Journal Article

Exploratory data analysis in large-scale genetic studies

Yik Y. Teo

in Biostatistics

Volume 11, issue 1, pages 70-81
Published in print January 2010 | ISSN: 1465-4644
Published online October 2009 | e-ISSN: 1468-4357 | DOI: https://dx.doi.org/10.1093/biostatistics/kxp038

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Genome-wide association studies (GWAS) have become the method of choice for investigating the genetic basis of common diseases and complex traits. The immense scale of these experiments is unprecedented, involving thousands of samples and up to a million variables. The careful execution of exploratory data analysis (EDA) prior to the actual genotype–phenotype association analysis is crucial as this identifies problematic samples and poorly assayed genetic polymorphisms that, if undetected, can compromise the outcome of the experiment. EDA of such large-scale genetic data sets thus requires specialized numerical and graphical strategies, and this article provides a review of the current exploratory tools commonly used in GWAS.

Keywords: Exploratory data analysis; Genetic association studies

Journal Article.  4738 words.  Illustrated.

Subjects: Probability and Statistics