DOI
https://doi.org/10.25772/0MNQ-KS97
Defense Date
2025
Document Type
Thesis
Degree Name
Master of Science
Department
Molecular Biology and Genetics
First Advisor
Brien Riley
Second Advisor
Tan Hoang Nguyen
Abstract
The underlying pathogenesis of MDD remains poorly understood, with multiple competing hypotheses regarding its exact etiology. As a heterogeneous disorder with relatively low heritability, large-scale genome-wide association studies of MDD have relied on increasingly large sample sizes to identify common genetic variants associated with the disorder. However, the rare variant genetic architecture of MDD is still largely unknown. The aim of the present analysis was to use the AoU—a United States-based biobank that integrates healthcare records from over 50 healthcare systems and possesses whole genome sequencing data for participants—to perform a gene set–based analysis of MDD. This analysis presented several challenges that had to be addressed at each step of the process. First, because MDD lacks a definitive biological reference standard, phenotyping was conducted using EHRs, with case and control definitions refined through sensitivity analyses. Second, a polygenic risk score was constructed to account for the effects of common variants in the gene set–based rare variant analysis. Third, the rare variant analysis was performed using a suite of variant annotation tools to prioritize gene-sets that mediate MDD risk through brain tissue. Through this approach, the analysis aims: (1) to establish a foundational framework for analyzing MDD within the AoU and (2) to identify gene-sets associated with EHR-derived MDD that illuminate the underlying rare variant genetic architecture of the disorder.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
8-8-2025