Document Type
Open Access Thesis
Embargo Period
3-1-2018
Degree Program
Public Health
Degree Type
Master of Science (M.S.)
Year Degree Awarded
2017
Month Degree Awarded
September
Abstract
Epigenetic changes (specifically DNA methylation) have been associated with adverse health outcomes; however, unlike genetic markers that are fixed over the lifetime of an individual, methylation can change. Given that there are a large number of methylation sites, measuring them repeatedly introduces multiple testing problems beyond those that exist in a static genetic context. Using simulations of epigenetic data, we considered different methods of controlling the false discovery rate. We considered several underlying associations between an exposure and methylation over time.
We found that testing each site with a linear mixed effects model and then controlling the false discovery rate (FDR) had the highest positive predictive value (PPV), a low number of false positives, and was able to differentiate between differential methylation that was present at only one time point vs. a persistent relationship. In contrast, methods that controlled FDR at a single time point and ad hoc methods tended to have lower PPV, more false positives, and/or were unable to differentiate these conditions.
Validation in data obtained from Project Viva found a difference between fitting longitudinal models only to sites significant at one time point and fitting all sites longitudinally.
First Advisor
Ken Kleinman
Second Advisor
Brian Whitcomb
Third Advisor
Matthias Steinrücken
Recommended Citation
Leap, Katie, "Multiple Testing Correction with Repeated Correlated Outcomes: Applications to Epigenetics" (2017). Masters Theses. 559.
https://scholarworks.umass.edu/masters_theses_2/559
Included in
Bioinformatics Commons, Biostatistics Commons, Computational Biology Commons, Longitudinal Data Analysis and Time Series Commons