 Meta-Analysis of 28,141 Individuals Identifies Common Variants within Five New Loci That Influence Uric Acid ConcentrationsMelanie Kolz, Toby Johnson, Serena Sanna, Alexander Teumer, Veronique Vitart, Markus Perola, Massimo Mangino, Eva Albrecht, Chris Wallace, Martin Farrall, Åsa Johansson, Dale R Nyholt, Yurii Aulchenko, Jacques S Beckmann, Sven Bergmann, Murielle Bochud, Morris Brown, Harry Campbell, for the EUROSPAN Consortium, John Connell, Anna Dominiczak, Georg Homuth, Claudia Lamina, Mark I McCarthy, for the ENGAGE Consortium, Thomas Meitinger, Vincent Mooser, Patricia Munroe, Matthias Nauck, John Peden, Holger Prokisch, Perttu Salo, Veikko Salomaa, Nilesh J Samani, David Schlessinger, Manuela Uda, Uwe Völker, Gérard Waeber, Dawn Waterworth, Rui Wang-Sattler, Alan F Wright, Jerzy Adamski, John B Whitfield, Ulf Gyllensten, James F Wilson, Igor Rudan, Peter Pramstaller, Hugh Watkins, for the PROCARDIS Consortium, Angela Doering, H-Erich Wichmann, for the KORA Study, Tim D Spector, Leena Peltonen, Henry Völzke, Ramaiah Nagaraja, Peter Vollenweider, Mark Caulfield, For The Wtccc, Thomas Illig and Christian Gieger PLOS Genetics, 2009, vol. 5, issue 6, 1-10 Abstract: Elevated serum uric acid levels cause gout and are a risk factor for cardiovascular disease and diabetes. To investigate the polygenetic basis of serum uric acid levels, we conducted a meta-analysis of genome-wide association scans from 14 studies totalling 28,141 participants of European descent, resulting in identification of 954 SNPs distributed across nine loci that exceeded the threshold of genome-wide significance, five of which are novel. Overall, the common variants associated with serum uric acid levels fall in the following nine regions: SLC2A9 (p = 5.2×10−201), ABCG2 (p = 3.1×10−26), SLC17A1 (p = 3.0×10−14), SLC22A11 (p = 6.7×10−14), SLC22A12 (p = 2.0×10−9), SLC16A9 (p = 1.1×10−8), GCKR (p = 1.4×10−9), LRRC16A (p = 8.5×10−9), and near PDZK1 (p = 2.7×10−9). Identified variants were analyzed for gender differences. We found that the minor allele for rs734553 in SLC2A9 has greater influence in lowering uric acid levels in women and the minor allele of rs2231142 in ABCG2 elevates uric acid levels more strongly in men compared to women. To further characterize the identified variants, we analyzed their association with a panel of metabolites. rs12356193 within SLC16A9 was associated with DL-carnitine (p = 4.0×10−26) and propionyl-L-carnitine (p = 5.0×10−8) concentrations, which in turn were associated with serum UA levels (p = 1.4×10−57 and p = 8.1×10−54, respectively), forming a triangle between SNP, metabolites, and UA levels. Taken together, these associations highlight additional pathways that are important in the regulation of serum uric acid levels and point toward novel potential targets for pharmacological intervention to prevent or treat hyperuricemia. In addition, these findings strongly support the hypothesis that transport proteins are key in regulating serum uric acid levels.Author Summary: Elevated serum uric acid levels cause gout and are a risk factor for cardiovascular disease and diabetes. The regulation of serum uric acid levels is under a strong genetic control. This study describes the first meta-analysis of genome-wide association scans from 14 studies totalling 28,141 participants of European descent. We show that common DNA variants at nine different loci are associated with uric acid concentrations, five of which are novel. These variants are located within the genes coding for organic anion transporter 4 (SLC22A11), monocarboxylic acid transporter 9 (SLC16A9), glucokinase regulatory protein (GCKR), Carmil (LRRC16A), and near PDZ domain-containing 1 (PDZK1). Gender-specific effects are shown for variants within the recently identified genes coding for glucose transporter 9 (SLC2A9) and the ATP-binding cassette transporter (ABCG2). Based on screening of 163 metabolites, we show an association of one of the identified variants within SLC16A9 with DL-carnitine and propionyl-L-carnitine. Moreover, DL-carnitine and propionyl-L-carnitine were strongly correlated with serum UA levels, forming a triangle between SNP, metabolites and UA levels. Taken together, these associations highlight pathways that are important in the regulation of serum uric acid levels and point toward novel potential targets for pharmacological intervention to prevent or treat hyperuricemia. Date: 2009 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pgen00:1000504 DOI: 10.1371/journal.pgen.1000504 Access Statistics for this article More articles in PLOS Genetics from Public Library of Science |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.