Untargeted metabolome quantitative trait locus mapping associates variation in urine glycerate to mutant glycerate kinase.

TitleUntargeted metabolome quantitative trait locus mapping associates variation in urine glycerate to mutant glycerate kinase.
Publication TypeJournal Article
Year of Publication2012
AuthorsCazier J-B, Kaisaki PJ, Argoud K, Blaise BJ, Veselkov K, Ebbels TMD, Tsang T, Wang Y, Bihoreau M-T, Mitchell SC, Holmes EC, Lindon JC, Scott J, Nicholson JK, Dumas M-E, Gauguier D
JournalJ Proteome Res
Volume11
Issue2
Pagination631-42
Date Published2012 Feb 3
ISSN1535-3907
KeywordsAnalysis of Variance, Animals, Chromosome Mapping, Computer Simulation, Genomics, Glyceric Acids, Lod Score, Male, Metabolome, Metabolomics, Mice, Mice, Inbred BALB C, Nuclear Magnetic Resonance, Biomolecular, Phosphotransferases (Alcohol Group Acceptor), Quantitative Trait Loci
Abstract

With successes of genome-wide association studies, molecular phenotyping systems are developed to identify genetically determined disease-associated biomarkers. Genetic studies of the human metabolome are emerging but exclusively apply targeted approaches, which restricts the analysis to a limited number of well-known metabolites. We have developed novel technical and statistical methods for systematic and automated quantification of untargeted NMR spectral data designed to perform robust and accurate quantitative trait locus (QTL) mapping of known and previously unreported molecular compounds of the metabolome. For each spectral peak, six summary statistics were calculated and independently tested for evidence of genetic linkage in a cohort of F2 (129S6xBALB/c) mice. The most significant evidence of linkages were obtained with NMR signals characterizing the glycerate (LOD10-42) at the mutant glycerate kinase locus, which demonstrate the power of metabolomics in quantitative genetics to identify the biological function of genetic variants. These results provide new insights into the resolution of the complex nature of metabolic regulations and novel analytical techniques that maximize the full utilization of metabolomic spectra in human genetics to discover mappable disease-associated biomarkers.

DOI10.1021/pr200566t
Alternate JournalJ. Proteome Res.
PubMed ID22029865