Dataset Information
A robust clustering algorithm for identifying problematic samples in genome-wide association studies.
Ontology highlight
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-spencerContact
chris.spencer@well.ox.ac.ukSupplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Bellenguez C
PROVIDER: S-EPMC3244763 | biostudies-literature | 2012 Jan
REPOSITORIES: biostudies-literature
Publications
A robust clustering algorithm for identifying problematic samples in genome-wide association studies.
Bioinformatics (Oxford, England) 20111103 1
<h4>Summary</h4>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 in ...[more]