Increasing temporal sensitivity of omics association studies with epigenome-wide distributed lag models.

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, and these can 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, the EWDLM achieved an increased sensitivity for associations limited to specific periods of time compared with traditional 2-stage approaches. In a real-world EWDLM analysis, 353 cytosine-phosphate-guanine sites were identified at which DNA methylation measured at age 12 years was significantly associated with fine particulate matter exposure during pregnancy. The EWDLM provides an efficient and sensitive way to screen epigenomic data sets for associations with exposures localized to specific time periods.