scholarly journals Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases

2014 ◽  
Vol 39 (1) ◽  
pp. 11-19 ◽  
Author(s):  
Huann-Sheng Chen ◽  
Carolyn M. Hutter ◽  
Leah E. Mechanic ◽  
Christopher I. Amos ◽  
Vineet Bafna ◽  
...  
Keyword(s):  
Association Studies ◽  
Complex Diseases ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Simulation Tools ◽  
Genome Wide ◽  
Genetic Simulation

Identification of Critical Core Genes of Sarcoma Based on Centrality Analysis of Networks Nodes

2020 ◽  
Vol 10 (7) ◽  
pp. 1776-1784
Author(s):  
Shudong Wang ◽  
Jixiao Wang ◽  
Xinzeng Wang ◽  
Yuanyuan Zhang ◽  
Tao Yi
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Complex Diseases ◽  
Enrichment Analysis ◽  
Gene Interaction ◽  
Core Gene ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Gene Set ◽  
Genome Wide

Genome-wide association studies (GWAS) are powerful tools for identifying pathogenic genes of complex diseases and revealing genetic structure of diseases. However, due to gene-to-gene interactions, only a part of the hereditary factors can be revealed. The meta-analysis based on GWAS can integrate gene expression data at multiple levels and reveal the complex relationship between genes. Therefore, we used meta-analysis to integrate GWAS data of sarcoma to establish complex networks and discuss their significant genes. Firstly, we established gene interaction networks based on the data of different subtypes of sarcoma to analyze the node centralities of genes. Secondly, we calculated the significant score of each gene according to the Staged Significant Gene Network Algorithm (SSGNA). Then, we obtained the critical gene set HYC of sarcoma by ranking the scores, and then combined Gene Ontology enrichment analysis and protein network analysis to further screen it. Finally, the critical core gene set Hcore containing 47 genes was obtained and validated by GEPIA analysis. Our method has certain generalization performance to the study of complex diseases with prior knowledge and it is a useful supplement to genome-wide association studies.

scholarly journals Genome-Wide Association Studies: Progress in Identifying Genetic Biomarkers in Common, Complex Diseases

2007 ◽  
Vol 2 ◽  
pp. 117727190700200 ◽  
Author(s):  
Stephen F. Kingsmore ◽  
Ingrid E. Lindquist ◽  
Joann Mudge ◽  
William D. Beavis
Keyword(s):  
Genetic Heterogeneity ◽  
Complex Traits ◽  
Biomarker Discovery ◽  
Association Studies ◽  
Complex Diseases ◽  
Human Diseases ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Biomarkers ◽  
Genome Wide

Novel, comprehensive approaches for biomarker discovery and validation are urgently needed. One particular area of methodologic need is for discovery of novel genetic biomarkers in complex diseases and traits. Here, we review recent successes in the use of genome wide association (GWA) approaches to identify genetic biomarkers in common human diseases and traits. Such studies are yielding initial insights into the allelic architecture of complex traits. In general, it appears that complex diseases are associated with many common polymorphisms, implying profound genetic heterogeneity between affected individuals.

Do genome-wide association scans have potential for translation?

2012 ◽  
Vol 50 (2) ◽  
Author(s):  
Margarida C. Lopes ◽  
Eleftheria Zeggini ◽  
Kalliope Panoutsopoulou
Keyword(s):  
Clinical Practice ◽  
Association Studies ◽  
Complex Diseases ◽  
Genome Wide Association ◽  
Response To Treatment ◽  
Genome Wide Association Studies ◽  
Novel Treatments ◽  
Common Diseases ◽  
Genome Wide

AbstractThe success of genome-wide association studies (GWAS) in identifying replicating associations has greatly contributed to understanding of the genetic aetiology of complex diseases. This review discusses and provides examples of the potential of GWAS findings to be translated into clinical practice, i.e., diagnosis, prediction, prognosis, novel treatments and response to treatment of common diseases. The biological insights afforded by newly-identified robust associations represent the largest, albeit indirect, translational contribution of GWAS.

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