Abundant local interactions in the 4p16.1 region suggest functional mechanisms underlying SLC2A9 associations with human serum uric acid

The genetic basis of urate control and gout: Insights into molecular pathogenesis from follow-up study of genome-wide association study loci

This review focuses on the post-genome-wide association study (GWAS) era in gout, i.e., the translation of GWAS genetic association signals into biologically informative knowledge. Analytical and experimental follow-up of individual loci, based on the identification of causal genetic variants, reveals molecular pathogenic pathways. We summarize in detail the largest GWAS in urate to date, then we review follow-up studies and molecular insights from ABCG2, HNF4A, PDZK1, MAF, GCKR, ALDH2, ALDH16A1, SLC22A12, SLC2A9, ABCC4, and SLC22A13, including the role of insulin signaling. One common factor in these pathways is the importance of transcriptional control, including the HNF4α transcription factor. The new molecular knowledge reveals new targets for intervention to manage urate levels and prevent gout.

Characterization of a Compound Heterozygous SLC2A9 Mutation That Causes Hypouricemia

Renal hypouricemia is a rare genetic disorder. Hypouricemia can present as renal stones or exercise-induced acute renal failure, but most cases are asymptomatic. Our previous study showed that two recessive variants of SLC22A12 (p.Trp258*, pArg90His) were identified in 90% of the hypouricemia patients from two independent cohorts: the Korean genome and epidemiology study (KoGES) and the Korean Cancer Prevention Study (KCPS-II). In this work, we investigate the genetic causes of hypouricemia in the rest of the 10% of unsolved cases. We found a novel non-synonymous mutation of SLC2A9 (voltage-sensitive uric acid transporter) in the whole-exome sequencing (WES) results. Molecular dynamics prediction suggests that the novel mutation p.Met126Val in SLCA9b (p.Met155Val in SLC2A9a) hinders uric acid transport through a defect of the outward open geometry. Molecular analysis using Xenopus oocytes confirmed that the p.Met126Val mutation significantly reduced uric acid transport but does not affect the SLC2A9 protein expression level. Our results will shed light on a better understanding of SLC2A9-mediated uric acid transport and the development of a uric acid-lowering agent.

Genomic dissection of 43 serum urate-associated loci provides multiple insights into molecular mechanisms of urate control

High serum urate is a prerequisite for gout and associated with metabolic disease. Genome-wide association studies (GWAS) have reported dozens of loci associated with serum urate control; however, there has been little progress in understanding the molecular basis of the associated loci. Here, we employed trans-ancestral meta-analysis using data from European and East Asian populations to identify 10 new loci for serum urate levels. Genome-wide colocalization with cis-expression quantitative trait loci (eQTL) identified a further five new candidate loci. By cis- and trans-eQTL colocalization analysis, we identified 34 and 20 genes, respectively, where the causal eQTL variant has a high likelihood that it is shared with the serum urate-associated locus. One new locus identified was SLC22A9 that encodes organic anion transporter 7 (OAT7). We demonstrate that OAT7 is a very weak urate-butyrate exchanger. Newly implicated genes identified in the eQTL analysis include those encoding proteins that make up the dystrophin complex, a scaffold for signaling proteins and transporters at the cell membrane; MLXIP that, with the previously identified MLXIPL, is a transcription factor that may regulate serum urate via the pentose-phosphate pathway and MRPS7 and IDH2 that encode proteins necessary for mitochondrial function. Functional fine mapping identified six loci (RREB1, INHBC, HLF, UBE2Q2, SFMBT1 and HNF4G) with colocalized eQTL containing putative causal SNPs. This systematic analysis of serum urate GWAS loci identified candidate causal genes at 24 loci and a network of previously unidentified genes likely involved in control of serum urate levels, further illuminating the molecular mechanisms of urate control.

