Journal Article

GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population

Eleni Giannoulatou, Christopher Yau, Stefano Colella, Jiannis Ragoussis and Christopher C. Holmes

in Bioinformatics

Volume 24, issue 19, pages 2209-2214
Published in print October 2008 | ISSN: 1367-4803
Published online July 2008 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btn386
GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population

Show Summary Details

Preview

Summary: 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.

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

Contact: cholmes@stats.ox.ac.uk

Journal Article.  4199 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.