Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232, USA.
Bioinformatics. 2011 Jan 1;27(1):95-102. doi: 10.1093/bioinformatics/btq615. Epub 2010 Nov 2.
An important question that has emerged from the recent success of genome-wide association studies (GWAS) is how to detect genetic signals beyond single markers/genes in order to explore their combined effects on mediating complex diseases and traits. Integrative testing of GWAS association data with that from prior-knowledge databases and proteome studies has recently gained attention. These methodologies may hold promise for comprehensively examining the interactions between genes underlying the pathogenesis of complex diseases.
Here, we present a dense module searching (DMS) method to identify candidate subnetworks or genes for complex diseases by integrating the association signal from GWAS datasets into the human protein-protein interaction (PPI) network. The DMS method extensively searches for subnetworks enriched with low P-value genes in GWAS datasets. Compared with pathway-based approaches, this method introduces flexibility in defining a gene set and can effectively utilize local PPI information.
We implemented the DMS method in an R package, which can also evaluate and graphically represent the results. We demonstrated DMS in two GWAS datasets for complex diseases, i.e. breast cancer and pancreatic cancer. For each disease, the DMS method successfully identified a set of significant modules and candidate genes, including some well-studied genes not detected in the single-marker analysis of GWA studies. Functional enrichment analysis and comparison with previously published methods showed that the genes we identified by DMS have higher association signal.
dmGWAS package and documents are available at http://bioinfo.mc.vanderbilt.edu/dmGWAS.html.
最近全基因组关联研究 (GWAS) 的成功提出了一个重要问题，即如何检测单个标记/基因之外的遗传信号，以探索它们对介导复杂疾病和特征的综合影响。GWAS 关联数据与先验知识数据库和蛋白质组学研究的数据的综合测试最近受到了关注。这些方法可能有希望全面检查复杂疾病发病机制下基因之间的相互作用。
在这里，我们提出了一种密集模块搜索 (DMS) 方法，通过将 GWAS 数据集的关联信号集成到人类蛋白质-蛋白质相互作用 (PPI) 网络中，来识别复杂疾病的候选子网或基因。DMS 方法广泛搜索 GWAS 数据集中富含低 P 值基因的子网。与基于途径的方法相比，该方法在定义基因集方面具有灵活性，并可以有效地利用局部 PPI 信息。
我们在一个 R 包中实现了 DMS 方法，该方法还可以评估和图形化表示结果。我们在两个复杂疾病的 GWAS 数据集上演示了 DMS 方法，即乳腺癌和胰腺癌。对于每种疾病，DMS 方法都成功地确定了一组显著的模块和候选基因，其中包括一些在 GWAS 研究的单标记分析中未检测到的研究较多的基因。功能富集分析和与先前发表的方法的比较表明，我们通过 DMS 识别的基因具有更高的关联信号。
dmGWAS 包和文档可在 http://bioinfo.mc.vanderbilt.edu/dmGWAS.html 获得。
