title: GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population.
abstract: Current genotyping algorithms typically call genotypes by clustering allele-specific intensity data on a single nucleotide polymorphism (SNP) by SNP basis. This approach assumes the availability of a large number of control samples that have been sampled on the same array and platform. We have developed a SNP genotyping algorithm for the Illumina Infinium SNP genotyping assay that is entirely within-sample and does not require the need for a population of control samples nor parameters derived from such a population. Our algorithm exhibits high concordance with current methods and >99% call accuracy on HapMap samples. The ability to call genotypes using only within-sample information makes the method computationally light and practical for studies involving small sample sizes and provides a valuable independent quality control metric for other population-based approaches.
  
  
  BACKGROUND
  http://www.stats.ox.ac.uk/~giannoul/GenoSNP/.
authors.affiliation: Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX13TG, UK.
authors.forename: Eleni
authors.initials: E
authors.lastname: Giannoulatou
authors.forename: Christopher
authors.initials: C
authors.lastname: Yau
authors.forename: Stefano
authors.initials: S
authors.lastname: Colella
authors.forename: Jiannis
authors.initials: J
authors.lastname: Ragoussis
authors.forename: Christopher C
authors.initials: CC
authors.lastname: Holmes
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D005838:Genotype; D006801:Humans; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D044382:Population Groups / Q000235:genetics