Abstract 11158: Integration of Metabolomic and Genomic Data Highlights Novel Pathways For Mechanistic Exploration in Coronary Artery Disease
Background: Genome wide association studies (GWAS) have identified SNPs at >30 loci that are associated with coronary artery disease (CAD). Of these, ~10 loci are linked to established risk factors (RFs) for CAD: eight with hypercholesterolemia, and two with hypertension. Approximately 20 variants are not associated with any known RFs for CAD. We postulated that integrating metabolomic/lipidomic profiles and GWAS could provide insights into how SNPs might ultimately impact CAD.
Methods: We studied 2,076 participants in the Framingham Heart Study (FHS) Offspring cohort (mean age 55, 51% women). Genome-wide genotyping was performed using the Affymetrix 500K mapping array and the Affymetrix 50K gene-focused MIP array. Profiling of 217 metabolites (lipids, amino acids and amines, and intermediary metabolites) was performed using a 5500 QTRAP triple quadrupole mass spectrometer using previously published methods. The strongest associated single nucleotide polymorphism (SNP) within each of 33 CAD loci (published literature) was used to test for associations with each of the 217 metabolites. Results were deemed significant at a Bonferroni-corrected threshold of p<6.9E-6, accounting for 33 SNPs and 217 metabolites tested.
Results: Amongst the CAD loci not associated with known CAD RFs, we observed one significant SNP-metabolite association: Phosphatidycholine (PC) 32:0 (denotes the number of total carbons: denotes the number of double bonds) with SNP rs11556924 near gene ZC3HC1 at locus 7q32.2 (p<6.63E-6). This locus also had nominal associations with PC 34:4 (p<8.13E-5), Sphingomyelin 14:0 (p<2E-4), PC 32:2 (p<5E-4), PC 36:4 (p<5.2E-4), PC 40:6 (p<3.1E-3), and Sphingomyelin 16:1 (p<3.9E-3).
Conclusion: A more granular metabolomic/lipidomic analysis suggests a previously unknown association between the ZC3HC1 locus, a CAD locus not associated with any known CAD RFs, and PC/SM biology. Thus, integrating genomic and metabolomic data may help elucidate the relationship between genetic variants and CAD (as well as other diseases), and reveal potential candidate intermediaries for mechanistic studies.
- © 2013 by American Heart Association, Inc.