eQTL mapping and inherited risk enrichment analysis : a systems biology approach for coronary artery disease

TL;DR

This study integrates genome-wide association studies with gene expression data from multiple tissues to better understand the complex genetic architecture of coronary artery disease using novel bioinformatics tools.

Contribution

It introduces new bioinformatics methods to analyze gene networks, linking genetic variants to gene expression in CAD-relevant tissues, advancing understanding of complex inheritance.

Findings

Identification of regulatory gene networks involved in CAD

Integration of GWAS and gene expression data reveals new genetic associations

Development of tools for multi-gene, multi-variant analysis

Abstract

Despite extensive research during the last decades, coronary artery disease (CAD) remains the number one cause of death, responsible for near 50% of global mortality. A main reason for this is that CAD has a complex inheritance and etiology that unlike rare single gene disorders cannot fully be understood from studies of of genes one-by-one.In parallel, studies that simultaneously assess multiple, functionally associated genes are warranted. For this reason we undertook the Stockholm Atherosclerosis Gene Expression (STAGE) study that besides careful clinical characterization and genome-wide DNA genotyping also assessed the global gene expression profiles from seven CAD-relevant vascular and metabolic tissues. In this thesis report we show that by integrating GWAS with genetics of gene expression studies like STAGE, we can advance our understanding from the perspective of multiple genes and gene variants acting in conjunction to cause CAD in the form of regulatory gene networks. This is done through developing new bioinformatics tools and applying them to disease-specific, genetics of global gene expression studies like STAGE. These tools are necessary to go beyond our current limited single-gene understanding of complex traits, like CAD.