It is valuable to analyze the genome-wide association studies (GWAS) data for a complex disease phenotype in the context of the protein–protein interaction (PPI) network, as the related pathophysiology results from the function of interacting polyprotein pathways. The analysis may include the design and curation of a phenotype-specific GWAS meta-database incorporating genotypic and eQTL data linking to PPI and other biological datasets, and the development of systematic workflows for PPI network-based data integration toward protein and pathway prioritization. Here, we pursued this analysis for blood pressure (BP) regulation. The relational scheme of the implemented in Microsoft SQL Server BP-GWAS meta-database enabled the combined storage of: GWAS data and attributes mined from GWAS Catalog and the literature, Ensembl-defined SNP-transcript associations, and GTEx eQTL data. The BP-protein interactome was reconstructed from the PICKLE PPI meta-database, extending the GWAS-deduced network with the shortest paths connecting all GWAS-proteins into one component. The shortest-path intermediates were considered as BP-related. For protein prioritization, we combined a new integrated GWAS-based scoring scheme with two network-based criteria: one considering the protein role in the reconstructed by shortest-path (RbSP) interactome and one novel promoting the common neighbors of GWAS-prioritized proteins. Prioritized proteins were ranked by the number of satisfied criteria. The meta-database includes 6687 variants linked with 1167 BP-associated protein-coding genes. The GWAS-deduced PPI network includes 1065 proteins, with 672 forming a connected component. The RbSP interactome contains 1443 additional, network-deduced proteins and indicated that essentially all BP-GWAS proteins are at most second neighbors. The prioritized BP-protein set was derived from the union of the most BP-significant by any of the GWAS-based or the network-based criteria. It included 335 proteins, with ~ 2/3 deduced from the BP PPI network extension and 126 prioritized by at least two criteria. ESR1 was the only protein satisfying all three criteria, followed in the top-10 by INSR, PTN11, CDK6, CSK, NOS3, SH2B3, ATP2B1, FES and FINC, satisfying two. Pathway analysis of the RbSP interactome revealed numerous bioprocesses, which are indeed functionally supported as BP-associated, extending our understanding about BP regulation. The implemented workflow could be used for other multifactorial diseases.

中文翻译：

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基于蛋白质-蛋白质相互作用网络的 GWAS 和功能数据集成，用于血压调节分析

在蛋白质-蛋白质相互作用（PPI）网络的背景下分析复杂疾病表型的全基因组关联研究（GWAS）数据是有价值的，因为相关的病理生理学是由相互作用的多蛋白途径的功能产生的。分析可能包括设计和管理表型特异性 GWAS 元数据库，其中包含链接到 PPI 和其他生物数据集的基因型和 eQTL 数据，以及开发基于 PPI 网络的数据集成的系统工作流程，以实现蛋白质和通路优先级排序。在这里，我们对血压 (BP) 调节进行了分析。 Microsoft SQL Server BP-GWAS 元数据库中实现的关系方案可以组合存储：从 GWAS 目录和文献中挖掘的 GWAS 数据和属性、Ensembl 定义的 SNP 转录本关联以及 GTEx eQTL 数据。 BP-蛋白质相互作用组是根据 PICKLE PPI 元数据库重建的，通过将所有 GWAS-蛋白质连接成一个组件的最短路径扩展了 GWAS 推导的网络。最短路径中间体被认为与 BP 相关。对于蛋白质优先顺序，我们将一种新的基于 GWAS 的集成评分方案与两个基于网络的标准相结合：一个考虑蛋白质在最短路径 (RbSP) 相互作用组重建中的作用，另一个促进 GWAS 优先蛋白质的共同邻居。按满足标准的数量对优先蛋白质进行排序。该元数据库包含与 1167 个 BP 相关蛋白编码基因相关的 6687 个变体。 GWAS 推导的 PPI 网络包含 1065 个蛋白质，其中 672 个形成连接组件。 RbSP 相互作用组包含 1443 个额外的网络推导蛋白质，表明基本上所有 BP-GWAS 蛋白质最多都是第二邻居。优先的 BP 蛋白质集源自根据任何基于 GWAS 或基于网络的标准的最显着 BP 的联合。它包括 335 种蛋白质，其中约 2/3 是从 BP PPI 网络扩展推导出来的，另外 126 种蛋白质按至少两个标准优先排序。 ESR1 是唯一满足所有三个标准的蛋白质，排在前 10 名的依次是 INSR、PTN11、CDK6、CSK、NOS3、SH2B3、ATP2B1、FES 和 FINC，满足两个标准。 RbSP 相互作用组的通路分析揭示了许多生物过程，这些过程确实在功能上支持与 BP 相关，扩展了我们对 BP 调节的理解。实施的工作流程可用于其他多因素疾病。