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A Mechanism for Controlled Access to GWAS Data: Experience of the GAIN Data Access Committee
Author(s) -
Erin M. Ramos,
Corina Din-Lovinescu,
Ebony Bookman,
Lisa J. McNeil,
Carl C. Baker,
Georgy Godynskiy,
Emily Harris,
Thomas Lehner,
Catherine McKeon,
Joel Moss,
Vaurice L. Starks,
Stephen T. Sherry,
Teri A. Manolio,
Laura Lyman Rodriguez
Publication year - 2013
Publication title -
the american journal of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.661
H-Index - 302
eISSN - 1537-6605
pISSN - 0002-9297
DOI - 10.1016/j.ajhg.2012.08.034
Subject(s) - genome wide association study , mechanism (biology) , computer science , data sharing , data access , computational biology , data science , medicine , biology , genetics , database , single nucleotide polymorphism , gene , alternative medicine , genotype , philosophy , epistemology , pathology
The Genetic Association Information Network (GAIN) Data Access Committee was established in June 2007 to provide prompt and fair access to data from six genome-wide association studies through the database of Genotypes and Phenotypes (dbGaP). Of 945 project requests received through 2011, 749 (79%) have been approved; median receipt-to-approval time decreased from 14 days in 2007 to 8 days in 2011. Over half (54%) of the proposed research uses were for GAIN-specific phenotypes; other uses were for method development (26%) and adding controls to other studies (17%). Eight data-management incidents, defined as compromises of any of the data-use conditions, occurred among nine approved users; most were procedural violations, and none violated participant confidentiality. Over 5 years of experience with GAIN data access has demonstrated substantial use of GAIN data by investigators from academic, nonprofit, and for-profit institutions with relatively few and contained policy violations. The availability of GAIN data has allowed for advances in both the understanding of the genetic underpinnings of mental-health disorders, diabetes, and psoriasis and the development and refinement of statistical methods for identifying genetic and environmental factors related to complex common diseases.

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