Genome-wide association study of clinically defined gout identifies multiple risk loci and its association with clinical subtypes

Abstract

Objective: Gout, caused by hyperuricaemia, is a multifactorial disease. Although genome-wide association studies (GWASs) of gout have been reported, they included self-reported gout cases in which clinical information was insufficient. Therefore, the relationship between genetic variation and clinical subtypes of gout remains unclear. Here, we first performed a GWAS of clinically defined gout cases only.

Methods: A GWAS was conducted with 945 patients with clinically defined gout and 1213 controls in a Japanese male population, followed by replication study of 1048 clinically defined cases and 1334 controls.

Results: Five gout susceptibility loci were identified at the genome-wide significance level (p<5.0×10(-8)), which contained well-known urate transporter genes (ABCG2 and SLC2A9) and additional genes: rs1260326 (p=1.9×10(-12); OR=1.36) of GCKR (a gene for glucose and lipid metabolism), rs2188380 (p=1.6×10(-23); OR=1.75) of MYL2-CUX2 (genes associated with cholesterol and diabetes mellitus) and rs4073582 (p=6.4×10(-9); OR=1.66) of CNIH-2 (a gene for regulation of glutamate signalling). The latter two are identified as novel gout loci. Furthermore, among the identified single-nucleotide polymorphisms (SNPs), we demonstrated that the SNPs of ABCG2 and SLC2A9 were differentially associated with types of gout and clinical parameters underlying specific subtypes (renal underexcretion type and renal overload type). The effect of the risk allele of each SNP on clinical parameters showed significant linear relationships with the ratio of the case-control ORs for two distinct types of gout (r=0.96 [p=4.8×10(-4)] for urate clearance and r=0.96 [p=5.0×10(-4)] for urinary urate excretion).

Conclusions: Our findings provide clues to better understand the pathogenesis of gout and will be useful for development of companion diagnostics.

Keywords: Arthritis; Gene Polymorphism; Gout.

Figure 1

Manhattan plot of a genome-wide association analysis of gout. X-axis shows chromosomal positions. Y-axis shows -log₁₀ p values. The upper and lower dotted lines indicate the genome-wide significance threshold (p=5.0×10⁻⁸) and the cut-off level for selecting single-nucleotide polymorphisms for replication study (p=1.0×10⁻⁵), respectively.

Figure 2

Regional association plots for six discovered loci of gout. Five regions exceeding the genome-wide significance level (A–E) and one region showing a suggestive association (F). The highest association signal in each panel is located on ABCG2 (A), SLC2A9 (B), MYL2-CUX2 (C), GCKR (D), CNIH-2 (E) and MAP3K11 (F). Region within 250 kb from single-nucleotide polymorphism (SNP) showing lowest p value is displayed. (Top panel) Plots of -log₁₀ p values for the test of SNP association with gout in the genome-wide association study stage. SNP showing the lowest p value is depicted as a pink diamond. Other SNPs are colour-coded according to the extent of linkage disequilibrium (measured in r²) with SNP showing the lowest p value. (Middle panel) Recombination rates (centimorgans per Mb) estimated from HapMap Phase II data are plotted. (Bottom panel) RefSeq genes. Genomic coordinates are based on Genomic Reference Consortium GRCh37.

Figure 3

Relationships between effects of risk alleles on clinical parameters and ORs in case–subtype heterogeneity test. (A) FE_UA and (B) urinary urate excretion (UUE). The seven single-nucleotide polymorphisms (SNPs) listed in table 2 were examined. OR in case–subtype heterogeneity test is an estimate of the ratio of the case–control OR for the renal overload (ROL) type to that for the renal underexcretion (RUE) type. If an SNP has a stronger effect for the ROL type than for the RUE type, it takes a value >1. Diamonds and lines represent point estimates and their 95% CIs. Pearson's correlation coefficient (r) between the effect on clinical parameters and natural logarithm of OR in case–subtype heterogeneity test and its significance were examined. FE_UA, fractional excretion of urate clearance.

Figure 4

Differential effects by risk allele on clinical parameters and gout. (A) The risk alleles of ABCG2 increase UUE and FE_UA, which leads to the overloading effect on renal urate excretion and increases the risk of the ROL-type gout. Therefore, patients with risk alleles for the ROL-type gout would be given urate synthesis inhibitors. (B) The risk allele of SLC2A9 reduces UUE and FE_UA, which reflects a decreased renal urate excretion, thereby increasing the risk of the RUE-type gout. Patients with risk alleles for the RUE-type gout would be administered uricosuric agents. FE_UA, fractional excretion of urate clearance; ROL, renal overload; RUE, renal underexcretion; SUA, serum uric acid; UUE, urinary urate excretion.