Integrative Analysis of Genetic Associations and Gene-Expression Regulations

This dissertation focuses on approaches which integrate both gene-expression data and genetic association results to extend insight into disease pathogenesis. We investigate and proffer strategies to accurately estimate cell type composition using both single cell and single nucleus RNA-Seq data in Brain (Chapter 2). We map cis regulatory effects in the retinal transcriptome and integrate data about trait associated variants to identify potential target genes which are involved in Age-related Macular Degeneration (Chapter 3). We propose a likelihood framework to model trans-regulatory effect size distribution and apply the method to four tissues, using summary level data, from the Genotype-Tissue Expression Consortium (Chapter 4