Gene Prospector: An evidence gateway for evaluating potential susceptibility genes and interacting risk factors for human diseases

Details

• Alternative Title:
  BMC Bioinformatics
• Personal Author:
  Yu, Wei ; Wulf, Anja ; Liu, Tiebin ; Khoury, Muin J ; Gwinn, Marta
• Description:
  Background
  
  Millions of single nucleotide polymorphisms have been identified as a result of the human genome project and the rapid advance of high throughput genotyping technology. Genetic association studies, such as recent genome-wide association studies (GWAS), have provided a springboard for exploring the contribution of inherited genetic variation and gene/environment interactions in relation to disease. Given the capacity of such studies to produce a plethora of information that may then be described in a number of publications, selecting possible disease susceptibility genes and identifying related modifiable risk factors is a major challenge. A Web-based application for finding evidence of such relationships is key to the development of follow-up studies and evidence for translational research.
  
  Results
  
  We compared Gene Prospector results for the query "Parkinson" with a list of 13 leading candidate genes (Top Results) from a curated, specialty database for genetic associations with Parkinson disease (PDGene). Nine of the thirteen leading candidate genes from PDGene were in the top 10th percentile of the ranked list from Gene Prospector. In fact, Gene Prospector included more published genetic association studies for the 13 leading candidate genes than PDGene did.
  
  Conclusion
  
  Gene Prospector provides an online gateway for searching for evidence about human genes in relation to diseases, other phenotypes, and risk factors, and provides links to published literature and other online data sources. Gene Prospector can be accessed via .
  
  
• Subjects:
  Computational Biology Databases, Genetic Genetic Diseases, Inborn Genetic Predisposition To Disease Humans Internet Polymorphism, Single Nucleotide Risk Factors Software
• Source:
  BMC Bioinformatics. 2008; 9:528.
• Document Type:
  Journal Article
• Volume:
  9
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:5cbbed56b52c31dddc6e06ce0f4ff584d8636a34e5978d59a14a64d137bac22e
• Download URL:
  https://stacks.cdc.gov/view/cdc/3360/cdc_3360_DS1.pdf
• File Type:
  [PDF - 367.16 KB ]