Genome-wide association study of clinically defined gout identifies multiple risk loci and its association with clinical subtypes. Issue 4 (2nd February 2015)

• Record Type: Journal Article
• Authors: Matsuo, Hirotaka; Yamamoto, Ken; Nakaoka, Hirofumi; Nakayama, Akiyoshi; Sakiyama, Masayuki; Chiba, Toshinori; Takahashi, Atsushi; Nakamura, Takahiro; Nakashima, Hiroshi; Takada, Yuzo; Danjoh, Inaho; Shimizu, Seiko; Abe, Junko; Kawamura, Yusuke; Terashige, Sho; Ogata, Hiraku; Tatsukawa, Seishiro; Yin, Guang; Okada, Rieko; Morita, Emi; Naito, Mariko; Tokumasu, Atsumi; Onoue, Hiroyuki; Iwaya, Keiichi; Ito, Toshimitsu; Takada, Tappei; Inoue, Katsuhisa; Kato, Yukio; Nakamura, Yukio; Sakurai, Yutaka; Suzuki, Hiroshi; Kanai, Yoshikatsu; Hosoya, Tatsuo; Hamajima, Nobuyuki; Inoue, Ituro; Kubo, Michiaki; Ichida, Kimiyoshi; Ooyama, Hiroshi; Shimizu, Toru; Shinomiya, Nariyoshi;
• Is Part Of: Annals of the rheumatic diseases. Volume 75:Issue 4(2016)
• Page Start: 652
• Page End: 659
• Publication Date: 2015-02-02
• Subjects: Gout -- Arthritis -- Gene Polymorphism; Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2014-206191
• Languages: English
• ISSNs: 0003-4967

Abstract

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.