Trans-ancestral dissection of urate- and gout-associated major loci SLC2A9 and ABCG2 reveals primate-specific regulatory effects

Gout is a complex inflammatory arthritis affecting ~20% of people with an elevated serum urate level (hyperuricemia). Gout and hyperuricemia are essentially specific to humans and other higher primates, with varied prevalence across ancestral groups. SLC2A9 and ABCG2 are major loci associated with both urate and gout in multiple ancestral groups. However, fine mapping has been challenging due to extensive linkage disequilibrium underlying the associated regions. We used trans-ancestral fine mapping integrated with primate-specific genomic information to address this challenge. Trans-ancestral meta-analyses of GWAS cohorts of either European (EUR) or East Asian (EAS) ancestry resulted in single-variant resolution mappings for SLC2A9 (rs3775948 for urate and rs4697701 for gout) and ABCG2 (rs2622621 for gout). Tests of colocalization of variants in both urate and gout suggested existence of a shared candidate causal variant for SLC2A9 only in EUR and for ABCG2 only in EAS. The fine-mapped gout variant rs4697701 was within an ancient enhancer, whereas rs2622621 was within a primate-specific transposable element, both supported by functional evidence from the Roadmap Epigenomics project in human primary tissues relevant to urate and gout. Additional primate-specific elements were found near both loci and those adjacent to SLC2A9 overlapped with known statistical epistatic interactions associated with urate as well as multiple super-enhancers identified in urate-relevant tissues. We conclude that by leveraging ancestral differences trans-ancestral fine mapping has identified ancestral and functional variants for SLC2A9 or ABCG2 with primate-specific regulatory effects on urate and gout.

Pleiotropic effect of the ABCG2 gene in gout: involvement in serum urate levels and progression from hyperuricemia to gout

Background

The ABCG2 Q141K (rs2231142) and rs10011796 variants associate with hyperuricaemia (HU). The effect size of ABCG2 rs2231142 on urate is ~ 60% that of SLC2A9, yet the effect size on gout is greater. We tested the hypothesis that ABCG2 plays a role in the progression from HU to gout by testing for association of ABCG2 rs2231142 and rs10011796 with gout using HU controls.

Methods

We analysed 1699 European gout cases and 14,350 normouricemic (NU) and HU controls, and 912 New Zealand (NZ) Polynesian (divided into Eastern and Western Polynesian) gout cases and 696 controls. Association testing was performed using logistic and linear regression with multivariate adjusting for confounding variables.

Results

In Europeans and Polynesians, the ABCG2 141K (T) allele was associated with gout using HU controls (OR = 1.85, P = 3.8E^− 21 and OR_meta = 1.85, P = 1.3E^− 03, respectively). There was evidence for an effect of 141K in determining HU in European (OR = 1.56, P = 1.7E^− 18) but not in Polynesian (OR_meta = 1.49, P = 0.057). For SLC2A9 rs11942223, the T allele associated with gout in the presence of HU in European (OR = 1.37, P = 4.7E^− 06), however significantly weaker than ABCG2 rs2231142 141K (P_Het = 0.0023). In Western Polynesian and European, there was epistatic interaction between ABCG2 rs2231142 and rs10011796. Combining the presence of the 141K allele with the rs10011796 CC-genotype increased gout risk, in the presence of HU, 21.5-fold in Western Polynesian (P = 0.009) and 2.6-fold in European (P = 9.9E^− 06). The 141K allele of ABCG2 associated with increased gout flare frequency in Polynesian (P_meta = 2.5E^− 03).

Conclusion

These data are consistent with a role for ABCG2 141K in gout in the presence of established HU.

Epistatic interaction between PKD2 and ABCG2 influences the pathogenesis of hyperuricemia and gout

BACKGROUND

Genetic background affects serum urate concentration and gout risk, especially regarding these variants in the urate-transporter gene ABCG2. However, the role of epistasis between PKD2 and ABCG2 on the pathogenesis of gout is poorly understood. Here we assess this epistatic interaction in the progression from elevated serum urate to gout.

RESULTS

We identified two epistatic interaction pairs (rs2728121: rs1481012 and rs2728121: rs2231137) were associated with urate levels in 4914 Chinese individuals (Pint = 0.018 and 0.004, respectively). Using subgroup analysis for gender and BMI, we found the degree of associations was varied by gender and BMI. The SNP pair rs2728121:rs1481012 influenced urate levels in females and overweight subjects (Pint = 0.006 and 0.022, respectively), but rs2728121:rs2231137 did in males, overweight and normal-weight subjects (Pint = 0.017, 0.047 and 0.013, respectively). Consistent results were also observed in associations between these epistatic interactions with hyperuricemia. Next, the SNP pair rs2728121:rs2231137 was identified to influence the development of gout from both hyperuricemia and healthy (Pint = 0.035 and 0.001, respectively), especially in males (Pint = 0.030 and 0.001, respectively). Furthermore, we demonstrated that interacting regions were enriched by regulatory elements. Finally, we observed a strong gene co-expression pattern between PKD2 and ABCG2 (r = 0.743, P = 5.83E-06).

