Genome-Wide Association Studies(GWASs) aim to identify genetic variants that are associated with disease by assaying and analyzing hundreds of thousands of Single Nucleotide Polymorphisms(SNPs). Although tradition...Genome-Wide Association Studies(GWASs) aim to identify genetic variants that are associated with disease by assaying and analyzing hundreds of thousands of Single Nucleotide Polymorphisms(SNPs). Although traditional single-locus statistical approaches have been standardized and led to many interesting findings, a substantial number of recent GWASs indicate that for most disorders, the individual SNPs explain only a small fraction of the genetic causes. Consequently, exploring multi-SNPs interactions in the hope of discovering more significant associations has attracted more attentions. Due to the huge search space for complicated multilocus interactions, many fast and effective methods have recently been proposed for detecting disease-associated epistatic interactions using GWAS data. In this paper, we provide a critical review and comparison of eight popular methods, i.e., BOOST, TEAM, epi Forest, EDCF, SNPHarvester, epi MODE, MECPM, and MIC, which are used for detecting gene-gene interactions among genetic loci. In views of the assumption model on the data and searching strategies, we divide the methods into seven categories. Moreover, the evaluation methodologies,including detecting powers, disease models for simulation, resources of real GWAS data, and the control of false discover rate, are elaborated as references for new approach developers. At the end of the paper, we summarize the methods and discuss the future directions in genome-wide association studies for detecting epistatic interactions.展开更多
Parkinson’s disease(PD)is recognized as the second most common neurodegenerative disorder after Alzheimer disease.Although a fascinating 200-year journey of research has revealed the multifaceted nature of PD[1,2],it...Parkinson’s disease(PD)is recognized as the second most common neurodegenerative disorder after Alzheimer disease.Although a fascinating 200-year journey of research has revealed the multifaceted nature of PD[1,2],its fundamental features are the loss of dopaminergic neurons in the substantia nigra pars compacta(SNpc)and depletion of dopamine(DA)in the striatum.Iron accumulates in normal brains with aging.Such展开更多
Objective: To investigate the mechanisms of Panax notoginseng saponins(PNS) in treating coronary heart disease(CHD) by integrating gene interaction network and functional enrichment analysis. Methods: Text minin...Objective: To investigate the mechanisms of Panax notoginseng saponins(PNS) in treating coronary heart disease(CHD) by integrating gene interaction network and functional enrichment analysis. Methods: Text mining was used to get CHD and PNS associated genes. Gene–gene interaction networks of CHD and PNS were built by the Gene MANIA Cytoscape plugin. Advanced Network Merge Cytoscape plugin was used to analyze the two networks. Their functions were analyzed by gene functional enrichment analysis via DAVID Bioinformatics. Joint subnetwork of CHD network and PNS network was identified by network analysis. Results: The 11 genes of the joint subnetwork were the direct targets of PNS in CHD network and enriched in cytokine-cytokine receptor interaction pathway. PNS could affect other 85 genes by the gene–gene interaction of joint subnetwork and these genes were enriched in other 7 pathways. The direct mechanisms of PNS in treating CHD by targeting cytokines to relieve the inflammation and the indirect mechanisms of PNS in treating CHD by affecting other 7 pathways through the interaction of joint subnetwork of PNS and CHD network. The genes in the 7 pathways could be potential targets for the immunologic adjuvant, anticoagulant, hypolipidemic, anti-platelet and anti-hypertrophic activities of PNS. Conclusion: The key mechanisms of PNS in treating CHD could be anticoagulant and hypolipidemic which are indicated by analyzing biological functions of hubs in the merged network.展开更多
With the continuing development and improvement of genome-wide techniques, a great number of candidate genes are discovered. How to identify the most likely disease genes among a large number of candidates becomes a f...With the continuing development and improvement of genome-wide techniques, a great number of candidate genes are discovered. How to identify the most likely disease genes among a large number of candidates becomes a fundamental challenge in human health. A common view is that genes related to a specific or similar disease tend to reside in the same neighbourhood of biomolecular networks. Recently, based on such observations,many methods have been developed to tackle this challenge. In this review, we firstly introduce the concept of disease genes, their properties, and available data for identifying them. Then we review the recent computational approaches for prioritizing candidate disease genes based on Protein-Protein Interaction(PPI) networks and investigate their advantages and disadvantages. Furthermore, some pieces of existing software and network resources are summarized. Finally, we discuss key issues in prioritizing candidate disease genes and point out some future research directions.展开更多
