DOI
https://doi.org/10.25772/J5V3-VJ91
Author ORCID Identifier
0000-0001-8169-9754
Defense Date
2019
Document Type
Dissertation
Degree Name
Doctor of Philosophy
Department
Human Genetics
First Advisor
Dr. Timothy P. York
Second Advisor
Dr. Roxann Roberson-Nay
Abstract
Background. DNA methylation (DNAm) is a removable chemical modification to the DNA sequence intimately associated with genomic stability, cellular identity, and gene expression. DNAm patterning reflects joint contributions from genetic, environmental, and behavioral factors. As such, differences in DNAm patterns may explain interindividual variability in risk liability for complex traits like major depression (MD). Hundreds of significant DNAm loci have been identified using cross-sectional association studies. This dissertation builds on that foundational work to explore novel statistical approaches for longitudinal DNAm analyses. Methods. Repeated measures of genome-wide DNAm and social and environmental determinants of health were collected up to six times across pregnancy and the first year postpartum as part of the Pregnancy, Race, Environment, Genes (PREG) Study. Statistical analyses were completed using a combination of the R statistical environment, Bioconductor packages, MplusAutomate, and Mplus software. Prenatal maternal DNAm was measured using the Infinium HumanMethylation450 Beadchip. Latent growth curve models were used to analyze repeated measures of maternal DNAm and to quantify site-level DNAm latent trajectories over the course of pregnancy. The purpose was to characterize the location and nature of prenatal DNAm changes and to test the influence of clinical and demographic factors on prenatal DNAm remodeling. Results. Over 1300 sites had DNAm trajectories significantly associated with either maternal age or lifetime MD. Many of the genomic regions overlapping significant results replicated previous age and MD-related genetic and DNAm findings. Discussion. Future work should capitalize on the progress made here integrating structural equation modeling (SEM) with longitudinal omics-level measures.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
8-6-2019
Included in
Biological Psychology Commons, Genomics Commons, Longitudinal Data Analysis and Time Series Commons, Microarrays Commons, Other Genetics and Genomics Commons, Statistical Methodology Commons