Email updates

Keep up to date with the latest news and content from Genome Medicine and BioMed Central.

Journal App

google play app store
Open Access Method

Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma

Johannes Raffler12, Werner Römisch-Margl1, Ann-Kristin Petersen3, Philipp Pagel4, Florian Blöchl4, Christian Hengstenberg5, Thomas Illig67, Christa Meisinger8, Klaus Stark59, H-Erich Wichmann10, Jerzy Adamski11, Christian Gieger3, Gabi Kastenmüller1 and Karsten Suhre112*

Author Affiliations

1 Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany

2 Faculty of Biology, Ludwig-Maximilians-Universität, Großhaderner Straße 2, 82152 Planegg-Martinsried, Germany

3 Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany

4 numares GmbH, Josef-Engert-Str. 9, 93053 Regensburg, Germany

5 Klinik und Poliklinik für Innere Medizin II, University of Regensburg, Franz-Josef-Strauss-Allee 11, 93053 Germany

6 Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany

7 Hannover Unified Biobank, Hannover Medical School, Carl-Neuberg-Straße 1, 30625 Hannover, Germany

8 Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany

9 Department of Epidemiology and Preventive Medicine, University of Regensburg, Franz-Josef-Strauss-Allee 11, 93053 Germany

10 Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany

11 Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany

12 Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City, Qatar Foundation, P.O. BOX 24144, Doha, Qatar

For all author emails, please log on.

Genome Medicine 2013, 5:13  doi:10.1186/gm417


See related Research highlight by Inouye and Abraham, http://genomemedicine.com/content/5/2/14

Published: 15 February 2013

Abstract

Nuclear magnetic resonance spectroscopy (NMR) provides robust readouts of many metabolic parameters in one experiment. However, identification of clinically relevant markers in 1H NMR spectra is a major challenge. Association of NMR-derived quantities with genetic variants can uncover biologically relevant metabolic traits. Using NMR data of plasma samples from 1,757 individuals from the KORA study together with 655,658 genetic variants, we show that ratios between NMR intensities at two chemical shift positions can provide informative and robust biomarkers. We report seven loci of genetic association with NMR-derived traits (APOA1, CETP, CPS1, GCKR, FADS1, LIPC, PYROXD2) and characterize these traits biochemically using mass spectrometry. These ratios may now be used in clinical studies.