| Literature DB >> 21886157 |
So-Youn Shin1, Ann-Kristin Petersen2, Nicole Soranzo1, Christian Gieger2, Karsten Suhre3,4,5, Robert P Mohney6, David Meredith7, Brigitte Wägele3,8, Elisabeth Altmaier3, Panos Deloukas1, Jeanette Erdmann9, Elin Grundberg1,10, Christopher J Hammond10, Martin Hrabé de Angelis11,12, Gabi Kastenmüller3, Anna Köttgen13, Florian Kronenberg14, Massimo Mangino10, Christa Meisinger15, Thomas Meitinger16,17, Hans-Werner Mewes3,8, Michael V Milburn6, Cornelia Prehn11, Johannes Raffler3,4, Janina S Ried1, Werner Römisch-Margl3, Nilesh J Samani18, Kerrin S Small10, H-Erich Wichmann19,20,21, Guangju Zhai10, Thomas Illig22, Tim D Spector10, Jerzy Adamski11.
Abstract
Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.Entities:
Mesh：
Year: 2011 PMID： 21886157 PMCID： PMC3832838 DOI： 10.1038/nature10354
Source DB: PubMed Journal: Nature ISSN： 0028-0836 Impact factor: 49.962