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Meta-Analysis
. 2015 Mar 26;10(3):e0119752.
doi: 10.1371/journal.pone.0119752. eCollection 2015.

Modulation of genetic associations with serum urate levels by body-mass-index in humans

Jennifer E Huffman  1 Eva Albrecht  2 Alexander Teumer  3 Massimo Mangino  4 Karen Kapur  5 Toby Johnson  6 Zoltán Kutalik  5 Nicola Pirastu  7 Giorgio Pistis  8 Lorna M Lopez  9 Toomas Haller  10 Perttu Salo  11 Anuj Goel  12 Man Li  13 Toshiko Tanaka  14 Abbas Dehghan  15 Daniela Ruggiero  16 Giovanni Malerba  17 Albert V Smith  18 Ilja M Nolte  19 Laura Portas  20 Amanda Phipps-Green  21 Lora Boteva  1 Pau Navarro  1 Asa Johansson  22 Andrew A Hicks  23 Ozren Polasek  24 Tõnu Esko  25 John F Peden  12 Sarah E Harris  26 Federico Murgia  20 Sarah H Wild  27 Albert Tenesa  28 Adrienne Tin  13 Evelin Mihailov  10 Anne Grotevendt  29 Gauti K Gislason  30 Josef Coresh  31 Pio D'Adamo  7 Sheila Ulivi  32 Peter Vollenweider  33 Gerard Waeber  33 Susan Campbell  1 Ivana Kolcic  24 Krista Fisher  10 Margus Viigimaa  34 Jeffrey E Metter  14 Corrado Masciullo  8 Elisabetta Trabetti  17 Cristina Bombieri  17 Rossella Sorice  16 Angela Döring  35 Eva Reischl  36 Konstantin Strauch  37 Albert Hofman  15 Andre G Uitterlinden  15 Melanie Waldenberger  36 H-Erich Wichmann  38 Gail Davies  9 Alan J Gow  9 Nicola Dalbeth  39 Lisa Stamp  40 Johannes H Smit  41 Mirna Kirin  27 Ramaiah Nagaraja  42 Matthias Nauck  29 Claudia Schurmann  3 Kathrin Budde  29 Susan M Farrington  1 Evropi Theodoratou  27 Antti Jula  43 Veikko Salomaa  11 Cinzia Sala  8 Christian Hengstenberg  44 Michel Burnier  45 Reedik Mägi  10 Norman Klopp  46 Stefan Kloiber  47 Sabine Schipf  48 Samuli Ripatti  49 Stefano Cabras  50 Nicole Soranzo  51 Georg Homuth  3 Teresa Nutile  16 Patricia B Munroe  6 Nicholas Hastie  1 Harry Campbell  27 Igor Rudan  52 Claudia Cabrera  53 Chris Haley  28 Oscar H Franco  15 Tony R Merriman  21 Vilmundur Gudnason  18 Mario Pirastu  20 Brenda W Penninx  54 Harold Snieder  19 Andres Metspalu  10 Marina Ciullo  16 Peter P Pramstaller  23 Cornelia M van Duijn  55 Luigi Ferrucci  14 Giovanni Gambaro  56 Ian J Deary  9 Malcolm G Dunlop  1 James F Wilson  27 Paolo Gasparini  7 Ulf Gyllensten  57 Tim D Spector  4 Alan F Wright  1 Caroline Hayward  1 Hugh Watkins  58 Markus Perola  59 Murielle Bochud  60 W H Linda Kao  31 Mark Caulfield  6 Daniela Toniolo  8 Henry Völzke  48 Christian Gieger  2 Anna Köttgen  61 Veronique Vitart  1
Affiliations
Meta-Analysis

Modulation of genetic associations with serum urate levels by body-mass-index in humans

Jennifer E Huffman et al. PLoS One. .

Abstract

We tested for interactions between body mass index (BMI) and common genetic variants affecting serum urate levels, genome-wide, in up to 42569 participants. Both stratified genome-wide association (GWAS) analyses, in lean, overweight and obese individuals, and regression-type analyses in a non BMI-stratified overall sample were performed. The former did not uncover any novel locus with a major main effect, but supported modulation of effects for some known and potentially new urate loci. The latter highlighted a SNP at RBFOX3 reaching genome-wide significant level (effect size 0.014, 95% CI 0.008-0.02, Pinter= 2.6 x 10-8). Two top loci in interaction term analyses, RBFOX3 and ERO1LB-EDARADD, also displayed suggestive differences in main effect size between the lean and obese strata. All top ranking loci for urate effect differences between BMI categories were novel and most had small magnitude but opposite direction effects between strata. They include the locus RBMS1-TANK (men, Pdifflean-overweight= 4.7 x 10-8), a region that has been associated with several obesity related traits, and TSPYL5 (men, Pdifflean-overweight= 9.1 x 10-8), regulating adipocytes-produced estradiol. The top-ranking known urate loci was ABCG2, the strongest known gout risk locus, with an effect halved in obese compared to lean men (Pdifflean-obese= 2 x 10-4). Finally, pathway analysis suggested a role for N-glycan biosynthesis as a prominent urate-associated pathway in the lean stratum. These results illustrate a potentially powerful way to monitor changes occurring in obesogenic environment.

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Conflict of interest statement

Competing Interests: The SHIP study had one commercial funding source “Siemens AG” but, as stated in the financial disclosure part, the funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript and therefore this does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Mean effect across BMI strata of allelic substitutions at representative variants displaying genome-wide significant association with SU in at least one BMI stratum and displaying nominally significant difference in effect size across BMI strata.
Effect size is on standardised age-adjusted SU levels. Error bars indicate the standard errors of the mean effect estimates within a BMI category. Horizontal lines indicate nominally significant (p < 0.05) differences in mean effect sizes between BMI categories, ** indicates significance at the 1% level taking into account the multiple comparisons performed. Differences in mean effect sizes between BMI strata were tested pairwise using the classical z-test, and Pdiff denotes the 2-sided test corresponding P-value. Lean: BMI < 25 kg/m2, overweight: 25 ≤ BMI ≤ 30 kg/m2, obese: BMI > 30 kg/m2.
Fig 2
Fig 2. Forest plots of effect sizes within BMI stratum for variants with the two most significant mean effect size differences between BMI stratum.
A. RBMS1-TANK locus and B. TSPYL5 locus. The overall inverse—variance-weighted mean effect per BMI stratum is calculated assuming fixed effect across studies and represented by a lozenge, associated P-value displayed as P. Measure of heterogeneity between studies is reported (I-squared) with associated P-value for significance (p). Pdiff is the test of difference in mean-effect size P-value. For study abbreviations and references, see S1 Table.

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