CONCLUSION

Our findings indicate epistasis between PKD2 and ABCG2 influence serum urate concentrations, hyperuricemia and gout risk, thus providing insight into the pathogenesis of gout.

Influence of Genetic and Non-Genetic Risk Factors for Serum Uric Acid Levels and Hyperuricemia in Mexicans

Risk of hyperuricemia is modified by genetic and environmental factors. Our aim was to identify factors associated with serum uric acid levels and hyperuricemia in Mexicans. A pilot Genome-wide association study GWAS was performed in a subgroup of participants (n = 411) from the Health Workers Cohort Study (HWCS). Single nucleotide polymorphisms (SNPs) associated with serum uric acid levels were validated in all the HWCS participants (n = 1939) and replicated in independent children (n = 1080) and adult (n = 1073) case-control studies. The meta-analysis of the whole HWCS and replication samples identified three SLC2A9 SNPs: rs1014290 (p = 2.3 × 10⁻⁶⁴), rs3775948 (p = 8.2 × 10⁻⁶⁴) and rs11722228 (p = 1.1 × 10⁻¹⁷); and an ABCG2 missense SNP, rs2231142 (p = 1.0 × 10⁻¹⁸). Among the non-genetic factors identified, the visceral adiposity index, smoking, the metabolic syndrome and its components (waist circumference, blood pressure, glucose and hyperlipidemia) were associated with increased serum uric acid levels and hyperuricemia (p < 0.05). Among the female HWCS participants, the odds ratio for hyperuricemia was 1.24 (95% CI, 1.01–1.53) per unit increase in soft drink consumption. As reported in other studies, our findings indicate that diet, adiposity and genetic variation contribute to the elevated prevalence of hyperuricemia in Mexico.

Multiple Epistasis Interactions Within MHC Are Associated With Ulcerative Colitis

Successful searching for epistasis is much challenging, which generally requires very large sample sizes and/or very dense marker information. We exploited the largest Crohn's disease (CD) dataset (18,000 cases + 34,000 controls) and ulcerative colitis (UC) dataset (14,000 cases + 34,000 controls) to date. Leveraging its dense marker information and the large sample size of this IBD dataset, we employed a two-step approach to exhaustively search for epistasis. We detected abundant genome-wide significant (p < 1 × 10^-13) epistatic signals, all within the MHC region. These signals were reduced substantially when conditional on the additive background, but still nine pairs remained significant at the Immunochip-wide level (P < 1.1 × 10^-8) in conditional tests for UC. All these nine epistatic interactions come from the MHC region, and each explains on average 0.15% of the phenotypic variance. Eight of them were replicated in a replication cohort. There are multiple but relatively weak interactions independent of the additive effects within the MHC region for UC. Our promising results warrant the search for epistasis in large data sets with dense markers, exploiting dependencies between markers.

Genomic Influences on Hyperuricemia and Gout

Genome-wide association studies (GWAS) have identified nearly 30 loci associated with urate concentrations that also influence the subsequent risk of gout. The ABCG2 Q141 K variant is highly likely to be causal and results in internalization of ABCG2, which can be rescued by drugs. Three other GWAS loci contain uric acid transporter genes, which are also highly likely to be causal. However identification of causal genes at other urate loci is challenging. Finally, relatively little is known about the genetic control of progression from hyperuricemia to gout. Only 4 small GWAS have been published for gout.

Genome-wide association analysis identifies multiple loci associated with kidney disease-related traits in Korean populations

