Literature DB >> 31128465

Systematic analysis of genes and diseases using PheWAS-Associated networks.

Ali Khosravi1, Morteza Kouhsar1, Bahram Goliaei2, B Jayaram3, Ali Masoudi-Nejad4.   

Abstract

Several scientific sources have reported different causes of various diseases. One of these factors is genetic variation. Natural selection, molecular evolution and susceptibility to external conditions are the main causes of genetic variations. Phenome-Wide Association Studies (PheWAS) can emphasize the associations of genetic variations and diseases. The systematic analysis of these associations can highlight various important aspects of gene correlations and disease relationships. In this study, we have investigated a systematic approach to analyze associated networks of genes and diseases to explore novel scientific information. We have constructed the Associated Gene Network (AGN, n = 1769) and the Associated Disease Network (ADN, n = 503) based on common diseases and genes, respectively. We have evaluated these networks based on topological measures and compared them with a randomized null network. The comparative modular analysis based on size and quantity is a clear indication of the significance of these networks. We have found numerous novel associations of genes involved in different diseases. We have also found different diseases related to one another, which can correlate scientific evidence. We have verified our analysis through GO and KEGG enrichment for different case studies and concluded that AGN and ADN can be used as reference biological networks for various purposes such as drug design and drug repurposing.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Keywords:  Associated disease network; Associated gene network; PheWAS associations; Systems biology

Year:  2019        PMID: 31128465     DOI: 10.1016/j.compbiomed.2019.04.037

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  2 in total

1.  Active repurposing of drug candidates for melanoma based on GWAS, PheWAS and a wide range of omics data.

Authors:  Ali Khosravi; B Jayaram; Bahram Goliaei; Ali Masoudi-Nejad
Journal:  Mol Med       Date:  2019-06-20       Impact factor: 6.354

2.  A fuzzy logic-based computational method for the repurposing of drugs against COVID-19.

Authors:  Yosef Masoudi-Sobhanzadeh; Hosein Esmaeili; Ali Masoudi-Nejad
Journal:  Bioimpacts       Date:  2021-08-10
  2 in total

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