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

Association of nephrolithiasis and gene for glucose transporter type 9 (SLC2A9): study of 145 patients

AIM

To investigate the association of nephrolithiasis and solute carrier family 2, facilitated glucose transporter, member 9 (SLC2A9), also known as glucose transporter type 9, Glut9.

METHODS

A total of 145 participants were recruited in the period April-October 2008 from the Department of Mineral Research of the Medical School Osijek, Osijek, Croatia; 58 (40%) had confirmed nephrolithiasis and 87 (60%) were asymptomatic. Four single nucleotide polymorphisms (SNP) from the SLC2A9 gene were genotyped in both groups (rs733175, rs6449213, rs1014290, and rs737267).

RESULTS

There was a weak but significant association of all 4 SNPs and nephrolithiasis (P=0.029 for rs733175; P=0.006 for rs6449213; P=0.020 for rs1014290, and P=0.011 for rs737267). Logistic regression in an age- and sex-adjusted model suggested that genotype C/T for rs6449213 had odds ratio for nephrolithiasis of 2.89 (95% confidence interval 1.13-7.40). This SNP explained a total of 4.4% of nephrolithiasis variance.

CONCLUSION

Development of nephrolithiasis may be associated with SLC2A9 gene. Further studies are needed to clarify the role of SLC2A9 gene as a link between uric acid and nephrolithiasis.

Prioritizing GWAS results: A review of statistical methods and recommendations for their application

Genome-wide association studies (GWAS) have rapidly become a standard method for disease gene discovery. A substantial number of recent GWAS indicate that for most disorders, only a few common variants are implicated and the associated SNPs explain only a small fraction of the genetic risk. This review is written from the viewpoint that findings from the GWAS provide preliminary genetic information that is available for additional analysis by statistical procedures that accumulate evidence, and that these secondary analyses are very likely to provide valuable information that will help prioritize the strongest constellations of results. We review and discuss three analytic methods to combine preliminary GWAS statistics to identify genes, alleles, and pathways for deeper investigations. Meta-analysis seeks to pool information from multiple GWAS to increase the chances of finding true positives among the false positives and provides a way to combine associations across GWAS, even when the original data are unavailable. Testing for epistasis within a single GWAS study can identify the stronger results that are revealed when genes interact. Pathway analysis of GWAS results is used to prioritize genes and pathways within a biological context. Following a GWAS, association results can be assigned to pathways and tested in aggregate with computational tools and pathway databases. Reviews of published methods with recommendations for their application are provided within the framework for each approach.

What lies behind serum urate concentration? Insights from genetic and genomic studies

Many factors, including genetic components and acquired factors such as obesity and alcohol consumption, influence serum uric acid (urate) concentrations. Since serum urate concentrations are determined by the balance between renal urate excretion and the volume of urate produced via purine metabolism, urate transporter genes as well as genes coding for enzymes involved in purine metabolism affect serum urate concentrations. URAT1 was the first transporter affecting serum urate concentrations to be identified. Using the characterization of this transporter as an indicator, several transporters have been shown to transport urate, allowing the construction of a synoptic renal urate transport model. Notable re-absorptive urate transporters are URAT1 at apical membranes and GLUT9 at basolateral membranes, while ABCG2, MRP4 (multidrug resistance protein 4) and NPT1 are secretive transporters at apical membranes. Recent genome-wide association studies have led to validation of the in vitro model constructed from each functional analysis of urate transporters, and identification of novel candidate genes related to urate metabolism and transport proteins, such as glucokinase regulatory protein (GKRP), PDZK1 and MCT9. However, the function and physiologic roles of several candidates, as well as the influence of acquired factors such as obesity, foods, or alcoholic beverages, remain unclear.

Sex and age interaction with genetic association of atherogenic uric acid concentrations

BACKGROUND

High serum uric acid levels are associated with gout, atherosclerosis and cardiovascular disease. Three genes (SLC2A9, ABCG2, and SLC17A3) were reported to be involved in the regulation of uric acid levels.

