Multiple sclerosis (MS) is a complex disease in which the immune system attacks the central nervous system. The molecular mechanisms contributing to the etiology of MS remain poorly understood. Genome-wide association studies (GWAS) of MS have identified a small number of genetic loci significant at the genome level, but they are mainly non-coding variants. Network-assisted analysis may help better interpret the functional roles of the variants with association signals and potential translational medicine application. The Dense Module Searching of GWAS tool (dmGWAS version 2.4) developed in our team is applied to 2 MS GWAS datasets (GeneMSA and IMSGC GWAS) using the human protein interactome as the reference network. A dual evaluation strategy is used to generate results with reproducibility. Approximately 7500 significant network modules were identified for each independent GWAS dataset, and 20 significant modules were identified from the dual evaluation. The top modules included GRB2, HDAC1, JAK2, MAPK1, and STAT3 as central genes. Top module genes were enriched with functional terms such as “regulation of glial cell differentiation” (adjusted p-value = 2.58 × 10− 3), “T-cell costimulation” (adjusted p-value = 2.11 × 10− 6) and “virus receptor activity” (adjusted p-value = 1.67 × 10− 3). Interestingly, top gene networks included several MS FDA approved drug target genes HDAC1, IL2RA, KEAP1, and RELA, Our dmGWAS network analyses highlighted several genes (GRB2, HDAC1, IL2RA, JAK2, KEAP1, MAPK1, RELA and STAT3) in top modules that are promising to interpret GWAS signals and link to MS drug targets. The genes enriched with glial cell differentiation are important for understanding neurodegenerative processes in MS and for remyelination therapy investigation. Importantly, our identified genetic signals enriched in T cell costimulation and viral receptor activity supported the viral infection onset hypothesis for MS.

中文翻译：

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密集模块搜索与多发性硬化症相关的基因网络。

多发性硬化症（MS）是一种复杂的疾病，免疫系统会攻击中枢神经系统。导致MS病因的分子机制仍然知之甚少。MS的全基因组关联研究（GWAS）已确定了少数在基因组水平上重要的遗传基因座，但它们主要是非编码变异。网络辅助分析可能有助于更好地解释具有关联信号和潜在转化医学应用的变体的功能角色。我们团队开发的GWAS密集模块搜索工具（dmGWAS版本2.4）以人蛋白质相互作用组作为参考网络，被应用于2个MS GWAS数据集（GeneMSA和IMSGC GWAS）。双重评估策略用于产生可重复的结果。为每个独立的GWAS数据集确定了大约7500个重要的网络模块，并且通过双重评估确定了20个重要的模块。最重要的模块包括GRB2，HDAC1，JAK2，MAPK1和STAT3作为中心基因。顶层模块基因富含功能术语，例如“调节神经胶质细胞分化”（调整后的p值= 2.58×10−3），“ T细胞共刺激”（调整后的p值= 2.11×10−6）和“病毒受体活性”（调整后的p值= 1.67×10−3）。有趣的是，顶级基因网络包括MS FDA批准的几个药物靶基因HDAC1，IL2RA，KEAP1和RELA，我们的dmGWAS网络分析突出显示了顶级模块中的几个基因（GRB2，HDAC1，IL2RA，JAK2，KEAP1，MAPK1，RELA和STAT3），有望解释GWAS信号并链接至MS药物靶标。富含神经胶质细胞分化的基因对于理解MS中的神经退行性过程和髓鞘再生研究非常重要。重要的是，我们鉴定出的富含T细胞共刺激和病毒受体活性的遗传信号支持了MS的病毒感染发作假说。