Genome-wide association study of clinically defined gout identifies multiple risk loci and its association with clinical subtypes.
Genome-wide association study of clinically defined gout identifies multiple risk loci and its association with clinical subtypes.
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DOI：
10.1136/annrheumdis-2014-206191
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发表时间：
2016-04
影响因子：
27.4
通讯作者：
Shinomiya N
中科院分区：
文献类型：
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作者：
Matsuo H;Yamamoto K;Nakaoka H;Nakayama A;Sakiyama M;Chiba T;Takahashi A;Nakamura T;Nakashima H;Takada Y;Danjoh I;Shimizu S;Abe J;Kawamura Y;Terashige S;Ogata H;Tatsukawa S;Yin G;Okada R;Morita E;Naito M;Tokumasu A;Onoue H;Iwaya K;Ito T;Takada T;Inoue K;Kato Y;Nakamura Y;Sakurai Y;Suzuki H;Kanai Y;Hosoya T;Hamajima N;Inoue I;Kubo M;Ichida K;Ooyama H;Shimizu T;Shinomiya N
高尿酸血症引起的痛风是一种多因素疾病。虽然已经报道了痛风的全基因组关联研究（GWAS），但它们包括自我报告的痛风病例，其中临床信息不足。因此，遗传变异与痛风临床亚型之间的关系仍不清楚。在这里，我们首先进行了GWAS的临床定义的痛风病例。在日本男性人群中，对945例临床定义的痛风患者和1213例对照进行了GWAS，随后对1048例临床定义的病例和1334例对照进行了重复研究。在全基因组显著性水平上确定了5个痛风易感基因座（p<5.0×10−8），其中含有众所周知的尿酸盐转运蛋白基因（ABCG 2和SLC 2A 9）和另外的基因：rsl 260326（p=1.9×10−12; OR=1.36）（葡萄糖和脂质代谢基因），rs 2188380（p=1.6×10−23; OR=1.75）的MYL 2-CUX 2（与胆固醇和糖尿病相关的基因）和CNIH-2（谷氨酸信号调节基因）的rs 4073582（p=6.4×10−9; OR=1.66）。后两者被鉴定为新的痛风位点。此外，在确定的单核苷酸多态性（SNPs），我们证明，SNPs的ABCG 2和SLC 2A 9的差异与痛风的类型和临床参数的具体亚型（肾排泄不足型和肾超负荷型）。每个SNP的风险等位基因对临床参数的影响与两种不同类型痛风的病例对照OR比值呈显著线性关系（尿酸清除率r=0.96 [p=4.8×10−4]，尿尿酸排泄率r=0.96 [p=5.0×10−4]）。我们的研究结果为更好地了解痛风的发病机制提供了线索，并将有助于伴随诊断的发展。
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. 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. 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). Our findings provide clues to better understand the pathogenesis of gout and will be useful for development of companion diagnostics.