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

Ferrari, R., Kia, D. A., Tomkins, J. E., Hardy, J., Wood, N. W., Lovering, R. C., Lewis, P. A. and Manzoni, C. (2018) Stratification of candidate genes for Parkinson’s disease using weighted protein interaction network analysis. BMC Genomics, 19. 452. ISSN 1471-2164 doi: 10.1186/s12864-018-4804-9

Abstract/Summary

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.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.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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/77725
Item Type	Article
Refereed	Yes
Divisions	Life Sciences > School of Chemistry, Food and Pharmacy > School of Pharmacy > Division of Pharmacology
Uncontrolled Keywords	Bioinformatics, Networks, Functional genomics, Protein-protein interactions, Pathways, Neurodegeneration, Parkinson’s disease, GWAS
Publisher	BioMed Central
Download/View statistics	View download statistics for this item