Pathway-based genome-wide association analysis of coronary heart disease identifies biologically important gene sets

Abstract

Genome-wide association (GWA) studies of complex diseases including coronary heart disease (CHD) challenge investigators attempting to identify relevant genetic variants among hundreds of thousands of markers being tested. A selection strategy based purely on statistical significance will result in many false negative findings after adjustment for multiple testing. Thus, an integrated analysis using information from the learned genetic pathways, molecular functions, and biological processes is desirable. In this study, we applied a customized method, variable set enrichment analysis (VSEA), to the Framingham Heart Study data (404 467 variants, n ¼ 6421) to evaluate enrichment of genetic association in 1395 gene sets for their contribution to CHD. We identified 25 gene sets with nominal Po0.01; at least four sets are previously known for their roles in CHD: vascular genesis (GO:0001570), fatty-acid biosynthetic process (GO:0006633), fatty-acid metabolic process (GO:0006631), and glycerolipid metabolic process (GO:0046486). Although the four gene sets include 170 genes, only three of the genes contain a variant ranked among the top 100 in single-variant association tests of the 404 467 variants tested. Significant enrichment for novel gene sets less known for their importance to CHD were also identified: Rac 1 cell-motility signaling pathway (h_rac1 Pathway, Po0.001) and sulfur amino-acid metabolic process (GO:0000096, Po0.001). In summary, we showed that the pathway-based VSEA can help prioritize association signals in GWA studies by identifying biologically plausible targets for downstream searches of genetic variants associated with CHD.

Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-Wide SNP Data

“…For example, de las Fuentes et al [19] performed a pathway-based analysis of another independent GWAS dataset for the Framingham Heart Study and identified glycerolipid metabolism pathway to be significantly associated with CAD. In agreement with this study, a previous report showed that the level of serum triglyceride, a key element in glycerolipid metabolism, could be used as an effective predictor for CAD risk [20].…”

Candidate disease gene prediction using Gentrepid: application to a genome‐wide association study on coronary artery disease

“…Another point to note is the low concordance of the pathway enrichment results with the Framingham study (de las Fuentes et al. 2012). A few reasons for this include the fact that we used different gene data sets as input to which the methods are highly sensitive to (Glaab et al.…”

Identification of possible pathogenic pathways in Behçet’s disease using genome-wide association study data from two different populations

“…In this regard, pathway-based approaches to GWAS may be more helpful in a search for multiple genes involved in the same biological pathway, where the common variations in each of these genes have little correlation with disease risk. [8][9][10][11][12][13][14] It has been observed that genes that have aberrations associated with a given complex disease tend to be part of the same subnetwork of the overall protein-protein interaction (PPI) network. 15,16 Hence, to be able to explain the connections between genotypic and phenotypic data, perturbed network modules need to be detected.…”