A robust clustering algorithm for identifying problematic samples in genome-wide association studies.
Bioinformatics
; 28(1): 134-5, 2012 Jan 01.
Article
en En
| MEDLINE
| ID: mdl-22057162
ABSTRACT
SUMMARY:
High-throughput genotyping arrays provide an efficient way to survey single nucleotide polymorphisms (SNPs) across the genome in large numbers of individuals. Downstream analysis of the data, for example in genome-wide association studies (GWAS), often involves statistical models of genotype frequencies across individuals. The complexities of the sample collection process and the potential for errors in the experimental assay can lead to biases and artefacts in an individual's inferred genotypes. Rather than attempting to model these complications, it has become a standard practice to remove individuals whose genome-wide data differ from the sample at large. Here we describe a simple, but robust, statistical algorithm to identify samples with atypical summaries of genome-wide variation. Its use as a semi-automated quality control tool is demonstrated using several summary statistics, selected to identify different potential problems, and it is applied to two different genotyping platforms and sample collections.AVAILABILITY:
The algorithm is written in R and is freely available at www.well.ox.ac.uk/chris-spencer CONTACT chris.spencer@well.ox.ac.uk SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Análisis por Conglomerados
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Polimorfismo de Nucleótido Simple
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Estudio de Asociación del Genoma Completo
Tipo de estudio:
Etiology_studies
/
Incidence_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Female
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Humans
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Male
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2012
Tipo del documento:
Article
País de afiliación:
Reino Unido