scholarly journals Protein‐protein interaction networks of genes associated with different cognitively defined subtypes of late‐onset Alzheimer's disease in five white populations identify novel candidate genes

2020 ◽  
Vol 16 (S2) ◽  
Author(s):  
Shubhabrata Mukherjee ◽  
Jesse Mez ◽  
Emily H. Trittschuh ◽  
Andrew J. Saykin ◽  
Laura E. Gibbons ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Candidate Genes ◽  
Protein Interaction ◽  
Late Onset ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

scholarly journals Using whole-exome sequencing and protein interaction networks to prioritize candidate genes for germline cutaneous melanoma susceptibility

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sally Yepes ◽  
Margaret A. Tucker ◽  
Hela Koka ◽  
Yanzi Xiao ◽  
Kristine Jones ◽  
...  
Keyword(s):  
Network Analysis ◽  
Exome Sequencing ◽  
Candidate Genes ◽  
Protein Interaction ◽  
Cutaneous Melanoma ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Protein Interaction ◽  
Whole Exome ◽  
Protein Protein Interaction Networks

Abstract Although next-generation sequencing has demonstrated great potential for novel gene discovery, confirming disease-causing genes after initial discovery remains challenging. Here, we applied a network analysis approach to prioritize candidate genes identified from whole-exome sequencing analysis of 98 cutaneous melanoma patients from 27 families. Using a network propagation method, we ranked candidate genes by their similarity to known disease genes in protein–protein interaction networks and identified gene clusters with functional connectivity. Using this approach, we identified several new candidate susceptibility genes that warrant future investigations such as NGLY1, IL1RN, FABP2, PRKDC, and PROSER2. The propagated network analysis also allowed us to link families that did not have common underlying genes but that carried variants in genes that interact on protein–protein interaction networks. In conclusion, our study provided an analysis perspective for gene prioritization in the context of genetic heterogeneity across families and prioritized top potential candidate susceptibility genes in our dataset.

scholarly journals Candidate gene prioritization for chronic obstructive pulmonary disease using expression information in protein–protein interaction networks

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wan Li ◽  
Yihua Zhang ◽  
Yahui Wang ◽  
Zherou Rong ◽  
Chenyu Liu ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Candidate Genes ◽  
Pulmonary Disease ◽  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

Abstract Background Identifying or prioritizing genes for chronic obstructive pulmonary disease (COPD), one type of complex disease, is particularly important for its prevention and treatment. Methods In this paper, a novel method was proposed to Prioritize genes using Expression information in Protein–protein interaction networks with disease risks transferred between genes (abbreviated as PEP). A weighted COPD PPI network was constructed using expression information and then COPD candidate genes were prioritized based on their corresponding disease risk scores in descending order. Results Further analysis demonstrated that the PEP method was robust in prioritizing disease candidate genes, and superior to other existing prioritization methods exploiting either topological or functional information. Top-ranked COPD candidate genes and their significantly enriched functions were verified to be related to COPD. The top 200 candidate genes might be potential disease genes in the diagnosis and treatment of COPD. Conclusions The proposed method could provide new insights to the research of prioritizing candidate genes of COPD or other complex diseases with expression information from sequencing or microarray data.

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