VA Boston Healthcare System, Massachusetts Veterans Epidemiology and Research Information Center, (MAVERIC), 150 S. Huntington Avenue, Boston, 02130, USA.
Center for Complex Network Research, Department of Physics, Northeastern University, Boston, 02115, USA.
Sci Rep. 2022 Sep 1;12(1):14914. doi: 10.1038/s41598-022-19244-y.
Understanding the genetic relationships between human disorders could lead to better treatment and prevention strategies, especially for individuals with multiple comorbidities. A common resource for studying genetic-disease relationships is the GWAS Catalog, a large and well curated repository of SNP-trait associations from various studies and populations. Some of these populations are contained within mega-biobanks such as the Million Veteran Program (MVP), which has enabled the genetic classification of several diseases in a large well-characterized and heterogeneous population. Here we aim to provide a network of the genetic relationships among diseases and to demonstrate the utility of quantifying the extent to which a given resource such as MVP has contributed to the discovery of such relations. We use a network-based approach to evaluate shared variants among thousands of traits in the GWAS Catalog repository. Our results indicate many more novel disease relationships that did not exist in early studies and demonstrate that the network can reveal clusters of diseases mechanistically related. Finally, we show novel disease connections that emerge when MVP data is included, highlighting methodology that can be used to indicate the contributions of a given biobank.
了解人类疾病之间的遗传关系可以为更好的治疗和预防策略提供依据，特别是对于那些同时患有多种合并症的个体而言。GWAS Catalog 是研究遗传疾病关系的常用资源，这是一个来自各种研究和人群的 SNP-表型关联的大型、精心维护的存储库。其中一些人群包含在大型生物库中，如百万退伍军人计划（MVP），这使得在一个特征明确、异质的大型人群中对多种疾病进行遗传分类成为可能。在这里，我们旨在提供一个疾病间遗传关系的网络，并展示量化 MVP 等特定资源在发现这些关系方面的贡献程度的效用。我们使用基于网络的方法来评估 GWAS Catalog 存储库中数千个特征之间的共享变体。我们的结果表明，存在许多早期研究中不存在的新的疾病关系，并表明该网络可以揭示具有机制相关性的疾病簇。最后，我们展示了包含 MVP 数据时出现的新的疾病关联，突出了可以用于指示特定生物库贡献的方法。
