Draisma Harmen H M, Pool René, Kobl Michael, Jansen Rick, Petersen Ann-Kristin, Vaarhorst Anika A M, Yet Idil, Haller Toomas, Demirkan Ayşe, Esko Tõnu, Zhu Gu, Böhringer Stefan, Beekman Marian, van Klinken Jan Bert, Römisch-Margl Werner, Prehn Cornelia, Adamski Jerzy, de Craen Anton J M, van Leeuwen Elisabeth M, Amin Najaf, Dharuri Harish, Westra Harm-Jan, Franke Lude, de Geus Eco J C, Hottenga Jouke Jan, Willemsen Gonneke, Henders Anjali K, Montgomery Grant W, Nyholt Dale R, Whitfield John B, Penninx Brenda W, Spector Tim D, Metspalu Andres, Slagboom P Eline, van Dijk Ko Willems, 't Hoen Peter A C, Strauch Konstantin, Martin Nicholas G, van Ommen Gert-Jan B, Illig Thomas, Bell Jordana T, Mangino Massimo, Suhre Karsten, McCarthy Mark I, Gieger Christian, Isaacs Aaron, van Duijn Cornelia M, Boomsma Dorret I
Department of Biological Psychology, VU University Amsterdam, van der Boechorststraat 1, Amsterdam 1081 BT, The Netherlands.
The EMGO+ Institute for Health and Care Research, VU University Medical Center, Van der Boechorststraat 7, Amsterdam 1081 BT, The Netherlands.
Nat Commun. 2015 Jun 12;6:7208. doi: 10.1038/ncomms8208.
Metabolites are small molecules involved in cellular metabolism, which can be detected in biological samples using metabolomic techniques. Here we present the results of genome-wide association and meta-analyses for variation in the blood serum levels of 129 metabolites as measured by the Biocrates metabolomic platform. In a discovery sample of 7,478 individuals of European descent, we find 4,068 genome- and metabolome-wide significant (Z-test, P < 1.09 × 10(-9)) associations between single-nucleotide polymorphisms (SNPs) and metabolites, involving 59 independent SNPs and 85 metabolites. Five of the fifty-nine independent SNPs are new for serum metabolite levels, and were followed-up for replication in an independent sample (N = 1,182). The novel SNPs are located in or near genes encoding metabolite transporter proteins or enzymes (SLC22A16, ARG1, AGPS and ACSL1) that have demonstrated biomedical or pharmaceutical importance. The further characterization of genetic influences on metabolic phenotypes is important for progress in biological and medical research.
代谢物是参与细胞代谢的小分子，可以使用代谢组学技术在生物样本中检测到。在此，我们展示了通过百泰派克代谢组学平台测量的129种代谢物血清水平变异的全基因组关联研究和荟萃分析结果。在一个由7478名欧洲血统个体组成的发现样本中，我们发现单核苷酸多态性（SNP）与代谢物之间存在4068个全基因组和全代谢组显著（Z检验，P < 1.09×10⁻⁹）关联，涉及59个独立SNP和85种代谢物。59个独立SNP中有5个是血清代谢物水平的新发现，并在一个独立样本（N = 1182）中进行了重复验证。这些新的SNP位于编码代谢物转运蛋白或酶（SLC22A16、ARG1、AGPS和ACSL1）的基因内部或附近，这些基因已证明具有生物医学或药学重要性。进一步表征遗传因素对代谢表型的影响对于生物学和医学研究的进展至关重要。
