Department of Epidemiology and Biostatistics, Imperial College London, Norfolk Place, London W2 1PG, UK.
Hum Mol Genet. 2011 Sep 1;20(17):3494-506. doi: 10.1093/hmg/ddr248. Epub 2011 Jun 8.
Rheumatoid arthritis (RA) is the commonest chronic, systemic, inflammatory disorder affecting ∼1% of the world population. It has a strong genetic component and a growing number of associated genes have been discovered in genome-wide association studies (GWAS), which nevertheless only account for 23% of the total genetic risk. We aimed to identify additional susceptibility loci through the analysis of GWAS in the context of biological function. We bridge the gap between pathway and gene-oriented analyses of GWAS, by introducing a pathway-driven gene stability-selection methodology that identifies potential causal genes in the top-associated disease pathways that may be driving the pathway association signals. We analysed the WTCCC and the NARAC studies of ∼5000 and ∼2000 subjects, respectively. We examined 700 pathways comprising ∼8000 genes. Ranking pathways by significance revealed that the NARAC top-ranked ∼6% laid within the top 10% of WTCCC. Gene selection on those pathways identified 58 genes in WTCCC and 61 in NARAC; 21 of those were common (P(overlap)< 10(-21)), of which 16 were novel discoveries. Among the identified genes, we validated 10 known RA associations in WTCCC and 13 in NARAC, not discovered using single-SNP approaches on the same data. Gene ontology functional enrichment analysis on the identified genes showed significant over-representation of signalling activity (P< 10(-29)) in both studies. Our findings suggest a novel model of RA genetic predisposition, which involves cell-membrane receptors and genes in second messenger signalling systems, in addition to genes that regulate immune responses, which have been the focus of interest previously.
类风湿关节炎(RA)是最常见的慢性、全身性、炎症性疾病,影响全球约 1%的人口。它具有很强的遗传成分,在全基因组关联研究(GWAS)中发现了越来越多的相关基因,但这些基因仅占总遗传风险的 23%。我们旨在通过对生物功能的 GWAS 分析来识别额外的易感基因。我们通过引入一种通路驱动的基因稳定性选择方法,在通路和基因定向 GWAS 分析之间架起了桥梁,该方法可以识别出与疾病通路相关的最显著关联的潜在因果基因,这些基因可能是驱动通路关联信号的原因。我们分析了约 5000 例和 2000 例 WTCCC 和 NARAC 研究,分别分析了包含约 8000 个基因的 700 条通路。按重要性对通路进行排序后发现,NARAC 的前 6%左右的最高排名通路约占 WTCCC 的前 10%。对这些通路进行基因选择后,在 WTCCC 中确定了 58 个基因,在 NARAC 中确定了 61 个基因;其中 21 个基因重叠(P(overlap)<10(-21)),其中 16 个是新发现的。在所确定的基因中,我们在 WTCCC 中验证了 10 个已知的 RA 关联,在 NARAC 中验证了 13 个关联,这些关联在使用相同数据的单 SNP 方法中并未发现。对所确定基因的基因本体论功能富集分析显示,两项研究均显著过度表达信号活性(P<10(-29))。我们的研究结果表明,除了以前关注的调节免疫反应的基因外,RA 遗传易感性还涉及细胞膜受体和第二信使信号系统中的基因,这为我们提供了一个新的 RA 遗传易感性模型。