Chronic kidney disease (CKD) is an important social health problem characterized by a decrease in the kidney glomerular filtration rate (GFR). In this study, we analyzed genome-wide association studies for kidney disease-related traits using data from a Korean adult health screening cohort comprising 7,064 participants. Kidney disease-related traits analyzed include blood urea nitrogen (BUN), serum creatinine, estimated GFR, and uric acid levels. We detected two genetic loci (SLC14A2 and an intergenic region) and 8 single nucleotide polymorphisms (SNPs) associated with BUN, 3 genetic loci (BCAS3, C17orf82, ALDH2) and 6 SNPs associated with serum creatinine, 3 genetic loci (BCAS3, C17orf82/TBX2, LRP2) and 7 SNPs associated with GFR, and 14 genetic loci (3 in ABCG2/PKD2, 2 in SLC2A9, 3 in intergenic regions on chromosome 4; OTUB1, NRXN2/SLC22A12, CDC42BPG, RPS6KA4, SLC22A9, and MAP4K2 on chromosome 11) and 84 SNPs associated with uric acid levels. By comparing significant genetic loci associated with serum creatinine levels and GFR, rs9895661 in BCAS3 and rs757608 in C17orf82 were simultaneously associated with both traits. The SNPs rs11710227 in intergenic regions on chromosome 3 showing significant association with BUN is newly discovered. Genetic variations of multiple gene loci are associated with kidney disease-related traits, and differences in associations between kidney disease-related traits and genetic variation are dependent on the population. The meanings of the mutations identified in this study will need to be reaffirmed in other population groups in the future.

Mendelian randomization analysis indicates serum urate has a causal effect on renal function in Chinese women

PURPOSE

High levels of serum uric acid can predict the progression of stage I and II chronic kidney disease (CKD), but whether serum urate is an independent risk factor or has causal impact on serum creatinine (SCr) and renal function remains unclear.

METHODS

Mendelian randomization was used to determine whether serum uric acid had a causal effect on renal function, represented by estimated glomerular filtration rate (eGFR), with potential confounding factors, in 3734 subjects from the Taizhou Longitudinal Study. In the two-stage least squares method of Mendelian randomization, serum uric acid level was selected as the exposure, genetic risk score of uric acid transporters was selected as the instrumental variable, and SCr and eGFR were selected as the outcomes.

RESULTS

The result of the analysis showed that increased serum uric acid was not a causal effect on renal function, but it was a causal effect on reducing estimated glomerular filtration rate in both the female population and the subjects who were under 65 years old. We also found that increased serum uric acid levels led to impaired renal function only in the subjects with normal eGFR values. In addition, the serum uric acid was a risk factor for renal function in the subjects with relatively high levels of fasting glucose or who were currently smokers.

CONCLUSIONS

Although serum urate is not an independent risk factor for renal dysfunction, it has a causal effect on renal dysfunction in either female or individuals of under 65, or normal eGFR, or high level of fasting glucose, or current smokers.

Further investigations of the W-test for pairwise epistasis testing

Background: In a recent paper, a novel W-test for pairwise epistasis testing was proposed that appeared, in computer simulations, to have higher power than competing alternatives. Application to genome-wide bipolar data detected significant epistasis between SNPs in genes of relevant biological function. Network analysis indicated that the implicated genes formed two separate interaction networks, each containing genes highly related to autism and neurodegenerative disorders.

Methods: Here we investigate further the properties and performance of the W-test via theoretical evaluation, computer simulations and application to real data.

Results: We demonstrate that, for common variants, the W-test is closely related to several existing tests of association allowing for interaction, including logistic regression on 8 degrees of freedom, although logistic regression can show inflated type I error for low minor allele frequencies,  whereas the W-test shows good/conservative type I error control. Although in some situations the W-test can show higher power, logistic regression is not limited to tests on 8 degrees of freedom but can instead be tailored to impose greater structure on the assumed alternative hypothesis, offering a power advantage when the imposed structure matches the true structure.

Conclusions: The W-test is a potentially useful method for testing for association - without necessarily implying interaction - between genetic variants disease, particularly when one or more of the genetic variants are rare. For common variants, the advantages of the W-test are less clear, and, indeed, there are situations where existing methods perform better. In our investigations, we further uncover a number of problems with the practical implementation and application of the W-test (to bipolar disorder) previously described, apparently due to inadequate use of standard data quality-control procedures. This observation leads us to urge caution in interpretation of the previously-presented results, most of which we consider are highly likely to be artefacts.

