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
Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa, Saitama, Japan.
Department of Medical Chemistry, Kurume University School of Medicine, Kurume, Fukuoka, Japan.
Ann Rheum Dis. 2016 Apr;75(4):652-9. doi: 10.1136/annrheumdis-2014-206191. Epub 2015 Feb 2.
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.
痛风是一种由高尿酸血症引起的多因素疾病。尽管已有痛风的全基因组关联研究（GWAS）报道，但这些研究纳入的是自我报告的痛风病例，临床信息不足。因此，基因变异与痛风临床亚型之间的关系仍不清楚。在此，我们首次仅对临床确诊的痛风病例进行了GWAS。
在日本男性人群中，对945例临床确诊的痛风患者和1213例对照进行了GWAS，随后对1048例临床确诊病例和1334例对照进行了重复研究。
在全基因组显著性水平（p<5.0×10⁻⁸）上鉴定出5个痛风易感位点，其中包含著名的尿酸转运蛋白基因（ABCG2和SLC2A9）以及其他基因：GCKR（葡萄糖和脂质代谢相关基因）的rs1260326（p=1.9×10⁻¹²；OR=1.36）、MYL2-CUX2（与胆固醇和糖尿病相关的基因）的rs2188380（p=1.6×10⁻²³；OR=1.75）以及CNIH-2（谷氨酸信号调节相关基因）的rs4073582（p=6.4×10⁻⁹；OR=1.66）。后两个位点被鉴定为新的痛风位点。此外，在鉴定出的单核苷酸多态性（SNP）中，我们证明ABCG2和SLC2A9的SNP与痛风类型以及特定亚型（肾脏排泄减少型和肾脏负荷过载型）的临床参数存在差异关联。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r=0.96 [p=5.0×10⁻⁴]）。
我们的研究结果为更好地理解痛风的发病机制提供了线索，并将有助于伴随诊断的开发。
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