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Updated: Jun 21, 2026
16:37
Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization
Published on: August 5, 2008
Genome-wide association database developed in the Japanese Integrated Database Project
Asako Koike1, Nao Nishida, Ituro Inoue
1Central Research Laboratory, Hitachi Ltd, Tokyo, Japan. asako.koike.ea@hitachi.com
Journal of Human Genetics
|July 25, 2009
Summary
A new public database manages genome-wide association study (GWAS) data, enabling researchers to share and compare findings on genetic factors linked to complex diseases. This resource accelerates discovery by providing accessible, integrated data for disease-related studies.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- High-throughput single-nucleotide polymorphism (SNP)-typing technologies have advanced genome-wide association studies (GWAS).
- Numerous genetic factors associated with complex diseases have been identified through GWAS.
- Effective data management and sharing are crucial for continued progress in genetic research.
Purpose of the Study:
- To establish a public repository database (DB) for continuous and intensive management of GWAS data.
- To facilitate data sharing and collaboration among researchers.
- To accelerate disease-related studies by providing user-friendly access to integrated GWAS information.
Main Methods:
- Development of a public repository database (DB) for GWAS data.
- Inclusion of study design, quality control, allele/genotype frequencies, and statistical analysis results as publicly available data.
- Storage of individual genotyping and raw data under restricted access with authorization.
- Implementation of a user-friendly graphic viewer for data visualization.
- Integration of a distributed annotation system for data interpretation and comparison across studies and platforms.
Main Results:
- A comprehensive GWAS DB has been created, offering free access to public data such as study design and analysis results.
- Restricted access is provided for individual genotyping and raw data upon authorization.
- The DB features a graphic viewer to enhance user-friendliness for researchers, including those new to GWAS.
- Functionality for comparing results from different institutions and platforms is available.
- A distributed annotation system facilitates data interpretation by integrating external data.
Conclusions:
- The GWAS DB provides a centralized platform for managing and sharing valuable genetic association data.
- The user-friendly interface and data integration capabilities are expected to accelerate the discovery of disease-related genetic factors.
- Enhanced data accessibility and comparability through the DB will foster collaboration and advance complex disease research.
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