Interactions within the MHC contribute to the genetic architecture of celiac disease

Interaction analysis of GWAS can detect signal that would be ignored by single variant analysis, yet few robust interactions in humans have been detected. Recent work has highlighted interactions in the MHC region between known HLA risk haplotypes for various autoimmune diseases. To better understand the genetic interactions underlying celiac disease (CD), we have conducted exhaustive genome-wide scans for pairwise interactions in five independent CD case-control studies, using a rapid model-free approach to examine over 500 billion SNP pairs in total. We found 14 independent interaction signals within the MHC region that achieved stringent replication criteria across multiple studies and were independent of known CD risk HLA haplotypes. The strongest independent CD interaction signal corresponded to genes in the HLA class III region, in particular PRRC2A and GPANK1/C6orf47, which are known to contain variants for non-Hodgkin's lymphoma and early menopause, co-morbidities of celiac disease. Replicable evidence for statistical interaction outside the MHC was not observed. Both within and between European populations, we observed striking consistency of two-locus models and model distribution. Within the UK population, models of CD based on both interactions and additive single-SNP effects increased explained CD variance by approximately 1% over those of single SNPs. The interactions signal detected across the five cohorts indicates the presence of novel associations in the MHC region that cannot be detected using additive models. Our findings have implications for the determination of genetic architecture and, by extension, the use of human genetics for validation of therapeutic targets.

Major histocompatibility complex harbors widespread genotypic variability of non-additive risk of rheumatoid arthritis including epistasis

Genotypic variability based genome-wide association studies (vGWASs) can identify potentially interacting loci without prior knowledge of the interacting factors. We report a two-stage approach to make vGWAS applicable to diseases: firstly using a mixed model approach to partition dichotomous phenotypes into additive risk and non-additive environmental residuals on the liability scale and secondly using the Levene's (Brown-Forsythe) test to assess equality of the residual variances across genotype groups per marker. We found widespread significant (P < 2.5e-05) vGWAS signals within the major histocompatibility complex (MHC) across all three study cohorts of rheumatoid arthritis. We further identified 10 epistatic interactions between the vGWAS signals independent of the MHC additive effects, each with a weak effect but jointly explained 1.9% of phenotypic variance. PTPN22 was also identified in the discovery cohort but replicated in only one independent cohort. Combining the three cohorts boosted power of vGWAS and additionally identified TYK2 and ANKRD55. Both PTPN22 and TYK2 had evidence of interactions reported elsewhere. We conclude that vGWAS can help discover interacting loci for complex diseases but require large samples to find additional signals.

Immunochip Analyses of Epistasis in Rheumatoid Arthritis Confirm Multiple Interactions within MHC and Suggest Novel Non-MHC Epistatic Signals

OBJECTIVE

Studying statistical gene-gene interactions (epistasis) has been limited by the difficulties in performance, both statistically and computationally, in large enough sample numbers to gain sufficient power. Three large Immunochip datasets from cohort samples recruited in the United Kingdom, United States, and Sweden with European ancestry were used to examine epistasis in rheumatoid arthritis (RA).

METHODS

A full pairwise search was conducted in the UK cohort using a high-throughput tool and the resultant significant epistatic signals were tested for replication in the United States and Swedish cohorts. A forward selection approach was applied to remove redundant signals, while conditioning on the preidentified additive effects.

RESULTS

We detected abundant genome-wide significant (p < 1.0e-13) epistatic signals, all within the MHC region. These signals were reduced substantially, but a proportion remained significant (p < 1.0e-03) in conditional tests. We identified 11 independent epistatic interactions across the entire MHC, each explaining on average 0.12% of the phenotypic variance, nearly all replicated in both replication cohorts. We also identified non-MHC epistatic interactions between RA susceptible loci LOC100506023 and IRF5 with Immunochip-wide significance (p < 1.1e-08) and between 2 neighboring single-nucleotide polymorphism near PTPN22 that were in low linkage disequilibrium with independent interaction (p < 1.0e-05). Both non-MHC epistatic interactions were statistically replicated with a similar interaction pattern in the US cohort only.

CONCLUSION

There are multiple but relatively weak interactions independent of the additive effects in RA and a larger sample number is required to confidently assign additional non-MHC epistasis.

Association of SLC2A9 genotype with phenotypic variability of serum urate in pre-menopausal women

