A Structured Approach to Evaluating Life Course Hypotheses: Moving Beyond Analyses of Exposed Versus Unexposed in the Omics Context.

In this study, we performed simulations and empirical analyses to evaluate the performance of the SLCMA when applied to genome-wide DNA methylation (DNAm). Using simulations, we compared five statistical inference tests used with SLCMA (n=700), assessing the family-wise error rate, statistical power, and confidence interval coverage to determine whether inference based on these tests was valid in the presence of substantial multiple testing and small effects, two hallmark challenges of inference from omics data. In the empirical analyses, we evaluated the time-dependent relationship of childhood abuse with genome-wide DNAm (n=703). In simulations, selective inference and max-|t|-test performed best: both controlled family-wise error rate and yielded moderate statistical power. Empirical analyses using SLCMA revealed time-dependent effects of childhood abuse on DNAm. Our findings show that SLCMA, applied and interpreted appropriately, can be used in high-throughput settings to examine time-dependent effects underlying exposure-outcome relationships over the life course. We provide recommendations for applying the SLCMA in omics settings and encourage researchers to move beyond analyses of exposed versus unexposed. PMID: 33125040 [PubMed - as supplied by publisher]
Source: Am J Epidemiol - Category: Epidemiology Authors: Tags: Am J Epidemiol Source Type: research