RESEARCH

DESIGN AND METHODS

SNPs rs2231142 (ABCG2) and rs1165205 (SLC17A3) were genotyped in three cohorts (n=4492) and combined with previously genotyped SNPs within SLC2A9 (rs6855911, rs7442295, rs6449213, rs12510549).

RESULTS

Each copy of the minor allele decreased uric acid levels by 0.30-0.38 mg/dL for SLC2A9 (p values: 10(-20)-10(-36)) and increased levels by 0.34 mg/dL for ABCG2 (p=1.1x10(-16)). SLC17A3 influenced uric acid levels only modestly. Together the SNPs showed graded associations with uric acid levels of 0.111 mg/dL per risk allele (p=3.8x10(-42)). In addition, we observed a sex-specific interaction of age with the association of SLC2A9 SNPs with uric acid levels, where increasing age strengthened the association of SNPs in women and decreased the association in men.

CONCLUSIONS

Genetic variants within SLC2A9,ABCG2 and SLC17A3 show highly significant associations with uric acid levels, and for SNPs within SLC2A9 this association is strongly modified by age and sex.

Trp64Arg polymorphism of the ADRB3 gene predicts hyperuricemia risk in a population from southern Spain

OBJECTIVE

To study the role of Trp64Arg polymorphism of the ADRB3 gene in the risk of developing hyperuricemia in 1051 subjects from southern Spain, with a followup of 6 years. The inclusion of plasma levels of uric acid as a diagnostic criterion to define the metabolic syndrome is under discussion. Genes responsible for insulin resistance could contribute to the development of hyperuricemia. Previous cross-sectional studies have suggested ADRB3 as a possible candidate gene in the development of hyperuricemia and insulin resistance.

METHODS

A prospective, population-based, cohort study of 1051 persons examined in 1997-98 and reassessed at a second examination 6 years later. The metabolic phenotype was assessed at baseline and again at the followup. Insulin resistance was measured by homeostasis model assessment. The Trp64Arg polymorphism of ADRB3 was detected by real-time polymerase chain reaction. Subjects were considered normouricemic if their serum uric acid levels were <or=7 mg/dl for men or <or= 6 mg/dl for women.

RESULTS

Carriers of the Arg64 allele who were normouricemic at baseline had a higher risk of developing hyperuricemia 6 years later (p = 0.017, OR 2.3, 95% CI 1.1-4.6). Multivariate logistic regression analysis showed that the OR of having hyperuricemia at the 6-year followup was significantly associated with the Arg64 allele, after adjusting for age, weight gain, baseline levels of triglycerides, serum uric acid, and insulin resistance (OR 3.1, 95% CI 1.3-7.1).

CONCLUSION

Trp64Arg polymorphism of the ADRB3 gene predicted the risk of developing hyperuricemia in this adult population.

Homozygous SLC2A9 mutations cause severe renal hypouricemia

Hereditary hypouricemia may result from mutations in the renal tubular uric acid transporter URAT1. Whether mutation of other uric acid transporters produces a similar phenotype is unknown. We studied two families who had severe hereditary hypouricemia and did not have a URAT1 defect. We performed a genome-wide homozygosity screen and linkage analysis and identified the candidate gene SLC2A9, which encodes the glucose transporter 9 (GLUT9). Both families had homozygous SLC2A9 mutations: A missense mutation (L75R) in six affected members of one family and a 36-kb deletion, resulting in a truncated protein, in the other. In vitro, the L75R mutation dramatically impaired transport of uric acid. The mean concentration of serum uric acid of seven homozygous individuals was 0.17 +/- 0.2 mg/dl, and all had a fractional excretion of uric acid >150%. Three individuals had nephrolithiasis, and three had a history of exercise-induced acute renal failure. In conclusion, homozygous loss-of-function mutations of GLUT9 cause a total defect of uric acid absorption, leading to severe renal hypouricemia complicated by nephrolithiasis and exercise-induced acute renal failure. In addition to clarifying renal handling of uric acid, our findings may provide a better understanding of the pathophysiology of acute renal failure, nephrolithiasis, hyperuricemia, and gout.

