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Biostatistics Advance Access published online on October 14, 2009
Biostatistics, doi:10.1093/biostatistics/kxp038
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© The Author 2009. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Exploratory data analysis in large-scale genetic studies

Yik Y. Teo*

Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK, Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK and Department of Statistics and Applied Probability and Centre for Molecular Epidemiology, National University of Singapore, Singapore 117546 teo{at}well.ox.ac.uk

* To whom correspondence should be addressed.

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

Received January 5, 2009; revised April 6, 2009; revised July 22, 2009; accepted for publication September 14, 2009.


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