The Genetic Association Database (original) (raw)

Nature Genetics volume 36, pages 431–432 (2004)Cite this article

To the editor:

The increasing availability of polymorphism data has allowed more gene association studies to be carried out and the number of published genetic association studies is growing rapidly. Studies done secondarily to successful linkage studies over the last decade have also fueled the increase in published association studies. Although there are single-nucleotide polymorphism and human variation databases1,2, there is currently no public repository for genetic association data. It is difficult to query association data in a systematic manner or to integrate association data with other molecular databases. OMIM3, the main repository of genetic information for mendelian disorders, is largely text based and is of a historical narrative design, making it difficult to compare large sets of molecular data. Moreover, OMIM archives mature, high-quality data of high significance, the standard in rare mendelian disorders. Although this data is useful, OMIM does not routinely collect findings of lower significance or negative findings. The study of nonmendelian, common complex disorders is often a struggle to find disease relevance with lower significance values, and often conflicting evidence. Negative data are often not reported or are marginalized into obscure and less accessible scientific journals, resulting in a publication bias favoring positive genetic associations4. Here, we describe the development of a genetic association database (GAD; http://geneticassociationdb.nih.gov) that aims to collect, standardize and archive genetic association study data and to make it easily accessible to the scientific community.

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Figure 1: A simple search of positive associations for the disease schizophrenia.

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Authors and Affiliations

  1. Gene Expression and Genomics Unit, 333 Cassell Drive, National Institute on Aging, National Institutes of Health, Baltimore, 21224, Maryland, USA
    Kevin G Becker & Tiffani J Bright
  2. Johns Hopkins Asthma and Allergy Center, Johns Hopkins University, 5501 Hopkins Bayview Circle, Baltimore, 21224, Maryland, USA
    Kathleen C Barnes
  3. Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, 20892, Maryland, USA
    S Alex Wang

Authors

  1. Kevin G Becker
  2. Kathleen C Barnes
  3. Tiffani J Bright
  4. S Alex Wang

Corresponding author

Correspondence toKevin G Becker.

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Becker, K., Barnes, K., Bright, T. et al. The Genetic Association Database.Nat Genet 36, 431–432 (2004). https://doi.org/10.1038/ng0504-431

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