Insight in genome-wide association of metabolite quantitative traits by exome sequence analyses.
Demirkan A., Henneman P., Verhoeven A., Dharuri H., Amin N., van Klinken JB., Karssen LC., de Vries B., Meissner A., Göraler S., van den Maagdenberg AMJM., Deelder AM., C 't Hoen PA., van Duijn CM., van Dijk KW.
Metabolite quantitative traits carry great promise for epidemiological studies, and their genetic background has been addressed using Genome-Wide Association Studies (GWAS). Thus far, the role of less common variants has not been exhaustively studied. Here, we set out a GWAS for metabolite quantitative traits in serum, followed by exome sequence analysis to zoom in on putative causal variants in the associated genes. 1H Nuclear Magnetic Resonance (1H-NMR) spectroscopy experiments yielded successful quantification of 42 unique metabolites in 2,482 individuals from The Erasmus Rucphen Family (ERF) study. Heritability of metabolites were estimated by SOLAR. GWAS was performed by linear mixed models, using HapMap imputations. Based on physical vicinity and pathway analyses, candidate genes were screened for coding region variation using exome sequence data. Heritability estimates for metabolites ranged between 10% and 52%. GWAS replicated three known loci in the metabolome wide significance: CPS1 with glycine (P-value = 1.27×10-32), PRODH with proline (P-value = 1.11×10-19), SLC16A9 with carnitine level (P-value = 4.81×10-14) and uncovered a novel association between DMGDH and dimethyl-glycine (P-value = 1.65×10-19) level. In addition, we found three novel, suggestively significant loci: TNP1 with pyruvate (P-value = 1.26×10-8), KCNJ16 with 3-hydroxybutyrate (P-value = 1.65×10-8) and 2p12 locus with valine (P-value = 3.49×10-8). Exome sequence analysis identified potentially causal coding and regulatory variants located in the genes CPS1, KCNJ2 and PRODH, and revealed allelic heterogeneity for CPS1 and PRODH. Combined GWAS and exome analyses of metabolites detected by high-resolution 1H-NMR is a robust approach to uncover metabolite quantitative trait loci (mQTL), and the likely causative variants in these loci. It is anticipated that insight in the genetics of intermediate phenotypes will provide additional insight into the genetics of complex traits.