Exploratory data analysis in large-scale genetic studies.
Biostatistics
; 11(1): 70-81, 2010 Jan.
Article
em En
| MEDLINE
| ID: mdl-19828557
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Interpretação Estatística de Dados
/
Estudo de Associação Genômica Ampla
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2010
Tipo de documento:
Article