Holden Marit, Deng Shiwei, Wojnowski Leszek, Kulle Bettina
Norwegian Computing Center, Oslo, Norway, Department of Pharmacology, University of Mainz, Mainz, Germany.
Bioinformatics. 2008 Dec 1;24(23):2784-5. doi: 10.1093/bioinformatics/btn516. Epub 2008 Oct 14.
The power of genome-wide SNP association studies is limited, among others, by the large number of false positive test results. To provide a remedy, we combined SNP association analysis with the pathway-driven gene set enrichment analysis (GSEA), recently developed to facilitate handling of genome-wide gene expression data. The resulting GSEA-SNP method rests on the assumption that SNPs underlying a disease phenotype are enriched in genes constituting a signaling pathway or those with a common regulation. Besides improving power for association mapping, GSEA-SNP may facilitate the identification of disease-associated SNPs and pathways, as well as the understanding of the underlying biological mechanisms. GSEA-SNP may also help to identify markers with weak effects, undetectable in association studies without pathway consideration. The program is freely available and can be downloaded from our website.
全基因组单核苷酸多态性(SNP)关联研究的效能受到诸多因素的限制,其中包括大量的假阳性检测结果。为了提供一种补救方法,我们将SNP关联分析与最近开发的用于促进全基因组基因表达数据处理的通路驱动基因集富集分析(GSEA)相结合。由此产生的GSEA-SNP方法基于这样一种假设,即疾病表型潜在的SNP在构成信号通路的基因或具有共同调控的基因中富集。除了提高关联定位的效能外,GSEA-SNP还可能有助于识别与疾病相关的SNP和通路,以及理解潜在的生物学机制。GSEA-SNP也可能有助于识别在不考虑通路的关联研究中无法检测到的弱效应标记。该程序可免费获取,可从我们的网站下载。