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


Journal

Computers in biology and medicine
ISSN: 1879-0534
Titre abrégé: Comput Biol Med
Pays: United States
ID NLM: 1250250

Informations de publication

Date de publication:
06 2019
Historique:
received: 04 03 2019
revised: 28 04 2019
accepted: 28 04 2019
pubmed: 28 5 2019
medline: 29 7 2020
entrez: 26 5 2019
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 31128465
pii: S0010-4825(19)30145-3
doi: 10.1016/j.compbiomed.2019.04.037
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

311-321

Informations de copyright

Copyright © 2019 Elsevier Ltd. All rights reserved.

Auteurs

Ali Khosravi (A)

Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.

Morteza Kouhsar (M)

Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.

Bahram Goliaei (B)

Department of Biophysics & Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.

B Jayaram (B)

Department of Chemistry & Supercomputing Facility for Bioinformatics & Computational Biology, Kusuma School of Biological Sciences, Indian Institute of Technology Delhi, India.

Ali Masoudi-Nejad (A)

Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. Electronic address: amasoudin@ut.ac.ir.

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Classifications MeSH