There is a pressing need today to mechanistically interpret sets of genomic variants associated with diseases. Here we present a tool called ‘VarSAn’ that uses a network analysis algorithm to identify pathways relevant to a given set of variants. VarSAn analyzes a configurable network whose nodes represent variants, genes and pathways, using a Random Walk with Restarts algorithm to rank pathways for relevance to the given variants, and reports P-values for pathway relevance. It treats non-coding and coding variants differently, properly accounts for the number of pathways impacted by each variant and identifies relevant pathways even if many variants do not directly impact genes of the pathway. We use VarSAn to identify pathways relevant to variants related to cancer and several other diseases, as well as drug response variation. We find VarSAn's pathway ranking to be complementary to the standard approach of enrichment tests on genes related to the query set. We adopt a novel benchmarking strategy to quantify its advantage over this baseline approach. Finally, we use VarSAn to discover key pathways, including the VEGFA-VEGFR2 pathway, related to de novo variants in patients of Hypoplastic Left Heart Syndrome, a rare and severe congenital heart defect.

中文翻译：

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VarSAn：使用网络分析将通路与一组基因组变异相关联

今天迫切需要机械地解释与疾病相关的基因组变异集。在这里，我们提出了一个名为“VarSAn”的工具，它使用网络分析算法来识别与给定变体集相关的路径。VarSAn 分析其节点代表变体、基因和通路的可配置网络，使用带有重启的随机游走算法对通路与给定变体的相关性进行排序，并报告通路相关性的 P 值。它以不同的方式处理非编码和编码变体，正确考虑每个变体影响的途径数量，并识别相关途径，即使许多变体不直接影响途径的基因。我们使用 VarSAn 来识别与癌症和其他几种疾病相关的变异以及药物反应变异相关的途径。我们发现 VarSAn 的通路排名与查询集相关基因富集测试的标准方法是互补的。我们采用了一种新颖的基准测试策略来量化其相对于这种基线方法的优势。最后，我们使用 VarSAn 发现关键通路，包括 VEGFA-VEGFR2 通路，这些通路与左心发育不全综合征（一种罕见且严重的先天性心脏缺陷）患者的从头变异相关。