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Meta-analysis of 28,141 individuals identifies common variants within five new loci that influence uric acid concentrations.

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 = 5.2x10(-201)), ABCG2 (p = 3.1x10(-26)), SLC17A1 (p = 3.0x10(-14)), SLC22A11 (p = 6.7x10(-14)), SLC22A12 (p = 2.0x10(-9)), SLC16A9 (p = 1.1x10(-8)), GCKR (p = 1.4x10(-9)), LRRC16A (p = 8.5x10(-9)), and near PDZK1 (p = 2.7x10(-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 = 4.0x10(-26)) and propionyl-L-carnitine (p = 5.0x10(-8)) concentrations, which in turn were associated with serum UA levels (p = 1.4x10(-57) and p = 8.1x10(-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.

SUBMITTER: Kolz M

PROVIDER: S-EPMC2683940 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

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Meta-analysis of 28,141 individuals identifies common variants within five new loci that influence uric acid concentrations.

Kolz Melanie M Johnson Toby T Sanna Serena S Teumer Alexander A Vitart Veronique V Perola Markus M Mangino Massimo M Albrecht Eva E Wallace Chris C Farrall Martin M Johansson Asa A Nyholt Dale R DR Aulchenko Yurii Y Beckmann Jacques S JS Bergmann Sven S Bochud Murielle M Brown Morris M Campbell Harry H Connell John J Dominiczak Anna A Homuth Georg G Lamina Claudia C McCarthy Mark I MI Meitinger Thomas T Mooser Vincent V Munroe Patricia P Nauck Matthias M Peden John J Prokisch Holger H Salo Perttu P Salomaa Veikko V Samani Nilesh J NJ Schlessinger David D Uda Manuela M Völker Uwe U Waeber Gérard G Waterworth Dawn D Wang-Sattler Rui R Wright Alan F AF Adamski Jerzy J Whitfield John B JB Gyllensten Ulf U Wilson James F JF Rudan Igor I Pramstaller Peter P Watkins Hugh H Doering Angela A Wichmann H-Erich HE Spector Tim D TD Peltonen Leena L Völzke Henry H Nagaraja Ramaiah R Vollenweider Peter P Caulfield Mark M Illig Thomas T Gieger Christian C

PLoS genetics 20090605 6

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 ...[more]

PMID: 19503597

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Project description:Uric acid (UA) is a complex phenotype influenced by both genetic and environmental factors as well as their interactions. Current genome-wide association studies (GWASs) have identified a variety of genetic determinants of UA in Europeans; however, such studies in Asians, especially in Chinese populations remain limited.A two-stage GWAS was performed to identify single nucleotide polymorphisms (SNPs) that were associated with serum uric acid (UA) in a Chinese population of 12,281 participants (GWAS discovery stage included 1452 participants from the Dongfeng-Tongji cohort (DFTJ-cohort) and 1999 participants from the Fangchenggang Area Male Health and Examination Survey (FAMHES). The validation stage included another independent 8830 individuals from the DFTJ-cohort). Affymetrix Genome-Wide Human SNP Array 6.0 chips and Illumina Omni-Express platform were used for genotyping for DFTJ-cohort and FAMHES, respectively. Gene-environment interactions on serum UA levels were further explored in 10,282 participants from the DFTJ-cohort.Briefly, we identified two previously reported UA loci of SLC2A9 (rs11722228, combined P = 8.98 × 10-31) and ABCG2 (rs2231142, combined P = 3.34 × 10-42). The two independent SNPs rs11722228 and rs2231142 explained 1.03% and 1.09% of the total variation of UA levels, respectively. Heterogeneity was observed across different populations. More importantly, both independent SNPs rs11722228 and rs2231142 were nominally significantly interacted with gender on serum UA levels (P for interaction = 4.0 × 10-2 and 2.0 × 10-2, respectively). The minor allele (T) for rs11722228 in SLC2A9 has greater influence in elevating serum UA levels in females compared to males and the minor allele (T) of rs2231142 in ABCG2 had stronger effects on serum UA levels in males than that in females.Two genetic loci (SLC2A9 and ABCG2) were confirmed to be associated with serum UA concentration. These findings strongly support the evidence that SLC2A9 and ABCG2 function in UA metabolism across human populations. Furthermore, we observed these associations are modified by gender.

Project description:ObjectiveGlycated hemoglobin (HbA₁(c)), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA₁(c). We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA₁(c) levels.Research design and methodsWe studied associations with HbA₁(c) in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA₁(c) loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening.ResultsTen loci reached genome-wide significant association with HbA(1c), including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 × 10⁻²⁶), HFE (rs1800562/P = 2.6 × 10⁻²⁰), TMPRSS6 (rs855791/P = 2.7 × 10⁻¹⁴), ANK1 (rs4737009/P = 6.1 × 10⁻¹²), SPTA1 (rs2779116/P = 2.8 × 10⁻⁹) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10⁻⁹), and four known HbA₁(c) loci: HK1 (rs16926246/P = 3.1 × 10⁻⁵⁴), MTNR1B (rs1387153/P = 4.0 × 10⁻¹¹), GCK (rs1799884/P = 1.5 × 10⁻²⁰) and G6PC2/ABCB11 (rs552976/P = 8.2 × 10⁻¹⁸). We show that associations with HbA₁(c) are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (% HbA₁(c)) difference between the extreme 10% tails of the risk score, and would reclassify ∼2% of a general white population screened for diabetes with HbA₁(c).ConclusionsGWAS identified 10 genetic loci reproducibly associated with HbA₁(c). Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA₁(c) levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA₁(c).

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Meta-analysis of 28,141 individuals identifies common variants within five new loci that influence uric acid concentrations.

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Meta-analysis of 28,141 individuals identifies common variants within five new loci that influence uric acid concentrations.

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