Pathway-based analysis in genome-wide association study (GWAS) is being widely used to uncover novel multi-genic functional associations. Many of these pathway-based methods have been used to test the enrichment of the associated genes in the pathways, but exhibited low powers and were highly affected by free parameters. We present the novel method and software GSA-SNP2 for pathway enrichment analysis of GWAS P-value data. GSA-SNP2 provides high power, decent type I error control and fast computation by incorporating the random set model and SNP-count adjusted gene score. In a comparative study using simulated and real GWAS data, GSA-SNP2 exhibited high power and best prioritized gold standard positive pathways compared with six existing enrichment-based methods and two self-contained methods (alternative pathway analysis approach). Based on these results, the difference between pathway analysis approaches was investigated and the effects of the gene correlation structures on the pathway enrichment analysis were also discussed. In addition, GSA-SNP2 is able to visualize protein interaction networks within and across the significant pathways so that the user can prioritize the core subnetworks for further studies. GSA-SNP2 is freely available at https://sourceforge.net/projects/gsasnp2.

中文翻译：

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使用GSA-SNP2的GWAS摘要数据的有效途径富集和网络分析

全基因组关联研究（GWAS）中基于路径的分析被广泛用于发现新型的多基因功能关联。这些基于途径的方法中有许多已被用于测试途径中相关基因的富集，但表现出低功效，并受到自由参数的高度影响。我们提出了新方法和软件GSA-SNP2用于GWAS P的途径富集分析值数据。GSA-SNP2通过合并随机集模型和SNP计数调整后的基因得分，可提供强大的I类错误控制和快速计算能力。在一项使用模拟和真实GWAS数据进行的比较研究中，与现有的六种现有的基于富集的方法和两种独立的方法（替代途径分析方法）相比，GSA-SNP2展现出了强大的功能和最佳的金标准优先阳性途径。基于这些结果，研究了途径分析方法之间的差异，并讨论了基因相关结构对途径富集分析的影响。此外，GSA-SNP2能够可视化重要路径之内和之间的蛋白质相互作用网络，因此用户可以确定核心子网络的优先级，以进行进一步研究。GSA-SNP2可从以下网址免费获得：