Direct quantitative trait locus mapping of mammalian metabolic phenotypes in diabetic and normoglycemic rat models

Characterizing the relationships between genomic and phenotypic variation is essential to understanding disease etiology. Information-dense data sets derived from pathophysiological 1 , proteomic 2 , 3 and transcriptomic 4 profiling have been applied to map quantitative trait loci (QTLs). Metabolic...

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Veröffentlicht in:Nature genetics 2007-05, Vol.39 (5), p.666-672
Hauptverfasser: Dumas, Marc-Emmanuel, Wilder, Steven P, Bihoreau, Marie-Thérèse, Barton, Richard H, Fearnside, Jane F, Argoud, Karène, D'Amato, Lisa, Wallis, Robert H, Blancher, Christine, Keun, Hector C, Baunsgaard, Dorrit, Scott, James, Sidelmann, Ulla Grove, Nicholson, Jeremy K, Gauguier, Dominique
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Sprache:eng
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Zusammenfassung:Characterizing the relationships between genomic and phenotypic variation is essential to understanding disease etiology. Information-dense data sets derived from pathophysiological 1 , proteomic 2 , 3 and transcriptomic 4 profiling have been applied to map quantitative trait loci (QTLs). Metabolic traits, already used in QTL studies in plants 5 , are essential phenotypes in mammalian genetics to define disease biomarkers. Using a complex mammalian system, here we show chromosomal mapping of untargeted plasma metabolic fingerprints derived from NMR spectroscopic analysis 6 in a cross between diabetic and control rats. We propose candidate metabolites for the most significant QTLs. Metabolite profiling in congenic strains provided evidence of QTL replication. Linkage to a gut microbial metabolite (benzoate) can be explained by deletion of a uridine diphosphate glucuronosyltransferase. Mapping metabotypic QTLs provides a practical approach to understanding genome-phenotype relationships in mammals and may uncover deeper biological complexity, as extended genome 7 (microbiome) perturbations that affect disease processes through transgenomic effects 8 may influence QTL detection.
ISSN:1061-4036
1546-1718
DOI:10.1038/ng2026