System to predict novel genetic disease associations

Information

  • Research Project
  • 6934988
  • ApplicationId
    6934988
  • Core Project Number
    R43HG003667
  • Full Project Number
    1R43HG003667-01
  • Serial Number
    3667
  • FOA Number
  • Sub Project Id
  • Project Start Date
    5/3/2005 - 19 years ago
  • Project End Date
    10/31/2005 - 19 years ago
  • Program Officer Name
    BONAZZI, VIVIEN
  • Budget Start Date
    5/3/2005 - 19 years ago
  • Budget End Date
    10/31/2005 - 19 years ago
  • Fiscal Year
    2005
  • Support Year
    1
  • Suffix
  • Award Notice Date
    5/2/2005 - 19 years ago
Organizations

System to predict novel genetic disease associations

DESCRIPTION (provided by applicant): Rapid advances in biotechnology and clinical studies have produced an information bottleneck in data processing that has hindered application of such critical scientific data for use in medicine and disease diagnosis. Omicia will develop a novel genetics based informatics infrastructure to overcome this bottleneck that can predict the functional outcome of gene mutations and their relationship to human disease by integrating diverse knowledge bases in a consistent and structured manner. Implementation of the technology will reduce the cost and enhance the effectiveness of genetic association studies, population profiling analyses and pharmacogenomic applications, as well as potentially identify novel gene targets for disease. Our technology will mine classification systems for large number of genes and create statistical associations as well as integrate ontologies to allow for reliable inferences about functions of genes and their relation to disease phenotypes. Importantly, the predictive capabilities of the technology will assign functions to orphan genes and genetic markers for which very little functional information is available. The IT system will consist of a classification of genes into families using shared attributes in a number of well-structured domains to create a gene inference system (GIS) by comparing gene ontologies in GO with disease ontologies in MeSH. It will also use the well-annotated HGMD database to infer novel disease-related function to unannotated polymorphisms such as those in dbSNP. These studies are designed to demonstrate the feasibility of the technology so that it can be validated through future genetic association studies in a Phase II SBIR.

IC Name
NATIONAL HUMAN GENOME RESEARCH INSTITUTE
  • Activity
    R43
  • Administering IC
    HG
  • Application Type
    1
  • Direct Cost Amount
  • Indirect Cost Amount
  • Total Cost
    99757
  • Sub Project Total Cost
  • ARRA Funded
  • CFDA Code
    172
  • Ed Inst. Type
  • Funding ICs
    NHGRI:99757\
  • Funding Mechanism
  • Study Section
    ZRG1
  • Study Section Name
    Special Emphasis Panel
  • Organization Name
    OMICIA, INC.
  • Organization Department
  • Organization DUNS
    148382315
  • Organization City
    EMERYVILLE
  • Organization State
    CA
  • Organization Country
    UNITED STATES
  • Organization Zip Code
    94608
  • Organization District
    UNITED STATES