UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Stratification of candidate genes for Parkinson's disease using weighted protein-protein interaction network analysis

Ferrari, R; Kia, DA; Tomkins, JE; Hardy, J; Wood, NW; Lovering, RC; Lewis, PA; (2018) Stratification of candidate genes for Parkinson's disease using weighted protein-protein interaction network analysis. BMC Genomics , 19 , Article 452. 10.1186/s12864-018-4804-9. Green open access

Text (Published article)
Ferrari_Stratification of candidate.pdf - ["content_typename_Published version" not defined]

Download (2MB) | Preview
Text (Supplementary methods)

Download (417kB) | Preview
[img] Archive (Supplementary files)

Download (2MB)


Background: Genome wide association studies (GWAS) have helped identify large numbers of genetic loci that significantly associate with increased risk of developing diseases. However, translating genetic knowledge into understanding of the molecular mechanisms underpinning disease (i.e. disease-specific impacted biological processes) has to date proved to be a major challenge. This is primarily due to difficulties in confidently defining candidate genes at GWAS-risk loci. The goal of this study was to better characterize candidate genes within GWAS loci using a protein interactome based approach and with Parkinson’s disease (PD) data as a test case. // Results: We applied a recently developed Weighted Protein-Protein Interaction Network Analysis (WPPINA) pipeline as a means to define impacted biological processes, risk pathways and therein key functional players. We used previously established Mendelian forms of PD to identify seed proteins, and to construct a protein network for genetic Parkinson’s and carried out functional enrichment analyses. We isolated PD-specific processes indicating ‘mitochondria stressors mediated cell death’, ‘immune response and signaling’, and ‘waste disposal’ mediated through ‘autophagy’. Merging the resulting protein network with data from Parkinson’s GWAS we confirmed 10 candidate genes previously selected by pure proximity and were able to nominate 17 novel candidate genes for sporadic PD. // Conclusions: With this study, we were able to better characterize the underlying genetic and functional architecture of idiopathic PD, thus validating WPPINA as a robust pipeline for the in silico genetic and functional dissection of complex disorders.

Type: Article
Title: Stratification of candidate genes for Parkinson's disease using weighted protein-protein interaction network analysis
Open access status: An open access version is available from UCL Discovery
DOI: 10.1186/s12864-018-4804-9
Publisher version: https://doi.org/10.1186/s12864-018-4804-9
Language: English
Additional information: Copyright © The Author(s) 2018. Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Keywords: Bioinformatics, Networks, Functional genomics, Protein-protein interactions, Pathways, Neurodegeneration, Parkinson’s disease, GWAS
URI: http://discovery.ucl.ac.uk/id/eprint/10051345
Downloads since deposit
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item