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

摘要

Despite extensive research during the last decades, coronary artery disease (CAD) remains the number one cause of death, responsible for near 50% of global mortal- ity. 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, function- ally associated genes are warranted. For this reason we undertook the Stockholm Atherosclerosis Gene Expression (STAGE) study that besides careful clinical charac- terization and genome-wide DNA genotyping also assessed the global gene expression profiles from seven CAD-relevant vascular and metabolic tissues. In paper I, we used STAGE to develop a bioinformatics tool for efficient eQTL mapping called kruX based on Kruskal-Wallis statistics test. kruX excels in de- tecting a higher proportion of nonlinear expression quantitative expression traits loci (eQTLs) compared to other established methods. This tool was developed for Python, MATLAB, and R and is available online. In paper II, we applied kruX to detect eQTLs across the seven tissues in STAGE and assessed their tissue speci- ficity. A tool for analyzing inherited risk enrichment was also developed assessing CAD association (i.e., risk enrichment) of STAGE eQTLs according to genome-wide association studies (GWAS) of CAD. We found that eQTLs active across multiple vascular and metabolic tissues are more enriched in inherited risk for CAD than tissue-specific eQTLs. In paper III, we integrate the analysis of STAGE data with data from GWAS of CAD to identify 30 regulatory-gene networks causal for CAD. In paper IV, we again used kruX to investigate STAGE eQTLs for three established candidate genes in CAD and atherosclerosis (ALOX5, ALOX5AP, and LTA4H). In addition, we used the Athero-Express dataset of genotype and atherosclerotic carotid plaque characteristics to further elucidate the role of these genes in atherosclerosis development. In sum, in this thesis report we show that by integrating GWAS with genet- ics 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.