DOI
https://doi.org/10.25772/24XD-NG53
Author ORCID Identifier
https://orcid.org/0000-0001-8098-5791
Defense Date
2023
Document Type
Dissertation
Degree Name
Doctor of Philosophy
Department
Integrative Life Sciences
First Advisor
Silviu-Alin Bacanu
Abstract
Genome-wide association studies (GWAS) of psychiatric disorders (PD) yield numerous loci with significant signals, but often they do not implicate specific protein coding genes. Because GWAS risk loci are enriched in expression/protein/methylation quantitative loci (e/p/mQTL, hereafter xQTL), transcriptome/proteome/methylome-wide association studies (T/P/MWAS, hereafter XWAS), which integrate information from GWAS and x-level (mRNA, protein or DNA methylation levels) coming from largest xQTL studies, can link GWAS signals to effects on specific genes. For gene level analyses, researchers use mendelian randomization (MR) methods to fine-map the association between x-levels and trait. However, none of the previous studies ever jointly analyzed XWAS of multiple traits to improve the signal detection for underpowered traits and tissues.
In our investigation, we performed both brain/blood univariate and joint (cross-trait cross-tissue) XWAS analyses for nine PD. We identified shared immune signals within the Major Histocompatibility Complex (MHC) region on chromosome 6 and other type of signals elsewhere in the genome across multiple PD. Notably, some actionable findings may relate to vitamins, such as vitamin B6 (cofactor of KYAT3) for post-traumatic stress disorder and omega-3 and vitamin D gene sets for bipolar disorder.
To improve signal detection, researchers proposed to aggregate signals in pathways/gene sets (GS). However, there is no MR method yet for fine-mapping GS and, also, most of the existing methods do not adjust for the linkage disequilibrium (LD) between statistics for adjacent genes. To further refine causal inference between complex disorders (including PD) and GS, we developed a novel mendelian randomization (MR) GS enrichment (MR-GSE) procedure that naturally adjusts its statistics for LD. Our method first generates a “synthetic” GWAS for each MSigDB GS by aggregating summary statistics for x-levels related to genes in a GS. Second, it conducts a generalized summary-data-based MR (GSMR) analysis using synthetic GS GWAS as exposure and trait GWAS as outcome. When we applied MR-GSE to the nine investigated PD, we uncovered signals that largely fall into seven categories: neuron, immune, mRNA translation, miRNA, cytoskeleton, apoptosis, supplements related GS and some unexpected GS findings.
Rights
© Huseyin Gedik
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
8-1-2023