  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.