The SLC2A9 gene, that encodes a renal uric acid reuptake transporter, has genetic variants that explain ∼3% of variance in urate levels. There are previous reports of non-additive interaction between SLC2A9 genotype and environmental factors which influence urate control. Therefore, our aim was to further investigate the general phenomenon that such non-additive interactions contribute to genotype-specific association with variance at SLC2A9. Data from 14135 European individuals were used in this analysis. The measure of variance was derived from a ranked inverse normal transformation of residuals obtained by regressing known urate-influencing factors (sex, age, and body mass index) against urate. Variant rs6449173 showed the most significant effect on serum urate variance at SLC2A9 (P = 7.9 × 10^-14), which was maintained after accounting for the effect on average serum urate levels (P = 0.022). Noting the stronger effect in a sub-cohort that consisted of pre-menopausal women and younger men, the participants were stratified into males and pre-menopausal and post-menopausal women. This revealed a strong effect on variance in pre-menopausal women (P = 3.7 × 10^-5) with a weak effect in post-menopausal women (P = 0.032) and no effect in men (P = 0.22). The T-allele of rs6449173, which associates with increased urate levels, was associated with the greater variance in urate. There was a non-additive interaction between rs6449173 genotype and female gender in control of serum urate levels that was driven by a greater increase in urate levels associated with the T-allele in women. Female hormones, and/or other factors they influence or are associated with (such as iron levels, temperature, testosterone) interact with SLC2A9 genotype in women to determine urate levels. The association of SLC2A9 with greater variance in pre-menopausal women may reflect the cyclical changes resulting from menstruation.

An update on the genetic architecture of hyperuricemia and gout

Genome-wide association studies that scan the genome for common genetic variants associated with phenotype have greatly advanced medical knowledge. Hyperuricemia is no exception, with 28 loci identified. However, genetic control of pathways determining gout in the presence of hyperuricemia is still poorly understood. Two important pathways determining hyperuricemia have been confirmed (renal and gut excretion of uric acid with glycolysis now firmly implicated). Major urate loci are SLC2A9 and ABCG2. Recent studies show that SLC2A9 is involved in renal and gut excretion of uric acid and is implicated in antioxidant defense. Although etiological variants at SLC2A9 are yet to be identified, it is clear that considerable genetic complexity exists at the SLC2A9 locus, with multiple statistically independent genetic variants and local epistatic interactions. The positions of implicated genetic variants within or near chromatin regions involved in transcriptional control suggest that this mechanism (rather than structural changes in SLC2A9) is important in regulating the activity of SLC2A9. ABCG2 is involved primarily in extra-renal uric acid under-excretion with the etiological variant influencing expression. At the other 26 loci, probable causal genes can be identified at three (PDZK1, SLC22A11, and INHBB) with strong candidates at a further 10 loci. Confirmation of the causal gene will require a combination of re-sequencing, trans-ancestral mapping, and correlation of genetic association data with expression data. As expected, the urate loci associate with gout, although inconsistent effect sizes for gout require investigation. Finally, there has been no genome-wide association study using clinically ascertained cases to investigate the causes of gout in the presence of hyperuricemia. In such a study, use of asymptomatic hyperurcemic controls would be expected to increase the ability to detect genetic associations with gout.

Genetic variations in the CLNK gene and ZNF518B gene are associated with gout in case-control sample sets

A genome-wide association study of gout in European populations identified 12 genetic variants strongly associated with risk of gout, but it is unknown whether these variants are also associated with gout risk in Chinese populations. A total of 145 patients with gout and 310 healthy control patients were recruited for a case-control association study. Twelve SNPs of CLNK and ZNF518B gene were genotyped, and association analysis was performed. Odds ratios (ORs) with 95 % confidence intervals (CIs) were used to assess the association. Overall, we found four risk alleles for gout in patients: the allele "G" of rs2041215 and rs1686947 in the CLNK gene by dominant model (OR 1.66; 95 % CI 1.04-2.63; p = 0.031) (OR 2.19; 95 % CI 1.38-3.46; p = 0.001) and additive model (OR 1.39; 95 % CI 1.00-1.93; p = 0.049) (OR 1.67; 95 % CI 1.19-2.32; p = 0.003), respectively, and the allele "A" of rs10938799 and rs10016022 in ZNF518B gene by recessive model (OR 4.66; 95 % CI 1.44-15.09; p = 0.008) (OR 4.54; 95 % CI 1.23-16.76; p = 0.020). Further haplotype analysis showed that the TCATTCTGA haplotype of CLNK was more frequent among patients with gout (adjusted OR 0.48; 95 % CI 0.24-0.95; p = 0.036). Additionally, polymorphisms of rs2041215, rs10938799, and rs17467273 were also correlated with clinical pathological parameters. This study provides evidence for gout susceptibility genes, CLNK and ZNF518B, in a Chinese population, which may have potential as diagnostic and prognostic marker for gout patients.