A Computational Approach in Identification of Putative Risk Genes in Parkinsons Disease

Abstract

This study focussed on identification of risk genes involved in PD through analysis of microarray data. The two methods were applied viz; WGCNA and DEGs Analysis to identify important genes that are downregulated or upregulated in PD. Both methods show high agreement with each other and also with the available biomedical literature available on this neurodegenerative disease. On the basis of their p–value, 20 significantly upregulated and 19 significantly downregulated genes were found to be playing role in the manifestation of motor and non motor symptoms of the disease, as interpreted from the enrichment analysis.Gene expression dataset of Parkinson’s Disease (PD) used in this study was obtained from the GeneExpression Omnibus namely GSE8397, GSE20164, and GSE20295 (Edgar, 2002). Among the important genes extracted, the top downregulated and upregulated genes were studied using enrichment analysis. Out of the 19 common downregulated genes, 10 were directly associated with neuron development and differentiation. Two of the genes, FGF13 and CDC42 were associated with multiple signalling pathways. The gene NSF was found to be enriched with GABAergic synapse, associated with the predominating inhibitory neurotransmitter in the mammalian CNS. The inhibitory synapses are thought to provide a brake to neural firing.The upregulated genes DDIT4, HSPB1, NUPR1, GPNMB and CH13L1 were enriched with apoptotic signalling pathway while MT1M, MT1E, MT1F, MT1X were associated with mineral absorption pathways. Genes like CDC42 have already been reported to be potential diagnostic markers of PD in clinic (Chi et al., 2018).