慕尼黑大学Karsten Suhre等科学家发现了能导致脂质代谢紊乱的多个基因变异。这类基因基因变异常导致糖尿病的发生。这项新的研究发现或许有助于科学家更好的了解糖尿病的发病机制以及临床表现,从而达到更好的诊断和治疗糖尿病的目的。这篇研究报告发表在最近的Nature Genetics杂志上。
Karsten Suhre从九个不同基因中发现了与脂质代谢紊乱相关的基因变异,并首次发现糖尿病风险基因MTNR1B和GCKR基因与脂质代谢之间的联系。
该课题组首先确定1,800名实验参与者的血液样本中163种代谢产物的浓度,然后对这些代谢产物进行全基因组相关性研究,寻找与某些常见的基因变异(如SNPs)之间可能的联系,再通过多次独立重复试验验证。(生物谷Bioon.com)
生物谷推荐原始出处:
Nature Genetics 27 December 2009 | doi:10.1038/ng.507
A genome-wide perspective of genetic variation in human metabolism
Thomas Illig1,13, Christian Gieger1,13, Guangju Zhai2, Werner R?misch-Margl3, Rui Wang-Sattler1, Cornelia Prehn4, Elisabeth Altmaier3,5, Gabi Kastenmüller3, Bernet S Kato2, Hans-Werner Mewes3,6, Thomas Meitinger7,8, Martin Hrabé de Angelis4,9, Florian Kronenberg10, Nicole Soranzo2,11, H-Erich Wichmann1,12, Tim D Spector2, Jerzy Adamski4,9 & Karsten Suhre3,5
Serum metabolite concentrations provide a direct readout of biological processes in the human body, and they are associated with disorders such as cardiovascular and metabolic diseases. We present a genome-wide association study (GWAS) of 163 metabolic traits measured in human blood from 1,809 participants from the KORA population, with replication in 422 participants of the TwinsUK cohort. For eight out of nine replicated loci (FADS1, ELOVL2, ACADS, ACADM, ACADL, SPTLC3, ETFDH and SLC16A9), the genetic variant is located in or near genes encoding enzymes or solute carriers whose functions match the associating metabolic traits. In our study, the use of metabolite concentration ratios as proxies for enzymatic reaction rates reduced the variance and yielded robust statistical associations with P values ranging from 3 × 10?24 to 6.5 × 10?179. These loci explained 5.6%–36.3% of the observed variance in metabolite concentrations. For several loci, associations with clinically relevant parameters have been reported previously.
1 Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
2 Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
3 Institute of Bioinformatics and Systems Biology, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
4 Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
5 Faculty of Biology, Ludwig-Maximilians-Universit?t, Planegg-Martinsried, Germany.
6 Department of Genome Oriented Bioinformatics, Life and Food Science Center Weihenstephan, Technische Universit?t München, Freising-Weihenstephan, Germany.
7 Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
8 Institute of Human Genetics, Klinikum rechts der Isar, Technische Universit?t München, Munich, Germany.
9 Institute of Experimental Genetics, Life and Food Science Center Weihenstephan, Technische Universit?t München, Freising-Weihenstephan, Germany.
10 Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria.
11 The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK.
12 Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universit?t and Klinikum Grosshadern, Munich, Germany.
These authors contributed equally to this work.