title: A robust clustering algorithm for identifying problematic samples in genome-wide association studies.
abstract: CONCLUSIONS
  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.
  
  
  BACKGROUND
  The algorithm is written in R and is freely available at www.well.ox.ac.uk/chris-spencer
  
  
  BACKGROUND
  chris.spencer@well.ox.ac.uk
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK.
authors: Céline
authors: C
authors: Bellenguez
authors: Amy
authors: A
authors: Strange
authors: Colin
authors: C
authors: Freeman
authors: Peter
authors: P
authors: Donnelly
authors: Chris C A
authors: CC
authors: Spencer
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D015331:Cohort Studies; D005260:Female; D055106:Genome-Wide Association Study; D006801:Humans; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis; D020641:Polymorphism, Single Nucleotide