylation across the epigenome at age 12 years, EWDLM achieved an increased sensitivity for associations limited to specific periods of time compared to traditional two-stage approaches. In a real-world EWDLM analysis, 353 CpG sites at which DNAm measured at age 12 was significantly associated with PM2.5 exposure during pregnancy were identified. EWDLM is a novel method that provides an efficient and sensitive way to screen epigenomic datasets for associations with exposures localized to specific time periods.