Common polymorphisms influencing serum uric acid levels contribute to susceptibility to gout, but not to coronary artery disease

Background

Recently, a large meta-analysis including over 28,000 participants identified nine different loci with association to serum uric acid (UA) levels. Since elevated serum UA levels potentially cause gout and are a possible risk factor for coronary artery disease (CAD) and myocardial infarction (MI), we performed two large case-control association analyses with participants from the German MI Family Study. In the first study, we assessed the association of the qualitative trait gout and ten single nucleotide polymorphisms (SNP) markers that showed association to UA serum levels. In the second study, the same genetic polymorphisms were analyzed for association with CAD.

Methods and Findings

A total of 683 patients suffering from gout and 1,563 healthy controls from the German MI Family Study were genotyped. Nine SNPs were identified from a recently performed genome-wide meta-analysis on serum UA levels (rs12129861, rs780094, rs734553, rs2231142, rs742132, rs1183201, rs12356193, rs17300741 and rs505802). Additionally, the marker rs6855911 was included which has been associated with gout in our cohort in a previous study. SNPs rs734553 and rs6855911, located in SLC2A9, and SNP rs2231142, known to be a missense polymorphism in ABCG2, were associated with gout (p = 5.6*10⁻⁷, p = 1.1*10⁻⁷, and p = 1.3*10⁻³, respectively). Other SNPs in the genes PDZK1, GCKR, LRRC16A, SLC17A1-SLC17A3, SLC16A9, SLC22A11 and SLC22A12 failed the significance level. None of the ten markers were associated with risk to CAD in our study sample of 1,473 CAD cases and 1,241 CAD-free controls.

Conclusion

SNP markers in SLC2A9 and ABCG2 genes were found to be strongly associated with the phenotype gout. However, not all SNP markers influencing serum UA levels were also directly associated with the clinical manifestation of gout in our study sample. In addition, none of these SNPs showed association with the risk to CAD in the German MI Family Study.

Replication of the five novel loci for uric acid concentrations and potential mediating mechanisms

Uric acid (UA) is the final catabolic product of purine metabolism and elevated levels are associated with diabetes and cardiovascular disease. A recent meta-analysis of genome-wide association studies totalling 28,141 participants identified five novel loci associated with serum UA levels. In our population-based cohort of 7795 subjects, we replicated four of these five loci; PDZK1 (rs12129861, P = 1.07 x 10(-3)), glucokinase regulator protein (GCKR) (rs780094, P = 4.83 x 10(-4)), SLC16A9 (rs742132, P = 0.047) and SLC22A11 (rs17300741, P = 6.13 x 10(-3)), but not LRRC16A (rs742132, P = 0.645). Serum UA concentration is a complex trait, closely associated to renal UA handling (fractional UA excretion, P < 1 x 10(-300)), renal function (serum creatinine, P < 1 x 10(-300)) and the metabolic syndrome (including fasting insulin, P = 2.48 x 10(-232); insulin resistance, P = 2.51 x 10(-258); waist circumference, P < 1 x 10(-300)) and systolic blood pressure (P = 1.93 x 10(-219)). Together these factors explain 67% of the variance in UA levels. Therefore, we sought to determine the potential contribution of these factors to the association of these novel loci with UA levels, by including them as additional explanatory variables in our analyses, and by considering them as alternative response variables. The association with the GCKR locus is attenuated by serum triglycerides and fractional UA excretion. We also observed the GCKR locus to be associated with total cholesterol (P = 7.52 x 10(-6)), triglycerides (P = 2.65 x 10(-9)), fasting glucose (P = 0.011), fractional UA excretion (P = 3.36 x 10(-5)) and high-sensitive CRP (P = 1.18 x 10(-3)) also after adjusting for serum UA levels. We argue that GCKR locus affects serum UA levels through a factor that also affects triglycerides.