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

Autor: Ferolito B; VA Boston Healthcare System, Massachusetts Veterans Epidemiology and Research Information Center, (MAVERIC), 150 S. Huntington Avenue, Boston, 02130, USA. brian.ferolito@va.gov., do Valle IF; 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., Gerlovin H; VA Boston Healthcare System, Massachusetts Veterans Epidemiology and Research Information Center, (MAVERIC), 150 S. Huntington Avenue, Boston, 02130, USA., Costa L; VA Boston Healthcare System, Massachusetts Veterans Epidemiology and Research Information Center, (MAVERIC), 150 S. Huntington Avenue, Boston, 02130, USA., Casas JP; VA Boston Healthcare System, Massachusetts Veterans Epidemiology and Research Information Center, (MAVERIC), 150 S. Huntington Avenue, Boston, 02130, USA.; Brigham and Women's Hospital, Division of Aging, Department of Medicine, Harvard Medical School, Boston, 02115, USA., Gaziano JM; VA Boston Healthcare System, Massachusetts Veterans Epidemiology and Research Information Center, (MAVERIC), 150 S. Huntington Avenue, Boston, 02130, USA.; Brigham and Women's Hospital, Division of Aging, Department of Medicine, Harvard Medical School, Boston, 02115, USA., Gagnon DR; VA Boston Healthcare System, Massachusetts Veterans Epidemiology and Research Information Center, (MAVERIC), 150 S. Huntington Avenue, Boston, 02130, USA.; School of Public Health, Department of Biostatistics, Boston University, Boston, 02215, USA., Begoli E; Oak Ridge National Laboratory, Oak Ridge, 37830, USA., Barabási AL; Center for Complex Network Research, Department of Physics, Northeastern University, Boston, 02115, USA., Cho K; VA Boston Healthcare System, Massachusetts Veterans Epidemiology and Research Information Center, (MAVERIC), 150 S. Huntington Avenue, Boston, 02130, USA.; Brigham and Women's Hospital, Division of Aging, Department of Medicine, Harvard Medical School, Boston, 02115, USA.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 Sep 01; Vol. 12 (1), pp. 14914. Date of Electronic Publication: 2022 Sep 01.
DOI: 10.1038/s41598-022-19244-y
Abstrakt: 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.
(© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)
Databáze: MEDLINE
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