基金supported by the Molecular Basis of Disease (MBD) program at Georgia State Universitysupported in part by the National Natural Science Foundation of China (Nos. 61379108 and 61232001)
文摘Genome-Wide Association Studies(GWASs) aim to identify genetic variants that are associated with disease by assaying and analyzing hundreds of thousands of Single Nucleotide Polymorphisms(SNPs). Although traditional single-locus statistical approaches have been standardized and led to many interesting findings, a substantial number of recent GWASs indicate that for most disorders, the individual SNPs explain only a small fraction of the genetic causes. Consequently, exploring multi-SNPs interactions in the hope of discovering more significant associations has attracted more attentions. Due to the huge search space for complicated multilocus interactions, many fast and effective methods have recently been proposed for detecting disease-associated epistatic interactions using GWAS data. In this paper, we provide a critical review and comparison of eight popular methods, i.e., BOOST, TEAM, epi Forest, EDCF, SNPHarvester, epi MODE, MECPM, and MIC, which are used for detecting gene-gene interactions among genetic loci. In views of the assumption model on the data and searching strategies, we divide the methods into seven categories. Moreover, the evaluation methodologies,including detecting powers, disease models for simulation, resources of real GWAS data, and the control of false discover rate, are elaborated as references for new approach developers. At the end of the paper, we summarize the methods and discuss the future directions in genome-wide association studies for detecting epistatic interactions.
基金supported by grants from the National Natural Science Foundation of China(81430024,31771124,31571054,and 31371081)Excellent Innovative Team of Shandong Province and Taishan Scholars Construction Project
文摘Parkinson’s disease(PD)is recognized as the second most common neurodegenerative disorder after Alzheimer disease.Although a fascinating 200-year journey of research has revealed the multifaceted nature of PD[1,2],its fundamental features are the loss of dopaminergic neurons in the substantia nigra pars compacta(SNpc)and depletion of dopamine(DA)in the striatum.Iron accumulates in normal brains with aging.Such
基金Supported by the National Natural Science Foundation of China(No.81173116)
文摘Objective: To investigate the mechanisms of Panax notoginseng saponins(PNS) in treating coronary heart disease(CHD) by integrating gene interaction network and functional enrichment analysis. Methods: Text mining was used to get CHD and PNS associated genes. Gene–gene interaction networks of CHD and PNS were built by the Gene MANIA Cytoscape plugin. Advanced Network Merge Cytoscape plugin was used to analyze the two networks. Their functions were analyzed by gene functional enrichment analysis via DAVID Bioinformatics. Joint subnetwork of CHD network and PNS network was identified by network analysis. Results: The 11 genes of the joint subnetwork were the direct targets of PNS in CHD network and enriched in cytokine-cytokine receptor interaction pathway. PNS could affect other 85 genes by the gene–gene interaction of joint subnetwork and these genes were enriched in other 7 pathways. The direct mechanisms of PNS in treating CHD by targeting cytokines to relieve the inflammation and the indirect mechanisms of PNS in treating CHD by affecting other 7 pathways through the interaction of joint subnetwork of PNS and CHD network. The genes in the 7 pathways could be potential targets for the immunologic adjuvant, anticoagulant, hypolipidemic, anti-platelet and anti-hypertrophic activities of PNS. Conclusion: The key mechanisms of PNS in treating CHD could be anticoagulant and hypolipidemic which are indicated by analyzing biological functions of hubs in the merged network.
文摘With the continuing development and improvement of genome-wide techniques, a great number of candidate genes are discovered. How to identify the most likely disease genes among a large number of candidates becomes a fundamental challenge in human health. A common view is that genes related to a specific or similar disease tend to reside in the same neighbourhood of biomolecular networks. Recently, based on such observations,many methods have been developed to tackle this challenge. In this review, we firstly introduce the concept of disease genes, their properties, and available data for identifying them. Then we review the recent computational approaches for prioritizing candidate disease genes based on Protein-Protein Interaction(PPI) networks and investigate their advantages and disadvantages. Furthermore, some pieces of existing software and network resources are summarized. Finally, we discuss key issues in prioritizing candidate disease genes and point out some future research directions.