整合全基因组遗传变异和单核细胞表达数据揭示了人类中转调控基因模块。
Integrating genome-wide genetic variations and monocyte expression data reveals trans-regulated gene modules in humans.
机构信息
INSERM UMRS 937, Pierre and Marie Curie University (UPMC, Paris 6) and Medical School, Paris, France.
出版信息
PLoS Genet. 2011 Dec;7(12):e1002367. doi: 10.1371/journal.pgen.1002367. Epub 2011 Dec 1.
One major expectation from the transcriptome in humans is to characterize the biological basis of associations identified by genome-wide association studies. So far, few cis expression quantitative trait loci (eQTLs) have been reliably related to disease susceptibility. Trans-regulating mechanisms may play a more prominent role in disease susceptibility. We analyzed 12,808 genes detected in at least 5% of circulating monocyte samples from a population-based sample of 1,490 European unrelated subjects. We applied a method of extraction of expression patterns-independent component analysis-to identify sets of co-regulated genes. These patterns were then related to 675,350 SNPs to identify major trans-acting regulators. We detected three genomic regions significantly associated with co-regulated gene modules. Association of these loci with multiple expression traits was replicated in Cardiogenics, an independent study in which expression profiles of monocytes were available in 758 subjects. The locus 12q13 (lead SNP rs11171739), previously identified as a type 1 diabetes locus, was associated with a pattern including two cis eQTLs, RPS26 and SUOX, and 5 trans eQTLs, one of which (MADCAM1) is a potential candidate for mediating T1D susceptibility. The locus 12q24 (lead SNP rs653178), which has demonstrated extensive disease pleiotropy, including type 1 diabetes, hypertension, and celiac disease, was associated to a pattern strongly correlating to blood pressure level. The strongest trans eQTL in this pattern was CRIP1, a known marker of cellular proliferation in cancer. The locus 12q15 (lead SNP rs11177644) was associated with a pattern driven by two cis eQTLs, LYZ and YEATS4, and including 34 trans eQTLs, several of them tumor-related genes. This study shows that a method exploiting the structure of co-expressions among genes can help identify genomic regions involved in trans regulation of sets of genes and can provide clues for understanding the mechanisms linking genome-wide association loci to disease.
从人类转录组中可以得到一个主要的预期,即描述全基因组关联研究中确定的关联的生物学基础。到目前为止,只有少数顺式表达数量性状基因座(eQTL)与疾病易感性有可靠的关联。反式调控机制可能在疾病易感性中发挥更突出的作用。我们分析了 1490 个欧洲无关个体的基于人群的样本中至少 5%的循环单核细胞样本中检测到的 12808 个基因。我们应用了一种独立成分分析提取表达模式的方法来识别共同调控基因的集合。然后,将这些模式与 675350 个 SNP 相关联,以确定主要的反式作用调节因子。我们检测到三个与共同调控基因模块显著相关的基因组区域。这些位点与多个表达性状的关联在 Cardiogenics 中得到了复制,这是一项独立研究,其中 758 个个体的单核细胞表达谱可用。12q13 位点(先导 SNP rs11171739)先前被鉴定为 1 型糖尿病位点,与包括两个顺式 eQTL(RPS26 和 SUOX)和 5 个反式 eQTL 在内的一个模式相关联,其中一个(MADCAM1)是介导 1 型糖尿病易感性的潜在候选物。12q24 位点(先导 SNP rs653178)已显示出广泛的疾病多效性,包括 1 型糖尿病、高血压和乳糜泻,与强烈相关的血压水平模式相关联。该模式中最强的反式 eQTL 是CRIP1,这是癌症中细胞增殖的已知标志物。12q15 位点(先导 SNP rs11177644)与由两个顺式 eQTL(LYZ 和 YEATS4)驱动的模式相关联,包括 34 个反式 eQTL,其中几个是肿瘤相关基因。这项研究表明,一种利用基因之间共同表达结构的方法可以帮助识别参与基因集反式调控的基因组区域,并为理解将全基因组关联位点与疾病联系起来的机制提供线索。
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