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基于全基因组关联研究的牙病综合基因集富集分析揭示的关联信号。

Association signals unveiled by a comprehensive gene set enrichment analysis of dental caries genome-wide association studies.

机构信息

Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.

出版信息

PLoS One. 2013 Aug 14;8(8):e72653. doi: 10.1371/journal.pone.0072653. eCollection 2013.

Abstract

Gene set-based analysis of genome-wide association study (GWAS) data has recently emerged as a useful approach to examine the joint effects of multiple risk loci in complex human diseases or phenotypes. Dental caries is a common, chronic, and complex disease leading to a decrease in quality of life worldwide. In this study, we applied the approaches of gene set enrichment analysis to a major dental caries GWAS dataset, which consists of 537 cases and 605 controls. Using four complementary gene set analysis methods, we analyzed 1331 Gene Ontology (GO) terms collected from the Molecular Signatures Database (MSigDB). Setting false discovery rate (FDR) threshold as 0.05, we identified 13 significantly associated GO terms. Additionally, 17 terms were further included as marginally associated because they were top ranked by each method, although their FDR is higher than 0.05. In total, we identified 30 promising GO terms, including 'Sphingoid metabolic process,' 'Ubiquitin protein ligase activity,' 'Regulation of cytokine secretion,' and 'Ceramide metabolic process.' These GO terms encompass broad functions that potentially interact and contribute to the oral immune response related to caries development, which have not been reported in the standard single marker based analysis. Collectively, our gene set enrichment analysis provided complementary insights into the molecular mechanisms and polygenic interactions in dental caries, revealing promising association signals that could not be detected through single marker analysis of GWAS data.

摘要

基于基因集的全基因组关联研究 (GWAS) 数据分析方法最近已成为一种研究复杂人类疾病或表型中多个风险基因座联合效应的有效方法。龋齿是一种常见的、慢性的、复杂的疾病,会降低全球的生活质量。在这项研究中,我们应用基因集富集分析方法分析了一个主要的龋齿 GWAS 数据集,该数据集包含 537 例病例和 605 例对照。使用四种互补的基因集分析方法,我们分析了从分子特征数据库 (MSigDB) 中收集的 1331 个基因本体论 (GO) 术语。设定错误发现率 (FDR) 阈值为 0.05,我们确定了 13 个与 GO 显著相关的术语。此外,还有 17 个术语被进一步确定为边缘相关术语,因为它们是每种方法的最高排名术语,尽管它们的 FDR 高于 0.05。总共,我们确定了 30 个有前途的 GO 术语,包括“鞘脂代谢过程”、“泛素蛋白连接酶活性”、“细胞因子分泌的调节”和“神经酰胺代谢过程”。这些 GO 术语涵盖了广泛的功能,这些功能可能相互作用并有助于与龋齿发展相关的口腔免疫反应,这些功能在标准的基于单一标记物的分析中尚未报道过。总之,我们的基因集富集分析为龋齿的分子机制和多基因相互作用提供了补充见解,揭示了通过 GWAS 数据的单一标记物分析无法检测到的有前途的关联信号。

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