School of Nano-Biotech and Chemical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 689-798, Seoul, Korea.
Nucleic Acids Res. 2010 Jul;38(Web Server issue):W749-54. doi: 10.1093/nar/gkq428. Epub 2010 May 25.
Genome-wide association (GWA) study aims to identify the genetic factors associated with the traits of interest. However, the power of GWA analysis has been seriously limited by the enormous number of markers tested. Recently, the gene set analysis (GSA) methods were introduced to GWA studies to address the association of gene sets that share common biological functions. GSA considerably increased the power of association analysis and successfully identified coordinated association patterns of gene sets. There have been several approaches in this direction with some limitations. Here, we present a general approach for GSA in GWA analysis and a stand-alone software GSA-SNP that implements three widely used GSA methods. GSA-SNP provides a fast computation and an easy-to-use interface. The software and test datasets are freely available at http://gsa.muldas.org. We provide an exemplary analysis on adult heights in a Korean population.
全基因组关联(GWA)研究旨在确定与感兴趣性状相关的遗传因素。然而,GWA 分析的功效受到测试标记数量巨大的严重限制。最近,基因集分析(GSA)方法被引入 GWA 研究中,以解决具有共同生物学功能的基因集的关联问题。GSA 极大地提高了关联分析的功效,并成功识别了基因集的协调关联模式。在这方面已经有几种方法,但都存在一些局限性。在这里,我们提出了 GWA 分析中的 GSA 通用方法和一个独立的软件 GSA-SNP,它实现了三种广泛使用的 GSA 方法。GSA-SNP 提供了快速的计算和易于使用的界面。软件和测试数据集可在 http://gsa.muldas.org 免费获得。我们在韩国人群中提供了一个关于成人身高的示例分析。