Visualizing novel connections and genetic similarities across diseases using a network-medicine based approach

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 S...

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Bibliographic Details
Published in:Scientific reports 2022-09, Vol.12 (1), p.14914-14914, Article 14914
Main Authors: Ferolito, Brian, do Valle, Italo Faria, Gerlovin, Hanna, Costa, Lauren, Casas, Juan P., Gaziano, J. Michael, Gagnon, David R., Begoli, Edmon, Barabási, Albert-László, Cho, Kelly
Format: Article
Language:English
Subjects:
631/114
631/114/2408
631/208/205
631/208/205/2138
631/208/726/649
Comorbidity
Disease prevention
Genetic relationship
Humanities and Social Sciences
multidisciplinary
Population studies
Science
Science & Technology - Other Topics
Science (multidisciplinary)
Single-nucleotide polymorphism
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-19244-y