Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa, Japan.
Medical Squadron, Air Base Group, Western Aircraft Control and Warning Wing, Japan Air Self-Defense Force, Kasuga, Japan.
Ann Rheum Dis. 2020 May;79(5):657-665. doi: 10.1136/annrheumdis-2019-216644. Epub 2020 Apr 1.
Genome-wide meta-analyses of clinically defined gout were performed to identify subtype-specific susceptibility loci. Evaluation using selection pressure analysis with these loci was also conducted to investigate genetic risks characteristic of the Japanese population over the last 2000-3000 years.
Two genome-wide association studies (GWASs) of 3053 clinically defined gout cases and 4554 controls from Japanese males were performed using the Japonica Array and Illumina Array platforms. About 7.2 million single-nucleotide polymorphisms were meta-analysed after imputation. Patients were then divided into four clinical subtypes (the renal underexcretion type, renal overload type, combined type and normal type), and meta-analyses were conducted in the same manner. Selection pressure analyses using singleton density score were also performed on each subtype.
In addition to the eight loci we reported previously, two novel loci, and , were identified at a genome-wide significance level (p<5.0×10) from a GWAS meta-analysis of all gout patients, and other two novel intergenic loci, and , from normal type gout patients. Subtype-dependent patterns of Manhattan plots were observed with subtype GWASs of gout patients, indicating that these subtype-specific loci suggest differences in pathophysiology along patients' gout subtypes. Selection pressure analysis revealed significant enrichment of selection pressure on in addition to loci for all subtypes except for normal type gout.
Our findings on subtype GWAS meta-analyses and selection pressure analysis of gout will assist elucidation of the subtype-dependent molecular targets and evolutionary involvement among genotype, phenotype and subtype-specific tailor-made medicine/prevention of gout and hyperuricaemia.
对临床定义的痛风进行全基因组荟萃分析,以确定亚类特异性易感基因座。使用这些基因座的选择压力分析进行评估,以研究过去 2000-3000 年来日本人群特有的遗传风险。
对来自日本男性的 3053 例临床定义的痛风病例和 4554 例对照进行了两项全基因组关联研究(GWAS),使用了 Japonica Array 和 Illumina Array 平台。在导入后对约 720 万个单核苷酸多态性进行了荟萃分析。然后将患者分为四个临床亚型(肾功能不全排泄型、肾功能超负荷型、混合型和正常型),并以同样的方式进行荟萃分析。还对每个亚型的单倍体密度评分进行了选择压力分析。
除了我们之前报道的八个基因座外,在所有痛风患者的 GWAS 荟萃分析中,还在全基因组显著水平(p<5.0×10)确定了两个新的基因座 和 ,以及正常型痛风患者的两个新的基因间基因座 和 。痛风患者的亚型 GWAS 观察到依赖于亚型的曼哈顿图模式,表明这些亚类特异性基因座提示了患者痛风亚型的病理生理学差异。选择压力分析显示,除正常型痛风外,所有亚型都对 基因座存在选择压力的显著富集。
我们对痛风的亚型 GWAS 荟萃分析和选择压力分析的发现将有助于阐明基于基因型、表型和亚型特异性定制药物/预防痛风和高尿酸血症的分子靶标和进化作用。