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.http://www.stats.ox.ac.uk/~giannoul/GenoSNP/.

Original publication

DOI

10.1093/bioinformatics/btn386

Type

Journal article

Journal

Bioinformatics (Oxford, England)

Publication Date

10/2008

Volume

24

Pages

2209 - 2214

Addresses

Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX13TG, UK.

Keywords

Humans, Cluster Analysis, Models, Statistical, Computational Biology, Genotype, Polymorphism, Single Nucleotide, Algorithms, Population Groups