Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

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