Genome-wide significant loci: how important are they? Systems genetics to understand heritability of coronary artery disease and other common complex disorders

Abstract

Genome-wide association studies (GWAS) have been extensively used to study common complex diseases such as coronary artery disease (CAD), revealing 153 suggestive CAD loci, of which at least 46 have been validated as having genome-wide significance. However, these loci collectively explain <10% of the genetic variance in CAD. Thus, we must address the key question of what factors constitute the remaining 90% of CAD heritability. We review possible limitations of GWAS, and contextually consider some candidate CAD loci identified by this method. Looking ahead, we propose systems genetics as a complementary approach to unlocking the CAD heritability and etiology. Systems genetics builds network models of relevant molecular processes by combining genetic and genomic datasets to ultimately identify key "drivers" of disease. By leveraging systems-based genetic approaches, we can help reveal the full genetic basis of common complex disorders, enabling novel diagnostic and therapeutic opportunities.

Keywords: atherosclerosis; atherosclerotic plaque; genome-wide association study; myocardial infarction; primary prevention; regulatory gene networks.

FIGURE 1. Development of Rare Versus Complex Disease, Using CAD as the Example

(A) Rare disease does not develop in noncarriers of deoxyribonucleic acid (DNA) risk variants. The level of clinical manifestation varies with different rare diseases. Of note, the indicated sigmoidal curve for rare disease development merely reflects that most biological events follow this growth pattern; it does not indicate that this true for all rare diseases. (B) The development of complex diseases, such as coronary artery disease (CAD), generally follows a sigmoidal curve, characterized by a slow initial growth phase (30 to 50 years), reaching a triggering level to enter a subsequent rapid expansion phase (10 years), and finally, a slow-down phase in which clinical symptoms manifest. Subjects in genome-wide association studies (GWAS) are more likely to be defined as cases or controls predominantly on the basis of early disease processes, rather than due to later, largely overlapping processes. As a result, the likelihood of finding risk variants with genome-wide significance is higher for disease processes active in early CAD development. (C) Given the large overlap between cases and controls, increasing dependence of environmental contexts, and the shorter duration of late disease processes, DNA variants regulating these processes are likely associated with disease at nominally-significant p values in traditional analyses of genome-wide association datasets.

FIGURE 2. Risk Variants Identified in GWAS

(A) The gray triangle represents all DNA variants tested for CAD association in GWAS. The 5% (red triangle) have nominal significant association (p < 0.05). The very top of the central red triangle consists of risk variants associated with CAD at a level of genome-wide significance (p < 10⁻⁸). The middle section consists of risk variants associated with CAD at a level suggestive of genome-wide significance (p < 10⁻⁵). The base of the triangle consists of risk variants associated with CAD at a level of nominal significance (p < 0.05). On the right, the likely influences of different significance levels on context dependency, penetrance, and disease are indicated. On the far right is the likely mode of discovery for testing genome-wide significance (p < 10⁻⁸). DM = diabetes mellitus; GWA = genome-wide association; GWES = genome-wide network studies; other abbreviations as in Figure 1.

FIGURE 3. Genetics of CAD Gene Expression Studies

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.

CENTRAL ILLUSTRATION. Systems Genetics to Understand Coronary Artery Disease: Genetics of Genome-Wide Expression Studies

A wide range of genomic (deoxyribonucleic acid [DNA], ribonucleic acid [RNA], protein, and metabolite), clinical, imaging, and prior knowledge data (circles above the half-circle) are integrated to infer causal gene networks reflecting central disease processes in complex diseases. (Left) Genome-wide (GW) association datasets are integrated in the analysis of these networks to determine their load of inherited risk, which also indicates their level of causality. (Right) Key drivers in disease-causing networks are analyzed against transcriptomic data from studies of numerous approved and experimental drugs to identify drugs that either increase or decrease the activity of genes in the network (i.e., drug repurposing; heat map indicates gene activity in a network exposed to drugs). Identified drugs can be tested in models of complex disease (e.g., mouse models). The final goal is to enable diagnosis and treatment of patients on the basis of their molecular disease status, reflected by the activity status of disease networks. The center parallelogram shows disease-causing gene networks acting within and across tissues (red indicates atherosclerosis; brown indicates liver; orange indicates skeletal muscle; and blue indicates fat) that are integrated through communication (e.g., via blood). The central network indicates the presence of cross-tissue networks.