Please use this identifier to cite or link to this item: https://doi.org/10.1093/biostatistics/kxp038
Title: Exploratory data analysis in large-scale genetic studies
Authors: Teo, Y.Y. 
Keywords: Exploratory data analysis
Genetic association studies
Issue Date: Jan-2010
Citation: Teo, Y.Y. (2010-01). Exploratory data analysis in large-scale genetic studies. Biostatistics 11 (1) : 70-81. ScholarBank@NUS Repository. https://doi.org/10.1093/biostatistics/kxp038
Abstract: 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.
Source Title: Biostatistics
URI: http://scholarbank.nus.edu.sg/handle/10635/105142
ISSN: 14654644
DOI: 10.1093/biostatistics/kxp038
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