Skip to main content
. Author manuscript; available in PMC: 2016 Mar 3.
Published in final edited form as: J Am Coll Cardiol. 2015 Mar 3;65(8):830–845. doi: 10.1016/j.jacc.2014.12.033

FIGURE 3. Genetics of CAD Gene Expression Studies.

FIGURE 3

The STAGE (Stockholm Atherosclerosis Gene Expression) and STARNET (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task) genetics of gene expression studies (GGES). (A) Patient recruitment. Patients were included if eligible for coronary artery bypass grafting (CABG) or other non-CAD related indications for open thorax surgery and had no other severe systemic diseases (e.g., widespread cancer or active systemic inflammatory disease). The ethical committees of the Karolinska and Tartu University Hospitals approved the studies, and patients gave written consent (Dnr 004-02 and 2007/1521-32). (B) Open thorax surgery. Between 7 and 9 vascular and metabolic tissues of 124 (STAGE) and 600 (STARNET) CAD patients and 100 non-CAD control subjects (STARNET), all clinically well-characterized, were sampled during open thorax surgery as described (7). Ribonucleic acid (RNA) samples were isolated from the atherosclerotic arterial wall, internal mammary artery, liver, skeletal muscle (Sklm.), subcutaneous (s.c.) and visceral fat, whole blood, and primary blood monocytes differentiated into macrophages and foam cells in vitro. (C) Clinical endpoints. In each patient, a total of 114 clinical characteristics were screened, including the SYNTAX score (from pre-operative angiograms), which shows the clinical degree of coronary atherosclerosis. Currently, the 5-year follow-up of mortality (from CAD and other reasons) and CAD-related morbidity is being conducted. (D) Data generation. GenomeWideSNP_6 arrays (Affymetrix) were used for genotyping DNA, and Custom-made HuRSTA-2a520709 arrays (Affymetrix) were used for gene expression profiling (STAGE) and RNA sequencing (Illumina2500, STARNET). (E) Systems genetic analysis. Gene expression and RNA sequence data are used to define groups of genes acting together in modules and networks based on coexpression similarities (17,52,64,65). An eigengene value of each module is calculated and correlated with the phenotypic characteristics of the patients. For modules with strong phenotypic associations, Bayesian network algorithms are applied to infer key drivers genes (50,67,68) (yellow nodes) believed to serve as diagnostic markers and therapeutic targets. (F) Integrate GWA datasets. GWA datasets for CAD are reanalyzed to assess risk enrichment of identified regulatory gene networks. Abd. = abdominal; MI = myocardial infarction; MVR = mitral valve repair; other abbreviations as in Figures 1 and 2.