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Genome-Wide Association Studies—Data Generation, Storage, Interpretation, and Bioinformatics

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Abstract

Genome-wide association studies (GWAS) have had great success in identifying common genetic determinants of disease. One of the challenges posed by GWAS is the analysis of the large amount of data generated. This review aims to provide the non-geneticists with an overview of the different steps entailed in analysis of GWAS data, with an emphasis on popular bioinformatics tools available. GWAS data generation, analysis, and interpretation will be covered.

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Authors and Affiliations

  1. Department of Pathology & Molecular Medicine, Genetic and Molecular Epidemiology Laboratory, McMaster University, 1200 Main St. West MDCL Rm. 3206, Hamilton, ON, Canada, L8N 3Z5

    Guillaume Pare

  2. Department of Clinical Epidemiology & Biostatistics, Genetic and Molecular Epidemiology Laboratory, McMaster University, 1200 Main St. West MDCL Rm. 3206, Hamilton, ON, Canada, L8N 3Z5

    Guillaume Pare

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Correspondence to Guillaume Pare.

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Pare, G. Genome-Wide Association Studies—Data Generation, Storage, Interpretation, and Bioinformatics. J. of Cardiovasc. Trans. Res. 3, 183–188 (2010). https://doi.org/10.1007/s12265-010-9181-y

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  • DOI: https://doi.org/10.1007/s12265-010-9181-y

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