%0 Journal Article
%T Meta-Analysis of 28,141 Individuals Identifies Common Variants within Five New Loci That Influence Uric Acid Concentrations
%A Melanie Kolz equal contributor
%A Toby Johnson equal contributor
%A Serena Sanna £¿
%A Alexander Teumer £¿
%A Veronique Vitart £¿
%A Markus Perola £¿
%A Massimo Mangino £¿
%A Eva Albrecht £¿
%A Chris Wallace £¿
%A Martin Farrall £¿
%A £¿sa Johansson £¿
%A Dale R. Nyholt £¿
%A Yurii Aulchenko
%A Jacques S. Beckmann
%A Sven Bergmann
%A Murielle Bochud
%A Morris Brown
%A Harry Campbell
%A for the EUROSPAN Consortium
%A John Connell
%A Anna Dominiczak
%A Georg Homuth
%A Claudia Lamina
%A Mark I. McCarthy
%A for the ENGAGE Consortium
%A Thomas Meitinger
%A Vincent Mooser
%A Patricia Munroe
%A Matthias Nauck
%A John Peden
%A Holger Prokisch
%A Perttu Salo
%A Veikko Salomaa
%A Nilesh J. Samani
%A David Schlessinger
%A Manuela Uda
%A Uwe V£¿lker
%A G¨¦rard Waeber
%A Dawn Waterworth
%A Rui Wang-Sattler
%A Alan F. Wright
%A Jerzy Adamski
%A John B. Whitfield
%A Ulf Gyllensten
%A James F. Wilson
%A Igor Rudan
%A Peter Pramstaller
%A Hugh Watkins
%A for the PROCARDIS Consortium
%A Angela Doering
%A H.-Erich Wichmann
%A for the KORA Study
%A Tim D. Spector
%A Leena Peltonen
%A Henry V£¿lzke
%A Ramaiah Nagaraja
%A Peter Vollenweider
%A Mark Caulfield
%A for the WTCCC
%A Thomas Illig
%A Christian Gieger
%J PLOS Genetics
%D 2009
%I Public Library of Science (PLoS)
%R 10.1371/journal.pgen.1000504
%X 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¡Á10£¿201), ABCG2 (p = 3.1¡Á10£¿26), SLC17A1 (p = 3.0¡Á10£¿14), SLC22A11 (p = 6.7¡Á10£¿14), SLC22A12 (p = 2.0¡Á10£¿9), SLC16A9 (p = 1.1¡Á10£¿8), GCKR (p = 1.4¡Á10£¿9), LRRC16A (p = 8.5¡Á10£¿9), and near PDZK1 (p = 2.7¡Á10£¿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¡Á10£¿26) and propionyl-L-carnitine (p = 5.0¡Á10£¿8) concentrations, which in turn were associated with serum UA levels (p = 1.4¡Á10£¿57 and p = 8.1¡Á10£¿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.
%U http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000504