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

Advisor Name

Ken Kleinman

Co-advisor Name

Brian Whitcomb

Third Advisor Name

Matthias Steinrücken

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.

Available for download on Thursday, March 01, 2018

Share

COinS